Necroptosis is a lytic programmed cell death mediated by the RIPK1-RIPK3-MLKL pathway. The loss of Receptor-interacting serine/threonine-protein kinase 3 (RIPK3) expression and necroptotic potential have been previously reported in several cancer cell lines; however, the extent of this loss across cancer types, as well as its mutational drivers, were unknown. Here, we show that RIPK3 expression loss occurs progressively during tumor growth both in patient tumor biopsies and tumor xenograft models. Using a cell-based necroptosis sensitivity screen of 941 cancer cell lines, we find that escape from necroptosis is prevalent across cancer types, with an incidence rate of 83%. Genome-wide bioinformatics analysis of this differential necroptosis sensitivity data in the context of differential gene expression and mutation data across the cell lines identified various factors that correlate with resistance to necroptosis and loss of RIPK3 expression, including oncogenes BRAF and AXL. Inhibition of these oncogenes can rescue the RIPK3 expression loss and regain of necroptosis sensitivity. This genome-wide analysis also identifies that the loss of RIPK3 expression is the primary factor correlating with escape from necroptosis. Thus, we conclude that necroptosis resistance of cancer cells is common and is oncogene driven, suggesting that escape from necroptosis could be a potential hallmark of cancer, similar to escape from apoptosis. Necroptosis is a regulated process that triggers cell death, resulting in necrosis and inflammation. Cancer cells have been shown to lose their ability to die via necroptosis, but the genetic factors that drive this resistance remain unknown. Here, we have analyzed 941 different cancer cell types and found that 83% of them are fully resistant to necroptosis. In order to identify the mechanisms underlying necroptosis resistance in these cells, we performed bioinformatics analyses to identify genes whose overexpression or mutation correlate with this effect. We show that two major genes, which are frequently deregulated in cancer , are key drivers of the resistance to necroptosis, and that targeting these oncogenes with specific drugs reversed this resistance. We conclude that resistance to necroptosis is a common event in cancer that can be overcome by targeting the genes that drive this resistance, which subsequently allows stimulation of cancer cell death via necroptosis. Necroptosis is a necrotic programmed cell death pathway mediated by the RIPK1-RIPK3-MLKL signaling cascade . ReceptoDuring necroptosis activation, RIPK1 interacts with Receptor-interacting serine/threonine-protein kinase 3 (RIPK3) to form the necrosome, which in turn phosphorylates pseudokinase Mixed lineage kinase domain-like protein (MLKL) to mediate necrotic cell death via plasma membrane rupture –17. In aThe Tyro3, Axl, Mer (TAM) receptor family of tyrosine kinases plays a role in regulating cell growth, survival, and proliferation ,36. TAM BRAF is a major regulator of protein synthesis, cell survival, growth, and proliferation . OveractIn this study, we performed a necroptosis sensitivity screen in 941 human cancer cell lines to identify the mutational drivers of the RIPK3 expression loss and the consequent escape from necroptosis. We identified the oncogenic kinases BRAF and AXL, which were validated as potential mediators of this process, because their inhibition can rescue the loss of RIPK3 expression and result in regain of sensitivity to necroptosis. Interestingly, our tumor xenograft studies, as well as transcriptomics analyses of published RNAseq/microarray datasets of patient tumor biopsy samples, show that RIPK3 expression is lost progressively during tumorigenesis. Our results reveal a potential role of BRAF and AXL oncogenes in driving the loss of RIPK3 expression and escape from necroptosis in various cancers.In order to understand the relevance of necroptosis in tumor growth and the in vivo kinetics of the RIPK3 expression loss during tumorigenesis, we evaluated the changes in RIPK3 mRNA levels in published transcriptomics datasets. Six patient tumor biopsy studies –51 and oBecause the most robust RIPK3 expression loss was observed in ovarian cancer biopsies , we perfThese findings reveal that the loss of RIPK3 expression occurs progressively during tumorigenesis in vivo and that necroptosis is activated in tumors that express RIPK3.In order to identify the mechanisms driving RIPK3 expression loss in cancer cells, we performed a necroptosis sensitivity screen using a panel of 941 human cancer cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) collection, which represent various cancer types from 28 tissues ,54. A poTogether, these results suggest that the escape from necroptosis is found in most cancer cell lines, independent of tissue and cancer type.50 values . We used both databases because the GDSC and the CCLE database cell line collections overlap and the expression values obtained from two independent sources would increase the confidence in the obtained correlation results. Our correlation analyses revealed 634 genes whose expression positively correlated with the resistance to necroptosis . RIPK3 expression was the most negatively correlated with resistance to necroptosis  and its low expression was significantly enriched in necroptosis-resistant (NR) cell lines, confirming the validity of the screen and the analysis strategy (p = 8.45 × 10−7), while RIPK1 expression did not ; and (c) AXL is the predominant TAM kinase family member that is frequently overexpressed in cancer. Importantly, transcriptomics analysis of the screened 941 cancer cell lines revealed that high AXL and TYRO3 mRNA levels predict both resistance to necroptosis and low RIPK3 mRNA levels  its family member TYRO3 was also among the 634 genes that positively correlate with resistance to necroptosis; (b) out of the two TAM kinase family members, AXL expression showed the strongest positive correlation with TSZ-ICels Figs , but notels Figs .AXL expression levels also negatively correlated with RIPK3 expression in stomach adenocarcinoma tumors and acute myeloid leukemia , based o50, as well as a negative correlation between AXL-RIPK3 expression levels, was observed when expression values from the CCLE database were used for the analysis  are either AXLhigh  or TYRO3high , while the majority of those that are sensitive to necroptosis (low IC50) are RIPK3high and have low/medium AXL/TYRO3 levels . While the majority of the cells in the cluster 4 were RIPK3high, a fraction were RIPK3low, suggesting that high RIPK3 mRNA levels are not a prerequisite to undergo necroptosis and that sufficient RIPK3 protein is expressed in these cells to undergo necroptotic cell death. However, RIPK3 expression levels were more heterogeneous than the TSZ-IC50 values, and about 18% of the cell lines were fully resistant to necroptosis despite the presence of RIPK3 expression (clusters 5 and 6), suggesting that the escape from necroptosis may not be only due to loss of RIPK3 expression. Moreover, not all cell lines with high AXL/TYRO3 levels had lost RIPK3 expression (cluster 5). Additionally, about 14% of the cell lines with low RIPK3 levels and resistance to necroptosis did not have high AXL/TYRO3 levels, suggesting that other RIPK3 loss-driving forces may exist (cluster 7). Overall, this analysis revealed a great degree of heterogeneity in AXL/TYRO3 and RIPK3 expression levels and resistance to necroptosis in the screened lines, as well as the presence of high AXL/TYRO3 and concomitant low RIPK3 expression levels in about 56% of the NR lines, suggesting that high expression levels of AXL/TYRO3 could be potential predictors/biomarkers for loss of RIPK3 expression and necroptosis resistance in cancer.Clustering analysis  revealedA 4-day treatment of A375 and SkMel28 cancer cell lines, which have no initial RIPK3 expression , with low concentrations of AXL/TYRO3 inhibitor BMS-777607 resulted in a regain of RIPK3 expression at both mRNA and protein levels . ImportaOverall, these findings suggest that AXL/TYRO3 overexpression, frequently seen in cancers, promotes the loss of RIPK3 expression and escape from necroptosis, which may be reversed upon inhibition of these kinases. Moreover, high AXL/TYRO3 levels are potential predictors/biomarkers for loss of RIPK3 expression and necroptosis resistance in cancer.high versus NR-RIPK3low populations revealed 73 interesting genes, mutations of which may lead to necroptosis resistance via alternative pathways, independent of the RIPK3 expression suppression  versus necroptosis-sensitive (NS) cell lines. Our analysis revealed that several oncogenic mutations, including those of BRAF, are strongly