NFE2L2, and decreased dependencies on upstream activators of pathways, such as CDK6. SuperDendrix also reveals dozens of dependencies on lineage-specific transcription factors, identifies cancer-type-specific correlations between dependencies, and enables annotation of individual mutated residues.Recent genome-wide CRISPR-Cas9 loss-of-function screens have identified genetic dependencies across many cancer cell lines. Associations between these dependencies and genomic alterations in the same cell lines reveal phenomena such as oncogene addiction and synthetic lethality. However, comprehensive identification of such associations is complicated by complex interactions between genes across genetically heterogeneous cancer types. We introduce and apply the algorithm SuperDendrix to CRISPR-Cas9 loss-of-function screens from 769 cancer cell lines, to identify differential dependencies across cell lines and to find associations between differential dependencies and combinations of genomic alterations and cell-type-specific markers. These associations respect the position and type of interactions within pathways: for example, we observe increased dependencies on downstream activators of pathways, such as   SuperDendrix finds associations between sample features and CRISPR genetic dependenciesSomatic mutations are associated with 127 genetic dependencies from Project DepMapLineage-specific dependencies on transcription factors correlate with gene expressionIdentified associations agree with direction of interactions within oncogenic pathways Using SuperDendrix, Park et al. examine associations between genetic dependencies in 769 cancer cell lines. They report 127 genetic dependencies explained by combinations of mutually exclusive somatic mutations congregating into a few oncogenic pathways across cancer subtypes. These present a small number of prominent and highly specific genetic vulnerabilities in cancer. Genes whose perturbation results in a change in viability reveal potential cancer-specific genetic dependencies. Recent technologies such as genome-wide pooled RNAi,,,,A key problem in cancer biology is to identify the genes that cancer cells depend on for their growth and survival advantage. This knowledge both informs our understanding of cancer development and suggests therapeutic targets.,single genomic biomarkers,,,,,,,,,Several recent studies have attempted to systematically identify associations between differential dependencies and genomic alterations using data from large-scale RNAi and CRISPR datasets.20multiple biomarkers.,,A second group of studies identifies associations between differential dependencies and sets of a priori. Several related methods have also been developed to identify associations between genomic alterations and cancer dependencies measured from drug response experiments, including LOBICO,Spurious associations are a significant challenge in analyzing large-scale RNAi or CRISPR screens, since the number of phenotypes (gene perturbations) and number of features  are orders of magnitude larger than the number of samples. This challenge is further exacerbated when searching for sets of multiple biomarkers as the number of such sets is massive and the optimal set is unknown We introduce a new algorithm, SuperDendrix, to identify sets of approximately mutually exclusive genomic alteration and cell-type features that are associated with differential dependencies from large-scale perturbation experiments. SuperDendrix includes several key features: (1) a principled approach to identify and score differential dependencies using a 2-component mixture model; (2) a combinatorial model and optimization algorithm to find feature sets associated with differential dependencies; and (3) a model selection criterion to select the size of the associated set and a robust statistical test that accounts for different frequencies of genomic alterations across samples.increased dependencies on genes downstream in the same pathway. These associations generalize the phenomenon of oncogene addiction to oncogene pathway addiction.,decreased dependency on genes upstream in the same pathway. When including the cancer type as an additional feature for each cell line, SuperDendrix identifies a total of 227 differential dependencies that are associated with sets of mutations and/or cancer-type features, most prominently dependencies on lineage-specific transcription factors and a previously unreported association between TCF3 dependency and myeloma or blood cancers with mutations in BCL2.We apply SuperDendrix to identify associations between somatic mutations in cancer genes and differential dependencies in a large-scale CRISPR-Cas9 loss-of-function screens from DepMaphttps://github.com/raphael-group/superdendrix and results on DepMap datasets are available through the web portal at https://superdendrix-explorer.lrgr.io/.The SuperDendrix software is publicly available at P where each entry pgj of P indicates the viability of cell line j when gene g is knocked out. Each of these scores quantifies the dependency of a cell line on a gene. We refer to the dependency scores for a gene across cell lines  as a dependency profile.Cell viability measurements are from genome-wide CRISPR-Cas9 loss-of-function screens. We represent these measurements in a phenotype matrix A list of somatic alterations in each cell line. Here, we analyze somatic missense and nonsense mutations. Categorical information  of each cell line.We introduce SuperDendrix, an algorithm to identify sets of binary features such as genomic alterations and/or cell types that are (approximately) mutually exclusive and associated with a quantitative phenotype. While SuperDendrix is applicable to any quantitative phenotype, in this work we focus on the phenotype of cell viability change following genome-wide CRISPR-Cas9 loss-of-function screens. The inputs to SuperDendrix are as follows.SuperDendrix consists of three modules A: (1) scdifferential dependency profile for each gene knockout (row of P). This profile quantifies the magnitude of the effect on the gene knockout on each cell line relative to a background distribution. We assume that the dependency scores g are generated from two populations: a background population that is unaffected by the knockout and a responsive population that is affected by the knockout. We fit a two-component mixture model to the dependency scores g, or that gene g is a differential dependency. We designate component 1 to be the component with smaller mean and define the differential dependency score, or 2C score, j as the log ratio of the posterior probabilities that cell line j is from component 2 (j is from component 1 (The first module in SuperDendrix has two steps: (1) scoring differential dependencies from the dependency scores and (2) selecting the genomic alteration and cell-type features that will be evaluated for association. In the first step, we derive a A. This matrix contains two types of features. The first type are genomic alteration features. We define these features using the OncoKB databaseA are cell-type features. In this analysis, we construct a binary feature for each cancer type represented in the analyzed cell lines. By definition, these cancer-type features are mutually exclusive across cell lines.Next, we construct the genomic alteration and cell-type feature matrix M of features in A that are (1) approximately mutually exclusive and (2) associated with increased (or decreased) dependency. We derive the SuperDendrix weight W(M) of a set M that combines criteria (1) and (2) and use an integer linear program (ILP) to find the set M∗ of minimum (or maximum) weight W(M∗).The second module in SuperDendrix is a rigorous and practically efficient combinatorial optimization algorithm to find sets M∗ found in the second module, where each feature in W(M∗) is nearly the same as random features. Second, a permutation test assesses the statistical significance of the set The third module of SuperDendrix includes two steps. First, a model selection step identifies a subset ll lines , we use https://superdendrix-explorer.lrgr.io/. Further details of the SuperDendrix algorithm are in We also developed an interactive tool for visualization and exploration of the SuperDendrix results which is available at ,We used SuperDendrix to analyze the Avana dataset (20Q2/5.20.2020) from Project DepMap containing results of CRISPR-Cas9 loss-of-function screens of 18,119 genes across 769 cancer cell lines from 31 cancer types.ndencies .,BRAF, KRAS, NRAS, and PIK3CA (The first module of SuperDendrix computes that 511 (25%) of the ndencies . These 5dencies ,. Moreoved PIK3CA —a signifd PIK3CA . Finallyvariance .We derive genomic features for SuperDendrix using 399,559 non-synonymous coding mutations reported in Cancer Cell Line Encyclopedia (CCLE)BRAFARID1BWe used SuperDendrix to identify associations between sets of mutations and the 511 2C differential dependencies. SuperDendrix identified 91 single mutations and 36 sets of approximately mutually exclusive mutations that are significantly associated (FDR ≤ 0.2) with 127 differential dependencies . 