Category Archives: Adult Stem Cells

Eye stem cell transplant to treat blindness bolsters retinal function in monkeys – FierceBiotech

Retinal cell transplants are considered to be an attractive approach for treating blindness. Question is, where do you source the cells?

An international research team of scientists from Singapores Agency for Science, Technology and Research (A*STAR), the Icahn School of Medicine at Mount Sinai in New York and Germanys Eye Clinic Sulzbach is using a type of stem cell in the eye to grow the pigmented layer of retina thats essential for vision. The approach is showing promise in monkeys.

The findingssuggest that these retinal pigment epithelium (RPE) stem cell-derived RPE, or hRPESC-RPE, may be a useful source for cell replacement therapies to treatRPE-related blindness caused by diseases such as macular degeneration, the researchers suggest. The results are published in the journal Stem Cell Reports.

RPE is a layer of tissue that supports the neurosensory retina and is critical for vision. An estimated 200 million people live with diseases associated with RPE dysfunction, including macular degeneration. Early attempts at RPE replacement used cells from the patientan approach with limitationsscientists have been searching for treatment using different populations of stem cells.

In 2012, scientists identified a type of adult cell in the RPE that's normally dormant but that can be activated to take on a stem-cell-like state with self-renewing ability. These cells have the potential to differentiate into RPE cells and could therefore be used for RPE replacement therapies, the A*STAR-led team figured.

In their study, the researchers took hRPESC-RPE from donated adult eyes and grew them into RPE monolayers. When transplanted into the eyes of monkeys on a polymer scaffold, theRPE patches stably integrated for at least three months.

The stem cell-derived RPE patchespartially took over and were able to support normal light-sensing function, the team showed. Whats more, the method didnt cause vision-blocking retinal scarring that has been seen with other experimental approaches.

RELATED:Reprogrammed skin cells restore sight in mouse models of retinal disease

Multiple types of stem cells, includinghuman embryonic stem cells and human-induced pluripotent stem cells, have been proposed as alternative sources for retinal replacement. A team led by Mount Sinai previously used gene transfer to activate a type of retinal cells called Mller glial to adopt stem-cell-like characteristics. The team prompted the cells to divide into light-sensing rod photoreceptor cells in blind mice.

Researchers led by the National Institutes of Healths National Eye Institute used five chemicals to turn skin cells directly into rod photoreceptors.

The A*STAR-led researchers believetheir study demonstrates the potential of using hRPESC-RPE transplants as a treatment for macular degeneration. Further studies are needed to test the method in monkey models of eye disease to gauge the therapeutic effect, the researcher suggested.

If the cells succeed, they could serve as an unlimited resource for human RPE. Because the cells are harvested from human eyes, the researchers suggested establishing hRPESC-RPE donor banks to provide cells that match individual patients so there is noimmune rejection.

Original post:
Eye stem cell transplant to treat blindness bolsters retinal function in monkeys - FierceBiotech

Induction of muscle-regenerative multipotent stem cells from human adipocytes by PDGF-AB and 5-azacytidine – Science Advances

Abstract

Terminally differentiated murine osteocytes and adipocytes can be reprogrammed using platelet-derived growth factorAB and 5-azacytidine into multipotent stem cells with stromal cell characteristics. We have now optimized culture conditions to reprogram human adipocytes into induced multipotent stem (iMS) cells and characterized their molecular and functional properties. Although the basal transcriptomes of adipocyte-derived iMS cells and adipose tissuederived mesenchymal stem cells were similar, there were changes in histone modifications and CpG methylation at cis-regulatory regions consistent with an epigenetic landscape that was primed for tissue development and differentiation. In a non-specific tissue injury xenograft model, iMS cells contributed directly to muscle, bone, cartilage, and blood vessels, with no evidence of teratogenic potential. In a cardiotoxin muscle injury model, iMS cells contributed specifically to satellite cells and myofibers without ectopic tissue formation. Together, human adipocytederived iMS cells regenerate tissues in a context-dependent manner without ectopic or neoplastic growth.

The goal of regenerative medicine is to restore function by reconstituting dysfunctional tissues. Most tissues have a reservoir of tissue-resident stem cells with restricted cell fates suited to the regeneration of the tissue in which they reside (14). The innate regenerative capacity of a tissue is broadly related to the basal rate of tissue turnover, the health of resident stem cells, and the hostility of the local environment. Bone marrow transplants and tissue grafts are frequently used in clinical practice but for most tissues, harvesting and expanding stem and progenitor cells are currently not a viable option (5, 6). Given these constraints, research efforts have been focused on converting terminally differentiated cells into pluripotent or lineage-restricted stem cells (7, 8). However, tissues are often a complex mix of diverse cell types that are derived from distinct stem cells. Therefore, multipotent stem cells may have advantages over tissue-specific stem cells. To be of use in regenerative medicine, these cells would need to respond appropriately to regional cues and participate in context-dependent tissue regeneration without forming ectopic tissues or teratomas. Mesenchymal stem cells (MSCs) were thought to have some of these characteristics (911), but despite numerous ongoing clinical trials, evidence for their direct contribution to new tissue formation in humans is sparse, either due to the lack of sufficient means to trace cell fate in hosts in vivo or failure of these cells to regenerate tissues (12, 13).

We previously reported a method by which primary terminally differentiated somatic cells could be converted into multipotent stem cells, which we termed as induced multipotent stem (iMS) cells (14). These cells were generated by transiently culturing primary mouse osteocytes in medium supplemented with azacitidine (AZA; 2 days) and platelet-derived growth factorAB (PDGF-AB; 8 days). Although the precise mechanisms by which these agents promoted cell conversion was unclear, the net effect was reduced DNA methylation at the OCT4 promoter and reexpression of pluripotency factors (OCT4, KLF4, SOX2, c-MYC, SSEA-1, and NANOG) in 2 to 4% of treated osteocytes. iMS cells resembled MSCs with comparable morphology, cell surface phenotype, colony-forming unit fibroblast (CFU-F), long-term growth, clonogenicity, and multilineage in vitro differentiation potential. iMS cells also contributed directly to in vivo tissue regeneration and did so in a context-dependent manner without forming teratomas. In proof-of-principle experiments, we also showed that primary mouse and human adipocytes could be converted into long-term repopulating CFU-Fs by this method using a suitably modified protocol (14).

AZA, one of the agents used in this protocol, is a cytidine nucleoside analog and a DNA hypomethylating agent that is routinely used in clinical practice for patients with higher-risk myelodysplastic syndrome (MDS) and for elderly patients with acute myeloid leukemia (AML) who are intolerant to intensive chemotherapy (15, 16). AZA is incorporated primarily into RNA, disrupting transcription and protein synthesis. However, 10 to 35% of drug is incorporated into DNA resulting in the entrapment and depletion of DNA methyltransferases and suppression of DNA methylation (17). Although the relationship between DNA hypomethylation and therapeutic efficacy in MDS/AML is unclear, AZA is known to induce an interferon response and apoptosis in proliferating cells (1820). PDGF-AB, the other critical reprogramming agent, is one of five PDGF isoforms (PDGF-AA, PDGF-AB, PDGF-BB, PDGF-CC, and PDGF-DD), which bind to one of two PDGF receptors (PDGFR and PDGFR) (21). PDGF isoforms are potent mitogens for mesenchymal cells, and recombinant human (rh)PDGF-BB is used as an osteoinductive agent in the clinic (22). PDGF-AB binds preferentially to PDGFR and induces PDGFR- homodimers or PDGFR- heterodimers. These are activated by autophosphorylation to create docking sites for a variety of downstream signaling molecules (23). Although we have previously demonstrated induction of CFU-Fs from human adipocytes using PDGF-AB/AZA (14), the molecular changes, which underlie conversion, and the multilineage differentiation potential and in vivo regenerative capacity of the converted cells have not been determined.

Here, we report an optimized PDGF-AB/AZA treatment protocol that was used to convert primary human adipocytes, a tissue source that is easily accessible and requires minimal manipulation, from adult donors aged 27 to 66 years into iMS cells with long-term repopulating capacity and multilineage differentiation potential. We also report the molecular landscape of these human iMS cells along with that of MSCs derived from matched adipose tissues and the comparative in vivo regenerative and teratogenic potential of these cells in mouse xenograft models.

Primary mature human adipocytes were harvested from subcutaneous fat (Fig. 1A and table S1) and their purity confirmed by flow cytometry with specific attention to the absence of contaminating adipose-derived MSCs (AdMSCs) (fig. S1, A and B). As previously described (14), plastic adherent adipocytes were cultured in Alpha Minimum Essential Medium (MEM) containing rhPDGF-AB (200 ng/ml) and 20% autologous serum (AS) with and without 10 M AZA for 2 and 23 days, respectively (Fig. 1A). During daily observations, unilocular lipid globules were observed to fragment within adipocytes ~day 10 with progressive extrusion of fat into culture medium, coincident with changes in cell morphology (movie S1). Consistent with these observations, when fixed and stained with Oil Red O, adipocytes that were globular in shape at the start of culture resembled lipid laden stromal cells at day 12 and lipid-free stromal cells at day 25 (Fig. 1B).

(A) Generation and reprogramming of adipocytes. (B) Oil Red Ostained adipocytes (days 0, 12, and 25) during treatment with recombinant human platelet-derived growth factorAB (rhPDGF-AB) and AZA. (C) Flow cytometry plots of LipidTOX and PDGFR in adipocytes cultured as in (A). (D) CFU-F counts from treated and untreated adipocytes during conversion. (E) CFU-F counts from adipocytes treated (Rx) with indicated combinations of rhPDGF-AB, AZA, fetal calf serum (FCS), autologous serum (AS), or serum-free media (SFM). (F) CFU-F counts from adipocytes reprogrammed in the presence of 0, 1, or 10 M PDGFR/ inhibitor AG1296. (G) CFU-F counts per 400 reprogrammed adipocytes from three donor age groups (n = 3 for each) generated using indicated combinations of rhPDGF-AB and AZA. (H) Long-term growth of reprogrammed adipocytes from three donor age groups (n = 3 for each) generated using indicated combinations of rhPDGF-AB and AZA. (I) Long-term growth of iMS cells cultured in SFM or media supplemented with FCS, autologous, or allogeneic serum. Error bars indicate SD, n = 3; *P < 0.05, **P < 0.01, and ***P < 0.0001 calculated using either a Students t test (E and F) or a linear mixed model (H). Photo credit: Avani Yeola, UNSW Sydney.

To evaluate these changes in individual cells, we performed flow cytometry at multiple time points during treatment and probed for adipocyte (LipidTOX) (24) and stromal cell characteristics [PDGFR expression (25); Fig. 1C]. A subpopulation of adipocytes, when cultured in media supplemented with PDGF-AB/AZA and AS (Fig. 1C, top; treated), showed reduced LipidTOX staining intensity at day 10, with progressive reduction and complete absence in all cells by day 19. Adipocytes cultured in the absence of PDGF-AB/AZA retained LipidTOX staining, albeit with reduced intensity (Fig. 1C, bottom; untreated). Adipocytes expressed PDGFR [fig. S1C, (i) and (ii)] but not PDGFR (Fig. 1C) at day 0 but both the frequency and intensity of PDGFR staining increased from day 21. To record these changes in real time, we also continuously live-imaged treated adipocytes from days 15 to 25 and recorded the extrusion of fat globules, change in cell morphology from globular to stromal, and acquisition of cell motility and cell mitosis (movie S1 and fig. S1D). Intracellular fragmentation of fat globules was observed over time in untreated adipocytes (fig. S1E), consistent with variable LipidTOX staining intensity. CFU-F capacity was absent at day 10, present in day 15 cultures, and tripled by day 19 with no substantial increase at days 21, 23, and 25 (Fig. 1D). It is noteworthy that CFU-F potential was acquired before PDGFRA surface expression when adipocytes had started to display stromal cell morphology and had diminished fat content. There was also no CFU-F capacity in adipocytes cultured in MEM with fetal calf serum (FCS) or AS, unless supplemented with both PDGF-AB and AZA. CFU-F capacity was significantly higher with AS than with FCS and absent in serum-free media (SFM) (Fig. 1E and fig. S1F). As previously shown with reprogramming of murine osteocytes, there was dose-dependent inhibition of CFU-F capacity when AG1296, a potent nonselective PDGF receptor tyrosine kinase inhibitor (26), was added to the reprogramming media (Fig. 1F).

To evaluate the impact of patient age and concentrations of PDGF-AB and AZA on the efficiency of human adipocyte conversion, we harvested subcutaneous fat from donors aged 40 (n = 3), 41 to 60 (n = 3), and 61 (n = 3) years and subjected each to three different concentrations of PDGF-AB (100, 200, and 400 ng/ml) and three different concentrations of AZA (5, 10, and 20 M) (Fig. 1G). Although all combinations supported cell conversion in all donors across the three age groups, rhPDGF-AB (400 ng/ml) and 5 M AZA yielded the highest number of CFU-Fs (Fig. 1G). When these cultures were serially passaged in SFM (with no PDGF-AB/AZA supplementation, which was used for cell conversion only), adipocytes converted with reprogramming media containing rhPDGF-AB (400 ng/ml) and 5 M AZA were sustained the longest (Fig. 1H, fig. S2A, and table S2). The growth plateau that was observed even with these cultures [i.e., adipocytes converted with rhPDGF-AB (400 ng/ml) and 5 M AZA when expanded in SFM or FCS] was overcome when cells were expanded in either autologous or allogeneic human serum (Fig. 1I). The genetic stability of human iMS cells (RM0072 and RM0073) was also assessed using single-nucleotide polymorphism arrays and shown to have a normal copy number profile at a resolution of 250 kb (fig. S2B). Together, these data identify an optimized protocol for converting human primary adipocytes from donors across different age groups and show that these can be maintained long term in culture.

Given the stromal characteristics observed in human adipocytes treated with PDGF-AB/AZA (Fig. 1), we performed flow cytometry to evaluate their expression of MSC markers CD73, CD90, CD105, and STRO1 (13) and noted expression levels comparable to AdMSCs extracted from the same subcutaneous fat harvest (Fig. 2A). Primary untreated adipocytes (day 25 in culture) did not express any of these MSC markers (fig. S3A). The global transcriptomes of iMS cells and matched AdMSCs were distinct from untreated control adipocytes but were broadly related to each other [Fig. 2B, (i) and (ii)]. Ingenuity pathway analysis (IPA) using genes that were differentially expressed between AdMSCs versus adipocytes [3307 UP/4351 DOWN in AdMSCs versus adipocytes; false discovery rate (FDR) 0.05] and iMS versus adipocytes (3311 UP/4400 DOWN in iMS versus adipocytes; FDR 0.05) showed changes associated with gene expression, posttranslational modification, and cell survival pathways and organismal survival and systems development [Fig. 2B(iii)]. The number of differentially expressed genes between iMS cells and AdMSCs was limited (2 UP/26 DOWN in iMS versus AdMSCs; FDR 0.05) and too few for confident IPA annotation. All differentially expressed genes and IPA annotations are shown in table S3 (A to E, respectively).

(A) Flow cytometry for stromal markers on AdMSCs (green) and iMS cells (purple) from matched donors. Gray, unstained controls. (B) (i) Principal components analysis (PCA) plot of adipocyte, AdMSC, and iMS transcriptomes. (ii) Hierarchical clustering of differentially expressed genes (DEGs, FDR 0.05). (iii) Ingenuity pathway analysis (IPA) of DEG between AdMSCs/adipocytes (top) or iMS cells/adipocytes (bottom). The most enriched annotated biological functions are shown. (C) (i) Chromatin immunoprecipitation sequencing (ChIP-seq) profiles in AdMSCs and iMS cells from matched donors at a representative locus. Gray bar indicates differential enrichment. (ii) Volcano plots of H3K4me3, H3K27Ac, and H3K27me3 enrichment peaks significantly UP (red) or DOWN (blue) in iMS cells versus AdMSCs. (iii) IPA of corresponding genes. log2FC, log2 fold change. (D) (i) DNA methylation at a representative locus in AdMSCs and iMS cells from matched donors. (ii) Volcano plot of regions with significantly higher (red) or lower (blue) DNA methylation in iMS cells versus AdMSCs. (iii) IPA using genes corresponding to differentially methylated regions (DMRs). (E) OCT4, NANOG, and SOX2 expression in iPS, AdMSCs, and iMS cells. Percentage of cells expressing each protein is indicated. DAPI, 4,6-diamidino-2-phenylindole. (F) AdMSCs and iMS cells differentiated in vitro. Bar graphs quantify staining frequencies, error bars show SD, n = 3. ***P < 0.001 (Students t test). Photo credit: Avani Yeola, UNSW Sydney.