enriched in the NR cell lines, compared to the NS cell lines . Interespression .WT cell lines displaying low RIPK3 expression, consistent with its heterogeneous nature  could be of importance to explain this clinically vital phenomenon. We found that 38 out of 39 melanoma cell lines that have an activating BRAF mutation are fully resistant to necroptosis and have lost RIPK3 expression . Thus, aWe analyzed the mutational status of BRAF and AXL kinases in the cell lines used for the aforementioned xenograft transcriptomics study. Consistent with the notion that oncogenic BRAF and AXL kinases promote the loss of RIPK3 expression in cancer cells, 14 out of 20 cell lines that harbor mutations promoting BRAF activation or high levels of AXL (or TYRO3) experienced loss of RIPK3 expression during in vivo passaging, while 13 out of 16 cell lines that lack such mutations did not experience that effect . The latIt is possible that the selective pressure to lose RIPK3 expression during tumorigenesis comes from the necessity to evade immunity. For example, loss of RIPK3 in tumors would result in decreased cross-priming  and incrOur in vivo results and published tumor xenograft experiments using immunocompromised animals show that the adaptive immune response  is not necessary for the loss of RIPK3 expression in tumor cells. Hence, our findings suggest that RIPK3 loss may be dependent on a tumor cell–intrinsic mechanism in vivo or due to interactions with stromal or innate immune cells.RAS isoforms, known to be upstream of BRAF, were found among 20 oncogenes identified to positively correlate with resistance to necroptosis, further suggesting the involvement of BRAF in escape from necroptosis . Cancer RIPK3 expression has been previously shown to be controlled via transcriptional repression mechanisms that include promoter hypermethylation and regulation via transcription factor Sp1 ,60. BRAFNotably, both ABIN-1 and OPTN expression levels were found to strongly correlate with AXL expression across the analyzed 1,000 cell lines . It is nIt is interesting that both BRAF and RIPK3 are in the same kinome branch, namely, in the tyrosine-kinase like (TKL) family of kinases . NotablyIn conclusion, we provide the first systematic evidence that most human cancer cell lines escape from necroptosis, independent of their tissue of origin or cancer type, and identify the first two oncogenic alterations upstream of the RIPK3 expression suppression. We show that BRAF and AXL oncogene overactivation in cancers is likely to be among the driving forces for the loss of RIPK3 during tumorigenesis and the consequent escape from necroptosis, as well as other RIPK3-driven processes. Understanding the mechanism of escape from necroptosis in tumorigenesis is likely to pave the way for development of better anticancer therapies.BMS-777607 and TAK-632 were purchased from SelleckChem . Luminol (A8511), p-coumaric acid (C9008), Tween 20, and zVAD.fmk were from Sigma . DMSO (sc-20258) was from Santa Cruz Biotechnology . The following antibodies were used in this study: RIPK1 ; p-MLKL (S358) , ab187091); hMLKL ; and Actin , sc-81178). Smac mimetic SM-164 was custom synthesized  , 0.1% BME [fresh], 1 mM PMSF [fresh], 2× Complete protease inhibitor cocktail [Roche]) using VWR 200 Homogenizer, on ice. Lysates were cleared by centrifugation at 16,000g, 15 minutes, 4°. Protein concentrations were determined using Bradford reagent (BioRad). Protein samples were mixed with 5× SDS-PAGE sample buffer and frozen at −80° for storage.Experiments were performed as previously described . Tumor an was at least 3. Statistical analyses were performed using GraphPad Prism 7 or Microsoft Excel. Violin and bean plots were made using BoxPlotR (http://shiny.chemgrid.org/boxplotr/) "). Intersections of gene lists were made with CrossCheck  and VennMutational enrichment was done by dividing the number of mutations identified in NR cells by those identified in cells that were sensitive to the necroptosis treatment (NS) and then performing a z-test on this dataset. NR cells were defined as those that had no reduction in cell viability at 1 μM SM-164 concentration (the highest concentration used in the screen). The rest of the cell lines that exhibited reduction in cell viability were defined as NS.S1 DataData are given in indicated separate sheets.(XLSX)Click here for additional data file.S1 Fig(A) Loss of RIPK3 expression during tumorigenesis. RIPK3 expression is progressively lost during tumorigenesis. Ovarian PDX lysates obtained at the indicated in vivo passages were immunoblotted with the indicated antibodies. The lack of RIPK3 expression loss in all of the PDX samples highlights the heterogeneity of this event in cancer. (B) Effect of Nec-1 and GSK’872 on cell death induced by TSZ. The experiment shown in (C) As in (B), except indicated doses and the MLKL inhibitor NSA were used. The underlying data can be found in (TIF)Click here for additional data file.S2 Fig(A) Low-throughput confirmation of the screen observations regarding necroptosis resistance. Indicated cells were treated with TCZ  ± Nec-1 indicated treatments and cell survival was measured 16 hours later using CellTiterGlo. Means ± SEM are shown with t test p-values. (B) Frequency of necroptosis-resistant cancer cell lines across various tissues of origin. Some tissues did not have NS cancer cell lines , while only 40% of cancer cell lines from the biliary tract were resistant to necroptosis. (C) Numbers of cancer cell lines used in the screen across various tissues of origin. A total of 91 cell lines were derived from leukemia patients and 102 cell lines were from NSCLC. The underlying data can be found in (TIF)Click here for additional data file.S3 Fig(A) Outline of the data analysis strategy for differential necroptosis sensitivity data from the cell-based high-throughput screen described in (B) Low RIPK3 expression levels are enriched in cancer cell lines fully resistant to necroptosis. The GDSC database was employed for the analysis. Means, 10–90 percentile data points ± SEM are shown with t test p-values. The underlying data can be found in (TIF)Click here for additional data file.S4 Fig(A) Pearson correlation analysis using CCLE mRNA expression database for known necroptosis-related proteins was performed and a heatmap for the Pearson coefficients was generated using ClustVis. (B) High AXL expression positively correlates with low RIPK3 expression levels in SAC  and AML , according to Pearson and Spearman correlation analyses. Cell lines with either genomic AXL or RIPK3 mutations were omitted from the analysis (18 for SAC and 0 for AML). The results shown here are based upon data generated by the TCGA Research Network, using cBioportal. The underlying data can be found in (TIF)Click here for additional data file.S5 Fig(A) High AXL expression positively correlates with resistance to TSZ-induced necroptosis. Pearson correlation analysis for AXL mRNA levels versus TSZ-IC50 values for the screened cancer cell lines. Markers stacked at IC50 = 1 value indicate cell lines with no response to TSZ even at the highest SM-164 concentration of 1 μM. The CCLE database was employed for the analysis. (B) High AXL expression positively correlates with low RIPK3 expression levels. Pearson and Spearman correlation analyses were used. The CCLE database was employed for the analysis. (C) High AXL expression positively correlates with low RIPK3 expression levels. The GDSC database was employed for the analysis. Quartile analysis using one-way ANOVA was used to determine statistical significance. (D) High TYRO3 expression positively correlates with low RIPK3 expression levels. The GDSC database was employed for the analysis. Quartile analysis using one-way ANOVA was used to determine statistical significance. The underlying data can be found in (TIF)Click here for additional data file.S6 Fig(A) A total of 75 of the NR cell lines have not lost RIPK3 expression (RIPK3high). RIPK3high subpopulation was defined as cell lines with RIPK3 expression greater than the third quartile of the 941-cell-line population. NR subpopulation was defined as cell lines that showed no cell death at the highest SM-164 concentration (1 μM). (B) Fold enrichment of mutations in NR-RIPK3high versus NR-RIPK3low cell lines is plotted against the genes and mutation types. All the displayed hits pass the Fisher’s exact test with p < 0.05 for mutational enrichment in the NR-RIPK3high population. Types of mutations are indicated. The underlying data can be found in (TIF)Click here for additional data file.S7 Figp-values were used for the analysis. The underlying data can be found in Pearson (TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.