113 of NFE2L2  mutations is consistent with the oncogene addiction model.,NFE2L2 dependency in cell lines with KEAP1 inactivating mutations is consistent with KEAP1’s role in inhibiting NFE2L2 by targeting NFE2L2 for degradation via ubiquitination.,NFE2L2 in cell lines with KEAP1 inactivating mutations can be viewed as another form of oncogene addiction. These associations generalize the phenomenon of oncogene addiction to oncogenic pathway addiction:,First, SuperDendrix finds an association between the set {KEAP1(O), KEAP1(I), NFE2L2(A)} of three mutations and increased dependency on NFE2L2 B. The KENote that only a fraction of the associated mutations occurs in cell lines whose CERES score is below the hreshold B, demonsNFE2L2. For example, the KEAP1 G364C mutation is not reported as functional in OncoKB, but is located at a position that is reported to disrupt NFE2L2 repression.The associations with differential dependencies reported by SuperDendrix are also useful for annotating individual missense mutations. Specifically, several of the mutations in the KEAP1(O) feature—which include missense mutations that are unannotated in OncoKB—occur in cell lines with strong evidence of increased dependency on RB1 inactivating mutations and differential dependencies in E2F3, CCND1, and CDK6, three members of the RB1 pathway  and increased dependency on SHOC2,RAF1. The later association is consistent with the role of RAF1 as a mediator of RAS for signal transduction in the MAPK pathway during transformation.Third, SuperDendrix finds associations between 12 differential dependencies in the MAPK pathway and subsets of the approximately mutually exclusive mutation set {BRAF(A), KRAS(A), NRAS(A), HRAS(A)} A. These MAP2K1, MAPK1, MITF, and DUSP4. Associations with MAP2K1, MAPK1, and MITF are consistent with previous reports on conditional dependency on these genes in BRAF(V600E) melanoma.DUSP4 is intriguing because there are conflicting reports regarding DUSP4’s role in cancer. On the one hand, DUSP4 is reported to be a tumor suppressor that inhibits ERK1 and MAPK1 (ERK2) activity in the nucleus.,DUSP4 knockout is expected to result in decreased dependency. On the other hand, there are also reports of high DUSP4 expression in colorectal cancer,RAS or RAF mutations, suggesting that DUSP4 activity may contribute to oncogenesis in these cancers. Our finding that cell lines with BRAF(A) have increased dependency on DUSP4 is consistent with the oncogenic role. To investigate these competing hypotheses, we investigated the relationship between DUSP4 dependency and MAPK1, as DUSP4 is a negative regulator of MAPK1. We found that in cell lines with BRAF(A), DUSP4 dependency scores were significantly negatively correlated  with expression of MAPK1  mutations, there is no significant correlation between DUSP4 dependency and MAPK expression . These observations are consistent with the Goldilocks principleDUSP4 inhibition of MAPK1 is most essential in cell lines with hyperactive MAPK signaling due to BRAF(A) mutations.Also among associations identified in the MAPK pathway are associations between BRAF(A) mutations and increased dependencies on other downstream members of the MAPK signaling pathway including of MAPK1 B; i.e., PTPN11 and GRB2 in the MAPK pathway. Specifically, we find decreased dependency on PTPN11 in cell lines with KRAS(A)GRB2 in the same cell lines. The decreased dependency on PTPN11 is consistent with a previous report that cell lines with constitutive RAS or RAF signaling were insensitive to suppression of PTPN11.GRB2, it is intriguing that both proteins with decreased dependencies—PTPN11 and GRB2—are upstream of the RAS/RAF mutations that result in constitutive MAPK signaling. Thus, it makes sense that cell lines with constitutive activation of RAS or RAF signaling are insensitive to upstream activators of RAS signaling, analogous to the insensitivity of RB1-deficient cell lines to knockout of upstream regulators CDK6 and CCND1 reported above  mutation,SMARCA2 in cell lines with SMARCA4(I) mutation,STAG1 in cell lines with STAG2(I) mutation.CTNNB1 with the mutation set {APC(I), CTNNB1(A)} and increased dependency on TCF7L2 with the same set  than with BRAF(A) (SuperDendrix weight = −0.37).Next, we investigated associations between differential dependencies and cancer types. We augmented the mutation matrix with 31 cancer-type features, each feature representing one of the 31 cancer types in the Avana dataset. We ran SuperDendrix on the augmented mutation matrix and identified 298 differential dependencies that are significantly associated (FDR ≤ 0.2) with mutations and/or cancer types , with 22protein domain specific binding; in particular, 43 of the 227 differential dependencies are transcription factorsOf the 227 differential dependencies, 195 are increased dependencies upon gene knockout and the other 32 are decreased dependencies. These 227 differential dependencies are enriched (FDR ≤ 0.05) for 88 GO molecular function terms . The mosISL1, GATA3, and MYCN in neuroblastoma, all of which were recently reported as part of the core regulatory circuitry (CRC) in neuroblastoma and associated with superenhancers.THAP1 and TP53, which are consistent with their functional role in the associated cancer typesThe 43 transcription factor dependencies with cancer-type-specific associations cluster into a number of interesting groups . These iTCF3 and IRF4 which serve critical roles in determining B cell terminal differentiation into plasma cells (the cell type of myeloma cancer) via transcriptional regulatory activityTCF3 and the mutually exclusive set {myeloma, BCL2(A)} and the transcription factor IRF4 and {myeloma, lymphoma}. SuperDendrix also finds associations for three downstream targets of TCF3 and IRF4 transcription factors: BCL2 and {leukemia, myeloma, MEF2B(A)}, PIM2 and myeloma, and POU2AF1 and BCL2 dependency and BCL2 for transcriptional regulation.BCL2 in cell lines with activating mutations in the upstream transcriptional regulator MEF2B .Cancer-type-specific dependencies identified by SuperDendrix include associations between blood cancers  and several transcription factors involved in B cell developmentTCF3 and IRF4 have previously been suggested as dependencies in myeloma and are part of the core regulatory circuitry, promoting tumorigenesis in cooperation with aberrant MYC activity.MYC expression is higher in myeloma cell lines than other cancer types  and is significantly correlated with dependency on TCF3 and IRF4  that are part of the regulatory network mediated by super-enhancer activity.,MYC expression.LDB1 and LMO2 in leukemia.,MDM2 in skin and kidney cancers and EGFR in head and neck cancer.The remaining 184 cancer-type-specific differential dependencies that are not annotated as transcription factors are enriched (FDR ≤ 0.05) for 46 GO molecular function terms , with thIGF2BP1, IGF1R, IRS1, and IRS2 in neuroblastoma, Ewing’s sarcoma, pancreas, myeloma, or rhabdomyosarcoma. These dependencies are consistent with previous reports of dependencies on IGF1R in Ewing’s sarcoma and rhabdomyosarcoma.PIK3CA and BCL2 in some of the same cancer types, including PIK3CA in myeloma and rhabdomyosarcoma and BCL2 in leukemia and myeloma.,A prominent group of cancer-type-specific differential dependencies are six genes in the IGF1R and PI3K pathways A across IGF2BP1 and IGF1R (R = 0.48) in Ewing’s sarcoma (IGF2BP1 and IRS2 dependencies (R = 0.32) in Ewing’s sarcoma and neuroblastoma  and consequently were not reported in two recent studies,IGF1R with IRS1 and IGF1R with IRS2 across all cell lines, as previously reported.,Additionally, we observed cancer-type-specific correlations between dependency scores of pairs of genes in the IGF1R pathway in neuroblastoma and Ewing’s sarcoma. These include correlations between  sarcoma B and betblastoma C. ImportWe find that 107 of the 227 associations identified by SuperDendrix are validated in the Score dataset,Finally, we compared the associations identified by SuperDendrix with those identified in the DepMap dataset using a simple univariate