In the absence of significant basal differences in the transcriptomes of AdMSCs and iMS cells, and the use of a hypomethylating agent to induce adipocyte conversion into iMS cells, we examined global enrichment profiles of histone marks associated with transcriptionally active (H3K4me3 and H3K27Ac) and inactive (H3K27me3) chromatin. There were differences in enrichment of specific histone marks in matched AdMSCs versus iMS cells at gene promoters and distal regulatory regions [Fig. 2C(i) and fig. S3, B to D]. H3K4me3, H3K27ac, and H3K27me3 enrichments were significantly higher at 255, 107, and 549 regions and significantly lower at 222, 78, and 98 regions in iMS cells versus AdMSCs [Fig. 2C(ii) and table S4, A to C] and were assigned to 237, 84, and 350 and 191, 58, and 67 genes, respectively. IPA was performed using these gene lists to identify biological functions that may be primed in iMS cells relative to AdMSCs [Fig. 2C(iii) and table S4, D to F]. Among these biological functions, annotations for molecular and cellular function (cellular movement, development, growth, and proliferation) and systems development (general; embryonic and tissue development and specific; cardiovascular, skeletal and muscular, and hematological) featured strongly and overlapped across the different epigenetic marks.

We extended these analyses to also assess global CpG methylation in matched AdMSCs and iMS cells using reduced representation bisulfite sequencing [RRBS; (27)]. Again, there were loci with differentially methylated regions (DMRs) in iMS cells versus AdMSCs [Fig. 2D(i)] with increased methylation at 158 and reduced methylation at 397 regions among all regions assessed [Fig. 2D(ii) and table S4G]. IPA of genes associated with these DMRs showed a notable overlap in annotated biological functions [Fig. 2D(iii) and table S4H] with those associated with differential H3K4me3, H3K27Ac, and H3K27me3 enrichment [Fig. 2C(iii) and table S4, E to G]. Together, these data imply that although basal transcriptomic differences between iMS cells and AdMSCs were limited, there were notable differences in epigenetic profiles at cis-regulatory regions of genes that were associated with cellular growth and systems development.

We next compared iMS cells to adipocytes from which they were derived. Expression of genes associated with adipogenesis was depleted in iMS cells (fig. S4A and table S4I). The promoter regions of these genes in iMS cells had broadly retained an active histone mark (H3K4me3), but, in contrast with adipocytes, many had acquired an inactive mark (H3K27me3) (fig. S4B and table S4J). However, there were examples where iMS cells had lost active histone marks (H3K4me3 and H3K27ac) at gene promoters and potential regulatory regions and gained repressive H3K27me3 [e.g., ADIPOQ; fig. S4C(i)]. In contrast, stromal genes had acquired active histone marks and lost repressive H3K27me3 [e.g. EPH2A; fig. S4C(ii)]. It is noteworthy that promoter regions of genes associated with muscle and pericytes (table S4K) were enriched for active histone marks in iMS cells compared with adipocytes [fig. S4D, (i) and (ii)]. We also compared demethylated CpGs in iMS cells and adipocytes (fig. S4E). There were 7366 sites in 2971 genes that were hypomethylated in iMS cells, of which 236 showed increased expression and were enriched for genes associated with tissue development and cellular growth and proliferation (fig. S4E).

PDGF-AB/AZAtreated murine osteocytes (murine iMS cells), but not bone-derived MSCs, expressed pluripotency associated genes, which were detectable by immunohistochemistry in 1 to 4% of cells (14). To evaluate expression in reprogrammed human cells, PDGF-AB/AZAtreated human adipocytes and matched AdMSCs were stained for OCT4, NANOG, and SOX2 with expression noted in 2, 0.5, and 3.5% of iMS cells respectively, but no expression was detected in AdMSCs (Fig. 2E). In addition to these transcription factors, we also evaluated surface expression of TRA-1-60 and SSEA4. Both proteins were uniformly expressed on iPSCs and absent in AdMSCs [fig. S4F(i)] and adipocytes [fig. S4F(ii)]. Although TRA-1-60 was absent in iMS cells, most (78%) expressed SSEA4 but rarely (<1%) coexpressed OCT4 and NANOG [fig. S4F(i)].

MSCs can be induced to differentiate in vitro into various cell lineages in response to specific cytokines and culture conditions. To evaluate the in vitro plasticity of human iMS cells, we induced their differentiation along with matched AdMSCs and primary adipocytes, into bone, fat, and cartilage, as well as into other mesodermal Matrigel tube-forming assays for endothelial cells (CD31) and pericytes (PDGFR) and muscle (MYH, myosin heavy chain; SMA, smooth muscle actin), endodermal (hepatocyte; HNF4, hepatocyte nuclear factor ), and neuroectodermal (TUJ1; neuron specific class III beta tubulin) lineages (Fig. 2F and fig. S4G). Whereas primary adipocytes remained as such and were resistant to transdifferentiation, iMS cells and AdMSCs showed comparable differentiation potential with the notable exception that only iMS cells generated pericyte-lined endothelial tubes in Matrigel. In keeping with these findings, relative to AdMSCs, iMS cells showed permissive epigenetic marks at pericyte genes [increased H3K4me3 and H3K27Ac; EPHA2 and MCAM; fig. S4H(i); and reduced CpG methylation; NOTCH1, SMAD7, TIMP2, AKT1, and VWF; fig. S4H(ii)]. Together with the notable differences in epigenetic profiles, these functional differences and low-level expression of pluripotency genes in iMS cell subsets suggested that these cells could be more amenable than matched AdMSCs to respond to developmental cues in vivo.

To evaluate spontaneous teratoma formation and in vivo plasticity of iMS cells, we tagged these cells and their matched AdMSCs with a dual lentiviral reporter, LeGO-iG2-Luc2 (28), that expresses both green fluorescent protein (GFP) and luciferase under the control of the cytomegalovirus promoter (Fig. 3A). To test teratoma-initiating capacity, we implanted tagged cells under the right kidney capsules of NOD Scid Gamma (NSG) mice (n = 3 per treatment group) after confirming luciferase/GFP expression in cells in culture (fig. S5, A and B). Weekly bioluminescence imaging (BLI) confirmed retention of cells in situ [Fig. 3B(i)] with progressive reduction in signal over time [Fig. 3B(ii)] and the absence of teratomas in kidneys injected with either AdMSCs or iMS cells [Fig. 3B(iii)]. Injection of equivalent numbers of iPS cells and iPS + iMS cell mixtures (1:49) to approximate iMS fraction expressing pluripotency markers led to spontaneous tumor formation in the same timeframe [Fig. 3B(iii)].

(A) Generation of luciferase/GFP-reporter AdMSCs and iMS cells, and assessment of their in vivo function. (B) Assessment of teratoma initiating capacity; (i) bioluminescence images at 0, 2, 6, and 8 weeks after implantation of 1 106 matched AdMSCs and iMS cells (P2; RM0057; n = 2 per group) under the right kidney capsules. (ii) Quantification of bioluminescence. (iii) Gross kidney morphology 8 weeks following subcapsular implantation of cells (R) or vehicle control (L). (C) Assessment of in vivo plasticity in a posterior-lateral intertransverse lumbar fusion model; (i) bioluminescence images following lumbar implantation of 1 106 matched AdMSCs or iMS cells (P2; RM0038; n = 3 per group) at 1 and 365 days after transplant. (ii) Quantification of bioluminescence. (iii) Tissues (bone, cartilage, muscle, and blood vessels) harvested at 6 months after implantation stained with (left) hematoxylin and eosin or (right) lineage-specific anti-human antibodies circles/arrows indicate regions covering GFP and lineage markerpositive cells. Corresponding graphs show donor cell (GFP+) contributions to bone, cartilage, muscle, and blood vessels as a fraction of total (DAPI+) cells in four to five serial tissue sections. Bars indicate confidence interval, n = 3. Photo Credit: Avani Yeola, UNSW Sydney.

To evaluate whether iMS cells survived and integrated with damaged tissues in vivo, we implanted transduced human iMS cells and matched AdMSCs controls into a posterior-lateral intertransverse lumbar fusion mouse model (Fig. 3A) (29). Cells were loaded into Helistat collagen sponges 24 hours before implantation into the posterior-lateral gutters adjacent to decorticated lumbar vertebrae of NSG mice (n = 9 iMS and n = 9 AdMSC). Cell retention in situ was confirmed by intraperitoneal injection of d-luciferin (150 mg/ml) followed by BLI 24 hours after cell implantation, then weekly for the first 6 weeks and monthly up to 12 months from implantation [Fig. 3C(i)]. The BLI signal gradually decreased with time but persisted at the site of implantation at 12 months, the final assessment time point [Fig. 3C(ii)]. Groups of mice (n = 3 iMS and n = 3 AdMSC) were euthanized at 3, 6, and 12 months and tissues harvested from sites of cell implantation for histology and immunohistochemistry [Fig. 3C(iii)]. Although implanted iMS cells and AdMSCs were present and viable at sites of implantation at 3 months, there was no evidence of lineage-specific gene expression in donor human cells (fig. S5C). By contrast, at 6 months after implantation, GFP+ donor iMS cells and AdMSCs were shown to contribute to new bone (BMP2), cartilage (SOX9), muscle (MYH), and endothelium (CD31) at these sites of tissue injury [Fig. 3C(iii)]. The proportion of donor cells expressing lineage-specific markers in a corresponding tissue section was significantly higher in iMS cells compared with matched AdMSCs at 6 months [Fig. 3C(iii) and table S2] as well as 12 months (fig. S5, E and D, and table S2). There was no evidence of malignant growth in any of the tissue sections or evidence of circulating implanted GFP+ iMS cells or AdMSCs (fig. S5E). Together, these data show that implanted iMS cells were not teratogenic, were retained long term at sites of implantation, and contributed to regenerating tissues in a context-dependent manner with greater efficiency than matched AdMSCs.

Although appropriate to assess in vivo plasticity and teratogenicity of implanted cells, the posterior-lateral intertransverse lumber fusion mouse model is not suited to address the question of tissue-specific differentiation and repair in vivo. To this end, we used a muscle injury model (30) where necrosis was induced by injecting 10 M cardiotoxin (CTX) into the left tibialis anterior (TA) muscle of 3-month-old female severe combined immunodeficient (SCID)/Beige mice. CTX is a myonecrotic agent that spares muscle satellite cells and is amenable to the study of skeletal muscle regeneration. At 24 hours after injury, Matrigel mixed with either 1 106 iMS cells or matched AdMSCs (or no cells as a control) was injected into the damaged TA muscle. The left (injured) and right (uninjured control) TA muscles were harvested at 1, 2, or 4 weeks after injury to assess the ability of donor cells to survive and contribute to muscle regeneration without ectopic tissue formation (Fig. 4A; cohort A). Donor human iMS cells or AdMSCs compete with resident murine muscle satellite cells to regenerate muscle, and their regenerative capacity is expected to be handicapped not only by the species barrier but also by having to undergo muscle satellite cell commitment before productive myogenesis. Recognizing this, a cohort of mice was subject to a second CTX injection, 4 weeks from the first injury/cell implantation followed by TA muscle harvest 4 weeks later (Fig. 4A; cohort B).

(A) Generation of iMS and AdMSCs and their assessment in TA muscle injury model. (B) (i) Confocal images of TA muscle stained for human CD56+ satellite cells (red) and laminin basement membrane protein (green; mouse/human). Graph shows donor hCD56+ satellite cell fraction for each treatment group. (ii) Confocal images of TA muscle harvested at 4 weeks and stained for human spectrin (red) and laminin (green; mouse/human). For each treatment, the left panel shows a tile scan of the TA muscle and the right panel a high magnification confocal image. Graph shows contribution of mouse (M), human (H), or chimeric (C) myofibers in three to five serial TA muscle sections per mouse (n = 3 mice per treatment group). (C) Confocal images of TA muscle 4 weeks following re-injury with CTX, stained for human spectrin (red) and laminin (green; mouse/human). For each treatment, left panel shows a tile scan of the TA muscle, upper right panel a low-magnification image, and lower right panel a high magnification image of the area boxed above. Graph shows contribution of mouse (M), human (H), or chimeric (C) myofibers in three to five serial TA muscle sections per mouse (n = 3 mice per treatment group). Graph bars indicate confidence interval. *P < 0.05, **P < 0.01, and ***P < 0.001 (linear mixed model). Photo credit: Avani Yeola, UNSW Sydney.

In tissue sections harvested from cohort A, donor-derived muscle satellite cells (31) [hCD56 (Thermo Fisher Scientific, MA5-11563)+; red] were evident in muscles implanted with both iMS cells and AdMSCs at each time point but were most numerous at 2 weeks after implantation [Fig. 4B(i) and fig. S6A]. The frequency of hCD56+ cells relative to total satellite cells [sublaminar 4,6-diamidino-2-phenylindolepositive (DAPI+) cells] was quantified in three to five serial sections of TA muscles per mouse in each of three mice per treatment group and was noted to be higher following the implantation of iMS cells compared with AdMSCs at all time points [week 1, 5.6% versus 2.4%; week 2, 43.3% versus 18.2%; and week 4, 30.7% versus 14.6%; Fig. 4B(i), table S2, and fig. S6A]. Donor cell contribution to regenerating muscle fibers was also assessed by measuring human spectrin (32) costaining with mouse/human laminin [(33) at 4 weeks (Fig. 4B(ii)]. At least 1000 myofibers from three to five serial sections of TA muscles for each of three mice in each treatment group were scored for human [H; hSpectrin+ (full circumference); laminin+], murine (M; mouse; hSpectrin; laminin+), or mouse/human chimeric [C; hSpectrin+ (partial circumference); laminin+] myofibers. Although none of the myofibers seen in cross section appeared to be completely human (i.e., donor-derived), both iMS cells and AdMSCs contributed to chimeric myofibers [Fig. 4B(ii)]. iMS cell implants contributed to a substantially higher proportion of chimeric fibers than AdMSC implants (57.7% versus 30.7%; table S2). In cohort B, TA muscles were allowed to regenerate following the initial CTX injection/cell implantation, and re-injured 4 weeks later with a repeat CTX injection. In these mice, although total donor cell contributions to myofibers in TA muscles harvested 4 weeks after re-injury were comparable to that observed in cohort A, there were no myofibers that appeared to be completely human (Fig. 4C). There were substantially more human myofibers following iMS cell implants than with AdMSCs (9.7% versus 5.4%; table S2). There was no evidence of ectopic tissue formation in TA muscles following implantation of either iMS cells or AdMSCs in either cohort.

To assess the physiological properties of muscles regenerated with human myofibers, we performed tetanic force contractions in extensor digitorum longus (EDL) muscles following the schema shown in Fig. 4A. Tetanic forces evoked by electrical pulses of various stimulus frequencies were not significantly different between the experimental cohorts or between the experimental cohorts and control animals [fig. S6B, (i) to (iii)]. However, when challenged with a sustained train of electrical pulses [fig. S6C(i)], the iMS group demonstrated significantly greater absolute [fig. S6C(ii)] and specific [fig. S6C(iii)] forces over a 3- to 6-s period. Together, these data showed that iMS cells had the capacity to respond appropriately to the injured environment and contribute to tissue-specific regeneration without impeding function.

We have optimized a protocol, originally designed for mouse osteocytes, to convert human primary adipocytes into iMS cells. We show that these long-term repopulating cells regenerate tissues in vivo in a context-dependent manner without generating ectopic tissues or teratomas.

PDGF-AB, AZA, and serum are indispensable ingredients in reprograming media, but the underlying reasons for their cooperativity and the observed dose-response variability between patients are not known. PDGF-AB is reported to bind and signal via PDGFR- and PDGFR- but not PDGFR- subunits (21). Mouse osteocytes and human adipocytes lack PDGFR, although surface expression was detectable as cells transition during reprogramming [mouse; day 2 of 8 (14) and human day 21 of 25]. However, these cells express PDGFR (14). Given that PDGFR inhibition attenuates iMS cell production in both mice (14) and humans, a degree of facilitated binding of PDGF-AB to PDGF- subunits or signaling through a noncanonical receptor is likely to occur, at least at the start of reprogramming. PDGF-Bcontaining homo- and heterodimers are potent mitogens that increase the pool of undifferentiated fibroblasts and preosteoblasts with rhPDGF-BB used in the clinic to promote healing of chronic ulcers and bone regeneration (34). However, the unique characteristics of PDGF-AB but not PDGF-BB or PDGF-AA that facilitate reversal and plasticity of cell identity in combination with AZA and serum (14) remain unknown.

PDGF-AB was replenished in culture throughout the reprogramming period, but AZA treatment was limited to the first 2 days for both mouse osteocyte and human adipocyte cultures. DNA replication is required for incorporation of AZA into DNA (35) and hence DNA demethylation is unlikely to be an initiating event in the conversion of terminally differentiated nonproliferating cells such as osteocytes and mature adipocytes. However, the majority of intracellular AZA is incorporated into RNA, which could directly affect the cellular transcriptome and proteome as an early event (36, 37). It is feasible that subsequent redistribution of AZA from RNA to DNA occurs when cells replicate resulting in DNA hypomethylation as a later event (38).