Cancer cell lines are a cornerstone of cancer research but previous studies have shown that not all cell lines are equal in their ability to model primary tumors. Here we present a comprehensive pan-cancer analysis utilizing transcriptomic profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia to evaluate cell lines as models of primary tumors across 22 tumor types. We perform correlation analysis and gene set enrichment analysis to understand the differences between cell lines and primary tumors. Additionally, we classify cell lines into tumor subtypes in 9 tumor types. We present our pancreatic cancer results as a case study and find that the commonly used cell line MIA PaCa-2 is transcriptionally unrepresentative of primary pancreatic adenocarcinomas. Lastly, we propose a new cell line panel, the TCGA-110-CL, for pan-cancer studies. This study provides a resource to help researchers select more representative cell line models. Cell lines are used ubiquitously in cancer research but how well they represent the tumor type they were derived from is variable. Here, the authors compare transcriptomic profiles of 22 tumor types and cell lines and propose a new comprehensive cell line panel for pan-cancer studies. Prior studies in ovarian cancer3, liver cancer4, and breast cancer6 have shown that cell lines differ in their ability to represent the primary tumors they were derived from, suggesting that using more appropriate cell lines for cancer studies may increase the translatability of preclinical findings. While these previous studies are valuable resources for researchers studying select tumor types, there is a need for a comprehensive pan-cancer analysis of cell lines and primary tumors.Cancer cell lines are an integral part of cancer research and are routinely used to study cancer biology and to screen anti-tumor compounds. While they are relatively inexpensive and easy to grow under laboratory conditions, cell lines have known limitations as preclinical models of cancer and many promising candidate drug compounds have failed to show utility among patient populations7 research group has collected and characterized the molecular profiles of tumors from over 11,000 patients across 33 different tumor types. They provide clinical, transcriptomic, methylation, copy number, mutation, and proteomic data to facilitate the in-depth interrogation of cancer biology at multiple molecular and clinical levels. In addition, the Broad Institute’s Cancer Cell Line Encyclopedia8 is another large-scale research effort which characterized over 1000 human-derived cancer cell lines across 36 tumor types and provides transcriptomic, copy number, and mutation data.The generation of large public molecular data sets has allowed researchers to investigate cancer biology at a scale that was unheard of a decade ago. In particular, The Cancer Genome Atlas (TCGA)3. They created a cell line suitability score using features of HGSOC and discovered that the most commonly used cell lines do not seem to resemble HGSOC tumors, and the cell lines most representative of HGSOC have very few publications. Similarly, Chen et al. compared hepatocellular carcinoma primary tumor samples to cell lines using transcriptomic data and found that nearly half of the hepatocellular carcinoma cell lines in CCLE do not resemble their primary tumors4. In breast cancer, Jiang et al. compared gene expression, copy number alterations, mutations, and protein expression between cell lines and primary tumor samples5. They created another cell line suitability score by summing the correlations across all four molecular profiles, although it is notable that only gene expression and copy number alterations had a substantial effect on their score as mutations and protein expression had extremely low correlations across all cell lines (R < 0.1). In another breast cancer study, Vincent et al. compared transcriptomic data between cell lines and primary tumor samples and identified basal and luminal cell lines that were most similar to their respective breast cancer subtypes6. While these studies provide insight into specific tumor types, here we hope to provide researchers with a pan-cancer resource that is, to the best of our knowledge, the most comprehensive to date. In addition, unlike previous studies, we adjust for tumor purity which can be a significant confounder in primary tumor transcriptomic data9.Previous studies have integrated data from both of these data sets to evaluate cell lines as models of specific tumor types. For example, Domcke et al. focused primarily on copy number alterations and mutation data to evaluate cell lines as models of high-grade serous ovarian carcinomas (HGSOC)12. Breast cancers, for example, can be divided into five intrinsic molecular subtypes based on gene expression profiles with distinct clinical outcomes13. While much progress has been made in separating primary tumors into biologically distinct subtypes, few publications have attempted to apply these subtypes to cell line models. Our study seeks to provide subtype classifications for cell lines to aid researchers interested in subtype-specific studies or drug screens.Cancer is an incredibly heterogeneous disease that can often be stratified into clinically relevant subtypes with different prognosis and responses to treatments. While specific genomic alterations or histological markers have been used to stratify tumors, gene expression is commonly used to group tumors into molecular subtypes14. The NCI-60 panel contains 60 human tumor cell lines representing nine human tumor types: leukemia, colon, lung, central nervous system, renal, melanoma, ovarian, breast, and prostate. Over 100,000 antitumor compounds have been screened using this cell line panel, generating the largest cancer pharmacology database worldwide. While this cell line panel has provided valuable insight into mechanisms of drug response and cancer biology, new large public molecular data sets allow us to compare the NCI-60 cell lines to primary tumor samples and propose more representative cell lines for an improved cancer cell line panel.The National Cancer Institute’s NCI-60 cell lines are perhaps the most well-studied human cancer cell lines, and have been in use for nearly three decades by both academic and industrial institutions for drug discovery and cancer biology researchhttp://comphealth.ucsf.edu/TCGA110CL). Last, we selected the cell lines that were the most correlated to their primary tumor samples across 22 tumor types and propose a new cell line panel, the TCGA-110-CL, as a more appropriate and comprehensive panel for pan-cancer studies.In this study, we compared transcriptomic profiles from cell lines and primary tumor samples across the 22 tumor types covered by both TCGA and CCLE. We observed the confounding effect of primary tumor sample purity in our analysis, and we adjusted for purity in our correlation analysis and differential expression analysis of cell lines and primary tumor samples. We found that cell-cycle-related pathways are consistently upregulated in cell lines, while immune pathways are consistently upregulated across the primary tumor samples. Next, we classified cell lines into subtypes across nine tumor types. We then present our analysis of pancreatic adenocarcinoma (PAAD) cell lines and primary tumor samples and show that we are able to identify a cell line that originated from a different cell-type lineage compared with the primary tumor samples. Although only our PAAD analysis is presented in the main text, we also analyzed the other 21 tumor types and present our results as a web application and a resource to the cancer research community  to 0.49 in liver hepatocellular carcinoma Fig. . Within 3. Within the solid tumor cluster, tumor types from similar cell of origin generally clustered together, such as ovarian serous cystadenocarcinoma (OV) and uterine corpus endometrial carcinoma (UCEC), glioblastoma (GBM) and lower grade glioma (LGG), and esophageal carcinoma (ESCA) and HNSC. Interestingly, we observe that sometimes the highest correlation coefficients are not necessarily between cell line and primary tumor samples from the same tumor type. In fact, in 8/22 tumor types, primary tumor samples have higher correlation coefficients with other tumor cell lines than their own. These tumor types are BLCA (highest correlation with HNSC), CHOL (highest correlation with LIHC), ESCA (highest correlation with HNSC), LGG (highest correlation with GBM), STAD (highest correlation with COADREAD), LUSC (highest correlation with HNSC), LUAD (highest correlation with PAAD), and UCEC (highest correlation with OV). While this may indicate poor differentiation in the cell lines or primary tumor sample or lack of appropriate cell line models, many of these tumor types have higher correlations with a related tumor type .Our clustering analysis of cell line and primary tumors correlation coefficients largely captures known biological relationships between the tumor types Fig. . The fir3. Our results were highly correlated , which suggests that our cell line rankings capture much of the same information as more curated ranking methods that use genomic alterations.To verify that the results of our transcriptomic-based correlation approach were consistent with previous publications, we compared our cell line rankings for OV to the cell line rankings in Domcke et al. which evaluated high-grade ovarian cancer cell lines based on copy number alterations and selected mutations  between primary tumor sample purity and the cell line-primary tumor correlation coefficients, suggesting that tumor purity is a confounder in our correlation analysis. Furthermore, when we performed Gene Set Enrichment Analysis (GSEA) on the differential expression results using the hallmark gene sets from the MSigDB Collections19 and the hallmarks of cancer pathways20, we saw that the gene sets involved in immune processes are consistently upregulated in primary tumor samples, suggesting that the largest biological signal from the TCGA samples can likely be attributed to the immune cell infiltrate that are present in the primary tumor samples and absent in the pure cell line populations . In addition, we found that only one tumor type (LGG) retained significantly higher correlations between cell lines and the primary tumor samples in the top quartile of purity compared with cell lines and primary tumor samples in the bottom quartile of purity   Fig. . This PP16) Fig. .No individual genes were significantly upregulated in cell lines across 90% of the tumor types analyzed. However, gene sets involved in cell-cycle progression  and genome instability were significantly enriched in cell lines in our GSEA of MSigDB Hallmark Gene Sets and the Hallmarks of Cancer pathways Fig. . These r21, which has previously been used to predict the subtypes of cancer cell lines22. Briefly, this method involves generating gene templates for each subtype by identifying genes that are upregulated in each subtype compared with the other subtypes. The distances between the sample to be classified and each subtype template is then calculated, and the sample is predicted to belong to the subtype with the smallest template distance  methodnce Fig. .Fig. 3Ce22. We began with the 18 TCGA tumor types for which we had subtype information from TCGA publications39, and randomly divided these samples