test and UNCOVER.,,We introduced SuperDendrix, a method that incorporates a principled statistical model and a practically efficient combinatorial algorithm for analyzing differential gene dependencies from perturbation experiments. SuperDendrix scores differential dependencies using a two-component mixture model and identifies mutually exclusive sets of features—including genomic alterations and/or cancer types—that are associated with each differential dependency. Application of SuperDendrix to CRISPR-Cas9 loss-of-function screens in 769 cancer cell lines from Project DepMap revealed 511 differential dependencies and inferred associations between 127 (27.4%) of these dependencies and sets of somatic mutations in cancer genes. Many of these associations group into well-known cancer pathways such as NFE2L2, RB1, and MAPK. SuperDendrix reports that a higher fraction of differential dependencies are associated with mutations compared to previous analyses of RNAi and CRISPR screens.NFE2L2 dependency in cell lines with inactivating mutations in KEAP1 and MAPK1 dependency in cell lines with activating mutations in BRAF. These results are consistent with the notion that cancer cells develop addiction to an oncogenic pathway during cancer progression.,RB1 show decreased dependency on CDK6. These results show the importance of considering pathway topology in the design of cancer therapeutic strategies; for example, a current strategy for treating tumors with activating mutations in undruggable oncogenes is to inactivate downstream genes.DUSP4 is noted as a tumor suppressor due to its role in inhibiting MAPK signaling; however, we find increased dependency on DUSP4 in cell lines with activating mutations in BRAF suggesting that DUSP4 contributes to maintaining the balance of MAPK signaling in BRAF mutant tumors. These results suggest DUSP4 as a potential therapeutic target for cancer treatment. Our results also provide further predictions about the functional consequences of individual non-synonymous mutations and the function of individual genes. For example, we find that previously unannotated mutations in the dimerization domain of KEAP1 are associated with increased dependency on its downstream target NFE2L2.Our results show striking consistency between the directionality of dependencies (increased versus decreased), the type of interactions (activating versus inhibitory), and the position of dependencies and somatic mutations in pathways. In particular, oncogenic mutations in upstream pathway genes—such as activating mutations in an oncogene or inactivating mutations in a tumor suppressor—are associated with increased dependencies on genes that are downstream in the same pathway and that promote cancer; e.g., BCL2 is associated with leukemia, myeloma, and MEF2B mutations. Another prominent cancer-type-specific association found by SuperDendrix is increased dependency on IGF2BP1, a regulator of insulin growth factor receptor IGF1R, in Ewing’s sarcoma and neuroblastoma. We anticipate that with larger cohorts, there will be increased opportunities to identify these more subtle associations that include both cancer types and somatic mutations.SuperDendrix also identifies associations between differential dependencies and sets of cancer types or combinations of cancer types and somatic mutations. A large fraction (35%) of the cancer-type-specific associations found by SuperDendrix involve increased dependencies on lineage-specific transcription factors. Many of these lineage-specific transcription factors have been previously reported to be highly expressed or correlated with poor prognosis in cancers of corresponding types. We also identify associations that include both cancer types and somatic mutations. For example, we find that increased dependency on ,108There are several limitations and caveats in the current study. First, all of the reported associations are computational predictions. While we strove for high specificity in these predictions, further experimental validation is warranted. Second, while we identified mutation and cancer type features that are associated with a large fraction of the differential dependencies, many of the differential dependencies remain unexplained due to either weak statistical significance or lack of associated cell line features. Some possible reasons for these unexplained differential dependencies are (1) the small sample size of 769 cell lines which limits statistical power to find rare associations particularly because the cell lines originate from a heterogeneous collection of 31 cancer types; (2) examination of a limited class of genomic alterations, namely non-synonymous single-nucleotide mutations; and (3) our modeling assumption that the mutations that are associated with a differential dependency are mutually exclusive. Including copy number aberrations (CNA) and DNA methylation changes in the analysis will likely identify additional associations; however, since these alterations span larger genomic distances than single-nucleotide mutations, they require more careful decomposition into specific alteration features.TCF7L2 and the sets {APC(I), CTNNB1(A)} and {Colon,Gastric}). Extending SuperDendrix to simultaneously identify multiple sets of features might identify additional such dependencies, as previously shown for multiple sets of mutually exclusive mutations.,Beyond the limitations described above, there are several directions for future work, both the analysis and in further development of SuperDendrix. First, alternative dependency scores could be used as input to SuperDendrix.braphael@princeton.edu)Further information and requests for resources and data should be directed to and will be fulfilled by the Lead Contact, Benjamin J. Raphael  for each sample.3. Categorical information  of each sample.We introduce a new algorithm, SuperDendrix, to identify sets of binary features  that are approximately mutually exclusive and associated with a continuous-valued phenotype. The inputs to SuperDendrix are:While SuperDendrix is a general-purpose algorithm, here we describe the specific application where the phenotype scores are dependency scores from gene perturbation experiments and the binary features are somatic mutations . SuperDendrix includes three modules: (1) A module to identify and score differential dependencies using a two-component mixture model and to select genomic and cell-type features using mutation annotations; (2) A module to find sets of features that are approximately mutually exclusive and associated with differential dependencies using a combinatorial optimization algorithm; (3) A module to perform model selection and to evaluate statistical significance of associations.responsive to the perturbation while the remaining samples are unresponsive and have scores derived from a background distribution. Thus, we assume that the dependency scores are distributed according to a two-component mixture model. We fit each dependency profile with a differential dependencies.The first module in SuperDendrix includes two steps: the identification and scoring of differential dependencies and the selection of genomic and cell-type features. In the first step, we assume that a gene perturbation leads to two population of samples: a minority of samples are Gaussian distributions. We choose component 1 to be the component with smaller mean so that negative 2C scores indicate decreased viability, or increased dependency in response to knockout. Conversely, positive 2C scores indicate decreased dependency in response to knockout. We assume that a minority of cell lines are responsive to gene knockout and thus refer to the component that contains fewer cell lines as the responsive component and the component with more cells lines as the background component. We define the 2C profile, or differential dependency profile, increased dependency if its responsive component contains cell lines with negative 2C scores (increased dependency) and a decreased dependency if its responsive component contains cell lines with positive 2C scores (decreased dependency).For each differential dependency In the second step, we construct a genomic alteration and cell-type feature matrix Activating, Inactivating, or Other mutation features which we label as GENE(A), GENE(I), and GENE(O) using the OncoKB annotation according to the following rules:1.Mutations that are not oncogenic  are grouped into a feature, GENE(O).2.Oncogenic mutations  with Gain-of-function or Likely Gain-of-function effect are grouped into a feature, GENE(A).3.Oncogenic mutations with Loss-of-function or