In the absence of serum, we could neither convert primary human adipocytes into iMS cells nor perpetuate these cells long term in culture. The efficiency of conversion and expansion was significantly higher with human versus FCS and highest with AS. The precise serum factor(s) that are required for cell conversion in conjunction with PDGF-AB and AZA are not known. The volumes of blood (~50 ml 2) and subcutaneous fat (5 g) that we harvested from donors were not limiting to generate sufficient numbers of P2 iMS cells (~10 106) for in vivo implantation and are in the range of cell numbers used in prospective clinical trials using mesenchymal precursor cells for chronic discogenic lumbar back pain (NCT02412735; 6 106) and hypoplastic left heart syndrome (NCT03079401; 20 106).

Our motivation was to optimize a protocol that could be applied to primary uncultured and easily accessible cells for downstream therapeutic applications, and adipose tissue satisfied these criteria. We have not surveyed other human cell types for their suitability for cell conversion using this protocol. It would be particularly interesting to establish whether tissue-regenerative properties of allogeneic mesenchymal precursor populations that are currently in clinical trials could be boosted by exposure to PDGF-AB/AZA. However, given that iMS cells and MSCs share stromal cell characteristics, identifying a unique set of cell surface markers that can distinguish the former is a priority that would assist in future protocol development and functional assessment of iMS cells.

Producing clinical-grade autologous cells for cell therapy is expensive and challenging requiring suitable quality control measures and certification. However, the advent of chimeric antigen receptor T cell therapy into clinical practice (39) has shown that production of a commercially viable, engineered autologous cellular product is feasible where a need exists. Although there were no apparent genotoxic events in iMS cells at P2, ex vivo expansion of cells could risk accumulation of such events and long-term follow-up of ongoing and recently concluded clinical trials using allogeneic expanded mesenchymal progenitor cells will be instructive with regard to their teratogenic potential. The biological significance of the observed expression of pluripotency-associated transcription factors in 2 to 3% of murine and human iMS cells is unknown and requires further investigation. However, their presence did not confer teratogenic potential in teratoma assays or at 12-month follow-up despite persistence of cells at the site of implantation. However, this risk cannot be completely discounted, and the clinical indications for iMS or any cell therapy require careful evaluation of need.

In regenerating muscle fibers, it was noteworthy that iMS cells appeared to follow canonical developmental pathways in generating muscle satellite cells that were retained and primed to regenerate muscle following a second muscle-specific injury. Although iMS cells were generated from adipocytes, there was no evidence of any adipose tissue generation. This supports the notion that these cells have lost their native differentiation trajectory and adopted an epigenetic state that favored response to local differentiation cues. The superior in vivo differentiation potential of iMS cells vis--vis matched AdMSCs was consistent with our data showing that despite the relatively minor transcriptomic differences between these cell types, the epigenetic state of iMS cells was better primed for systems development. Another clear distinction between iMS cells and AdMSCs was the ability of the former to produce CD31+ endothelial tube-like structures that were enveloped by PDGFR+ pericytes. An obvious therapeutic application for iMS cells in this context is vascular regeneration in the setting of critical limb ischemia to restore tissue perfusion, an area of clear unmet need (40).

An alternative to ex vivo iMS cell production and expansion is the prospect of in situ reprogramming by local subcutaneous administration of the relevant factors to directly convert subcutaneous adipocytes into iMS cells, thereby eliminating the need for ex vivo cell production. AZA is used in clinical practice and administered as a daily subcutaneous injection for up to 7 days in a 28-day cycle, with responders occasionally remaining on treatment for decades (41). Having determined the optimal dose of AZA required to convert human adipocytes into iMS cells in vitro (2 days, 5 M), the bridge to ascertaining the comparable in vivo dose would be to first measure levels of AZA incorporation in RNA/DNA following in vitro administration and match the dose of AZA to achieve comparable tissue levels in vivo. A mass spectrometrybased assay was developed to measure in vivo incorporation of AZA metabolites (AZA-MS) in RNA/DNA and is ideally suited to this application (38). The duration of AZA administration for adipocyte conversion was relatively short (i.e., 2 days), but PDGF-AB levels were maintained for 25 days. One mechanism of potentially maintaining local tissue concentrations would be to engineer growth factors to bind extra cellular matrices and be retained at the site of injection. Vascular endothelial growth factor A (VEGF-A) and PDGF-BB have recently been engineered with enhanced syndecan binding and shown to promote tissue healing (42). A comparable approach could help retain PDGF-AB at the site of injection and maintain local concentrations at the required dose. While our current data show that human adipocytederived iMS cells regenerate tissues in a context-dependent manner without ectopic or neoplastic growth, these approaches are worth considering as an alternative to an ex vivo expanded cell source in the future.

Extended methods for cell growth and differentiation assays and animal models are available in the Supplementary Materials, and antibodies used are detailed in the relevant sections.

The primary objective of this study was to optimize conditions that were free of animal products for the generation of human iMS cells from primary adipocytes and to characterize their molecular landscape and function. To this end, we harvested subcutaneous fat from donors across a broad age spectrum and used multiple dose combinations of a recombinant human growth factors and a hypomethylating agent used in the clinic and various serum types. We were particularly keen to demonstrate cell conversion and did so by live imaging and periodic flow cytometry for single-cell quantification of lipid loss and gain of stromal markers. Using our previous report generating mouse iMS cells from osteocytes and adipocytes as a reference, we first characterized the in vitro properties of human iMS cells including (i) long-term growth, (ii) colony-forming potential, (iii) in vitro differentiation, and (iv) molecular landscape. Consistent with their comparative morphology, cell surface markers, and behavioral properties, the transcriptomes (RNA sequencing) were broadly comparable between iMS cells and matched AdMSCs, leading to investigation of epigenetic differences [Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) histone chromatin immunoprecipitation sequencing (ChIP-seq), and RRBS for DNA methylation differences] that might explain properties that were unique to iMS cells (expression of pluripotency factors, generation of endothelial tubes in vitro with pericyte envelopes, and in vivo regenerative potential). Context-dependent in vivo plasticity was assessed using a tissue injury model that was designed to promote bone/cartilage/muscle/blood vessel contributions from donor cells and simultaneously assess the absence of ectopic/malignant tissue formation by these cells (labeled and tracked in vivo using a bioluminescence/fluorescence marker). Tissue-specific regeneration and the deployment of canonical developmental pathways were assessed using a specific muscle injury model, and donor cell contributions in all injury models were performed on multiple serial tissue sections in multiple mice with robust statistical analyses (see below). Power calculations were not used, samples were not excluded, and investigators were not blinded. Experiments were repeated multiple times or assessments were performed at multiple time points. Cytogenetic and Copy Number Variation (CNV) analyses were performed on iMS and AdMSCs pretransplant, and their teratogenic potential was assessed both by specific teratoma assays and long-term implantation studies.

Subcutaneous fat and blood were harvested from patients undergoing surgery at the Prince of Wales Hospital, Sydney. Patient tissue was collected in accordance with National Health and Medical Research Council (NHMRC) National Statement on Ethical Conduct in Human Research (2007) and with approval from the South Eastern Sydney Local Health District Human Research Ethics Committee (HREC 14/119). Adipocytes were harvested as described (43). Briefly, adipose tissue was minced and digested with 0.2% collagenase type 1 (Sigma-Aldrich) at 37C for 40 min and the homogenized suspension passed through a 70-m filter, inactivated with AS, and centrifuged. Primary adipocytes from the uppermost fatty layer were cultured using the ceiling culture method (44) for 8 to 10 days. AdMSCs from the stromal vascular pellet were cultured in MEM + 20% AS + penicillin (100 g/ml) and streptomycin (250 ng/ml), and 200 mM l-glutamine (complete medium).

Adherent mature adipocytes were cultured in complete medium supplemented with AZA (R&D systems; 5, 10, and 20 M; 2 days) and rhPDGF-AB (Miltenyi Biotec; 100, 200, and 400 ng/ml; 25 days) with medium changes every 3 to 4 days. For inhibitor experiments, AG1296 was added for the duration of the culture. Live imaging was performed using an IncuCyte S3 [10 0.25numerical aperture (NA) objective] or a Nikon Eclipse Ti-E (20 0.45-NA objective). Images were captured every 30min for a period of 8 days starting from day 15. Twelve-bit images were acquired with a 1280 1024 pixel array and analyzed using ImageJ software. In vitro plasticity was determined by inducing the cells to undergo differentiation into various cell types using differentiation protocols adapted from a previous report (45).

Animals were housed and bred with approval from the Animal Care and Ethics Committee, University of New South Wales (UNSW; 17/30B, 18/122B, and 18/134B). NSG (NOD.Cg-PrkdcscidIl2rgtm1Wjl/SzJ) and SCID/Beige (C.B-Igh-1b/GbmsTac-Prkdcscid-Lystbg N, sourced from Charles River) strains were used as indicated. The IVIS Spectrum CT (Perkin Elmer) was used to capture bioluminescence. Briefly, 15 min after intraperitoneal injection of d-luciferin (150 mg/kg), images were acquired for 5 min and radiance (photon s1 cm2 sr1) was used for subsequent data analysis. The scanned images were analyzed using the Living Image 5.0 software (Perkin Elmer).

Teratoma assays (46) were performed on 3- to 4-month-old female NSG mice. Lentiviral-tagged cells (5 105) in 20 l of phosphate-buffered saline containing 80% Matrigel were injected under the right kidney capsule using a fine needle (26 gauges) and followed weekly by BLI until sacrifice at week 8. Both kidneys were collected, fixed in 4% paraformaldehyde (PFA) for 48 hours, embedded in optimal cutting temperature compound (OCT), cryosectioned, and imaged for GFP.

Posterior-lateral intervertebral disc injury model (29). Lentiviral-tagged (28) AdMSCs (1 106) or iMS cells were loaded onto Helistat collagen sponges and implanted into the postero-lateral gutters in the L4/5 lumbar spine region of anesthetized NSG mice following decortication of the transverse processes. Animals were imaged periodically for bioluminescence to track the presence of transplanted cells. At 3, 6, or 12 months, mice were euthanized, and spines from the thoracic to caudal vertebral region, including the pelvis, were removed whole. The specimens were fixed in 4% PFA for 48 hours, decalcified in 14% (w/v) EDTA, and embedded in OCT.

Muscle injury model (47). The left TA and EDL muscles of 3- to 4-month-old female SCID/Beige mice were injured by injection with 15 l of 10 M CTX (Latoxan). Confocal images of three to four serial sections (TA) per mouse were captured by Zen core/AxioVision (Carl Zeiss) and visualized by ImageJ with the colocalization and cell counter plugins [National Institutes of Health; (48)]. Tetanic force contractions were performed on EDL muscles (49).

Total RNA was extracted using the miRNeasy Mini Kit (Qiagen) according to manufacturers instructions, and 200 ng of total RNA was used for Illumina TruSeq library construction. Library construction and sequencing was performed by Novogene (HK) Co. Ltd. Raw paired-end reads were aligned to the reference genome (hg19) using STAR (https://github.com/alexdobin/STAR), and HTSeq (50) was used to quantify the transcriptomes using the reference refFlat database from the UCSC Table Browser (51). The resulting gene expression matrix was normalized and subjected to differential gene expression using DeSeq2 (52). Normalized gene expression was used to compute and plot two-dimensional principal components analysis, using the Python modules sklearn (v0.19.1; https://scikit-learn.org/stable/) and Matplotlib (v2.2.2; https://matplotlib.org/), respectively. Differentially expressed genes (log2 fold change |1|, adjusted P < 0.05) were the input to produce an unsupervised hierarchical clustering heat map in Partek Genomics Suite software (version 7.0) (Partek Inc., St. Louis, MO, USA). Raw data are available using accession GSE150720.

ChIP was performed as previously described (53) using antibodies against H3K27Ac (5 g per IP; Abcam, ab4729), H3K4Me3 (5 g per IP; Abcam ab8580), and H3K27Me3 (5 g per IP; Diagenode, C15410195). Library construction and sequencing were performed by Novogene (HK) Co. Ltd. Paired-end reads were aligned to the hg38 genome build using Burrows Wheeler Aligner (BWA) (54) duplicate reads removed using Picard (http://broadinstitute.github.io/picard/), and tracks were generated using DeepTools bamCoverage (https://deeptools.readthedocs.io/en/develop/). Peaks were called using MACS2 (55) with the parameter (P = 1 109). Differentially bound regions between the AdMSC and iMS were calculated using DiffBind (http://bioconductor.org/packages/release/bioc/vignettes/DiffBind/inst/doc/DiffBind.pdf) and regions annotated using ChIPseeker (56). Raw data are available using accession GSE151527. Adipocyte ChIP data were downloaded from Gene Expression Omnibus (GEO); accession numbers are as follows for the three histone marks: GSM916066, GSM670041, and GSM772771.

Total genomic DNA was extracted using the DNA MiniPrep Kit (Qiagen), and RRBS library construction and sequencing were performed by Novogene (HK) Co. Ltd. Raw RRBS data in fastq format were quality and adapter trimmed using trim_galore (0.6.4) with rrbs parameter (www.bioinformatics.babraham.ac.uk/projects/trim_galore). The trimmed fastq files were then aligned to a bisulfite-converted genome (Ensembl GRCh38) using Bismark (2.3.5), and methylation status at each CpG loci was extracted (57). The cytosine coverage files were converted to BigWig format for visualization. Differentially methylated cytosines (DMCs) and DMRs were identified using methylKit (1.10) and edmr (0.6.4.1) packages in R (3.6.1) (58, 59). DMCs and DMRs were annotated using ChIPseeker (56), and pathway enrichment was performed as detailed below. Raw data are available using accession number GSE151527. Adipocyte RRBS data were downloaded from GEO: GSM2342293 and GSM2342392.

IPA (Qiagen) was used to investigate enrichment in molecular and cellular functions, systems development and function, and canonical pathways.

Statistical analysis was performed in SAS. For the dose-optimization experiments (Fig. 1), a linear mixed model with participant-level random effects was used to estimate maximum time by dose level and age group. A linear mixed model with participant-level random effects was used to analyze statistical differences in lineage contribution outcomes between treatment groups (Fig. 3) and at different time points posttransplant, to estimate the percentage of cells by treatment and lineage. For the in vivo regeneration experiment (Fig. 4), a linear model was used to model the percent of cells over time for each group. Quadratic time terms were added to account for the observed increase from 1 to 2 weeks and decrease from 2 to 4 weeks. In the muscle regeneration experiment, a linear model was applied to cohort A and cohort B, to estimate and compare percent cells by treatment and source. Statistical modeling data are included in table S2.

Acknowledgments: We are indebted to the patients who donated tissue to this project. We thank E. Cook (Prince of Wales Private Hospital), B. Lee (Mark Wainwright Analytical Centre, UNSW Sydney), and technicians at the UNSW BRC Facility for assistance with sample and data collection and animal care; Y. Huang for technical assistance; and A. Unnikrishnan and C. Jolly for helpful discussions and critical reading of the manuscript. We acknowledge the facilities and scientific and technical assistance of the National Imaging Facility, a National Collaborative Research Infrastructure Strategy (NCRIS) capability, at the BRIL (UNSW). The STRO-1 antibody was a gift from S. Gronthos, University of Adelaide, Australia. Funding: We acknowledge the following funding support: A.Y. was supported by an Endeavour International Postgraduate Research scholarship from the Australian Government. S.S. is supported by an International Postgraduate Student scholarship from UNSW and the Prince of Wales Clinical School. P.S. is supported by an International Postgraduate Student scholarship from UNSW. M.L.T. and D.D.M. acknowledge funding from St. Vincents Clinic Foundation and Arrow BMT Foundation. K.A.K. acknowledges funding from Australian Research Council (FT180100417). J.M. is supported, in part, by the Olivia Lambert Foundation. M.K. is supported by a NHMRC Program Grant (APP1091261) and NHMRC Principal Research Fellowship (APP1119152). L.B.H. acknowledges funding from MTPConnect MedTech and Pharma Growth Centre (PRJ2017-55 and BMTH06) as part of the Australian Governmentfunded Industry Growth Centres Initiative Programme and The Kinghorn Foundation. D.B. is supported by a Peter Doherty Fellowship from the National Health and Medical Research Council of Australia, a Cancer Institute NSW Early Career Fellowship, the Anthony Rothe Memorial Trust, and Gilead Sciences. R.M. acknowledges funding from Jasper Medical Innovations (Sydney, Australia). J.E.P., V.C., and E.C.H. acknowledge funding from the National Health and Medical Research Council of Australia (APP1139811). Author contributions: The project was conceived by V.C. and J.E.P., and the study design and experiments were planned by A.Y., V.C., and J.E.P. Most of the experiments and data analyses were performed by A.Y., guided and supervised by V.C. and J.E.P. S.S., R.A.O., C.A.L., D.C., F.Y., M.L.T., P.S., T.H., J.R.P., P.H., W.R.W., and V.C. performed additional experiments and data analyses, with further supervision from R.M., C.P., J.A.I.T., D.C., J.W.H.W., L.B.H., D.B., and E.C.H. Statistical analyses were performed by J.O. R.M., D.D.M., J.M., K.A.K., and M.K. provided critical reagents. The manuscript was written by A.Y., J.A.I.T., V.C., and J.E.P., and reviewed and agreed to by all coauthors. Competing interests: V.C. and J.E.P. are named inventors on a patent A method of generating cells with multi-lineage potential (US 9982232, AUS 2013362880). All other authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.