into training sets (80%) and test sets (20%). After generating our initial subtype templates using the training set of primary tumor samples, we removed genes that are differentially expressed between primary tumors and cell lines as we wanted to enrich our subtype templates for genes that are consistent between primary tumors and cell line models. We also filtered out genes that are not highly expressed in at least a subset of the cell lines, as we wanted to retain genes that are robust and informative in cell lines. This filtering step can also enrich for cancer-intrinsic genes, since cell lines are pure populations of cancer cells. To verify that our classifier is still able to predict tumor subtypes after enriching for cell line relevant genes, we applied the classifier to the test set of held out primary tumor samples. Overall, 9/18 tumor types had a classification accuracy greater than or equal to 80% in the test set. We then applied the classifiers of these nine tumor types to their respective cell lines and predicted the subtypes of the individual cell lines , LUAD , and SKCM . This is likely because certain tumor subtypes have a higher rate of cell line generation than others due to their biology. We present the results of our cell line subtype predictions in Supplementary Data Like Sveen et. al, we modified this method to create a classifier that can be applied to cancer cell lines after training the classifier on primary tumor samplesnes Fig. . While a40. We utilize these subtypes in our study of PAAD presented here. While only the analysis for PAAD is shown, analysis of the other tumor types are available in our web application . For each tumor type, we adjusted for primary tumor purity and compared the expression profiles of the primary tumor samples to the 932 cell line expression profiles in a correlation analysis. We included tumor subtype predictions for the nine tumor types where the prediction accuracy in the test set was greater ≥80%.PAAD is often diagnosed at an advanced stage, and is predicted to become the second leading cause of cancer mortality by the year 2030. PAAD tumors can be divided into basal or classical molecular subtypes, with significantly lower survival associated with the basal subtype41. This suggests that our correlation approach is able to distinguish between cell lines derived from different cell types or cell lines that may not be representative of PAAD. Of potential concern, the cell line with the lowest median correlation coefficient, MIA PaCa-2, is commonly used as an adenocarcinoma cell line model and has over 1000 PubMed citations.We compared the correlations between PAAD primary tumor samples and all 932 cell lines grouped by cell line tissue of origin Fig. . The PAA40. We did not see strong clustering by primary tumor subtypes in our primary tumor/cell line correlation matrix  have been noted to produce high or moderate amounts of mucin43, which the Moffit et. al paper found to be present in increased levels in the classical subtype40. In addition, the Collison et al. publication, whose classical subtype genes significantly overlapped with the Moffit et al. classical subtype genes (20/22), predicted that both CAPAN-1 and HPAF-II belong to the classical subtype44. This suggests that these two pancreatic cell lines may indeed reflect the classical subtype, despite the Moffit et al. publication classifying them as basal40.We then used the Nearest Template Prediction method to predict the subtypes of the pancreatic cancer cell lines Fig. . After dCorrelations between the pancreatic cell lines and the primary tumors in each individual subtype were also calculated .In our case study comparing pancreatic cell lines to pancreatic primary tumor samples, we found that the pancreatic cell lines are more representative of pancreatic primary tumor samples than cell lines from other tissues of origin. We also found a group of cell lines with significantly lower correlations with the primary tumors, suggesting that these cell lines may not be appropriate models of primary PAAD tumors. Indeed, the pancreatic cancer cell line with the worst median correlation was shown to express neuroendocrine differentiationFinally, we propose the TCGA-110-CL cell line panel as a resource for pan-cancer studies. It encompasses 22 different tumor types and contains the cell lines most correlated with their primary tumor samples. Although some tumor types have higher correlations than others, our aim was to propose a comprehensive cell line panel and we did not set a correlation coefficient cutoff for cell line inclusion. We hope that using more representative cell lines in our pan-cancer panel will improve our ability to translate cell line findings into patients.3, and we were able to identify a pancreatic cell line that was derived from a neuroendocrine tumor rather than a PAAD. Last, the focus of our study was on transcriptomics which is only one potential metric for determining cell line suitability, depending on the research question being asked. However, we believe this study is a valuable general resource for researchers who can, for example, use it to identify potentially problematic cell lines that may not be representative of the primary tumors they are studying.There are several limitations of our study that should be recognized. Although we were not able to match all of the cell lines from CCLE to primary tumor samples in TCGA, we were able to match a majority of the cell lines (71%) to a corresponding primary tumor type, and we provide analysis for less common tumor types whose cell lines have not been well studied. In addition, although our cell line findings lack experimental validation, our findings were highly correlated to previous publicationsIn future studies, we hope to integrate other types of molecular data such as mutation, copy number alteration, and methylation profiles to provide a multi-omic comparison of cell lines and primary tumor samples. In particular, genomic alterations are important for targeted therapies, which act on specific mutant isoforms and we hope to incorporate this information in our future cell line studies.By leveraging expression profiles from thousands of primary tumor and cell line samples, our study has created a comprehensive pan-cancer resource to aid researchers in selecting the most representative cell line models. We hope that using more appropriate cell line models for cancer studies will allow the research community to better understand cancer biology and translate more in vitro findings into clinically relevant therapies.n = 679) to their respective TCGA tumor type. We used these matched CCLE cell lines for comparison with TCGA primary tumor samples. These samples encompass the following 22 tumor types: BLCA, BRCA, CHOL, COADREAD, DLBC, ESCA, GBM, LGG, HNSC, KIRC, LAML, LIHC, LUAD, LUSC, MESO, OV, PAAD, PRAD, SKCM, STAD, THCA, and UCEC. For the correlation analysis based on cell line tissue of origin, all 934 CCLE samples were used.CCLE cell lines were manually matched to TCGA tumor types using the CCLE Cell Line Annotations file (CCLE_sample_info_file_2012–10–18.txt), which contains histological information for each cell line. While 934 CCLE samples were available in the OSF open-access repository, we were able to match ~70% of the samples (45 (https://osf.io/gqrz9/). This repository contains 12,307 RNA-seq samples from both the CCLE and the TCGA databases, which have been uniformly processed from raw data. Transcript alignment and quantification were performed using kallisto (version 0.43.0), and both transcript per million (TPM) values and transcript counts are available in the repository. The transcript counts were downloaded and summarized to the gene level for this analysis. We then performed upper-quartile normalization and the log transformed data. Because two different sequencing platforms (GAII and HiSeq) were used by TCGA to sequence five tumor types , we used ComBat to correct for these sequencing platform differences . We then computed tumor purity using ESTIMATE47 for all of the TCGA tumors and averaged the ABSOLUTE and ESTIMATE values. The purity estimates using ABSOLUTE were highly correlated with the purity estimates using ESTIMATE , and adjusted for tumor purity in the primary tumor samples using linear regression. For each tumor type, we then selected the 5000 most variable genes ranked by interquartile range (IQR) across the primary tumor samples only. We decided to use 5000 genes based on previous studies4, although we tried increasing the number of genes  and found our results to be remarkably robust  reflects the degree to which a gene set is overrepresented at the top or bottom of the ranked list of genes. We downloaded the 50 Hallmark gene sets from the MSigDB Collections19, and created our own gmx file for the Hallmarks of cancer pathways using gene sets from the Oncology Models Forum20.For our GSEA analysis, we ranked our genes by their log fold-change values. We then used the GSEAPreanked21 method for our subtype analysis. To generate the subtype templates for each tumor type, we collected subtype information from TCGA publications. We then randomly split the TCGA samples into training (80%) and test set (20%). We used the training set to generate the templates for the NTP method by performing differential expression analysis between each subtype versus all other subtypes with voom and quantile normalization. We selected template genes that had LFC >1 and FDR <0.01 for each subtype. To enrich for cell line relevant genes, we then removed genes that were differentially expressed between cell lines and primary tumors with LFC >2 and genes that were not in the top 50% of expression in at least two cell lines. Next, we used these filtered subtype templates to predict the subtypes of the primary tumors held out in the test set using the NTP method. If the classification accuracy in the test set was ≥80%, we then applied it to the cell lines to predict the cell line subtypes.We used the Broad Institute’s Nearest Template Prediction (NTP)Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Reporting Summary