Likely Loss-of-function effect are grouped into a feature, GENE(I).4.Oncogenic mutations with other effects are grouped into a feature, GENE(O).For each GENE in OncoKB, we group mutations from the input list into Using the OncoKB mutation features derived above, we construct the feature matrix Next, we generate binary features that represent the cell type of each sample using information from metadata such as the primary tissue. In the application in this paper, we use cancer types as the cell-type features. Each cancer-type feature has the value 1 for samples of the corresponding cancer type and the value 0 for samples of other cancer types. Note that the cancer-type features are mutually exclusive by definition.We now combine the two sets of features and create an augmented binary feature matrix The second module in SuperDendrix finds a subset First, for each score If the mutations in In the case where all samples have equal score, i.e., Following the nomenclature in machine learning, the problem considered in DendrixNext, we aim to find a set Problem 1 ). Given a binary feature matrix OWXTC is NP-hard because it generalizes the Maximum Weight Submatrix Problem which was shown to be NP-hardWe formulate the OWXTC as an integer linear program (ILP) as follows. First, we define binary variables Then the OWXTC in the case of increased dependency is equivalent to the following ILP.For finding associations with decreased dependencies, we replace Note that the SuperDendrix weight and the ILP are similar, but not identical, to those presented previously.The third module of SuperDendrix consists of two steps. First, since the optimal size all mutations , and then use the first module in SuperDendrix to select the OncoKB mutation features. We compare the weight Next, we evaluate the statistical significance of the association between feature set We downloaded the Avana [20Q2/5.20.2020] datasetWe downloaded non-synonymous somatic mutation data [20Q2/5.20.2020] for the same cell lines from the Cancer Cell Line Encyclopedia (CCLE)We run SuperDendrix using sets of at most 3 mutations and sets of at most 5 mutations and/or cancer types.,We compare two sets of differential dependencies identified from the DepMap data using the two-component mixture model from SuperDendrix and Normality Likelihood Ratio Test (NormLRT).,SuperDendrix identified 511 2C differential dependencies while NormLRT identified 949 differential dependencies using the same LRT score threshold of 125 from the original study.SuperDendrix outperforms NormLRT in identifying known dependencies, achieving both higher precision and recall for Sanger priority targets ). In addition, differential dependencies from SuperDendrix contain fewer nonessential genes than NormLRT differential dependencies (NormLRT: 3.2% (30/949), 2C: 0.8% (4/511)). We consider nonessential genes which are rarely expressed as the negative control gene set since the differential dependencies are unlikely to be non-essential (unexpressed) for cellular activity.We also ran SuperDendrix on the dependency probabilities obtained from the Avana dataset. These probabilities are computed from reference gene sets of unexpressed genes and essential genes and attempt to quantify the probability that a CERES score represents a true dependency.In the first module of SuperDendrix, we identified differential dependencies from the dependency probabilities using the following 3SuperDendrix identified significant associations  networks. First, we add edges to the PPI network for each association between a mutation and a differential dependency identified by SuperDendrix. We then find subnetworks that are connected in physical interactions and dense in genetic dependencies. This approach automates some of the manual annotation that we performed to identify oncogenic pathway addiction.TAZ dependency with TP53 mutations is consistent with a recent reportWe searched for the densest connected subnetworks of 6 different sizes  from a dual network of 176,839 physical interaction edges from HINT+HI networkrDendrix . TAZ is We used the dataset from genome-wide CRISPR-Cas9 screens conducted as part of the Cancer Dependency Map at Wellcome Sanger InstituteFirst, we downloaded the datasetFor each association identified by SuperDendrix in the Avana dataset, we compared the dependency scores of cell lines containing at least one of the features with dependency scores of the cell lines without any feature. We excluded the associations for which dependency or feature data is not available in the Score dataset. We found that associations between 45/110 differential dependencies and mutations and associations between 146/210 differential dependencies and cancer types and/or mutations identified by SuperDendrix are statistically significant in the Score dataset  with mutations using SuperDendrix, 36 of which are associated with more than one mutation. In contrast, Tsherniak et al.CCND3 dependency and CCND3 activating mutation (5 cell lines), a previously reported oncogene addiction, as part of the mutation set We find that the univariate analysis and SuperDendrix have some overlap in their reported associations, but also substantial differences . Only 65We conducted a systematic univariate analysis to search for associations between mutation features and differential dependencies. Specifically, for each mutation and each differential dependency we compare the CERES dependency scores in cell lines with and without the mutation using the Wilcoxon rank-sum test. We perform this test for all 897 mutations and 511 differential dependencies identified in the first module of SuperDendrix, for a total of 458,367 tests. This univariate analysis identified 201 significant associations  as an additional binary feature in the feature matrix of SuperDendrix. We used the MSI status for each cell line reported in Chan et al., 2019.WRN dependency is more significantly associated with WRN dependency identified by SuperDendrix is WRN; a higher fraction of the WRN than the MSI cell lines  was previously reported to be associated with both NFE2L2 , a relatively rare mutation occurring in 15/767 cell lines, to be associated with FANCG dependency  D. These Taken together, these results show that the univariate test and SuperDendrix have different trade-offs in the identification of associations: the univariate test is confounded by mutation rate, reporting many associations with frequently mutated genes and in cell lines with high mutation rates. In contrast, SuperDendrix identifies associations with rarely mutated genes that are mutually exclusive of associations with more frequently mutated genes, but might miss some associations in samples with extremely high mutation rates  which lead to co-occurrence between driver and passenger mutations.We conducted a systematic univariate analysis to search for associations between differential dependencies and combinations of cancer type and/or mutation features. We analyzed a total of 474,208 pairs consisting of one of 511 differential dependencies and one of 928 features (31 cancer types and 897 mutations). This univariate analysis identified 861 significant associations  that are associated (via Wilcoxon rank-sum test) with 280 differential dependencies, compared to the 87 sets of mutations in 84 genes that are associated with 127 differential dependencies identified by SuperDendrix. Most of the SELECT associations are dominated by mutations in a small number of well-known cancer genes . For exaSuperDendrix identified differential dependencies on 43 transcription factors that are significantly associated with specific cancer types. Cancer-type-specific dependencies on transcription factors have been reported previously to be associated also with expression of these genes.SOX10 and high SOX10 expression correspond to Skin cancer  nuclear bodies (NB).,,THAP1 which leads to loss of PML function resulted in decreased dependency or even prolonged cell survival in leukemia and lymphoma. We also find decreased dependency on TP53 in TP53 is its wild-type function as a tumor suppressor. A previous study reports that knocking out TP53 in cells with functional wild-type TP53 where p53 acts as a tumor suppressor will induce growth advantage in those cells [@giacomelli2018mutational]. In our results, we noticed that many of the rhabdoid and kidney cancer cell lines as well as skin cancer cell lines with TP53. We thus tested for association between decreased dependency on TP53 and TP53 wild-type cell lines as suggested previously.Two of the 43 transcription factors with cancer-type specific differential dependencies, https://github.com/lrgr/superdendrix-explorer (Zenodo: https://doi.org/10.5281/zenodo.5878914), and the browser itself is publicly available at https://superdendrix-explorer.lrgr.io/.We release a public, open-source web browser to view and explore SuperDendrix results. Users can choose which genetic dependency profile and which mutations they want to view or preload an association identified as significant by the SuperDendrix software. The browser displays a waterfall plot, indicating the dependency score and mutation status in each cell line. It also includes two bar plots on top of the waterfall plot that indicate tissue type and number of mutations per cell line. Users can interact with the plots by scrolling over bars in the waterfall plot. On mouse over, the browser displays tooltips listing information about the given cell line such as tissue type. Users can also select a range of cell lines in the bar plot at the top to zoom in. The plots provide an easy way to quickly assess whether the dependency scores in cell lines with user-specified mutations or cancer types are extreme relative to the other cell lines. The code for the SuperDendrix browser is open-source at