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Induction of muscle-regenerative multipotent stem cells from human adipocytes by PDGF-AB and 5-azacytidine - Science Advances

RoosterBio Partners with Sartorius to Advance Cell and Gene Therapy Manufacturing – GlobeNewswire

January 14, 2021 10:00 ET | Source: RoosterBio

FREDERICK, Md., Jan. 14, 2021 (GLOBE NEWSWIRE) -- RoosterBio Inc., a leading supplier of human mesenchymal stem/stromal cell (hMSC) working cell banks, highly engineered media and hMSC bioprocess systems, today announces the signing of a strategic collaboration with Sartorius, a leading international partner of life science research and the biopharmaceutical industry. The collaboration aims to advance the scale-up of hMSC manufacturing for regenerative medicine by leveraging the best-in-class solutions of both companies to significantly reduce process development efforts, industrialize the supply chain and accelerate the development and commercialization of groundbreaking cell-based regenerative cures.

RoosterBio and Sartorius will create a set of GMP-compatible, customer-centric protocols using RoosterBios hMSC and media systems, alongside Sartoriuss single use manufacturing technologies, process control software and cell analysis tools of hMSC final product manufacturing. Cell expansion will be rapidly optimized using Sartoriuss benchtop Ambr system and MODDE design of experiment software allowing the technical team to compare cultures in identically sized, multi-parallel bioreactors to gain process information and optimized conditions in a short timeline. Sartoriuss scalable Biostat STR production bioreactors will then be used to scale up to 50L as part of this collaboration, with the system benefitting from scalability to 2000L. Sartorius equipment will also be leveraged to develop post-harvest processing methods with the kSep system as well as process and quality analytics. This joint effort will simplify multiple steps in therapeutic development by providing robust, streamlined, end-to-end platform technologies and protocols that can be implemented for rapid scale up of manufacturing processes, allowing product developers to significantly speed up their development timelines.

Taking hMSC manufacturing to the thousand-liter scale is critical in meeting product dose requirements in commercial manufacturing, said RoosterBio CEO Margot Connor. For truly robust and standardized production in the field, a highly controlled manufacturing strategy is needed, with the implementation of automation, process monitoring and control to increase batch scale, consistency and efficiency. This collaboration is well-positioned to accomplish the clinical scale requirements of regenerative medicine product developers while laying foundation for true commercial scale manufacturing.

With the combination of technologies and tools of RoosterBio and Sartorius we support our customers to develop stem cell and therapies faster, better and more cost-efficient. Scalability is key in commercial manufacturing and this cooperation will help to meet the requirements of our customers even better, said Hugo de Wit, Head of Advanced Therapies at Sartorius.

Both companies aim to use the data from this collaboration to provide co-learning and development opportunities to support the growing cell and gene therapy industry.

About RoosterBio

RoosterBio, Inc. is a privately held cell manufacturing platform technology company focused on accelerating the development of a sustainable Regenerative Medicine industry, one customer at a time. RoosterBio's products are high-volume, affordable, and well-characterized adult human mesenchymal stem/stromal cells (hMSCs) paired with highly engineered media systems. RoosterBio has simplified and standardized how living cells are purchased, expanded, and used in development, leading to marked time and costs savings for customers. RoosterBio's innovative products are ushering in a new era of productivity and standardization into the field. Visit http://www.roosterbio.com.

About Sartorius The Sartorius Group is a leading international partner of life science research and the biopharmaceutical industry. With innovative laboratory instruments and consumables, the Groups Lab Products & Services Division concentrates on serving the needs of laboratories performing research and quality control at pharma and biopharma companies and those of academic research institutes. The Bioprocess Solutions Division with its broad product portfolio focusing on single-use solutions helps customers to manufacture biotech medications and vaccines safely and efficiently. The Group has been annually growing by double digits on average and has been regularly expanding its portfolio by acquisitions of complementary technologies. In fiscal 2019, the company earned sales revenue of some 1.83 billion euros. At the end of 2019, more than 9,000 people were employed at the Groups approximately 60 manufacturing and sales sites, serving customers around the globe.

Visit http://www.sartorius.com

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RoosterBio: Carrie Zhang, Director of Marketing czhang@roosterbio.com

Sartorius:Andre Hofmann, Head of Public Relations andre.hofmann@sartorius.com

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RoosterBio Partners with Sartorius to Advance Cell and Gene Therapy Manufacturing - GlobeNewswire

Reversing The Aging Clock With Epigenetic Reprogramming – Bio-IT World

By Deborah Borfitz

January 13, 2021 | As aging researchers are aware, birthday candles are not a good guide to either human health or longevity. But there is an abundance of clues in the genome and, as suggested by studies in animals, some of age-related damage is reversible by removing or reprogramming problematic cells or blocking the activity of key proteins.

As it turns out, DNA methylationa frequently-used biomarker of biological ageis not just marking time like a clock on the wall but actually controlling time within cells, according to David Sinclair, an expert on aging at Harvard Medical School and cofounder of 4-year-old Life Biosciences. The revelation emerged from a study recently published in Nature (DOI: 10.1038/s41586-020-2975-4) where Harvard researchers showed, for the first time, that the pattern of DNA methylation in the genome can be safely reset to a younger age.

It was in fact a prerequisite to restoring youthful function and vision in old mice, says Sinclair, who has spent most of his adult life studying the epigenetic changes associated with aging. Up until a few years ago, he thought the process was unidirectional and that cells ultimately lost their identity and malfunctioned or became cancerous.

It seemed crazy to try to get proteins to return to the place they were in young cells, Sinclair says. Proteins move around in response to age-associated DNA damage and end up in the wrong places on the genome, causing the wrong genes to be turned on, but scientists did not know if proteins could go back, where the instructions were stored, or if they were being stored at all.

As covered in his 2019 bestseller Lifespan, Sinclair now believes that aging is the result of the so-called epigenetic changes scrambling how the body reads genetic code. Were essentially looking for the polish to get the cell to read the genome correctly again, he says, a process he likens to recovering music on a scratched CD.

Yamanaka Factors

Sinclair and his research associates have been focusing on the eye, in part because retinal tissues start aging soon after birth, he explains. While a damaged optic nerve can heal in a newborn, the injury is irreversible in a 1-year-old.

Yuancheng Lu, a former student of Sinclairs, was also interested in the eye because his family has a vision-correction business and recognized sight loss as a huge unmet need, he continues. We thought if we could take the age of those retinal cells back far enough, but not so far that they lose their identity, we might be able to see regrowth of the optic nerve if it was damaged.

Among the foundational work was a 2016 study in Cell (DOI: 10.1016/j.cell.2016.11.052) by Life Biosciences cofounder Juan Carlos Izpisua Belmonte (Salk Institute for Biological Studies) who partially erased cellular markers of aging in mice that aged prematurely, as well as in human cells, by turning on Yamanaka factors Oct4, Sox2, Klf4, and c-Myc (OSKM) highly expressed in embryonic stem cells. Short-term induction of OSKM ameliorated hallmarks of aging and modestly extended lifespan in the short-lived mice.

The lifespan gain was widely dismissed as an artifact of shocking a mouse, says Sinclair, since the mice died if the treatment continued for more than two days. Although the human health implications appeared unlikely, his Harvard team decided to try the approach using an adeno-associated virus as a vehicle to deliver the youth-restoring OSKM genes into the retinas of aging mice.

The technology kept killing the mice or causing them to get cancer until Lu decided to drop the c-Myc genean oncogenein his experiments using human skin cells. He looked at [damaged] cells that had been expressing OSK for three weeks and the nerves were growing back toward the brain to an unprecedented degree. Moreover, the cells got older by the damage and younger by the treatment.

As the broader team went on to show in the Nature paper, the trio of Yamanaka factors effectively made cells younger without causing them to lose their identity (i.e., turning back into induced pluripotent stem cells) or fueling tumor growth even after a year of continuous treatment of the entire body of a mouse. If anything, the mice had fewer tumors over the course of the study, says Sinclair.

Although the mice needed to be autopsied to definitively measure tumor burden, Sinclair says the study will be repeated to learn if the epigenetic reprogramming technique can increase lifespan.

Findings have implications beyond the treatment of age-related diseases specific to the eye, says Sinclair. Aging researchers have published studies showing other types of tissues, including muscle and kidney cells, can also be rejuvenated.

Clocked Results

In the latest study using mice, epigenetic reprogramming was found to have three beneficial effects on the eye: promotion of optic nerve regeneration, reversal of vision loss with a condition mimicking human glaucoma, and reversal of vision loss in aging animals without glaucoma. The latter finding, from Sinclairs vantage point, is the most important one. This is ultimately a story about finding a repository of youthful information in old cells that can reverse aging.

Results of all three experiments are noteworthy and have commonly thought to be three separate processes, says Sinclair. That is only because the fields of aging and acute and chronic disease are distinct disciplines that rarely talk to each other.

The Harvard team is pioneering a new way to tackle diseases of aging by addressing the underlying cause. This is the first time, as far as Sinclair is aware, where nerve damage was studied in old rather than young animals. In the case of glaucoma and most diseases, aging is considered largely irrelevant, when of course we know glaucoma is a disease of aging.

A variety of aging clocks, including some the research team built themselves, have been deployed for studies because they are considered the most accurate predictor of biological age and future health, says Sinclair. As embryos, cells lay down different patterns of methylation to ensure they remember their purpose over the next 80 to 100 years.

For unknown reasons, methyl groups get predictably added and subtracted from DNA bases across cell and tissue types and even species, Sinclair says. In 2013, UCLAs Steve Horvath (another Life Biosciences cofounder) showed that machine learning could be used to pick out the hot spots and predict individual lifespan depending on how far above or below the DNA methylation line they sit (Genome Biology, DOI: 10.1186/gb-2013-14-10-r115).

A multitude of aging clocks have since been developed. Eventually, we will need some standardization in the field, but there is nothing super-mysterious about aging clocks, says Sinclair. One of my grad students could probably get you one by the end of the day.

Booming Field

Aging research is a rapidly accelerating field and epigenetic reprogramming is poised to become a particularly active area of inquiry. In terms of numbers, there are still only a dozen or so labs intensely working on this, but there are probably a hundred others I am aware of who are getting into it, says Sinclair.

Life Biosciences began with four labs, but new ones are now joining on an almost weekly basis, he adds. Collaborators have expanded work to the ear and other areas of the body beyond the eye, he adds.

Were also reducing the cost of the DNA clock test by orders of magnitude so [biological age prediction] can be done on millions of people, he continues. In the future, aging clocks are expected to be a routine test in physicians arsenal to guide patient care as well as to monitor response to cancer treatment.

Harvard University has already licensed two patents related to the technology used by the aging researchers to Life Biosciences, Sinclair says. The company has built a scientific team with a group of world-class advisors who developed gene therapy for the eye, which will be tested first for the treatment of glaucoma.

The role of chaperone-mediated autophagy in aging and age-related diseases is another promising area of research being pursued by Life Biosciences Ana Maria Cuervo, M.D, Ph.D., professor, and co-director of the Institute of Aging Studies at the Albert Einstein College of Medicine. Cuervo recently reported at a meeting that fasting-induced autophagy, the cells natural mechanism for removes unnecessary or dysfunctional components, can greatly extend the lifespan of mice. She believes the triggering of this process might one day help treat diseases such as macular degeneration and Alzheimers.

The specialty of Manuel Serrano, Ph.D., the fourth company cofounder, is cellular senescence and reprogramming and how they relate to degenerative diseases of the lung, kidney, and heart. He isan internationally recognized scientist who has made significant contributions to cancer and aging research and works in the Institute for Research Biomedicine in Barcelona.

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Reversing The Aging Clock With Epigenetic Reprogramming - Bio-IT World

New Models in Organoids Market Open New Vistas in Stem Cell Research for Cancer, Global Valuation to Reach US$ 12.8 Bn by 2030: TMR – PRNewswire

ALBANY, N.Y., Jan. 12, 2021 /PRNewswire/ -- Organoids are stem cell-derived 3D culture systems and are usually derived from induced pluripotent stem cells (iPSCs) and multipotent adult tissue stem cells (ASCs). The technologies in the organoids market have emerged as a novel culture used for human disease modelling. Their amazing capability in recapitulating in vivo anatomy and physiology of organs is utilized to open new paradigms in cell biology areas such as in gene therapy, regenerative medicine, and cancer research. Most prominently, researchers and industry players have harnessed the potential of organoids in regenerative medicine and tissue engineering.

Advent of new methods in generating 3D structures are opening new vistas in human disease modelling, particularly in virology. The utilization of these in drug discovery and personalized medicine will transform medical care in years to come. Europe and North America have emerged as the new hotspots for patient-derived human organoid studies in the global organoids market.

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The revenue of global organoid market is projected to climb from US$ 1.7 Bn in 2019 to touch the mark of US$ 12.8 Bn by 2030.

Key Findings of Organoids Market

In the backdrop of the need for new approaches of studying the pathogenesis of currently emerging Covid-19, organoids market is replete with incredible revenue potential for stakeholders. Researchers are relentlessly working toward new organoids approaches for understanding tissue tropism of SARS-CoV-2. In the last few years, the strides in the organoids market has unarguably expanded the armamentarium of virologists studying infectious diseases. A case in point was Zika virus infection.

Patient-derived human organoids are increasingly being leveraged upon by researchers to open new avenues in tissue engineering and regenerative medicine. These 3D-based cultures have been able to overcome the limitations of 2D cancer-derived cell lines, notably in bladder, colorectal, brain, and liver cancer. There is demand for new patient-derived cell lines for cancer sample biobanking. Integrating biobanking with tumor modelling has undoubtedly expanded the avenue in cancer care. This is also expanding the avenue for precision medicine, the relevance of which is gather traction in patient care.

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Over the years, the organoid market has made some remarkable strides on the back of collaborations between researchers in universities and medical experts in healthcare institutes. Next-gen organoid development for Covid-19 is a case in point where there has been surge in research funding. Giant leaps made by genome editing systems have expanded the avenue in genome engineering of human stem cells. This will test new methods of generating human organoid models. Another researcher directions attracting investments are in development of cerebral organoids for neurological diseases.

Organoids Market: Key Driving Factors and Promising Avenues

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Organoids Market: Competitive Dynamics

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New Models in Organoids Market Open New Vistas in Stem Cell Research for Cancer, Global Valuation to Reach US$ 12.8 Bn by 2030: TMR - PRNewswire

Health Canada Approves ONUREG (azacitidine tablets), First Maintenance Therapy for Patients in Remission from Acute Myeloid Leukemia – Canada NewsWire

AML is a heterogeneous clonal disorder characterized by immature myeloid cell proliferation and bone marrow failure, and is the most common form of acute leukemia in adults, accounting for approximately 80 per cent of adult cases.2,3,4 An estimated 40-60 per cent of patients aged 60 years and older and 60-80 per cent of patients under 60 years old will obtain complete remission through induction chemotherapy (IC); however, 50 per cent will relapse within a year.5,6 Once a relapse occurs, long-term survival averages at six months.7 In 2015, an estimated 1,235 Canadians were diagnosed with AML and the overall incidence rate in Canada is 3.46/100,000 people.8,9

"While the majority of patients with AML achieve a complete remission with intensive chemotherapy, many remission patients will experience disease relapse, especially if they were not eligible for a stem cell transplant. Until now, there has been no established standard of care for Canadians who are in remission from AML, but are not eligible for a stem cell transplant," noted Dr. Andre Schuh, Princess Margaret Cancer Centre, Toronto. "The approval of ONUREG is significant because it gives transplant ineligible patients with AML in remission a new treatment option that may improve their survival".

ONUREG is a nucleoside metabolic inhibitor that is taken orally and works by preventing cancer cells from growing. ONUREG becomes incorporated into the building blocks of cells (deoxyribonucleic acid (DNA) and ribonucleic acid (RNA)), which interferes with the production of new DNA and RNA. This is thought to kill cancerous cells in leukemia.10

"The approval of ONUREG is an extension of our ongoing commitment to Canadians living with blood cancer," said Al Reba, General Manager, Bristol Myers Squibb Canada. "We are proud that this therapy will help to fill a significant need for Canadians living in remission from AML and hope that it will have a positive impact on their everyday life."