O6-methylguanine DNA methyltransferase (MGMT) strongly influences the effectiveness of cancer treatment with chemotherapeutic alkylating agents, and MGMT status in cancer cells could potentially contribute to tailored therapies for individual patients. However, the promoter methylation and immunohistochemical assays presently used for measuring MGMT in clinical samples are indirect, cumbersome and sometimes do not accurately report MGMT activity. Here we directly compare the accuracy of 6 analytical methods, including two fluorescent reporter assays, against the in vitro MGMT activity assay that is considered the gold standard for measuring MGMT DNA repair capacity. We discuss the relative advantages of each method. Our data indicate that two recently developed fluorescence-based assays measure MGMT activity accurately and efficiently, and could provide a functional dimension to clinical efforts to identify patients who are likely to benefit from alkylating chemotherapy.The DNA repair protein  O6-methylguanine DNA Methyltransferase  repairs DNA damage induced by endogenous, environmental, and therapeutic alkylating agents, and is the most important pathway for repairing O6-alkylguanine adducts in human cells [O6-methylguanine (O6-MeG) cytotoxic DNA lesions induced by the cancer chemotherapy agents Temozolomide and Dacarbazine, and cytotoxic O6-chloroethylguanine lesions induced by cross-linking agents such as BCNU [an cells , 2. MGMT as BCNU . These c as BCNU , and are as BCNU , highligAlthough MGMT diminishes the effectiveness of cancer therapies, it also plays a critical role protecting normal tissues from DNA damage. Over 10-fold inter-individual variation in MGMT activity has been observed in normal tissues , 7, and Presently, MGMT status is assessed in clinical samples primarily using MGMT promoter methylation as a proxy for MGMT expression, and in some cases using immunohistochemistry; however, both methods can fail to reflect MGMT levels accurately. For example high levels of MGMT expression are possible in tumors with MGMT promoter hypermethylation due to expression of a previously unrecognized enhancer element . Such obSeven B-lymphoblastoid cell lines including TK6 (RRID CVCL_0561) , TK6+MGM7 cells were collected, washed twice with PBS, and suspended in 400 μL of lysis buffer comprising 50 mM Tris, pH 7.5, 1 mM EDTA, 1 mM DTT, 5% glycerol, 50 mM NaCl, and 1 mM AEBSF (protease inhibitor). Cells were disrupted by sonication, cell debris was pelleted by centrifugation at 14,000g for 30 minutes at 4°C, supernatants were collected, and total protein concentration was determined using a BCA assay. Cell lysates were incubated at 37°C for 30 minutes with 4 pmoles of a 32P 5’-end labeled duplex comprising an oligonucleotide, GAACTXCAGCTCCGTGCTGGCCC, in which X represents O6MeG, and the corresponding complementary oligonucleotide. The reaction products were then purified by phenol/chloroform extraction and ethanol precipitation, dissolved, and finally digested using PstI restriction enzyme. O6MeG blocks PstI cleaveage, providing the basis for a gel-shift assay for the extent of MGMT-dependent lesion removal. Digests were analyzed by SDS PAGE followed by autoradiographic imaging, and densitometry was used to calculate the percentage of cleaved oligonucleotide. The linear range of the MGMT assay was established for each cell line by varying the amount cell lysate (between 10 and 400 μg) incubated with the duplex. MGMT activity in each cell lysate was calculated from the slope of a linear best fit of the percentage of oligonucleotide cleaved versus total protein concentration.Oligonucleotide cleavage assays were performed as described previously , and illPreparation of cell lysates and immunoblotting were performed as described previously . BrieflyqPCR data were described previously . Total RO6MeG-containing oligonucleotides that were annealed to single-stranded plasmid DNA, followed by primer extension and ligation. The DNA lesion induces transcriptional errors that result in expression of functional mPlum fluorescent protein, unless repair removes O6MeG, the source of transcriptional errors. As a result, cells that efficiently repair O6MeG express relatively low levels of mPlum fluorescent protein, whereas MGMT deficient cells express relatively high levels of mPlum fluorescent protein. Transient transfection, flow cytometric analysis, and calculation of DNA repair capacity were described previously [FM-HCR assays have been published previously . Brieflyeviously .6 cells from cell lines using a QIAamp DNA Kit, and bisulfite conversion of 1 microgram of the resulting gDNA was carried out using an EpiTect Bisulfite Kit (Qiagen). For PCR detection of unmethylated MGMT promoter sequences, the following primers were used: 5’TTTGTGTTTTGATGTTTGTAGGTTTTTGT-3’, 5’AACTCCACACTCTTCCAAAAACAAAACA-3’. For detection of methylated DNA, the following primers were used: 5’TTTCGACGTTCGTAGGTTTTCGC-3’, 5’GCACTCTTCCGAAAACGAAACG-3’. Approximately 100 ng of gDNA was combined with primers at a final concentration of 400 nM, and amplified with 1 unit of AmpliTaq Gold DNA polymerase for 35 cycles . PCR products were analyzed on a 3% agarose gel visualized with ethidium bromide  comprising a short DNA oligomer containing a fluorophore and an O6-benzylguanine nucleoside that is modified with a quencher dye [Cell lysates were prepared from approximately 10cher dye . Repair For each method, error bars represent standard deviation from three biological replicates . One-way ANOVA with Tukey’s multiple comparisons test was used to determine the ability of assays to distinguish MGMT levels or activity among the cell lines. All statistical analyses were carried out in Graphpad Version 7.0c.O6MeG generating alkylating agents observed previously for the larger set of cell lines [A panel of 24 lymphoblastoid cell lines derived from apparently healthy individuals from diverse genetic backgrounds , Coriellll lines , which cll lines .The gold standard radiolabeled oligonucleotide-based biochemical MGMT assay revealed an approximately 100-fold range of MGMT activity in the samples, with the following rank order established using the biochemical MGMT assay: TK6 < Coriell #5 < Coriell #4 < Coriell #12 < Coriell #14 < Coriell #16 < TK6+MGMT. The available quantitative methods for assessing MGMT status in human lymphoblastoid cell lines have been compared with this biochemical assay . All sixIn vitro biochemical MGMT activity assays can be regarded to be the “gold standard” for measuring MGMT activity because they directly measure the level of repair activity of MGMT protein using a chemically defined substrate with a radiolabel that permits detection of very low levels of repair activity. However, the assay has several technical drawbacks. Assay conditions must be optimized to determine the range of cell lysate protein concentrations that produce a linear response, which is cell-line dependent (Compare x-axes in panel C of FM-HCR assays distinguished cells with low MGMT activity (Cell lines #4 and #5) from cells that lack MGMT activity (TK6), and thus exhibited the same dynamic range as the gold standard biochemical assay . The sensitivity of FM-HCR is achieved in part because of the ability to detect individual cells harboring unrepaired DNA lesions that lead to transcriptional errors and fluorescent protein expression; whereas a minor subpopulation of repair deficient cells may be lost in ensemble measurements, they can be readily detected by FM-HCR. Together with qPCR and the fluorescent NR-1 probe assay (below), FM-HCR required the least amount of active laboratory time (1.5 hours) for analysis of up to 4 samples in parallel.32P-oligonucleotide-based biochemical assay, FM-HCR and qPCR, were below the limit of detection by western blotting. Western blots also required approximately twice the active laboratory time  as the least labor-intensive assays.As has been observed by others , MGMT prMGMT transcript levels measured by qPCR analysis, reported previously , correlaMGMT activity as measured with the NR-1 probe correlated well with activity measured using the biochemical MGMT assay, however two cell lines (#4 and #5), which had the lowest activity as judged by the biochemical assay, were below the limit of detection. Analysis using the NR-1 probe required approximately 1.5 hours for analysis of up to 4 samples in parallel.O6-benzylguanine [Promoter methylation was detected, as expected, in TK6, previously shown to exhibit MGMT promoter methylation, and in TK6+MGMT, which is derived from TK6 and expresses MGMT constitutively under a CMV promoter . Strikinlguanine . AnalysiThe time-sensitive nature of cancer treatment as well as the serious side effects and risk of therapy-related cancers in patients treated with radiotherapy and chemotherapy have motivated a search for biomarkers that can predict whether specific therapies will work for individual patients . The suc8) of the six assays used. These considerations render oligonucleotide-based assays too labor intensive for clinical use.Although the biochemical MGMT assay is considered a gold standard because it provides a sensitive quantitative measure of functional MGMT levels in cell lysates, it was by far the most time consuming  is larger than that of the NR-1 probe (4.3). Although the NR-1 probe failed to detect the very low levels of MGMT activity in extracts from cell lines #4 and #5, the probe did distinguish significant differences in activity among cell lines with higher MGMT activity , whereas FM-HCR did not . HoweverClinical translation will require studies in primary patient samples to determine how well these assays predict therapeutic outcomes assessed using standard measures such as the Response Evaluation Criteria in Solid Tumors (RECIST). For cell-based assays, optimization of tumor processing to maximize live cell content may be needed. Strategies for excluding signal from tumor stroma should be considered, for example using cell surface markers or immunomagnetic separation.Limitations of existing assays have usually resulted in binary classification of tumor MGMT status, making it difficult to assess whether the distinction between tumors with low MGMT and those that are completely lacking MGMT is clinically important. However, available data are consistent with a continuous relationship between MGMT activity and temozolomide sensitivity . QuantitMany cancers exhibit alterations in MGMT activity that may be exploited when treating patients with alkylating chemotherapeutic agents, and MGMT activity in normal tissues may predict inter-individual differences in alkylating agent toxicity. However, there remains a critical need for accurate, functional assays that could be used to identify individuals with MGMT-deficient cancers. The recently developed FM-HCR assays and fluorescent NR-1 probe both overcome the problems associated with currently used indirect methods of measuring MGMT activity, and merit consideration as alternatives for use in pre-clinical studies and in clinical trials involving cancers where MGMT status may be associated with therapeutic outcomes.S1 FigO6MeG DNA lesion results in an 8 nt 32P labeled fragment detectable by polyacrylamide gel electrophoresis. B) Polyacrylamide gel analysis of restriction digest products following treatment of 4 pmol of oligonucleotide with 10–400 μg of protein extracts from 7 lymphoblastoid cell lines for 30 minutes at 37 °C. C) Determination of linear range and calculation of MGMT activity. Slopes calculated from the data in these plots are reported in Panel A.A) Oligonucleotide digest assay for MGMT activity. Repair of a PstI cleavage blocking (PDF)Click here for additional data file.S2 FigProtein levels were below the limit of detection for Coriell #5, Coriell #4, and TK6.(TIFF)Click here for additional data file.S3 FigEach lane shows PCR products obtained from amplification of bisulfite converted genomic DNA from the indicated cell lines with primers specific for unmethylated DNA (U), or methylated DNA (M).(PDF)Click here for additional data file.S1 Table32P Oligo, fmoles cleaved oligonucleotide per microgram protein lysate; NR-1, Fluorescence signal, arbitrary units; FM-HCR, % Reporter Expression; Western Blot, MGMT protein levels as a percentage of actin protein levels; qPCR, MGMT transcript levels normalized to MGMT transcript levels in TK6+MGMT.Units are as follows: (DOCX)Click here for additional data file.S2 TableCell lines that were significantly different from one another for each MGMT assay are indicated with an asterisk “*”; those that were not significantly different are marked “ns”.(DOCX)Click here for additional data file.