Complexes were fully characterized and their structures were determined by X-ray diffraction. Their biological activity was assessed for several selected human tumor cell lines: Jurkat (human leukaemic T-cell lymphoma), HCT-116 , HeLa (human cervix epitheloid carcinoma), MCF-7 (human breast adenocarcinoma), MDA-MB-231 (human mammary gland adenocarcinoma), A549 , Caco-2 , and for non-cancerous 3T3 cells (murine fibroblasts). The cytotoxicity of 1 is comparable to that of cisplatin, and superior to that of 2 in all cell lines. It is a correlation between IC50 values of 1 and 2 in the eight studied cell types, promising a potential use as anti-proliferative drugs. Moreover, for Jurkat cell line, complexes 1 and 2, show an enhanced activity. DFT and docking calculations on the NF-κB protein, Human Serum Albumin (HSA), and DNA were performed for 1 and 2 to correlate with their biological activities.Metallodrugs form a large family of therapeutic agents against cancer, among which is cisplatin, a paradigmatic member. Therapeutic resistance and undesired side effects to Pt(II) related drugs, prompts research on different metal–ligand combinations with potentially enhanced biological activity. We present the synthesis and biological tests of novel palladium(II) complexes containing bisdemethoxycurcumin (BDMC)  Cancer is still one of the main causes of death in the world, for which one of the most common treatment for oncological diseases is based on combined therapies which use platinum metallodrugs such as cisplatin ,2,3, oxaκB, MMP-2, and -9 signaling pathways, presumed the possibility of using the BDMC as a potential preventive agent against human cervical cancer metastasis [Several literature studies are also dedicated to assessing the biological activity of bisdemethoxycurcumin, the third component of turmeric extract. Based on the presumption that this minor component of turmeric is more stable as the other two derivatives (curcumin and demethoxycurcumin), significant anti-cancer properties have been observed. Bisdemethoxycurcumin inhibits cell proliferation, metastasis, and tumor growth and induces apoptosis in tumor cells. Moreover, it generates ROS levels in breast cancer, lung cancer, gastric cancer, and ovarian cancer ,35. Indetastasis . Althougtastasis ,38. Sevetastasis , preparatastasis , or creatastasis  to enhantastasis .1 and 2, which were derived as a continuation of our work on palladium(II) complexes previously reported. Biological tests were performed for complexes 1 and 2 against Jurkat (human leukaemic T-cell lymphoma), HCT-116 , HeLa (human cervix epitheloid carcinoma), MCF-7 (human breast adenocarcinoma), MDA-MB-231 (human mammary gland adenocarcinoma), A549 , Caco-2 , and for non-cancerous 3T3 cells (murine fibroblasts) cell lines. Computational modeling of these compounds and their interactions with human serum albumin (HSA), the transcription factor NF-κB and a short DNA sequence was carried out to assess their cytotoxic properties.In this work, we have synthesized and tested the cytotoxic activities of two Pd(II) complexes containing the 1,7-bis(4-hydroxyphenyl)hepta-1,6-diene-3,5-dione (BDMC) ligand, herein referred to as Recent studies on diverse pytochemicals, pointed also the capacity of curcumin to regulate the signaling pathways through the inhibition of NF-κB transcription factor ,43,44. M1 and 2 containing bisdemethoxycurcumin have been synthesized following a previously reported method [1H- and 13C-NMR, HR-MS and IR) confirmed the proposed structural formulas of compounds 1 and 2. Moreover, complexes 1 and 2 were recrystallized from a mixture of methanol:acetonitrile (10:2) to form suitable crystals for X-ray diffraction.Palladium(II) complexes d method ,31,32,33d method . Spectra1 crystallized in the C2 space group (No. 5), whereas complex 2 crystallized in the monoclinic P21/n space group (No. 14). The molecular structures of the cationic part of complex 1 and 2 are shown in  complex shown in  and theishown in . 1 and 2 confirm that the disordered square-planar geometry around the central atoms is very close to the ideal square-planar arrangement of the coordination sphere. The polyhedron coordination of palladium atoms in complex 1 consists of two oxygen atoms of the carbonyl moieties of bisdemethoxycurcumin (first molecule: Pd1—O distances are 1.995(4) and 2.007(4) Å, while in the second molecule: Pd2—O distances are 1.982(4) and 2.002(4) Å) and two nitrogen atoms of N,N,N′,N′-tetramethylcycohexane-1,2-diamine (first molecule: Pd1—N distances are 2.042(5) and 2.057(4) Å, and in the second molecule Pd2—N distances are 2.039(5) and 2.051(4) Å). In the case of complex 2, the polyhedron coordination of palladium atom contains two oxygen atoms of the carbonyl moieties of bisdemethoxycurcumin (Pd1—O distances are 1.991(2) and 1.987(2) Å) and two nitrogen atoms of N,N′-dimethylpiperazine (distances Pd1—N are 2.042(2) and 2.036(2) Å). A similarity between complex 2 and previously reported compounds [1 shows slightly larger values for Pd-O and Pd-N lengths (Pd—O distances are 1.975(4) and 1.981(4) Å, and Pd—N are 2.038(5) and 2.045(5) Å). The angles around the central atom in complex 1 are in the range of 86.2(3)–94.7(2)° for both central atoms and 73.2(1)–96.41(9)° for complex 2. The plane angles N1Pd1O2, N2Pd1O1, N3Pd2O6, and N4Pd2O5 are in the range of 173.5(1)–176.6(2)° for both central atoms in complex 1. The plane angles N1Pd1O2 and N2Pd1O1 for complex 2 are in a range of 168.3(9)–169.6(9)° , which explains the better rigidity geometry of N,N′-dimethylpiperazine ligand. The crystal structures of complexes 1 and 2 are stabilized by a network of intramolecular and intermolecular hydrogen bonds and Van der Waals interactions. A zig-zag 3D network with big cavities containing multi-disordered solvent molecules (acetonitrile) and acetate anions is observed in both complexes 1 and 2 (1 and 2 are given in The complex cations consist of bisdemethoxycurcumin ligand and appropriate amine ligand. Space disordered acetate anions and solvents were treated by OLEX2 software over the solvent mask (ompounds  has been69.6(9)° . Concernompounds , while i 1 and 2 . The cry1 and 2 at the ωB97XD/LANL2DZ level of theory with the use of the SMD continuous solvation model (water). The resulting structures  were obtained directly from the scoring functions set as default in Autodock Vina. All docking simulations were performed with AutoDock Vina using an exhaustiveness factor of 10 since the binding energies dropped severely for subsequent binding modes making it futile to assess those conformations. Binding energies (−ΔG1 and 2 show good affinities for HSA (PDB entry 4F5S), as shown in b) ranges between 6.00 and 7.43 kcal/mol for compound 1 and 5.82 to 7.60 kcal/mol for compound 2 for the 10 highest ranked modes of binding  than compound 1, as can be seen in b ranges from 5.33 to 6.10 kcal/mol for compound 1 and from 5.17 to 6.52 kcal/mol for compound 2. Compound 2 which forms a stable hydrogen bond with ASP243 throughout all the ten highest binding modes  sequence, but the selectivity for compound 2 is much higher across the ten highest ranked binding modes  complexes 0 µmol/L  and thei0 µmol/L .50 values, palladium(II) complex 1 shows overall better cytotoxicity than complex 2 , displaying lower IC50 values in all cell lines , which denotes that cytotoxicity caused by 1 and 2 tends to decrease in the same cell lines, notably for Jurkat, HeLa and HCT . Based on ICll lines . In A549 and HCT ; no asso2O3, tri-n-butyl borate 98%, n-butanamine, ethylacetate, palladium(II) acetate, methanol, chloroform) were of reagent grade and were used as purchased. Bisdemethoxycurcumin was synthesized as described previously in the literature [N,N,N′,N′-tetramethylcycohexane-1,2-diamine and N,N′-dimethylpiperazine, were prepared based on a reported [Chemicals used in syntheses  and 75 MHz (for 13C-NMR). All spectra were measured in CD3OD and the chemical shifts are reported relative to TMS used as an internal standard  3.6 kV, sheath gas (Sh.G) 5 psi, capillary voltage (CV) 41 V, capillary temperature 275 °C. Spectra were recorded for both complexes (1 and 2) from 0 to 800 .