Health Canada's approval of ONUREG is based upon findings from the QUAZAR AML-001 clinical trial. The QUAZAR study, a double-blind, randomized, placebo-controlled, multicenter Phase III study, involved adult patients 55 years or older living with AML. In the study, patients were randomized to Onureg or placebo within four months of achieving first CR/CRi following intensive induction chemotherapy and were not eligible for a stem cell transplant.11In the study, results showed the median overall survival (OS) was significantly longer with ONUREG versus placebo: 24.7 months versus 14.8 months [HR 0.69 (95% CI: 0.55, 0.86); p=0.0009], indicating a 31 per cent reduction in the risk of death in the ONUREG arm. Relapse-free survival (RFS), the key secondary endpoint in the study, supports the OS results. The median RFS was 10.2 months for ONUREG versus 4.8 months for placebo [HR 0.65 (95% CI: 0.52, 0.81); p=0.0001].12

About Bristol Myers Squibb CanadaBristol Myers Squibb Canada Co. is an indirect wholly-owned subsidiary of Bristol Myers Squibb Company, a global biopharmaceutical company whose mission is to discover, develop and deliver innovative medicines that help patients prevail over serious diseases. For more information about Bristol Myers Squibb global operations, visitwww.bms.com. Bristol Myers Squibb Canada Co. delivers innovative medicines for serious diseases to Canadian patients in the areas of cardiovascular health, oncology, and immunoscience. Bristol Myers Squibb Canada Co. employs close to 400 people across the country. For more information, please visitwww.bms.com/ca.

About Bristol Myers SquibbBristol Myers Squibb is a global biopharmaceutical company whose mission is to discover, develop and deliver innovative medicines that help patients prevail over serious diseases. For more information about Bristol Myers Squibb, visit us atBMS.comor follow us on LinkedIn, Twitter, YouTube, Facebookand Instagram.

References

_________________________

1 ONUREG Product Monograph, January 2021.

2 Saultz JN, Garzon R. J Clin Med 2016;5:33.

3Leukemia & Lymphoma Society of Canada. Acute Myeloid Leukemia. Available from https://www.llscanada.org/sites/default/files/National/CANADA/Pdf/InfoBooklets/AML%20Fact%20Sheet%2012-2019.pdf. Accessed December 11, 2020.

4De Kouchkovsky I, Abdul-Hay M. Blood Cancer J 2016;e441:DOI:10.1038/bcj.2016.50.

5Dohner et al. Blood. 2017;129(4):42447.

6SEER Cancer Statistics, 2007-2013.

7Xu J, et al. Medicine (Baltimore) 2018;97:e12102.

8Statistics Canada. Population estimates on July 1st, by age and sex. Available from https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1710000501&pickMembers%5B0%5D=1.1&pickMembers%5B1%5D=2.1&cubeTimeFrame.startYear=2015&cubeTimeFrame.endYear=2016&referencePeriods=20150101%2C20160101. Accessed December 11, 2020.

9Shysh et al. BMS Public Health (2018) 18:94.

10ONUREG Product Monograph, January 2021.

11ONUREG Product Monograph, January 2021.

12 ONUREG Product Monograph, January 2021.

SOURCE Bristol Myers Squibb Canada Co.

For further information: For media requests, please contact: Rachel Yates, Lead, Corporate Affairs, Bristol Myers Squibb Canada, [emailprotected]; Alannah Nugent, Account Executive, Health, Edelman, [emailprotected]

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Health Canada Approves ONUREG (azacitidine tablets), First Maintenance Therapy for Patients in Remission from Acute Myeloid Leukemia - Canada NewsWire

Stem Cells Market 2020 Research Study including Growth Factors, Types and Application to 2026| Covid-19 Impact – Farming Sector

Global Stem Cells Market Report mainly includes sales, revenue, trade, competition, investment, forecast and marketing of the product and the segments here include companies, types, applications, regions, countries, etc. The regions of Stem Cells market industry contain all Global market, especially in North America, Europe, Asia Pacific, Latin America and MEA.

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Data and information by Stem Cells market trends, by manufacturer, by region, by type, by application and etc., and custom research can be added according to specific requirements.

By Market Players: Osiris Therapeutics, Inc., Cytori Therapeutics, Inc., BrainStorm Cell Therapeutics Inc., U.S. Stem Cell, Inc., Takara Bio Inc., BioTime Inc., Cellular Engineering Technologies Inc., Astellas Pharma Inc., Caladrius Biosciences, Inc., STEMCELL Technologies Inc.

By Product Adult Stem Cell, Human Embryonic Stem Cell, Induced Pluripotent Stem Cell

By Source Autologous, Allogeneic,

By Application Regenerative Medicine, Drug Discovery and Development,

By End User Therapeutic Companies, Cell and Tissue Banks, Tools and Reagent Companies, Service Companies,

Stem Cells Market by Regions:

The Stem Cells Market contains the SWOT analysis of the market. Finally, the report contains the conclusion part where the opinions of the industrial experts are included.

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Points Covered in The Report:

Key Reasons to Purchase

Detailed TOC of 2019-2024 Global and Regional Stem Cells Production, Sales and Consumption Status and Prospects Professional Market Research Report

Chapter 1 Industry Overview of Stem Cells Market

1.1 Definition

1.2 Brief Introduction by Major Type

1.3 Brief Introduction by Major Application

1.4 Brief Introduction by Major Regions

1.4.1 United States

1.4.2 Europe

1.4.3 China

1.4.4 Japan

1.4.5 India

Chapter 2 Production Market Analysis of Stem Cells Market

2.1 Global Production Market Analysis

2.1.1 2013-2020 Global Capacity, Production, Capacity Utilization Rate, Ex-Factory Price, Revenue, Cost, Gross and Gross Margin Analysis

2.1.2 2013-2020 Major Manufacturers Performance and Market Share

2.2 Regional Production Market Analysis

2.2.1 2013-2020 Regional Market Performance and Market Share

2.2.2 United States Market

2.2.3 Europe Market

2.2.4 China Market

2.2.5 Japan Market

2.2.6 India Market

2.2.7 Market

Chapter 3 Sales Market Analysis of Stem Cells Market

3.1 Global Sales Market Analysis

3.2 Regional Sales Market Analysis

Chapter 4 Consumption Market Analysis of Stem Cells Market

4.1 Global Consumption Market Analysis

4.2 Regional Consumption Market Analysis

Chapter 5 Production, Sales and Consumption Market Comparison Analysis

5.1 Global Production, Sales and Consumption Market Comparison Analysis

5.2 Regional Production, Sales Volume and Consumption Volume Market Comparison Analysis

Chapter 6 Major Manufacturers Production and Sales Market Comparison Analysis

6.1 Global Major Manufacturers Production and Sales Market Comparison Analysis

6.2 Regional Major Manufacturers Production and Sales Market Comparison Analysis

Chapter 7 Major Type Analysis

7.1 2013-2020 Major Type Market Share

Chapter 8 Major Application Analysis

8.1 2013-2020 Major Application Market Share

Chapter 9 Industry Chain Analysis

9.1 Up Stream Industries Analysis

9.2 Manufacturing Analysis

9.3 Industry Chain Structure Analysis

Chapter 10 Global and Regional Stem Cells Market Forecast

10.1 Production Market Forecast

10.1.1 Global Market Forecast

10.1.2 Major Region Forecast

10.2 Sales Market Forecast

10.2.1 Global Market Forecast

10.2.2 Major Classification Forecast

10.3 Consumption Market Forecast

10.3.1 Global Market Forecast

10.3.2 Major Region Forecast

10.3.3 Major Application Forecast

Chapter 11 New Project Investment Feasibility Analysis

11.1 New Project SWOT Analysis

11.2 New Project Investment Feasibility Analysis

Chapter 12 Conclusions

Chapter 13 Appendix

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Stem Cells Market 2020 Research Study including Growth Factors, Types and Application to 2026| Covid-19 Impact - Farming Sector

The real reason behind goosebumps – Jill Lopez

If you've ever wondered why we get goosebumps, you're in good company -- so did Charles Darwin, who mused about them in his writings on evolution. Goosebumps might protect animals with thick fur from the cold, but we humans don't seem to benefit from the reaction much -- so why has it been preserved during evolution all this time?

In a new study, Harvard University scientists have discovered the reason: the cell types that cause goosebumps are also important for regulating the stem cells that regenerate the hair follicle and hair. Underneath the skin, the muscle that contracts to create goosebumps is necessary to bridge the sympathetic nerve's connection to hair follicle stem cells. The sympathetic nerve reacts to cold by contracting the muscle and causing goosebumps in the short term, and by driving hair follicle stem cell activation and new hair growth over the long term.

Published in the journalCell, these findings in mice give researchers a better understanding of how different cell types interact to link stem cell activity with changes in the outside environment.

"We have always been interested in understanding how stem cell behaviors are regulated by external stimuli. The skin is a fascinating system: it has multiple stem cells surrounded by diverse cell types, and is located at the interface between our body and the outside world. Therefore, its stem cells could potentially respond to a diverse array of stimuli -- from the niche, the whole body, or even the outside environment," said Ya-Chieh Hsu, the Alvin and Esta Star Associate Professor of Stem Cell and Regenerative Biology, who led the study in collaboration with Professor Sung-Jan Lin of National Taiwan University. "In this study, we identify an interesting dual-component niche that not only regulates the stem cells under steady state, but also modulates stem cell behaviors according to temperature changes outside."

A system for regulating hair growth

Many organs are made of three types of tissue: epithelium, mesenchyme, and nerve. In the skin, these three lineages are organized in a special arrangement. The sympathetic nerve, part of our nervous system that controls body homeostasis and our responses to external stimuli, connects with a tiny smooth muscle in the mesenchyme. This smooth muscle in turn connects to hair follicle stem cells, a type of epithelial stem cell critical for regenerating the hair follicle as well as repairing wounds.

The connection between the sympathetic nerve and the muscle has been well known, since they are the cellular basis behind goosebumps: the cold triggers sympathetic neurons to send a nerve signal, and the muscle reacts by contracting and causing the hair to stand on end. However, when examining the skin under extremely high resolution using electron microscopy, the researchers found that the sympathetic nerve not only associated with the muscle, but also formed a direct connection to the hair follicle stem cells. In fact, the nerve fibers wrapped around the hair follicle stem cells like a ribbon.

"We could really see at an ultrastructure level how the nerve and the stem cell interact. Neurons tend to regulate excitable cells, like other neurons or muscle with synapses. But we were surprised to find that they form similar synapse-like structures with an epithelial stem cell, which is not a very typical target for neurons," Hsu said.

Next, the researchers confirmed that the nerve indeed targeted the stem cells. The sympathetic nervous system is normally activated at a constant low level to maintain body homeostasis, and the researchers found that this low level of nerve activity maintained the stem cells in a poised state ready for regeneration. Under prolonged cold, the nerve was activated at a much higher level and more neurotransmitters were released, causing the stem cells to activate quickly, regenerate the hair follicle, and grow new hair.

The researchers also investigated what maintained the nerve connections to the hair follicle stem cells. When they removed the muscle connected to the hair follicle, the sympathetic nerve retracted and the nerve connection to the hair follicle stem cells was lost, showing that the muscle was a necessary structural support to bridge the sympathetic nerve to the hair follicle.

How the system develops

In addition to studying the hair follicle in its fully formed state, the researchers investigated how the system initially develops -- how the muscle and nerve reach the hair follicle in the first place.

"We discovered that the signal comes from the developing hair follicle itself. It secretes a protein that regulates the formation of the smooth muscle, which then attracts the sympathetic nerve. Then in the adult, the interaction turns around, with the nerve and muscle together regulating the hair follicle stem cells to regenerate the new hair follicle. It's closing the whole circle -- the developing hair follicle is establishing its own niche," said Yulia Shwartz, a postdoctoral fellow in the Hsu lab. She was a co-first author of the study, along with Meryem Gonzalez-Celeiro, a graduate student in the Hsu Lab, and Chih-Lung Chen, a postdoctoral fellow in the Lin lab.

Responding to the environment

With these experiments, the researchers identified a two-component system that regulates hair follicle stem cells. The nerve is the signaling component that activates the stem cells through neurotransmitters, while the muscle is the structural component that allows the nerve fibers to directly connect with hair follicle stem cells.

"You can regulate hair follicle stem cells in so many different ways, and they are wonderful models to study tissue regeneration," Shwartz said. "This particular reaction is helpful for coupling tissue regeneration with changes in the outside world, such as temperature. It's a two-layer response: goosebumps are a quick way to provide some sort of relief in the short term. But when the cold lasts, this becomes a nice mechanism for the stem cells to know it's maybe time to regenerate new hair coat."

In the future, the researchers will further explore how the external environment might influence the stem cells in the skin, both under homeostasis and in repair situations such as wound healing.

"We live in a constantly changing environment. Since the skin is always in contact with the outside world, it gives us a chance to study what mechanisms stem cells in our body use to integrate tissue production with changing demands, which is essential for organisms to thrive in this dynamic world," Hsu said.

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The real reason behind goosebumps - Jill Lopez

Synthetic lethality across normal tissues is strongly associated with cancer risk, onset, and tumor suppressor specificity – Science Advances

Abstract

Various characteristics of cancers exhibit tissue specificity, including lifetime cancer risk, onset age, and cancer driver genes. Previously, the large variation in cancer risk across human tissues was found to strongly correlate with the number of stem cell divisions and abnormal DNA methylation levels. Here, we study the role of synthetic lethality in cancer risk. Analyzing normal tissue transcriptomics data in the Genotype-Tissue Expression project, we quantify the extent of co-inactivation of cancer synthetic lethal (cSL) gene pairs and find that normal tissues with more down-regulated cSL gene pairs have lower and delayed cancer risk. Consistently, more cSL gene pairs become up-regulated in cells treated by carcinogens and throughout premalignant stages in vivo. We also show that the tissue specificity of numerous tumor suppressor genes is associated with the expression of their cSL partner genes across normal tissues. Overall, our findings support the possible role of synthetic lethality in tumorigenesis.

Cancers of different human tissues have markedly different molecular, phenotypic, and epidemiological characteristics, known as the tissue specificity in cancer. Various aspects of this intriguing phenomenon include a considerable variation in lifetime cancer risk, cancer onset age, and the genes driving the cancer across tissue types. The variation in lifetime cancer risk is known to span several orders of magnitude (1, 2). Such variation cannot be fully explained by the difference in exposure to carcinogens or hereditary factors and has been shown to strongly correlate with differences in the number of lifetime stem cell divisions (NSCD) estimated across tissues (2, 3). As claimed by Tomasetti and Vogelstein (2), these findings are consistent with the notion that tissue stem cell divisions can propagate mutations caused either by environmental carcinogens or random replication error (4). In addition, the importance of epigenetic factors in carcinogenesis has long been recognized (5), and Klutstein et al. (6) have recently reported that the levels of abnormal CpG island DNA methylation (LADM) across tissues are highly correlated with their cancer risk. Although both global (e.g., smoking and obesity) and various cancer typespecific (e.g., HCV infection for liver cancer) risk factors are well known (7), no factors other than NSCD and LADM have been reported to date to explain the across-tissue variance in lifetime cancer risk.

Besides lifetime cancer risk, cancer onset age, as measured by the median age at diagnosis, also varies among adult cancers (1). Although most cancers typically manifest later in life [more than 40 years old (1, 8)], some such as testicular cancer often have earlier onset (1). Many tumor suppressor genes (TSGs) and oncogenes are also tissue specific (911). For example, mutations in the TSG BRCA1 are predominantly known to drive the development of breast and ovarian cancer but rarely other cancer types (12). In general, factors explaining the overall tissue specificity in cancer could be tissue intrinsic (10, 13), and their elucidation can further advance our understanding of the forces driving carcinogenesis.

Synthetic lethality/sickness (SL) is a well-known type of genetic interaction, conceptualized as cell death or reduced cell viability that occurs under the combined inactivation of two genes but not under the inactivation of either gene alone. The phenomenon of SL interactions was first recorded in Drosophila (14) and then in Saccharomyces cerevisiae (15). In recent years, much effort has been made to identify SL interactions specifically in cancer, since targeting these cancer SLs (cSLs) has been recognized as a highly valuable approach for cancer treatment (1619). The effect of cSL on cancer cell viability has led us to investigate whether it plays an additional role even before tumors manifest, i.e., during carcinogenesis. In this study, we quantify the level of cSL gene pair co-inactivation in normal (noncancerous) human tissue as a measure of resistance to cancer development (termed cSL load, explained in detail below). We show that cSL load can explain a considerable level of the variation in cancer risk and cancer onset age across human tissues, as well as the tissue specificity of some TSGs. Together, these correlative findings support the effect of SL in impeding tumorigenesis across human tissues.