Embryology of normal web space creation and the genetics of syndactyly in humans and experimental animals are well described in the literature. In this review, the author offers a 3-step pathway of pathogenesis for syndactyly. The first step is initiated either by the overactivation of the WNT canonical pathway or the suppression of the Bone Morphogenetic Protein (BMP) canonical pathway. This leads to an overexpression of Fibroblast Growth Factor 8 (FGF8). The final step is the suppression of retinoic acid in the interdigital mesenchyme leading to suppression of both apoptosis and extracellular matrix (ECM) degradation, resulting in syndactyly. Studies on developmental biology and embryology of the upper limb have greatly improved our understanding of the genetics and phenotypes of various congenital hand abnormalities .The embryology of normal web space creation  and the FIBULIN1 (OMIM 608180) [The genetics literature classifies syndactyly into nine types . However 608180) , the tri 608180) .MMP11 gene also contains retinoic acid responsive elements [A 3-step pathway could explain the pathogenesis of almost all types of syndactyly in humans and experimental animals . The firelements .  Table LRP4 will lead to signal activation and syndactyly  [APC gene lead to beta-catenin accumulation. In most cases, a familial adenomatous polyposis phenotype (OMIM 175100) is seen. However, APC mutations may occasionally result in a Cenani–Lenz syndactyly phenotype [FMN1 deletions or duplications encompassing the GREM1-FMN1 genes [LRP4 is a strong suppressor of WNT signaling, and hence, loss-of-function mutations of  212780) . Loss-ofe OMIM 17100 is sehenotype . SimilarN1 genes .th and the 22nd embryonic stages (47–54 intrauterine days) [RAB23, TWIST1, and GLI3 will be associated with a high SHH activity and syndactyly  . The resne days) . In humane days) , 24, 25.ne days) –28. Hencndactyly .GJA1 lead to loss of the functional activity of CX43, which in turn leads to reduced BMPs and syndactyly  [The last protein acting on Step I is the CX43 protein. Mutations of  186100) .FGFR1/FGFR2 are associated with an increased activity of FGF8 and syndactyly. Overexpression of other FGFs such as FGF2 in the cranium also occurs, leading to craniosynostosis. The severity of craniofacial abnormalities versus hand syndactyly will also depend on the type of mutation. A classic example is Apert syndrome (OMIM 101200). Hand surgeons classify patients with Apert syndrome with FGFR2 mutations into two groups according to the severity of syndactyly/craniofacial defects [FGFR2. The resulting gain-of-function of the receptor in this mutation leads to an increased functional effect of FGF2 which is mainly expressed in the craniofacial skeleton. The second group have “mildly affected” heads but “severely affected” hands and carry the mutation Pro253Arg in FGFR2. The resulting gain-of-function of the receptor in this mutation results in loss of the ligand-binding specificity in which the abnormal receptor is able to bind to FGF10, which results in upregulation of ectodermal FGF8 through the FGF10–FGF8 loop [In Step II, acrocephalo-syndactyly syndromes caused by gain-of-function mutations of  defects , 31. TheFBLN1 mutations, OMIM 608180) also occurs at Step II. Normally, the FIBULIN1 protein binds to FGF8 with high affinity, modulating its activity and expression [The pathophysiology of the synpolydactyly type 2b  is probably related to HOXD13-GLI3R interactions. Chen et al. [Hoxd13 directly binds to Gli3r (the repressor form of Gli3). The mutated Hoxd13 directs Gli3r for a premature degradation. This will result in polydactyly, similar to the polydactyly phenotype caused by depletion of Gli3r. Brison et al. [HOXD13 gene in humans with synpolydactyly confirms a novel functional domain in HOXD13 which regulates digit number through its interaction with GLI3R.ndactyly  includin autopod . Hence,  autopod  reviewedn et al.  have shon et al.  have alsAnimal models of syndactyly may also be classified according to the 3-step pathway of pathogenesis .Lrp4 mutations develop polysyndactyly in their forelimbs and hindlimbs [Lrp4 will lead to signal activation and syndactyly [Mice with indlimbs . The Lrpndactyly .Fmn1 deletions and show oligosyndactyly, renal defects, and radio-ulnar synostosis [GREM1 gene [The murine limb deformity (Ld) model is caused by nostosis . The phenostosis  is also EM1 gene .Bmpr1a in the limb bud was associated with upregulation of both Fgf8 and Fgf4, resulting in syndactyly [Other animal models of syndactyly were created by suppression of Bmp signaling. Overexpression of the Bmp antagonist Noggin in mice resulted in extensive soft-tissue syndactyly and postaxial polydactyly . Inactivndactyly . Bmp2-dendactyly . Mice landactyly . SMADs 1ndactyly .Shh promotor. This results in overexpression of interdigital Shh, secondary suppression of the Bmp signaling, and finally syndactyly. The pathophysiology of this secondary suppression of Bmp was recently shown by Mouri et al. [Finally, the hammertoe (Hm) mutant mouse model shows syndactyly of digits 2–5. In this spontaneous mouse mutation, a 150 kb noncoding DNA fragment from chromosome 14 is inserted upstream of the i et al. . The ovei et al. .Jagged2. This resulted in an increased expression of Fgf8 and fusion of the middle three digits. Since Notch is also expressed in the thymus and the craniofacial area, mutant mice also exhibited cleft palate, tongue fusion, and thymic defects [Suppression of notch signaling in mice results in increased expression of Fgf8 in the AER and syndactyly . Notch 1 defects .Msx1/Msx2 double-mutant mice show variable phenotypes including oligodactyly, polydactyly, and syndactyly. This is associated with extended Fgf activity in the AER [ the AER .FRAS1 gene encodes an extracellular matrix protein involved in the establishment of the epidermal basement membrane. Mutations in FRAS1 in humans cause Fraser syndrome (OMIM 219000) with eye, kidney, and craniofacial defects [Fras1. The primary defect in these animals was the decreased Msx2 expression [The  defects . Hines e defects  identifipression . As mentpression .Nid1 or Nid2 in mice does not affect basement membrane formation and animals show no abnormalities. Mice lacking both Nidogens have defective ectodermal basement membrane of multiple organs including aberrant AER formation, altered distribution of Fgf8, and soft-tissue syndactyly [Basement membranes are extracellular matrices underlying the epithelium and endothelium of various organs including the AER. All basement membranes contain at least one member of Laminin, type IV collagen, and Nidogen families . Two Nidndactyly . Since bndactyly . This moRaldh2 gene. The enzyme oxidizes retinaldehyde to retinoic acid. Retinoic acid functions as a ligand for nuclear retinoic acid receptors (known as RAR) to induce the transcription of target genes. Hence, suppression of retinoic acid activity in experimental mice may be done either by targeting the retinaldehyde dehydrogenase-2 enzyme or the RAR receptors. Raldh2–/– autopods show syndactyly secondary to suppression of Mmp11 . In this model, Fgf8 expression is normal, indicating that retinoic acid acts downstream of Fgf8 in the pathogenesis of syndactyly [RARb/RARg double-mutant mice also show syndactyly secondary to the suppression of Mmp11 [Animal models with suppressed retinoic acid activity show syndactyly , 18. Thendactyly . RARb/RAHoxd13). The authors showed that the mutated Hoxd13 has a direct suppressive effect on retinoic acid in the autopod [Kuss et al.  used the autopod . Intraut autopod .Bid/Bim/Puma triple knockout mice show reduced apoptosis and syndactyly. Besides Mmp11, the ADAMTS group of peptides is essential in interdigital ECM degradation. Versican (a proteoglycan) is an important component of the interdigital ECM. ADAMTS mediates the cleavage of versican. Hence, mice deficient in ADAMTS exhibit syndactyly [The proteins Bid, Bim, and Puma act to activate Bax and Bak which are essential to initiate the apoptotic pathway at the mitochondrial level. Hence, ndactyly .Akirin2 null embryos are not able to survive beyond embryonic day 9.5. Transgenic mouse models with knockout of Akirin2 in the limb epithelium leads to a loss of interdigital cell death and an increase in cell proliferation, resulting in the retention of the interdigital web and soft-tissue syndactyly [Akirins (Akirin 1 and 2) are small nuclear proteins that localize to promoter and enhancer regions of genes. They function as “bridge” proteins to coordinate gene expression patterns . Akirin2ndactyly .Limb development is a complex process involving the action of signaling centers that coordinate spatially and temporally to sculpt a limb . Digit fThe current review offers a unified pathway that could explain the pathogenesis of syndactyly in humans and experimental animals. The pathway is a 3-step pathway, and hence, human and animal syndactyly may be classified accordingly Tables  and 2. TNoggin-null mice, syndactyly of Sp6 mutant mice, and isolated 4/5 metacarpal fusion in humans). These types of syndactyly are summarized in The pathway proposed in the current review explains the pathogenesis of most types/models of syndactyly. Certain types of syndactyly have a different pathway of pathogenesis (such as Poland syndrome syndactyly) or the pathogenesis is yet to be determined  is characterized by the unilateral absence or hypoplasia of the sternocostal head of the pectoralis major muscle and ipsilateral symbrachydactyly. However, several other pectoral muscle and hand defects have also been described . One chaFibrillin2 (Fbn2) loss-of-function mutations [The mouse mutant Shaker-with-syndactylism (Sy) is caused by utations . Fibrillutations . Since FBHLHA9 result in split hand-foot malformation with long-bone deficiency type 3  [BHLHA9 lead to mesoaxial synostotic syndactyly with phalangeal reduction  [Fingerin causes syndactyly, both in vivo and ex vivo. However, the exact pathogenesis of syndactyly is still unknown, and two different theories have been proposed [The Basic Helix-Loop-Helix Member A9  is a transcription factor which is expressed in the distal hand/foot plates . Duplicaproposed . As mentproposed . The secNOGGIN in humans leads to various phenotypes including brachydactyly type B2 (OMIM 611377) and various forms of synostosis/ankylosis/symphalangism . In contrast, cutaneous syndactyly is a prominent feature in Noggin-null mice [Mutations in ull mice . The exaull mice .Sp8 mutant mice exhibit severe loss of Fgf8 expression in the AER leading to limb truncations. In contrast, Sp6 mutant mice show a mild loss of Fgf8 expression and a mild syndactyly phenotype [Sp6  and Sp8  are transcription factors implicated in AER induction and maintenance . They arhenotype . The pathenotype . As a rehenotype . This pahenotype , 68.FGF16 [Fgf16 knockdown in experimental animals is known to affect Fgf8 expression in the AER as well as the expression of Shh expression in the zone of polarizing activity [Isolated fusion of the 4/5 metacarpal has been classified as syndactyly type 8 (OMIM 309630). This is the only type of syndactyly that is inherited as X-linked and is caused by nonsense mutations in FGF16 . The patactivity .Fbn2 mutations in mice show syndactyly, while FBN2 mutations in humans are associated with congenital contractual arachnodactyly (OMIM 121050). Another example is the mouse model of Apert syndrome which showed most of the classic craniofacial features, but none of the animals exhibited syndactyly [Our review also demonstrates that mutations in humans and animals do not always result in similar phenotypes. ndactyly . A thirdndactyly . In humaLRP4 mutations. The classic syndrome is characterized by bilateral hand and feet syndactyly along with metacarpal/metatarsal synostosis and phalangeal disorganization. Hand function in these patients is poor even after surgical correction [LRP4 gene [LRP4 gene cause a very mild phenotype with simple syndactyly and well-developed digits, without metacarpal synostosis/phalangeal disorganization [LRP4 lead to the most severe oligosyndactyly phenotype and may even be lethal with intrauterine demise [HOXD13 mutations, and a severe syndactyly phenotype is seen with homozygous HOXD13 mutations [Another observation from the current review is the correlation between the degree of pathway abnormality and the severity of syndactyly phenotype. One example is seen in Apert patients. The “severely affected” hand group has a more severe syndactyly phenotype and higher FGF8 overactivity when compared to the “mildly affected” hand group. Another example is Cenani–Lenz syndrome (OMIM 212780) caused by rrection . This reRP4 gene . In contnization . Furthere demise –76. A thutations , 77.LRP4 and APC mutations  and those directly leading to persistence of GREM1 expression  will have a similar Cenani–Lenz phenotype. Hence, syndactyly related to mutations of LRP4, APC, FMN1, and GREM1 should be grouped together. In contrast, human syndactyly related to high SHH signaling has a different syndactyly phenotype and is associated with preaxial polydactyly, although the pathogenesis is still at Step I  loss-of-function mutations [The 3-step pathway of pathogenesis provides a novel look at the correlation between the genetics and pathogenesis of syndactyly. The syndactyly is best classified according to the pathogenesis since the phenotype is affected by the site along the pathway. The pathway may also help to guide research on syndactyly types with undetermined pathogenesis. For example, the mouse mutant Shaker-with-syndactylism (Sy) is caused by utations . Since Futations , the pat


In this review, we discuss the immunomodulatory properties supporting the therapeutic use of MSCs in GVHD and contextualize the main clinical findings of recent trials using these cells. Critical parameters for the clinical translation of MSCs, including consistent production of MSCs according to Good Manufacturing Practices (GMPs) and informative potency assays for product quality control (QC), are addressed.Graft versus host disease (GVHD) is a common condition in patients subjected to allogeneic hematopoietic stem cell transplantation (HSCT). The immune cells derived from the grafted stem cells attack recipient’s tissues, including those from the skin, liver, eyes, mouth, lungs, gastrointestinal tract, neuromuscular system, and genitourinary tract, may lead to severe morbidity and mortality. Acute GVHD can occur within few weeks after the allogeneic cells have engrafted in the recipient while chronic GVHD may occur any time after transplant, typically within months. Although treatable by systemic corticosteroid administration, effective responses are not achieved for a significant proportion of patients, a condition associated with poor prognosis. The use of multipotent mesenchymal stromal cells (MSCs) as an alternative to treat steroid-refractory GVHD had improved last decade, but the results are still controversial. Some studies have shown improvement in the life quality of patients after MSCs treatment, while others have found no significant benefits. In addition to variations in trial design, discrepancies in protocols for MSCs isolation, characterization, and  Allogeneic hematopoietic stem cell transplant (HSCT) is a treatment for high risk hematological and malignant diseases. Conditioning regimen, immunosuppressive strategies, supportive care and prophylaxis for infectious disease are improving, reducing mortality related to transplant . HoweverGraft versus host disease occurs when immunologically competent donor T lymphocytes recognize recipient’s tissue as foreign resulting in damage in many organs systems including skin, liver, gastrointestinal tract, and lung. Clinical manifestations are classified as acute or chronic GVHD. In the past, all clinical manifestations of GVHD occurring before 100 days of transplant where classified as acute GVHD. When clinical manifestations occurred later on, after 100 days of transplant, it was considered chronic GVHD. In 2005, the National Institutes of Health (NIH) Consensus Conference determined new criteria of diagnosis and scoring and abolished the 100 days criterion. NIH Consensus considered acute and chronic GVHD as two distinguished symptoms without restriction of time .Among all patients undergoing allogeneic HSCT 30–50% will have acute GVHD (grade 1–4) and 14% of patients exhibit chronic GVHD. The most important acute GVHD risk factor is human leukocyte antigen (HLA) mismatch . Transplin vitro and in vivo and natural killer cells (NK cells) have been shown to modulate GVHD in a clinical trial reducing incidence of GVHD . Four studies using umbilical cord-derived MSC to prevent GVHD and one study using adipose-derived MSCs to treat GVHD have also been reported. These studies have compared MSCs treatment only with conventional treatment or historical control. Thus far, no studies have compared the impact of different biological sources of MSC in clinical outcome. Variabilities in trial design is one main limitation for carrying out a meta-analysis to determine which biological source is better or if they are all equivalent in terms of adverse effects and therapeutic benefits .de novo malignancies have been reported, although the clinical follow-up in the examined trials were rather short, ranging from 3 to 60 months . Infusion was safe and well tolerated in all studies (doses ranging from 0.1 to 10 million cells per kg) and there was no report of oncogenesis in the follow-up period. Trials using MSCs prophylactically had a median follow-up of 10–60 months. Follow-up of trials using MSCs as treatment varied from 2 to 23 months .As immunosuppressive treatment can reduce graft-versus-leukemia (GVL) effect, two studies reported relapse of malignant disease in short follow up . HoweverSafety regarding the use of MSCs also involves evaluation of possible complications related to embolism in capillary-rich organs. Among the most common routes of MSCs delivery, including topical application, intramuscular or direct injection into organs, intravenous and intra-arterial infusion, the preferred route is the intravenous . DespiteA study by In another experimental model, Altogether these studies indicate that patients in cell therapy protocols should be monitored for possible embolism or thrombus events related to MSCs infusion and, when applicable, antithrombotic therapies could be applied. Preclinical studies like these are of utmost importance to prevent translational failure. To this end, critical parameters such as the therapeutic window, delivery route, type of cells, immunogenicity, comorbidity, and concomitant treatment, should be considered in these studies.Evidence of efficacy is difficult to determine since the clinical studies published so far are highly heterogeneous. Some studies use MSCs as prophylactic scheme, with infusion in pre-determined days. Six studies analyzed incidence of acute GVHD and other six studies evaluated chronic GVHD , 2. PossOnly two controlled trials evaluated efficacy in acute GVHD treatment without difference in clinical manifestation in both groups (MSCs versus no MSCs). In one study with pediatric patients, complete and partial responses were reported in 58 and 17% patients, respectively . One trial evaluated MSC for treatment of chronic GVHD in 40 patients. In this study, complete and partial responses were observed in 15 and one patients, respectively  .As for safety, the cell administration route is a variable potentially impacting therapeutic effects. MSC systemic delivery is one of the most common administration route in cell therapy since it does not require an invasive procedure but, on the other hand, it relies on a transendothelial cell migration process toward the lesion site, which could have a direct influence in the treatment efficacy Mesenchymal stromal cells should be able to exit circulation and migrate to tissues/organs in order to repair local lesions due to GvHD. This process initially involves a contact between the cells and the endothelium and, subsequently, transmigration toward the target tissue/organ. MSCs rolling depends on presence of platelets  and diffEnhancing migration and homing of MSCs to target tissue/organ could be achieved by genetic engineering these cells to increase the expression of chemokines, integrins or selectins . This ap6 cells. Since this pioneer study, many others have been performed over the last decade establishing an excellent safety profile for both, autologous and allogeneic MSC infusions, over a range of cell doses and different schemes  or low dose (2 × 106 MSC/kg). Patients were stratified for dose levels between grades II and grades III/IV of acute GVHD. Standard steroid therapy using glucocorticosteroids and cyclosporine or tacrolimus, was continued at therapeutic dose levels. Seventy seven percent of patients had complete response and 16% partial response  