−1 (Infrared spectra were measured with a Nicolet 6700 FT-IR spectrophotometer in the range of 600–4000 cm−1 .1: To a solution of 0.21 g (0.68 mmol) bisdemethoxycurcumin in methanol (7 mL) was added dropwise the methanolic solution (10 mL) of 0.27 g (0.68 mmol) intermediate palladium(II) complex containing N,N,N′,N′-tetramethylcycohexane-1,2-diamine. The reaction mixture turned from dark-orange to yellow and an orange precipitate was formed. The precipitate was filtered off from the mother liquor, dried and identified as the final product. Complex 1: mp 184 °C (decomposed); 0.21 g yield (48%); 1H-NMR , δ (ppm): 7.42–7.49 , 6.80 , 6.65 , 5.82 , 3.21 , 2.85 , 2.83 , 2.19 , 1.90 , 1.81 , 1.43–1.53 , 1.21–1.29 . 13C-NMR , δ (ppm): 178.6 (2C), 159.8 (2C), 140.3 (2C), 129.6 (4C), 126.5 (2C), 121.4 (2C), 115.5 (4C), 104.0 (1C), 71.7 (2C), 47.6 (2C), 42.1 (2C), 24.08 (2C), 23.82 (2C). IR υ (cm−1): 3010, 2943, 2867, 2675, 1599, 1497, 1437, 1389, 1273, 1162, 1103, 996, 970, 936, 829, 783, 703, 662, 633. HR-MS C29H38N2O4Pd+ calc.583.1783; exp. 583.1794.Complex 2: Bisdemethoxycurcumin  was dissolved in 9 mL of methanol. To this solution was slowly added a solution of intermediate palladium(II) complex bounding N,N′-dimethylpiperazine  in 8 mL of methanol. The reaction was checked on TLC after 24 h and unreacted bisdemethoxycurcumin it was observed, therefore the reaction was kept on stirring at room temperature another 18 h. After that, the solvent was removed and the final product was isolated from the mixture by silica gel chromatography, using as eluent methanol: chloroform in a ratio 1:9. Palladium complex 2 was obtained as an orange powder. Complex 2: mp 195 °C (decomposed); 0.20 g yield (46%); 1H-NMR , δ (ppm): 7.50–7.45 , 6.80 , 6.65 , 5.82 , 3.89 , 2.74 , 2.62 , 1.89 . 13C-NMR , δ (ppm): 178.4 (2C), 159.7 (2C), 140.5 (2C), 129.6 (4C), 126.6 (2C), 121.2 (2C), 115.5 (4C), 104.3 (1C), 58.1 (4C), 45.2 (2C), 22.5 (1C). IR υ (cm−1): 3010, 2810, 2681, 2609, 1601, 1501, 1446, 1389, 1271, 1199, 1164, 1104, 994, 964, 828, 796, 704, 654. HR-MS C25H30N2O4Pd+ calc. 527.1157; exp. 527.1167.Complex 2 [Diffraction measurements were performed with a Stoe STADIVARI diffractometer equipped with Dectris Pilatus 300 K detector using a Genix3D Cu HF source  at 100 K employing a nitrogen gas open-flow cooler Cobra Oxford Cryosystems. Data reduction was achieved using X-Area  software package . The cry2 . All non2 , Mercury2  and OLEX2  were usewww.ccdc.cam.ac.uk/conts/retrieving.html (16 November 2020) .CCDC: 2044620-2044621 contains the supplementary crystallographic data for this paper. These data can be obtained free of charge via 1 and 2 was calculated with density functional theory-based methods at the ωB97XD/LANL2DZ level of theory with the Gaussian 16 suite of programs [1 and 2 using the AutoDock 4.2.6 suite of programs [1 and 2.The electronic structure of compounds programs . The LANprograms . This DNThe proliferation of cells was assessed from the absorbance at 490 nm wavelength with an automated Cytation™ 3 Cell Imaging Multi-Mode Reader .1 and 2. The Jurkat (human leukaemic T-cell lymphoma), HCT 116  and HeLa (human epitheloid cervix carcinoma) lines were cultured in RPMI 1640 medium . The MCF-7 (human breast adenocarcinoma), MDA-MB-231 (human mammary gland adenocarcinoma), A549 , Caco-2 , and noncancerous 3T3 (murine fibroblasts) cell lines were maintained in a growth medium consisting of high glucose Dulbecco’s Modified Eagle Medium with sodium pyruvate .Cell cultures: Seven human cancer cell lines and one noncancerous cell line were used for testing the synthesized complexes 2 in humidified air at 37 °C. Cell viability, estimated by trypan exclusion, was greater than 95% before each experiment.Cell cultivation: The growth medium was supplemented with a 10% fetal bovine serum, 1X HyClone™ Antibiotic/Antimycotic Solution . Cells were cultured in an atmosphere containing 5% CO3/well in 96-well polystyrene microplates . After 24 h, tested compounds 1 and 2 were added in various concentrations . After 72 h of incubation, 10 µL of MTS  was added to each well according to the CellTiter 96® Aqueous One Solution Cell Proliferation Assay protocol. The absorbance was measured at 490 nm with the automated Cytation™ 3 Cell Imaging Multi-Mode Reader  after 1 h of incubation. Three independent experiments were performed for each test. The results obtained from the MTS assay were used to determine the half-maximal inhibitory concentration (IC50) of each tested compound.MTS cell proliferation/viability assay: The cytotoxicity of complexes 1 and 2 was determined by MTS -5-(3-carboxymethoxyphenyl) -2-(4-sulfophenyl)-2H-tetrazolium) assay. Cells were seeded at a density of 5 × 101 and 2 have affinity to bind the ASP243 residue of NF-κB transcription factor, an amino acid located in the protein–protein interaction area. Therefore, the two complexes and especially 2 display a good potential to specifically target NF-κB. Compound 2 exhibits a systematically higher affinity for NF-κB than compound 1  complexes bring into consideration their antiproliferative potential and open the road to further examinations, mainly in vitro, as possible metallodrugs with anticancer activity.The NF-κB transcription factor is responsible for the fine tuning of the apoptotic process, cell cycle regulation, and cell differentiation in tumors. The crystal structure of NF-κB has two distinct druggable areas: the protein–protein interaction and the DNA-binding region . The commpound 1 , where −ent of 2 . Based opoptosis  and the


Zizyphus spina-christi (Rhamnaceae family) is an edible plant used in folk medicine. Therefore, it is of interest to report the cytotoxic effects of Z. spina-christi bark crude extract on human cell lines. Crude ethanol extract of Z. spina-christi barkwas fractionated with increasing polarity . The fractions were examined for their cytotoxicity against human colon cancer (HCT-116 and CACO-2), cervical cancer (HeLa and HEp-2), lung carcinoma(A-549), hepatocellular carcinoma (HepG-2), breast cancer (MCF-7) and prostate cancer (PC-3) cell lines using viability assay. Diethyl ether fraction of Z. spina-christi showed the highest cytotoxic effects among the four extracts of Z. spina-christi. TheIC50 of diethyl ether fraction was 7.14, 11.2, 11.6, 15.4, 39.8, 42.2, 84.2 and 153.8 µg/ml on HepG-2, A-549, CACO-2, HCT-116, MCF-7, PC-3, HeLa, and HEp-2 cell lines, respectively. Data shows that the diethyl ether fraction of Z. spina-christi showed effectivecytotoxic effects in colon, lung and hepatocellular cancer cell lines. Resistance to chemotherapeutic agents, and adverse side effects of radiation are problems in clinical cancer treatment . Drugs iEthanol, diethyl ether, chloroform, ethyl acetate, butanol, dimethyl sulfoxide (DMSO), crystal violet and trypan blue dye were purchased from Sigma . Fetal bovine serum, Dulbecco's Modified Eagle Medium (DMEM), HEPES (N-(2-Hydroxyethyl)piperazine-N'-(2-ethanesulfonic acid)) buffer solution, L-glutamine, gentamycin, trypsin-EDTA were purchased from Lonza, Basel, Switzerland. All other chemicals and solvents used in this study were of analytical grade.Fresh bark of the Z. spina-christi was collected from the Hail region, Saudi Arabia. The plant was identified by Dr. Sherif Sayed Sharawy and the specimen was deposited in the Herbarium, Department of Biology, College of Sciences, University of Hail. Theair-dried bark of Z. spina-christi was repeatedly extracted (3 times) at room temperature with absolute ethanol by percolation till complete exhaustion. The ethanol extract was filtered and solvents were evaporated under reduced pressure. The ethanol extract wasthen fractionated by successive extraction with the solvents of increasing polarity viz. diethyl ether, chloroform, ethyl acetate and n-butanol. The fractions were filtrated, and solvents were evaporated