To study the potential effects of cSL in normal, noncancerous tissues, we define a measure called cSL load, which quantifies the level of cSL gene pair co-inactivation based on gene expression of normal human tissues from the Genotype-Tissue Expression (GTEx) dataset (20). Specifically, we used a recently published reference set of genome-wide cSLs that are common to many cancer types, identified from both in vitro and The Cancer Genome Atlas (TCGA) cancer patient data (21) via the identification of clinically relevant synthetic lethality (ISLE) (table S1A) (22, 23). For each GTEx normal tissue sample, we computed the cSL load as the fraction of cSL gene pairs (among all the genome-wide cSLs) that have both genes lowly expressed in that sample (Methods; illustrated in Fig. 1). We further defined tissue cSL load (TCL) as the median cSL load value across all samples of each tissue type in GTEx (Methods and table S2A). We then proceed to test our hypothesis that TCL can be a measure of the level of resistance to cancer development intrinsic to each human tissue (outlined in Fig. 1).

This diagram illustrates the computation of cSL load for each sample and each tissue type (i.e., TCL) and depicts the outline of this study, where we attempted to explain the tissue-specific lifetime cancer risk, cancer onset age, and TSGs using TCL. See main text and Methods for details.

SL is widely known to be context specific across species, tissue types, and cellular conditions (24). In theory, a cancer-specific cSL gene pair can be co-inactivated in the normal tissue without reducing normal cell fitness, while conferring resistance to the emergence of malignantly transformed cells due to the lethal effect specifically on the cancer cells. Different normal tissues can have varied TCLs (representing the levels of cSL gene pair co-inactivation) as a result of their specific gene expression profiles, and we hypothesized that normal tissues with higher TCLs should have lower cancer risk, as transforming cancerous cells in these tissues will face higher cSL-mediated vulnerability and lethality. To test this hypothesis, we obtained data on the tissue-specific lifetime cancer risk in humans (Methods) and correlated that with the TCL values computed for the different tissue types. We find a strong negative correlation between the TCL (computed from older-aged GTEx samples, age 50 years) and lifetime cancer risk across normal tissues (Spearmans = 0.664, P = 1.59 104; Fig. 2A and table S2A). This correlation is robust, as comparable results are obtained when this analysis is carried out in various ways (e.g., different cutoffs for low expression of genes, different cSL network sizes, and different cancer typenormal tissue mappings; fig. S1 and note S3). We also showed that this correlation is not confounded by the number of poised genes associated with bivalent chromatin, variation in cancer driver gene expression, and immune cell or fibroblast abundance (notes S11 to S13 and figs. S12 to S14). Notably, the cSL load varies with age due to age-related gene expression changes, and the correlation with lifetime cancer risk is not found when the TCL is computed on samples from the young population (20 age < 50 years, Spearmans = 0.0251, P = 0.901; fig. S2A); this is consistent with the observation that lifetime cancer risk is mostly contributed by cancers occurring in older populations (1). We still see a marked negative correlation between TCL and lifetime cancer risk when analyzing samples from all age groups together (Spearmans = 0.49, P = 0.01; fig. S2B). Repeating these analyses using different control gene pairs including (i) random gene pairs, (ii) shuffled cSL gene pairs, and (iii) degree-preserving randomized cSL network (same size as the actual cSL network; note S4) results in significantly weaker correlations (empirical P < 0.001; fig. S3, A to C, and note S4), confirming that the associations found with cancer risk results from a cSL-specific effect.

(A) Scatterplot showing Spearmans correlations between lifetime cancer risk and TCL computed for the older population (age 50 years) (ranked values are used as lifetime cancer risk spans several orders of magnitude.) (B) Lifetime cancer risks across tissues were predicted using linear models (under cross-validation) containing different sets of explanatory variables: (i) TCL only, (ii) the number of stem cell divisions (NCSD) only, and (iii) TCL and NSCD (27 data points). The prediction accuracy is measured by Spearmans , shown by the bar plots. The result of a likelihood ratio test between models (ii) and (iii) is also displayed. (C) A similar bar plot as in (B) comparing the predictive models for cancer risk involving the following variables: (i) TCL only, (ii) the LADM only, and (iii) TCL and LADM combined (21 data points only due to the smaller set of LADM data). A model containing all the three variables does not increase the prediction power (Spearmans = 0.77 under cross-validation) and is not shown. (D) Bar plot showing the correlations between lifetime cancer risk with TCLs computed (age 50 years) using subsets of cSLs: hcSLs, lcSLs, and all cSLs. Spearmans and P values are shown. The hcSLs and lcSLs are identified using data of matched TCGA cancer types and GTEx normal tissues (Methods), which correspond to only a subset of tissue types. To facilitate comparison, here, the correlation for all cSLs was also computed for the same subset of tissues, and therefore, the resulting correlation coefficient is different from that in (A).

While the randomized cSL networks used in the control tests described above provide significantly weaker correlations with cancer risk than those observed with cSLs, many of these correlations are still significant by themselves (fig. S3, B and C). This suggests that there may be a possible association between the expression of single genes in the cSL network (cSL genes) and cancer risk. To investigate this, we computed the tissue cSL single-gene load (SGL; the fraction of lowly expressed cSL genes) for each tissue (Methods). We do find a significant negative correlation between tissue SGL levels and cancer risk (Spearmans = 0.49, P = 0.01; fig. S3D and note S5). This correlation vanishes when we use random sets of single genes (fig. S3F). However, after controlling for the single-gene effect, the partial correlation between TCL and cancer risk is still highly significant (Spearmans = 0.69, P = 6.10 105; fig. S3G), pointing to the dominant role of the SL genetic interaction effect (note S5).

We next compared the predictive power of TCL to those obtained with the previously reported measures of NSCD (2, 3) and LADM (6), using the set of GTEx tissue types investigated here (Methods). We first confirmed the strong correlations of NSCD and LADM with tissue lifetime cancer risk in our specific dataset (Spearmans = 0.72 and 0.74, P = 2.6 105 and 1.3 104, respectively; fig. S4). These correlations are stronger than the one we reported above between TCL and cancer risk. However, adding TCL to either NSCD or LADM in linear regression models leads to enhanced predictive models of cancer risk compared to those obtained with NSCD or LADM alone [log-likelihood ratio (LLR) = 2.18 and 2.39, P = 0.037 and 0.029, respectively]. Furthermore, adding TCL to each of these factors increases their prediction accuracy under cross-validation (Spearmans s from 0.67 and 0.69 with NSCD and LADM alone to 0.71 and 0.77, respectively; Fig. 2, B and C). LADM and NSCD are significantly correlated (Spearmans = 0.66, P = 0.02), while the TCL correlates only in a borderline significant manner with either NSCD (Spearmans = 0.57, P = 0.06) or LADM (Spearmans = 0.52, P = 0.08). Together, these observations support the hypothesis that TCL is associated with tissue cancer risk, with a partially independent role from either NSCD or LADM.

We have shown results that support the role of TCL in impeding cancer development, and we reason that such an effect is dependent on the notion that many of the cSLs are specific to cancer while having weaker or no lethal effects in normal tissues. We tested and found that the co-inactivation of cSL gene pairs is under much weaker negative selection in GTEx normal tissues versus matched TCGA cancers [Wilcoxon rank sum test P = 2.93 106 (fig. S5A), also shown using cross-validation (note S7)]. Moreover, we hypothesize that those cSLs with the highest specificity to cancer (i.e., with the strongest SL effect in cancer and no or the weakest effect on normal cells) should have the strongest effect on cancer development. To test this, we identified the subset of such cSLs (termed highly specific cSLs or hcSLs) and those with the lowest specificity to cancer (termed lowly specific cSLs or lcSLs; Methods) and recomputed the TCLs of all normal GTEx tissues using these two cSL subsets, respectively. The TCLs computed from the hcSLs correlate much stronger with cancer lifetime risk than those computed from the lcSLs (Spearmans = 0.593 versus 0.319; Fig. 2D), testifying that these cSLs with high functional specificity to cancer are more relevant to carcinogenesis. These hcSLs are enriched for cell cycle, DNA damage response, and immune-related genes [false discovery rate (FDR) < 0.05; table S5 and Methods], which are known to play key roles in tumorigenesis.

We have thus established that TCL in the older population is inversely correlated with lifetime cancer risk across tissues. We next hypothesized that higher cSL load in a given normal tissue in the young population may delay cancer onset, which typically occurs later (age >40 years) (1). To test this, we use the median age at cancer diagnosis (1) of a certain tissue as its cancer onset age (table S3 and Methods). We find that the TCL values (for age 40 years) are markedly correlated with cancer onset age (Spearmans = 0.502, P = 0.011; Fig. 3A). This result is again robust to variations in our methods to compute TCL and cancer onset age (fig. S6, table S3, and note S3). We note that the cancer onset age is not significantly correlated with lifetime cancer risk (Spearmans = 0.279, P = 0.28).

(A) Scatterplot showing Spearmans correlations between cancer onset age and TCL (age 40 years). (B) Bar plot showing the correlations between cancer onset age with TCLs computed (age 40 years) using subsets of cSLs: hcSLs, lcSL, and all cSLs. Spearmans and P values are shown. As in Fig. 2D, this analysis was done for a subset of GTEx normal tissues for which we had matched TCGA cancer types to identify the hcSLs and lcSLs (Methods); therefore, the correlation result for all cSLs is also different from that in (A).

Similar to our earlier analysis, we see that the TCLs computed from the hcSLs correlate much stronger with onset age than those from the lcSLs or all cSLs (Spearmans = 0.603 versus 0.157; Fig. 3B and fig. S7A) and also stronger than those obtained from control tests performed as before (empirical P < 0.001; fig. S7, B to D). As with the case of cancer risk, the observed correlation is dominated by the SL genetic interaction effects rather than the single-gene effects (fig. S7, E to G, and note S5).

To further corroborate the relevance of cSL load to carcinogenesis, we next investigated whether carcinogen treatment in normal (noncancer) cell lines and primary cells in vitro can lead to cSL load decrease. First, we analyzed gene expression data from a recent study where human primary hepatocytes, renal tube epithelial cells, and cardiomyocytes were treated with the carcinogen and hepatotoxin thioacetamide-S-oxide (25). We computed the cSL load in each cell type after treatment versus control and found a significant decrease of cSL load only in the hepatocytes (Wilcoxon rank sum test P = 0.014; Fig. 4A), which is consistent with thioacetamide-S-oxides role as a hepatotoxin and a carcinogen primarily in the liver. Second, we collected the gene expression signatures of chemotherapy drug treatments in a total of four primary cells and normal cell lines from the Connectivity Map (CMAP) (26). We quantified the drug-induced cSL load changes indirectly from the gene signatures (Methods), comparing the strongly mutagenic DNA-targeting drugs (n = 6) including alkylating agents and DNA topoisomerase inhibitors to the weak/nonmutagenic taxanes and vinca alkaloids (n = 5), which act on the cytoskeleton and not directly on DNA (27). We find that the strong mutagenic chemotherapy drugs lead to a significantly larger decrease in cSL load (Fig. 4B, P = 0.03 from a linear model controlling for cell type; Methods). The strong mutagenicity of alkylating agents and DNA topoisomerase inhibitors is consistent with their mechanisms of actions; they are also World Health Organization class I carcinogens (28), supported by incidence of secondary cancers in patients treated by these drugs for their primary cancers (29). In contrast, taxanes and vinca alkaloids have shown negative or weak/inconclusive results in mutagenic tests (27, 30). These results are not likely affected by cell death, as the cSL decreased specifically only for the two classes among all tested chemotherapy drugs. Although the CMAP dataset used for this analysis does not include cell viability information, the gene expression of the cells does not show an apoptotic signature after the drug treatment.

(A) Box plots showing the cSL loads in control versus thioacetamide-S-oxidetreated samples in human primary hepatocytes (liver), renal tube epithelial cells (kidney), and cardiomyocytes (heart), using the data from (25). One-sided Wilcoxon rank-sum test P values are shown. (B) Box plots showing the cSL load changes after treatment by different classes of chemotherapy drugs in four cell types, using the CMAP data (26). Asterisk indicates that the cSL load change is estimated indirectly from the CMAP drug treatment gene expression signatures (Methods). Strongly mutagenic drugs (n = 6), including alkylating agents (green points) and DNA topoisomerase inhibitors (purple points), lead to a significantly larger cSL load decrease compared to weak or nonmutagenic drugs (n = 5), including taxanes (red points) and vinca alkaloids (blue points); P = 0.03 from a linear model controlling for cell type. HA1E is an immortalized kidney cell line; PHH, primary human hepatocyte; ASC, adipose-derived stem cell; SKB, human skeletal myoblast. (C) Box plots showing the cSL load in samples of different stages of premalignant lesions in the lung (including normal tissue and lung squamous cell carcinoma) (28). The cSL load shows an overall decreasing trend from normal to different pre-cancer stages to cancer (one-sided Wilcoxon rank sum test of normal versus cancer P = 4.47 105; ordinal logistic regression has negative coefficient 28.7, P = 5.89 107).

Further beyond these in vitro findings, analyzing a recently published lung cancer dataset (31), we find that cSL load decreases progressively as cancers develop from normal tissues throughout the multiple stages of premalignant lesions in vivo (normal versus cancer Wilcoxon rank sum test P = 4.47 105, ordinal logistic regression P = 5.89 107 with negative coefficient 28.7; Fig. 4C). These results provide further evidence supporting cSL as a factor that may be involved in cancer development.

Given the role of cSLs in cancer development, we turned to ask whether cSL may also contribute to the tissue/cancer-type specificity of TSGs (10, 32). Specifically, we reasoned that the loss of function of a gene is unlikely to have cancer-driving effects in tissues where its cSL partner genes are lowly expressed, due to the synthetic lethal effect of such co-inactivation on the emerging cancer cells. In other words, this gene is unlikely to be a TSG in such tissues. To study this hypothesis, we obtained a list of TSGs together with the tissues in which their loss is annotated to have a tumor-driving function from the COSMIC database (table S6A) (11). We further identified the cSL partner genes of each such TSG using ISLE (Methods and table S6B) (22). In total, there are 23 TSGs for which we were able to identify more than one cSL partner gene. Consistent with our hypothesis, we find that in most of the cases, the cSL partner genes of TSGs have higher expression levels in the tissues where the TSGs are known drivers compared to the tissues where they are not established drivers (binomial test for the direction of the effect P = 0.023; Fig. 5A). We identified 10 TSGs whose individual effects are significant (FDR < 0.05) and cSL specific (as shown by the random control test), and all these 10 cases exhibit the expected direction of effect (labeled in Fig. 5A and table S6C; two example TSGs, FAS and BRCA1, are shown in Fig. 5B, details are in fig. S8 and Methods). Reassuringly, these findings disappear under randomized control tests involving random partner genes of the TSGs and shuffled TSGtissue type mappings (note S9), further consolidating the role of cancer-specific cSLs of normal tissues in cancer risk and development.

(A) For each tissue-specific TSG gene Gi, the expression levels of its cSL partner genes in the tissue type(s) where gene Gi is a TSG were compared to those where gene Gi is not an established TSG, using GTEx normal tissue expression data. The volcano plot summarizes the result of comparison with linear models. Positive linear model coefficients (x axis) mean that the expression levels of the cSL partner genes are, on average, higher in the tissue(s) where gene Gi is a TSG. Many cases have near-zero P values and are represented by points (half-dots) on the top border line of the plot. Overall, there is a dominant effect of the cSL partner genes of TSGs having higher expression levels in the tissues where the TSGs are known drivers (binomial test P = 0.023). All TSGs with FDR < 0.05 that also passed the random control tests are labeled. (B) Examples of two well-known TSGs, FAS and BRCA1, are given. The heatmaps display the normalized expression levels of their cSL partner genes (rows) in tissues of where these two genes are known to be TSGs [according to the annotation from the COSMIC database (11)] and in tissues where they are not established TSGs (columns), respectively. High and low expressions are represented by red and blue, respectively. For clarity, one typical tissue type where the TSG is a known driver (e.g., testis for FAS) and three other tissue types where the TSG is not an established driver (and the least frequently mutated) are shown.

In this work, we show that the cSL load in normal tissues is a strong predictor of tissue-specific lifetime cancer risk and is much stronger than the pertaining predictive power observed on the individual gene level. Consistently, we find that higher cSL load in the normal tissues from young people is associated with later onset of the cancers of that tissue. As far as we know, no other factor has been previously reported to be predictive of cancer onset age across tissues. Furthermore, cSL load decreases upon carcinogen treatment in vitro and during cancer development through stages of precancerous lesions in vivo. Last, we show that the activity status of cSL partners of TSGs can explain their tissue-specific inactivation.