without difference in both arms . In these studies, patients were treated with MSCs doses ranging from 10MSC dose .The MSCs anti-inflammatory properties as well as homing to sites of inflammation, immunomodulatory properties, and trophic influence on tissue repair, have made these cells very popular for clinical studies . Up to Fex vivo for clinical purposes, as an alternative method for regenerative therapy in many diseases. In any case, regulatory issues related to the safety, efficacy and quality of MSC therapies must be considered while preparing a cell- or tissue-based product for clinical and commercial use. Quality assurance (QA) and quality control (QC) must be provided in any cell manipulation under good manufacturing practices (GMP) grade.Currently, distinct strategies are used to produce human MSCs Thus far, there is no consensus on the production of MSCs regarding GMP system. The QC standards may be discussed individually on each project following the local regulatory agency authority. In Brazil, the regulatory agency , establish specific criteria for advanced therapy products to assure the quality, safety and efficacy of cell therapy in the country (Resolutions: RDC 214/2018 and RDC 260/2018). Cell viability, cytogenetics, potency tests and sterility tests to verify contamination by mycoplasma, aerobic and anaerobic bacteria, fungus, and special microorganisms such as filamentous fungus, are routinely used to ensure the quality of the cell product. Main GMP issues addressed by regulatory agencies in different countries are comparable and can be assessed in ex vivo expansion. A study by Nikitina and colleagues estimated that around 10% of MSC samples contain chromosomal aberrations after expansion, although the clinical consequences of such aberrations are unknown (According to the Foundation for the Accreditation of Cell Therapy (FACT) and Brazil’s ANVISA, cytogenetic testing is mandatory to release a cell product. Most manufacturing units perform karyotyping on the final cell product and the results are an important component of the release criteria. In case of any chromosomal abnormalities in the manipulated cells, the incoming samples may be karyotyped to ensure that the donor does not have any constitutive chromosomal abnormalities and that the alteration has probably originated in a cell clone during  unknown .However, since the potential clinical impact of minor changes in karyotyping is difficult to evaluate, not all services consider the cytogenetic testing as a release criterion .There are many different variations in existing manufacturing protocols for MSC production that may influence the final characteristics of the cells. The type of media supplementation is a typical example. Fetal bovine serum (FBS) is the most common supplementation used. FBS concentration in media ranges from 2 to 20%, with 10% FBS being the most common concentration. This variability on FBS concentration may result in different amounts of growth factors to stimulate cell survival and proliferation . Xeno-frEx vivo MSC expansion may be performed by conventional cell culture techniques or by using bioreactors. Considering autologous use, it is possible to produce lower cell quantity and the scale-out approach can be performed, using planar culture systems with multiple flasks in cell factories. For allogeneic use, MSCs can be expanded to a large number of cells in bioreactor systems  and cryopreserved in cell banks for future use . There are several bioreactors available for cell expansion such as stirred tank bioreactor, rocking bioreactor, hollow fiber bioreactor, and fixed bed bioreactor. The choice of bioreactor will depend on the aim to be achieved, i.e., final amount of cells required for infusion, type of cell (adherent or suspension growing cells), mid/long term use, etc .After MSC expansion, cells are harvested by using an enzyme, usually trypsin. Since this enzyme is normally of porcine origin, alternative GMP recombinant enzymes are available and their use should be prioritized. Mechanical detachment of cells cultured under GMP facilities is also possible by using a cell scraper tool, although detachment must be done gently in order to avoid cell damage and death. While enzymatic MSC detachment can be adapted in expansion protocols using bioreactors, the same adaptation is harder to achieve for mechanical detachment techniques.When high MSC doses and multiple cell infusions are required, which is typically the case of GVHD treatment, cryopreservation of previously expanded cells is an optimal solution to have high amounts of GMP-grade cells readily available for infusion. Nonetheless, cryopreservation is a critical step in MSC manipulation. Although either cryopreservation bags or regular cryotubes could be used for this purpose, the use of the latter is limited by the low volume of cell preparation that can be stored per vial . Cryopreservation media usually contain DMSO as cryoprotectant but it can cause some adverse effects when infused in patients. Typically, cells are stored using a ratio of 10% DMSO and 90% of serum. Thus, after thawing, some laboratories centrifuge and wash cells to remove DMSO. An alternative is to use cryoprotectants containing methylcellulose, sucrose, trehalose, glycerol, hydroxyethyl starch, and polyvinylpyrrolidone. Some companies already developed serum-free and xeno-free cryopreservation media  to circumvent toxicity. Importantly, validation of progressive freezing and thawing cycles is required to avoid significant loss of cell viability.ex vivo expanded MSC need to meet the quality standards required by regulatory agencies.Despite the lack of consensus in critical steps of MSCs production under GMP conditions, different protocols are available to attain high yields of expanded MSCs. However, these Cellular products intended for clinical use must also pass functional evaluation, a key part of a GMP quality control program. Currently, there is no specific release criterion required by the authorities for testing MSC potency. The most used and accepted potency tests for MSCs evaluate their ability to differentiate into three different cell types , to inhibit T-lymphocyte proliferation or to promote endothelial tube formation.in vitro assays that can be easily adapted for routine screening of cell preparations. Nonetheless, adoption of different assays for extensive functional characterization of cells is expensive and time consuming, which may delay the availability of freshly produced MSCs for infusion in patients at critical clinical conditions. Alternatively, a key test could be performed to evaluate a specific MSC property that is correlated with the aimed therapeutic effect. To this end, knowledge of the mechanism of cell action in vivo is of fundamental importance, which reinforces the relevance of basic stem cell biology studies.All these tests involve For instance, exploration of MSC immunosuppressive potential is well established in immunological-based diseases such as diabetes, multiple sclerosis, and GVHD. For this type of application, assaying MSC immunomodulatory activity would better suit a potency test then assaying MSC multipotency. This could be achieved by co-culture assays with T cells to evaluate MSC effects on T cell proliferation and/or by cytokine release by MSC .+ and CD8+ cells. This study suggested that cell-cell contact between MSC and effector cells was not necessary to inhibit T-lymphocyte proliferation (in vitro (feration . Howeverferation . MSC do in vitro .The immuno-suppressive activity of MSCs may also be determined after exposure to IFN-gamma. In this assay, presence of primed MSC could be indicated by expression of MHC class I, MHC II, PDL-1, or other modulatory molecules. Release of certain chemokine receptors such as CXCR3, CXCR4, CXCR5, and CCR7 could also be assayed to indicate immunomodulatory active MSC . As prevlow/D7fib+ve/LNGF+ve cells in samples derived from adults compared with samples derived from children. MSCs obtained from elderly people also showed increased levels of ROS, p21, and p53 proteins. The authors suggested that active MSCs derived from bone marrow decrease in number with age and that these cells are not as potent as the ones isolated from younger patients. Such aging effect on MSC properties should be considered when defining inclusion criteria for MSC donors in cell therapy protocols.Specific MSC properties involved in their expected therapeutic effect may be affected by different factors, including cryopreservation . Thus, pex vivo expansion of cells should help minimize product variability. Also, the choice of potency test is critical in evaluating suitability of the final product, since the desirable therapeutic effect may differ among clinical trials. In the case of GVHD, potency tests addressing MSCs immunomodulatory activity are key factors for obtaining high quality MSCs for therapeutic purposes. Allogeneic MSCs transplantation seem highly appropriate for GVHD treatment, due to the poor clinical condition of patients for tissue donation and lack of necessity of HLA match. In this scenario, the clinical use of cryopreserved third party MSC products offers the additional advantage of faster cell product availability, compared with autologous transplantation. Additional tests to address the cytotoxicity of recipient’s cells toward allogeneic MSCs should help refining the selection of eligible patients. Distribution of MSC products stored in cryobanks to different hospitals is also feasible, allowing potential therapeutic benefits for a greater number of patients in need.Evidences in the literature support safety of MSCs treatment for GVHD, whereas efficacy of this type of cell therapy still needs further clarification. Efficacy of MSC-based treatment is more evident for chronic GVHD patients. Double-blind randomized controlled trials with steroid-refractory GVHD patients should be designed to better address MSCs efficacy. Well-defined inclusion/exclusion criteria for patient accrual and a standardized protocol for GMP production of MSCs should facilitate multicenter studies and acquisition of faster results for clinical outcome assessment. Regarding MSCs production, uniformity in donor age and automated All authors wrote the manuscript and revised the final submitted version.OO was a visiting scholar at Departamento de Hemoterapia e Terapia Celular, Hospital Israelita Albert Einstein.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.