under reduced pressure.CACO-2 cells , HCT-116 cells (human colon carcinoma ATCC® CCL-247™), PC-3 cells (human prostate carcinoma ATCC® CRL-1435™), HeLa cells ,A-549 cells (lung carcinoma ATCC® CCL-185™), HepG-2 (hepatocellular carcinoma ATCC® HB-8065™), MCF-7 (breast cancer cell line ATCC® HTB-22™) and HEp-2 cells  were purchased from AmericanType Culture Collection (ATCC), USA.2 and weresub-cultured two times a week.The cells were propagated in DMEM supplemented with 10% heat-inactivated fetal bovine serum, 1% L-glutamine, HEPES buffer and 50µg/ml gentamycin. All cells will be maintained at 37°C in a humidified atmosphere with 5% COFor cytotoxicity assay, the cells were incubated with different concentrations of the fractions of Z. spina-christi bark extract for 48 h. After the incubation period, the viable cells yield will be determined by the colorimetric method .All resThe concentration of Z. spina-christi fractions required to cause cytotoxic effects in 50% of cancer cells (LD50) will be estimated from graphic plots of the dose-response curve for each concentration using Graphpad Prism software .The obtained results were analyzed using SPSS, IBM, Armonk, New York, United States to obtain the standard deviation (SD). The results were expressed as mean ± SD.The control cells showed high proliferation that has been taken as 100% viability (0% cytotoxicity). As shown in Diethyl ether fraction of Z. spina-christi extract showed the highest cytotoxic effects against the cervical cancer cell lines at all the tested concentrations in a dose-dependent manner (7.8 - 1000 mg/ml) .Other fAs shown in The diethyl ether fraction of Z. spina-christi extract showed a potent cytotoxic effect (>50% inhibition) against HepG-2 cell line from the concentration of 7.8 mg/ml . All theThe diethyl ether and ethyl acetate fractions of Z. spina-christi extract showed a cytotoxic effect against the MCF-7 cell line from the concentration of 7.8 mg/ml. This fraction showed a dosedependent increase in the cytotoxicity with about 80% of cytotoxiceffect at 1000 mg/ml . The chlThe diethyl ether fraction of Z. spina-christi extract showed a potent cytotoxic effect against PC-3 cell line from the concentration of 7.8 mg/ml. This fraction showed a dose-dependent increase in the cytotoxicity with more than 90% of cytotoxic effect at1000 mg/ml . The chlThe IC50 values of diethyl ether fraction is lower than other fractions against all the tested cell lines (Table 1 - see PDF). The IC50 values of diethyl ether fraction are less than 10 mg/ml against HepG-2, less than 20 mg/ml against A-549, CACO-2 andHCT-116, less than 50 mg/ml against MCF-7 and PC-2, and more than 100 mg/ml against HEp-2. The diethyl ether fraction is highly active against HepG-2 (IC50=7.1 mg/ml) and least active against HEp-2 (IC50=153.8 mg/ml). The cytotoxic potency of diethyl etherfraction against various cell lines is descended in the following order: HepG-2 > A-549 > CACO-2 > HCT-116 > MCF-7 > PC-3 > HeLa > HEp-2. Ethyl acetate fraction showed cytotoxic effects with the IC50 value of less than 100 mg/ml againstA-549, HepG-2, HCT-116, whereas chloroform and butanol fractions showed cytotoxic effects with the IC50 value less than 100 mg/ml only against HepG-2.Identification of new chemical entities through the biological activity-guided fractionation of plant materials is a novel approach to integrated drug discovery . The phyDiethyl ether fraction of Z. spina-christi bark extract exhibited remarkable cytotoxic activity against the cell lines . The fraction with low polarity (diethyl ether fraction) showed the highest cytotoxic effects than the fractions with high polarity . Comparing the IC50 of all thefractions against cancer cell lines revealed that diethyl ether extract has multi-fold potent cytotoxic activity compared to the second-highly active fraction (1 fold to 18 fold). The lower IC50 values represent the greater cytotoxic activity, whereas thehigher the IC50 values represent the lower cytotoxic activity. It was reported that tradecene, hexadecanoic acid, ethyl ester  and ethyl linoleate are found only in diethyl ether fraction, not in other fractions of Z. spina-christi bark extract. One or The fractions of ethanolic extract of Z. spina-christi bark showed cytotoxic effects on various human cancer cell lines . The cytotoxic effects of fractions against CACO-2, HeLa, HEp-2,HepG-2 and PC-3 are in the order of diethyl ether >> chloroform > butanol > ethyl acetate. The cytotoxic effect of fractions against HCT-116, A-549 and MCF-7 are in the order of diethyl ether >> ethyl acetate > chloroform > butanol. Thediethyl ether fraction of ethanolic extract of Z. spina-christi showed the highest activity compared to other highly-polar fractions  for further consideration in cancer drug discovery.


Epithelial–mesenchymal transition (EMT) is a dynamic and complex cellular process that is known to be hijacked by cancer cells to facilitate invasion, metastasis and therapeutic resistance. Several quantitative measures to assess the interplay between EMT and cancer progression are available, based on large scale genome and transcriptome data. However, these large scale multi-omics studies have repeatedly illustrated a lack of correlation in mRNA and protein abundances that may be influenced by diverse post-translational regulation. Hence, it is imperative to understand how changes in the EMT proteome are associated with the process of oncogenic transformation. To this effect, we developed a parallel reaction monitoring-based targeted proteomics method for quantifying abundances of EMT-associated proteins across cancer cell lines. Our study revealed that quantitative measurement of EMT proteome which enabled a more accurate assessment than transcriptomics data and revealed specific discrepancies against a backdrop of generally strong concordance between proteomic and transcriptomic data. We further demonstrated that changes in our EMT proteome panel might play a role in tumor transformation across cancer types. In future, this EMT panel assay has the potential to be used for clinical samples to guide treatment choices and to congregate functional information for the development and advancing novel therapeutics.The online version contains supplementary material available at 10.1186/s12967-021-03227-0. Epithelial-mesenchymal transition (EMT) is a cellular process that allows transdifferentiation of cells with a polarized epithelial phenotype to gain mesenchymal characteristics. It is a highly coordinated process that is regulated at genetic, epigenetic and protein levels by different regulators –3. EpithSeveral signalling cascades and downstream transcriptional regulators such as SNAIL, TWIST and ZEB are known to be associated with EMT , 11. AdvMass spectrometry and antibody arrays have been used to assess protein expression dynamics. Although mass spectrometry-based proteomics studies offered us to estimate quantative differential expression of many proteins associated with EMT process under different biological contexts , 21, theIn previous studies most of the approaches employed either the transcriptomics analysis or the mathematical modeling and were focused on classifying the dynamic state of the cellular phenotypes , 26. HowEMT is known to play an important role in oncogenic transformation. To examine EMT in the context of the oncogenic transformation of different organ types, we analyzed transcriptome data across different cancer types from Cancer Cell Line Encyclopedia (CCLE) using t-Distributed Stochastic Neighbor Embedding (t-SNE) method , 31. We Compared to RNA, proteins are more closely related to cellular phenotype and hence it is important to quantify protein levels of EMT-related markers. Currently, most of the studies focus on quantation of RNA expression and there is no high throughput protein level data to monitor changes related to EMT. The overarching objective of this study was to develop a multiplex PRM method to quantify EMT-associated proteins across a panel of cancer cell lines including lung, head and neck squamous cell carcinoma, urinary bladder, gall bladder, gastric and ovarian cancer. A panel of 37 well established EMT-associated proteins were curated from previously published studies . SelectiThe PRM assay was optimized for the detection of these stable isotope labelled (SIL) peptides in a complex mixture of the cell line peptides. Only peptides that were consistently detected were regarded as detectable targets and used for further analysis. Using the peptide retention time (RT), we generated a time-scheduled PRM method that analyzes 31 target