We have shown that the correlation between cSL and cancer risk in normal tissues may be explained by the fact that many of the cSLs are specific to cancer and have weak or no functional lethal effect in the normal tissues (Figs. 2D and 3B and fig. S5); therefore, normal tissues can bear relatively high cSL loads without being detrimentally affectedquite to the contrary, they become more resistant to cancer due to the latent effect of these cSLs on potentially emerging cancer cells. We emphasize that while we quantified the cSL loads using the normal tissue data from GTEx, the set of cSLs we used was derived exclusively in cancer from completely independent cancer datasets (and without using any information regarding lifetime cancer risk, onset, or tumor suppressor tissue specificity), so there is no circularity involved. The cSL load in normal tissues was computed to reflect the summed effects of individual cSL gene pairs. The underlying assumption is that the low expression of each cSL gene pair is synthetic sick (i.e., reducing cell fitness to some extent) and that the effects from different cSL gene pairs are additive, consistent with the ISLE method of cSL identification (22). Many experimental screenings of SL interactions also rely on techniques such as RNA interference that inhibits gene expression rather than completely knocks out a gene (33), and it is evident that most of the resulting SL gene pairs have milder than lethal effects. While these cSLs likely act via a diverse range of biological pathways and thus do not provide pathway-specific mechanisms, the additive cancer-specific lethal effect of such cSL gene pairs, however, could form a negative force impeding cancer development from normal tissues.

Obviously, as we are studying the across-tissue association between cSL load and cancer risk, it is essential to focus on cSLs that are common to many cancer types (i.e., pan-cancer). Therefore, we focused on cSLs identified computationally by ISLE via the analysis of the pan-cancer TCGA patient data (22). In contrast, most experimentally identified cSLs are obtained in specific cancer cell lines and are thus less likely to be pan-cancer [and possibly, less clinically relevant (22)]. However, for completeness, we also compiled a set of experimentally identified cSLs from published studies (22, 34) (note S1 and table S1B). The corresponding TCL values computed using this set of cSLs correlate significantly with lifetime cancer risk but not with cancer onset age; the correlation with cancer risk is also markedly weaker than that obtained from ISLE-derived cSLs [Spearmans = 0.433, P = 0.024 (fig. S9A), control tests and detailed analysis are explained in note S4]. These experimentally identified cSLs can explain some cases of tissue-specific TSGs including BRCA1 and BRCA2 (fig. S9E) but do not result in overall significant accountability for a large proportion of TSGs present in the analysis (like in Fig. 5A). This corroborates the importance of pan-cancer cSLs and their relevance to cancer risk.

TCL is not likely to be a corollary of NSCD and LADM [while LADM was thought to be closely related to NSCD (6)], as the cSL load is computed by analyzing expression data of bulk tissues, where stem cells occupy only a minor proportion. We have shown that TCL significantly adds to either NSCD or LADM in predicting lifetime cancer risk (Fig. 2, B and C), which also suggests that cSL load is an independent factor correlated with cancer risk with unique underlying mechanisms. Furthermore, NSCD is measured as the product of the rate of tissue stem cell division and the number of stem cells residing in a tissue (2), and we confirmed that TCL is correlated with lifetime cancer risk independent of both of these components (partial Spearmans = 0.510 and 0.567, P = 0.007 and 0.002, respectively; fig. S10, A and B). We additionally tested and verified that proliferation indices computed for the bulk normal tissues do not correlate with lifetime cancer risk across tissues (Spearmans = 0.062, P = 0.77; fig. S10C and note S10). Furthermore, we verified that our observed correlations are not confounded by the number of samples from each cancer or tissue type (fig. S11).

Since cSL load can vary with age, one may wonder whether cSL load could be extended to correlate with age-specific cancer risk within a tissue (as opposed to across tissues). However, variations in cancer risk across tissues and across ages can be driven by different factors. We did not find a consistent correlation between cSL load computed by age range and age-specific cancer risk in all tissue types (note S14 and fig. S15). Another extension to our current research question is studying the effect of higher-order genetic interactions on cancer risk, which is plausible but challenging to study due to the limited knowledge available on such complex interactions.

While revealing cSL as a previously unknown factor associated with cancer development, our study has several limitations. First, because of the importance of using pan-cancer cSLs as discussed above, we mainly relied on the cSLs computationally inferred by ISLE (22) as one of the most comprehensive pan-cancer cSL datasets. However, current cSL prediction algorithms are far from perfect and should not be regarded as the gold standard for general cSL identification. Only a minor fraction of the large number of predicted cSLs have been experimentally validated only in specific cell types. The cSLs inferred by ISLE should be best viewed as a set of candidate cSL pairs that emerge from genetic screen data in vitro but with further support from patient and phylogenetic data. Future studies that provide experimentally validated pan-cancer cSLs are needed to consolidate our current findings. Second, we have relied on analyzing the gene expression data of bulk tissues from GTEx and not the expression data of the specific cells of origin of the corresponding cancers. More refined future analysis is desirable using single-cell data across normal human tissues as such data becomes more widely available. Last, our study does not establish a causal relationship between the cSL load and the risk of cancer, as it is challenging to experimentally perturb a large number of cSLs simultaneously. The results shown are descriptive and association based, and the causal role of SLs in carcinogenesis remains to be studied mechanistically.

Together, our findings demonstrate strong associations between SL and cancer risk, onset time, and context specificity of tumor suppressors across human tissues. This suggests that beyond the effect on cancer after it has developed, cSL could also play an important role during the entire course of carcinogenesis, although further studies are needed to establish causality. While SL has been attracting tremendous attention as a way to identify cancer vulnerabilities and target them, this is the first time that its potential role in mediating cancer development is uncovered.

The cSL gene pairs computationally identified by the ISLE (identification of clinically relevant SL) pipeline were obtained from (22). We used the cSL network identified with FDR < 0.2 for the main text results, containing 21,534 cSL gene pairs, which is a reasonable size representing only about one cSL partner per gene on average. This also allows us to capture the effects of many weak genetic interactions. Nevertheless, we also used the cSL network with FDR < 0.1 (only 2326 cSLs) to demonstrate the robustness of the results to this parameter (notes S1 and S3). Each gene pair is assigned a significance score [the SL-pair score defined in (22)], that a higher score indicates that there is stronger evidence that the gene pair is SL in cancer. Out of these, we used 20,171 cSL gene pairs whose genes are present in the GTEx data (table S1A). The experimentally identified cSL gene pairs were collected from 18 studies [obtained from the supplementary data 1 of Lee et al. (22) except for those from Horlbeck et al. (34)]. Horlbeck et al. (34) provided a gene interaction (GI) score for each gene pair in two leukemia cell lines. Gene pairs with GI scores of <1 in either cell line were selected as cSLs. A total of 27,975 experimentally identified cSLs were obtained, out of which 27,538 have both their genes present in the GTEx data (table S1B).

The V6 release of GTEx (20) RNA sequencing (RNA-seq) data [gene-level reads per kilobase of transcript, per million mapped reads (RPKM) values] was obtained from the GTEx Portal (https://gtexportal.org/home/). The associated sample phenotypic data were downloaded from dbGaP (35) (accession number phs000424.vN.pN). For comparing the level of negative selection to co-inactivation of cSL gene pairs between normal and cancer tissues, the RNA-seq data of TCGA and GTEx as RNA-seq by expectation-maximization (RSEM) values that have been processed together with a consistent pipeline that helps to remove batch effects were downloaded from UCSC Xena (36). The expression data for each tissue type (normal or cancer) was normalized separately (inverse normal transformation across samples and genes) before being used for the downstream analyses. We mapped the GTEx tissue types to the corresponding TCGA cancer types (table S2B), resulting in one-on-many mappings, e.g., the normal lung tissue was mapped to both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC).

Lifetime cancer risk denotes the chance a person has of being diagnosed with cancer during his or her lifetime. Lifetime cancer risk data (table S2A) are from Tomasetti and Vogelstein (2), which are based on the U.S. statistics from the SEER (Surveillance, Epidemiology, and End Results) database (1). We derived the cancer onset age based on the age-specific cancer incidence data from the SEER database with the standard formula (37). Specifically, for each cancer type, SEER provides the incidence rates for 5-year age intervals from birth to 85+ years old. The cumulative incidence (CI) for a specific age range S is computed from the corresponding age-specific incidence rates (IRi, i S) as CI = 5i SIRi, and the corresponding risk is computed as risk = 1 exp(CI). The onset age for each cancer type (table S3) was computed as the age when the CI from birth is 50% of the lifetime CI (i.e., birth to 85+ years old). Usually, the onset age defined as such is between two ages where the actual CI data are available, so the exact onset age was obtained by linear interpolation. Alternative parameters were used to define onset age (note S3) to show the robustness of the correlation between TCL and cancer onset age based on different definitions.

For each sample, we computed the number of cancer-derived SL gene pairs that have both genes lowly expressed and divided it by the total number of cSLs available to get the cSL load per sample. In the ISLE method described in (22), low expression was defined as having expression levels below the 33 percentile in each tissue or cell type. Thus, the ISLE-derived cSL gene pairs were shown to exhibit synthetic sickness effects when both genes in the gene pair are expressed at levels below the 33 percentile in each tissue, even though this appears to be a very tolerant cutoff (22). We therefore adopted the same criterion for low expression for the main results, although we also explored other low expression cutoffs to demonstrate the robustness of the results (note S3).

TCL of each tissue type is the median value of the cSL loads of all the samples (or a subpopulation of samples) in that tissue, with the cSL load of a sample computed as above. For example, TCL for the older population (age 50 years) is the median cSL load for the samples of age 50 years in each tissue type. For analyzing the correlation between the TCLs computed from GTEx normal tissues and cancer risk, we mapped the GTEx tissue types to the corresponding cancer types for which lifetime risk data are available from Tomasetti and Vogelstein (2), resulting in 16 GTEx types mapped to 27 cancer types (table S2A). Gallbladder nonpapillary adenocarcinoma and osteosarcoma of arms, head, legs, and pelvis are not mapped to GTEx tissues and excluded from our analysis. Similarly for the correlation between TCLs and cancer onset age, we mapped GTEx tissue types to the tissue sites from the SEER database (as given in the data slot site recode ICD-O-3/WHO 2008) by their names (table S3).

To investigate the effect on the single-gene level, we computed the cSL SGL in a paralleling way to the computation of the cSL load. Among all the unique genes constituting the cSL network (i.e., cSL genes), we computed the fraction of lowly expressed cSL genes for each sample as the cSL SGL, where low expression was defined in the same way as the computation of cSL load as elaborated above. Similarly, tissue cSL SGL is the median value of the cSL SGLs of all the samples in a tissue.

The lifetime cancer risks across tissue types were predicted with linear models containing three different sets of explanatory variables: (i) the number of total stem cell divisions (NSCD) alone, (ii) TCL alone, and (iii) NSCD together with TCL. LLR test was used to determine whether model (iii) (the full model) is significantly better than model (i) (the null model) in predicting lifetime cancer risks. The three models were also used to predict the lifetime cancer risks with a leave-one-out cross-validation procedure, and the prediction performances were measured by Spearman correlation coefficient. A similar analysis was performed to predict lifetime cancer risks across tissue types with three linear models involving the level of abnormal DNA methylation levels of the tissues (6): (i) the number of LADM alone, (ii) TCL alone, and (iii) LADM together with TCL.

For each pair of GTEx normalTCGA cancer of the same tissue type (table S2B), we computed the fraction of samples where a cSL gene pair i has both genes lowly expressed (defined above) among the normal samples (fni) and cancer samples (fci) and computed a specific score as rsi = fni fci. We selected the hcSLs as those whose specific scores are greater than the 75% percentile of all scores and lcSLs as those with a score below the 25% percentile (table S4, A and B). We compared SL significance scores between the hcSLs and lcSLs in each tissue using a Wilcoxon rank sum test. For each type of the GTEx normal tissues used in this analysis (i.e., those that can be mapped to TCGA cancer types), we also computed the TCL as above but using the hcSLs, lcSLs, or all cSLs, respectively, and analyzed their correlation with lifetime cancer risk or cancer onset age across the tissues.

We designed an empirical enrichment test as below to account for the fact that each cSL consists of two genes. For the hcSLs in each tissue type and each given pathway from the Reactome database (38), we computed the odds ratio (OR) for the overlap between the genes in hcSLs and the genes within the pathway based on the Fishers exact test procedure, with the background being all the genes in the ISLE-inferred cSLs. A greater than 1 OR indicates that the hcSLs are positively enriched for the genes of the pathway. To determine the significance of the enrichment, we repeatedly and randomly sampled the same number of cSLs as that of the hcSLs, computed the ORs similarly, and computed the empirical P value as the fraction of cases where the OR from the random cSLs is greater than that from the hcSLs. We corrected for multiple testing across pathways with the Benjamini-Hochberg method.

The phase I CMAP (26) data were downloaded from the Gene Expression Omnibus database (GSE92742). Level 5 data that represent the consensus perturbation-induced differential expression signature were used. We focused on CMAP data that involve treatment by specific classes of chemotherapy drugs (mutagenic: alkylating agents and DNA topoisomerase inhibitors; nonmutagenic: taxanes and vinca alkaloids) in normal cell lines or primary cells. We identified a total of 11 drugs tested in four cell types. Given the signature (z score) of a drug treatment in a cell, we estimated the drug-induced cSL load change as follows1|S|((i,j)SI(zi<0.5zj<0.5)(i,j)SI(zi>0.5zj<0.5))where S is the set of cSLs, and |S| is the total number of cSL gene pairs. A gene pair is denoted by (i, j), and zi and zj are the z scores of gene i and gene j, respectively. I() is the indicator function. Intuitively, the above formula quantifies the number of cSL gene pairs where both genes are down-regulated with a z score cutoff of 0.5 (i.e., contributing to cSL load increase), minus the number of cSL gene pairs where either gene is up-regulated with a z score cutoff of 0.5 (i.e., contributing to cSL load decrease), normalized by the total number of cSL gene pairs. We then tested whether the mutagenic drugs lead to a larger decrease in cSL load compared to nonmutagenic drugs with a linear model that controls for both cell type and drug.

We obtained the list of TSGs and their associated tissue types from the COSMIC database (11) (https://cancer.sanger.ac.uk/cosmic/download, the Cancer Gene Census data; table S6A). For each TSG, their cSL partner genes were identified using the ISLE pipeline (22) with an FDR cutoff of 0.1 (table S6B). Here, the FDR cutoff is more stringent than that used for the pan-cancer genome-wide cSL network (FDR < 0.2 for the main results) since, here, FDR correction was performed for each TSG, corresponding to a much lower number of multiple hypotheses. As a result, the FDR correction has more power, and a relatively more stringent cutoff can give rise to a more reasonable number of cSL partner genes per TSG. We focused our analysis on 23 TSGs for which more than one cSL partner genes were identified (no cSL partner was identified for most of the other TSGs). The expression levels of the cSL partner genes were then compared between tissue type(s) where the TSG is a known driver and the rest of the tissues where the TSG is not an established driver with linear models. Specifically, the expression levels of the cSL partners were modeled with two explanatory variables: (i) driver status of the TSG in the tissue (binary) and (ii) cSL partner gene (categorical, indicating each of the cSL partner genes of a TSG). The coefficient and P value associated with variable (i) were used to analyze the general trend of differential expression among the cSL partner genes. Positive coefficients of variable (i) means that the expression levels of the cSL partner genes are, on average, higher in the tissue(s) where the TSG is a known driver compared to those in the tissues where the TSG is not an established cancer driver.

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Synthetic lethality across normal tissues is strongly associated with cancer risk, onset, and tumor suppressor specificity - Science Advances

New Approaches to the Treatment of Relapsed or Refractory Diffuse Large B-cell Lymphoma – Targeted Oncology

In the United States, the most common of the aggressive non-Hodgkin lymphomas (NHLs) is diffuse large B-cell lymphoma (DLBCL), which accounts for between 22% and 24% of newly diagnosed B-cell NHL cases.1 Although DLBCL can affect children and young adults, it is most commonly diagnosed in individuals between the ages of 65 and 74 years, with a median age at diagnosis of 66 years.2,3 Given the aggressive nature of DLBCL, patients often present with lymphadenopathy, extranodal involvement, and other constitutional symptoms that require immediate treatment.1

The treatment spectrum for DLBCL has expanded significantly in recent years, particularly for patients with relapsed or refractory (R/R) disease. Mechanisms of action differ greatly among agents, reflecting the complex pathophysiology and genetic variations of the disease. This article reviews the advances in DLBCL understanding that have led to the approval of new agents and subsequent utilization of new mechanisms.

The current standards of care for first-line DLBCL treatment include the combination chemoimmunotherapy regimen of rituximab, cyclophosphamide, doxorubicin hydrochloride, vincristine sulfate, and prednisone (R-CHOP). The varying numbers of cycles and use in combination with or without radiotherapy (RT) depends upon the stage of disease at presentation.1 The addition of rituximab to CHOP was associated with a 2-year event-free survival of 57% in elderly patients in a 2002 randomized trial (LNH-98.5), which, along with results of other trials, led to the FDA approval of this combination therapy.4,5 Although durable remission can be achieved with R-CHOP in about 60% of patients, its use has resulted in poorer long-term outcomes for patients with double-hit and triple-hit lymphomas (DHL and THL).1

In 2007, the International Harmonization Project issued guidelines on malignant lymphoma response criteria, defining relapsed disease as consisting of new lesions greater than 1.5 cm in any axis during or after the completion of therapy or a 50% or greater increase in the sum of the product of diameters of a previously involved node(s) or other lesion(s).6 The authors also defined refractory, or progressive, disease as entailing a 50% or greater increase in the size of a lymph node with a prior short-axis diameter of less than 1.0 cm to a size of 1.5 cm 1.5 cm (or a long-axis size of > 1.5 cm).6

For patients with R/R disease, high-dose chemotherapy and autologous stem cell transplant (ASCT) may offer the chance for cure, but several factors may limit the utility of this approach. For example, in the treatment of patients with MYC-positive R/R DLBCL, ASCT is considered controversial because it has produced poorer outcomes in patients with DHL.1 Additionally, patients who are older or have comorbidities may be inappropriate candidates for this approach,7 and patients with disease that is unresponsive to second-line chemotherapy may have poorer prognoses (ie, poorer rates of long-term survival) and incur added toxicity from the chemotherapy.7 Even when including patients who undergo high-dose, salvage chemotherapy and subsequent ASCT, patients with R/R DLBCL have a 1-year survival rate of 28%.1 Hence, in a search for improved outcomes in the R/R setting, clinical studies have focused on DLBCL subtypes, especially in those ineligible for transplant or who have relapsed following transplant.1

Another option for patients in the relapsed setting is chimeric antigen receptor (CAR) T-cell therapy, which entails the genetic modification of autologous T cells via cloned DNA plasmids carrying a viral recombinant vector in addition to T-cell receptor-expressing genes. CAR T-cell therapy plays an important role in the R/R DLBCL setting, with reported 2-year remissions and a complete response (CR) rate in 40% of patients and 25% DHL/THL patients.1 Other therapeutic classes that have been explored for DLBCL include phosphoinositide 3-kinase (PI3K) inhibitors, B-cell lymphoma 2 (BCL2) inhibitors, and checkpoint inhibitors.1,8-10

Given reduced survival in patients who are unresponsive to subsequent lines of therapy and the toxicity involved, a great need exists for novel agents in the R/R DLBCL setting. Recent entrants to the R/R DLBCL treatment landscape include the antibody-drug conjugate (ADC) polatuzumab vedotin-piiq, the selective inhibitor of nuclear export, selinexor, and the monoclonal antibody tafasitamab-cxix (TABLE 111-20).

Polatuzumab vedotin-piiq was approved by the FDA in 2019 and is indicated in combination with bendamustine and rituximab in adults with RR DLBCL not otherwise specified, following at least 2 previous therapies.11 It is an ADC wherein the monoclonal antibody is linked to an antimitotic agent, monomethyl auristatin E (MMAE). The ADC targets the B-cell surface protein CD79B and, after binding to the surface protein, is internalized by the cell. Lysosomal enzymes then cleave the link between the antibody and MMAE, the latter of which binds microtubules, thereby inhibiting cell division and inducing apoptosis.11

A 2020 phase 1b/2 study (NCT02257567) randomized patients with R/R DLBCL who were ineligible for ASCT to receive polatuzumab vedotin-piiq with bendamustine and rituximab (pola-BR) or bendamustine and rituximab (BR) alone.12 The phase 2 primary end point was CR; secondary end points included overall response rate (ORR) at end of treatment, superior overall response, duration of response (DOR), and progression-free survival (PFS) assessed per independent review committee (IRC).12 With a median follow-up of 22.3 months, the CR was significantly higher in the pola-BR group (40% vs 17.5% in the BR group; P = .026).12 Overall survival rate was also significantly higher in the pola-BR group (12.4 vs 4.7 months in the BR group; HR, 0.42; 95% CI, 0.24-0.75; P = .002).12 Similarly, median PFS was significantly longer at 9.5 months in the pola-BR group compared with 3.7 months in the BR group (HR, 0.36; 95% CI, 0.21-0.63; P < .001).12 Also, DOR was longer at 12.6 months in the pola-BR group vs 7.7 months in the BR group (HR, 0.47; 95% CI, 0.19-1.14).12 Finally, the pola-BR group had a 58% reduction in risk of death compared with the BR group (HR, 0.42; 95% CI, 0.24-0.75; P = .002).12 In terms of safety, grade 3/4 anemia, neutropenia, thrombocytopenia, and peripheral neuropathy occurred more frequently in the pola-BR group than in the BR group.12 Polatuzumab vedotin-piiq was deemed an effective agent that might provide a therapeutic option for patients with R/R DLBCL who were not ideal candidates for CAR T-cell therapy.12

In 2020, selinexor was approved by the FDA for use in adult patients with R/R DLBCL (including follicular lymphoma-derived DLBCL) after at least 2 lines of systemic treatment.13 Selinexor inhibits nuclear export of tumor suppressor proteins by blocking exportin 1.13

The FDA approval was based on results of the open-label single-arm phase 2 SADAL trial (NCT02227251), which included patients 18 years or older with DLBCL (based on pathologic confirmation) with an Eastern Cooperative Oncology Group (ECOG) score of 2 or less, who had 2 to 5 lines of prior therapy, and who had progressed following or were ineligible for ASCT.14 The primary end point of the SADAL trial was ORR (comprising patients with CR or PR per 2014 Lugano criteria), with secondary end points consisting of DOR and disease control rate.14 Patients received the 60-mg oral selinexor on the first and third day of each week until disease progression or unacceptable toxicity occurred.14

The updated phase 2b ORR was 28.3% with a disease control rate of 37% (95% CI, 28.6-46.0). Of 36 responders, CRs were reported in 13 evaluable patients and PRs were reported in 23 patients. At a median follow-up of 11.1 months, the median DOR was 9.3 months (95% CI, 4.8-23.0). For those with a CR, median DOR was 23.0 months (95% CI, 10.4-23.0); median DOR was 4.4 months for those with a PR (95% CI, 2.0not evaluable).14,15 To address potential differences by subtype, the SADAL trial also included a subgroup analysis of patients with the germinal center B-cell (GCB)like subtype (n = 59), which demonstrated an ORR of 33.9%, a 14% CR rate, and a 20% PR rate, whereas the patients with a non-GCB subtype (n = 63) had an ORR of 20.6%. At the time of data cutoff, 7% (n = 9) of patients showed continuing response.14,15 The SADAL trial also included 5 patients with the unclassified subtype, in 1 of whom a CR was achieved and in 2 of whom a PR was achieved.15 With respect to safety, 98% of patients in the SADAL trial had at least 1 treatment-emergent adverse event (TEAE). The most frequent grade 3/4 events were thrombocytopenia, neutropenia, anemia, fatigue, hyponatremia, and nausea.14 Among serious AEs affecting 48% of patients, the most common were pyrexia, pneumonia, and sepsis.14 Gastrointestinal AEs were reported in 80% of patients, hyponatremia in 61%, and central neurologic events (which included dizziness and altered mental status) in 25%.16 Trial investigators concluded that selinexor improved survival considerably and that it presented a nonchemotherapy oral option for patients with R/R DLBCL.14

Tafasitamab-cxix is a CD19-targeting monoclonal antibody that gained FDA approval in 2020 for use with lenalidomide in adults with R/R DLBCL who are ineligible for ASCT, including patients with low-grade lymphoma derived DLBCL.17 Tafasitamab-cxix binds to the pre-B and mature B-lymphocyte surface antigen CD19, which is expressed in DLBCL and other B-cell malignancies.17 Tafasitamab-cxix, once bound to CD19, facilitates B-lymphocyte lysis via apoptosis and immune effector mechanisms that encompass antibody-dependent cellular cytotoxicity and antibody-dependent cellular phagocytosis.17

The FDA approval of tafasitamab-cxix was based on data from the phase 2, single-arm, multicenter, open-label L-MIND trial (NCT02399085).17,18 The L-MIND trial included patients 18 years or older with R/R DLBCL who had received 1 to 3 previous therapies ( 1 of which incorporated a CD20-directed regimen), had an ECOG score of 0 to 2, and were ASCT ineligible.18 Patients were administered tafasitamab-cxix and lenalidomide in 28-day cycles and continued to receive tafasitamab-cxix every 2 weeks after cycle 12 until disease progression.18 Objective response rate (ie, PR and CR) was the primary end point per IRC, which implemented PET imaging; secondary end points included investigator-assessed objective response rate, DOR, OS, PFS, biomarker analyses, and safety.18 Eighty patients were included in the full analysis set (FAS), receiving tafasitamab-cxix plus lenalidomide.18 Of the FAS, the objective response rate was 60.0% (95% CI, 48.4%-70.8%) and the CR rate was 42.5% (34/80).18 The rate of patients achieving a 12-month DOR rate was comparable across subgroups, with 70.5% of patients who received 1 prior line of therapy achieving a 12-month DOR (95% CI, 47.2%-85.0%) and 72.7% of patients who had 2 or more prior lines of therapy achieving a 12-month DOR (95% CI, 46.3%-87.6%).18

Outcomes in patients with GCB DLBCL (n = 37) were promising, with an objective response rate of 48.6%, a 12-month DOR rate of 53.5%, and a 12-month OS rate of 65.4% (based upon the Hans algorithm). Outcomes in patients with non-GCB DLBCL (n = 21) were an improvement over those with the GCB subtype, with an objective response rate of 71.4%, a 12-month DOR rate of 83.1%, and a 12-month OS rate of 84.2%.18 IRC-evaluated data from a 2-year follow up of the L-MIND trial showed an objective response rate of 58.8% (47/80) and CR rate of 41.3% (33/80).19 The 2-year follow up data also showed a median DOR of 34.6 months, with a 31.6-month median OS and a 16.2-month median PFS.19

Safety data from the preliminary L-MIND trial results showed that the most frequent TEAEs (of any grade) were neutropenia (48%), thrombocytopenia (32%), anemia (31%), diarrhea (30%), pyrexia (22%), and asthenia (20%).20 A lenalidomide dose reduction was required in 42% of patients; 72% of patients could remain on daily lenalidomide at 20 mg or higher.20 Trial investigators concluded that the combination of tafasitamab-cxix and lenalidomide was well tolerated and did not lead to compounded AEs.20

The promising data from recent trialsparticularly from their DLBCL subtype based subgroupsunderscore the importance of understanding the unique prognoses and responses that these subtypes confer on patient outcomes. The establishment of DLBCL subtypes as prognostic and therapeutic response factors has fueled a search for more specific molecular targets in the disease process. In addition, the importance of subtype characterization is evidenced by ongoing diagnostic assay development (for use in conjunction with immunohistochemistry). As exemplified by the patient populations in these trials, new therapeutic options with distinct mechanisms of actions are needed for patients with R/R DLBCL who are ineligible for ASCT. Multiple studies of targeted agents in the R/R DLBCL setting are under way that include CAR T-cell, bispecific T-cell engager, programmed death receptor 1 (PD-1) inhibitor, and BCL2 inhibitor therapies.1 Continued development of clinically applicable diagnostics holds promise for improved prognostic capability and assessment of therapeutic response. With improved diagnostics, further elucidation of DLBCL-driver mutations can continue to provide additional DLBCL subtype-specific options and, hence, more treatments tailored to individual patients.

References 1. Liu Y, Barta SK. Diffuse large B-cell lymphoma: 2019 update on diagnosis, risk stratification, and treatment. Am J Hematol. 2019;94(5):604-616. doi:10.1002/ajh.25460 2. Diffuse large B-cell lymphoma. Lymphoma Research Foundation. Accessed October 12, 2020. https://lymphoma.org/aboutlymphoma/nhl/dlbcl/ 3. Cancer stat facts: NHL diffuse large B-cell lymphoma (DLBCL). National Cancer Institute. Accessed October 12, 2020. https://seer.cancer.gov/statfacts/html/dlbcl.html 4. Coiffier B, Lepage E, Briere J, et al. CHOP chemotherapy plus rituximab compared with CHOP alone in elderly patients with diffuse large B-cell lymphoma. N Engl J Med. 2002;346(4):235-242. doi:10.1056/NEJMoa011795 5. Rituxan plus CHOP approved for diffuse large B-cell lymphoma. Cancer Network. February 28, 2006. Accessed November 6, 2020. https://www.cancernetwork.com/view/rituxan-plus-chop-approved-diffuse-large-b-cell-lymphoma 6. Cheson BD, Pfistner B, Juweid ME, et al; International Harmonization Project on Lymphoma. Revised response criteria for malignant lymphoma. J Clin Oncol. 2007;25(5):579-586. doi:10.1200/JCO.2006.09.2403 7. Elstrom RL, Martin P, Ostrow K, et al. Response to second-line therapy defines the potential for cure in patients with recurrent diffuse large B-cell lymphoma: implications for the development of novel therapeutic strategies. Clin Lymphoma Myeloma Leuk. 2010;10(3):192-196. doi:10.3816/CLML.2010.n.030 8. Oki Y, Kelly KR, Flinn I, et al. CUDC-907 in relapsed/refractory diffuse large B-cell lymphoma, including patients with MYC-alterations: results from an expanded phase I trial. Haematologica. 2017;102(11):1923-1930. doi:10.3324/haematol.2017.172882 9. Ansell S, Gutierrez ME, Shipp MA, et al. A phase 1 study of nivolumab in combination with ipilimumab for relapsed or refractory hematologic malignancies (CheckMate 039). Blood. 2016; 128(22):183. doi:10.1182/blood.V128.22.183.183 10. Lesokhin AM, Ansell SM, Armand P, et al. Nivolumab in patients with relapsed or refractory hematologic malignancy: preliminary results of a phase Ib study. J Clin Oncol. 2016;34(23):2698-2704. doi:10.1200/JCO.2015.65.9789 11. POLIVY. Prescribing information. Genentech, Inc; 2020. Accessed October 22, 2020. https://www.gene.com/download/pdf/polivy_prescribing.pdf 12. Sehn LH, Herrera AF, Flowers CR, et al. Polatuzumab vedotin in relapsed or refractory diffuse large B-cell lymphoma. J Clin Oncol. 2020;38(2):155-165. doi:10.1200/JCO.19.00172 13. XPOVIO. Prescribing information. Karyopharm Therapeutics, Inc; 2020. Accessed October 22, 2020. https://www.karyopharm.com/wp-content/uploads/2019/07/NDA-212306-SN-0071-Prescribing-Information-01July2019.pdf 14. Kalakonda N, Maerevoet M, Cavallo F, et al. Selinexor in patients with relapsed or refractory diffuse large B-cell lymphoma (SADAL): a single-arm, multinational, multicentre, open-label, phase 2 trial. Lancet Haematol. 2020;7(7):e511-e522. doi:10.1016/S2352-3026(20)30120-4 15. Karyopharm reports updated data from the phase 2b SADAL study at the 2019 International Conference on Malignant Lymphoma. News release. Karyopharm. June 19, 2019. Accessed June 28, 2020. https://www.globenewswire.com/news-release/2019/ 06/19/ 1871363/0/en/Karyopharm-Reports-Updated-Data-from-the-Phase-2b-SADAL-Study-at-the-2019-International-Conference-on-Malignant-Lymphoma.html 16. FDA approves selinexor for relapsed/refractory diffuse large B-cell lymphoma. News release. FDA. June 22, 2020. Accessed June 28, 2020. https://www.fda.gov/drugs/resources-information-approved-drugs/fda-approves-selinexor-relapsedrefractory-diffuse-large-b-cell-lymphoma 17. Monjuvi. Prescribing information. MorphoSys US Inc; 2020. Accessed October 22, 2020. https://www.monjuvi.com/pi/monjuvi-pi.pdf 18. Duell J, Maddocks KJ, Gonzalez-Barca E, et al. Subgroup analyses from L-Mind, a phase II study of tafasitamab (MOR208) combined with lenalidomide in patients with relapsed or refractory diffuse large B-cell lymphoma. Blood. 2019;134(suppl 1):1582. doi:10.1182/blood-2019-122573 19. MorphoSys and Incyte announce long-term follow-up results from L-MIND study of tafasitamab in patients with r/r DLBCL. News release. Morpho-Sys. May 14, 2020. Accessed June 26, 2020. https://www.morphosys.com/media-investors/media-center/morphosys-and-incyte-announce-long-term-follow-up-results-from-l-mind 20. Salles GA, Duell J, Gonzlez-Barca E, et al. Single-arm phase II study of MOR208 combined with lenalidomide in patients with relapsed or refractory diffuse large B-cell lymphoma: L-Mind. Blood. 2018;132(suppl 1):227. doi:10.1182/blood-2018-99-113399

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