proteins (96 endogenous peptides + 96 SIL peptides), allowing 10 min time windows for monitoring across cell lines. Performance of the assay was assessed by evaluating linearity, LOQ-, and LOD using a reverse calibration curve strategy. A set of 31 best peptides corresponding to 31 proteins were selected based on quality control criteria specified in the methods section, and consistent detection across all the cell lines with 3 or more PRM transitions per peptide for quantitation during data analysis using skyline software  for which transcriptomics data is not available in the CCLE database. We observed clustering of these gall bladder cancer cell lines along with other cells showing mesenchymal phenotype in the proteomics data. This indicates that proteomics analysis can be useful in assigning the cellular phenotypes. However, certain outliers such as lung cancer cell line A549, and bladder cancer cell lines SW780 and VMCUB1 were also observed in the proteomics data, indicating proteomic heterogeneity.Further, we carried out Principal Component Analysis (PCA) using CCLE mRNA gene count data as well as PRM-based proteomic data for the EMT-related markers. PCA results showed consistent clustering of epithelial and mesenchymal cell lines in both the data sets Fig. b. The ep. A549, VMCUB1 and SW780, compared to epithelial cell lines such as RT112 and MCF7 and mesenchymal cell lines such as UMUC3 and MDAMB231  was observed in cell lines with high EMT score  and low expression was observed in cell lines with low EMT score  at both protein and transcript levels, concordant with their epithelial or mesenchymal phenotype Fig. d. HoweveTo further assess the EMT-related protein expression in clinical samples and understand how these proteins are associated with oncogenic transformation across different tumor types, we analyzed quantitative proteomics data from the CPTAC repository . Quantit. [, and George et al. [EMT is a dynamic change in cellular architecture that leads to changes in cell migration and invasion. Its role has been well documented in developmental process and closely associated with tumor dissemination and metastasis , 44. Sev. , and Geoe et al. . Anothere et al. . However. at both transcript and proteome levels [We have also analyzed pan-cancer transcriptomics data from 1037 cell lines in the CCLE database . Organize levels . A falsee levels . These rThe advent of advanced high throughput proteomic techniques has made it possible to study cellular proteome in context to cellular plasticity. Since then it has been repeatedly noted that transcriptome and proteome abundances do not correlate adequately to be considered as proxies for each other –38. The To this end, we have developed a PRM-based targeted proteomics method for the quantitative evaluation of several proteins related to EMT. We observed a higher abundance of epithelial phenotype-related proteins in known epithelial cell lines such as MCF7, Cal27 and FaDu along with a lower abundance of mesenchymal related proteins. Similarly, we observe a lower abundance of epithelial phenotype proteins in mesenchymal cell lines such as MDAMB231, J82 and UMUC3. These observations confirm that these cell lines generally exhibit a differentiating pattern of expression of EMT related proteins based on their cellular phenotype. Further, we observe a similar PCA-based clustering of both epithelial and mesenchymal cell lines into 2 distinct groups related to their phenotype and EMT scores with either transcriptome or PRM-based targeted proteomics data. This indicates that the PRM-based targeted proteomics data is largely concordant with the EMT scoring matrices that are based on transcriptomics., including cancers progressing from basal-squamous molecular subtype exemplified by cell lines such as VMCUB1 [Further, we observed clustering of gall bladder cancer cell lines , which are not represented in the CCLE transcriptome database, with other mesenchymal cell lines. OCUG-1 and NOZ have been characterized as moderately invasive cell lines while G-415 has been characterized as highly invasive , 49. In s VMCUB1 . FurtherCytokeratins are structural proteins that enable cellular integrity. Downregulation of the KRT8/KRT18 keratin pair is known to induce an increase in cell motility and invasion . We obse. has also reported a similar finding for colorectal cancer in terms of both tumor tissue and cell line samples, based on transcriptomics data. They have further hypothesized that these features of certain cancer types exhibiting epithelial or mesenchymal characteristics may be associated with embryonic ectodermal or mesodermal origins of these organs [To further explore how EMT-related proteins are expressed in clinical samples across multiple cancer types we analyzed quantitative proteomics data from the CPTAC database . Based oe organs . Thus, ohttps://portals.broadinstitute.org/ccle. To visualize the high-dimensional transcriptomics data we used the t-distributed stochastic neighbor embedding (t-SNE) algorithm implemented in the Rtsne package in R v.4.0.1 (http://www.R-project.org/) with perplexity value of 30 and at 1000 iterations, and all other arguments at their default values. Out of 18 cell lines that are used in the current study mRNA data for OCUG1, NoZ, G415 were not available in CCLE. For further analysis mRNA expression values of 20 selected proteins for 15 cell lines were used. Principal Component Analysis (PCA) was performed using the R-based “prcomp” function.The CCLE mRNA data and cell line annotations of 1037 cancer cell lines were retrieved from the CCLE portal at: CCLE has profiled Reverse Phase Protein Array (RPPA) data for 159 proteins in 889 cell lines. Out of 18 cancer cell lines that are included in our study, 7 cell lines have both available RPPA and transcriptomic data. 59 proteins were profiled in common between CCLE RPPA data and CCLE transcriptomic data. Pearson's correlation coefficient (r) was used to evaluate the relationship between mRNA expression and protein expression in cancer cell lines.https://cptac-data-portal.georgetown.edu/cptacPublic/) [To analyze the expression of EMT related protein in cancer tissue samples we used publicly available quantitative proteomics data from Clinical Proteomic Tumor Analysis Consortium (CPTAC) repository (Public/) . Proteom2 atmosphere. Cells were harvested at 70% confluency. Gall bladder cancer cell lines G-415 was sourced from RIKEN Bio Resource Center, Ibaraki, Japan and OCUG-1 and NOZ from Health Science Research Resources Bank, Osaka, Japan. Bladder cancer cell lines were received from Prof. Jean Paul Thiery .Cell lines used in the current study were grown as per vendor recommendations. Briefly the cells were cultured in their respective media as detailed in Additional file All the cell lines were grown in recommended media, and prior to harvesting washed with ice-cold phosphate-buffered saline thrice to remove media residuals. Cells were then harvested and lysed in urea lysis buffer . Protein concentration was measured using the bicinchoninic acid assay method as per the manufacturer’s protocol . 500 µg equivalent of protein from each sample were reduced using dithiothreitol  at 60 °C for 20 min and alkylated with iodoacetamide  for 10 min at room temperature. Protein was precipitated overnight at − 80 °C using ice-cold acetone. The samples were centrifuged at 12,000 rpm for 15 min; the acetone was removed and the pellet air-dried and then dissolved in 4 M urea. Proteins were then digested using lysyl endopeptidase, Mass Spectrometry Grade  at 1:100 enzyme to protein ratio for 4 h at 37 °C. After 4 h, the urea concentration was reduced from 4 to 2 M using 50 mM Triethylammonium bicarbonate (TEABC). The samples were then digested using tosyl phenylalanyl chloromethyl ketone (TPCK)-treated trypsin  at a 1:20 enzyme to protein ratio for 16 h at 37 °C. The samples were cleaned using Sep-Pak Classic C18 columns  and then completely dried before LC–MS/MS analysis.The peptides were analyzed on a QExactive plus mass spectrometer interfaced with RS-nanoLC 3000 nanoflow liquid chromatography system . 5 µg equivalent peptide digests were reconstituted in 0.1% formic acid and loaded onto a trap column  at a flow rate of 5 µl/min and resolved on analytical column  at a flow rate of 300 nl/min. The peptides were resolved using a step gradient of 5–25% solvent B (0.1% formic acid in 85% acetonitrile) from 8 to 60 min and 25–40% solvent B for 60–85 min. The mass spectrometer was operated in data-independent acquisition PRM mode. A survey full scan MS (from m/z 350–1700) was acquired in the Orbitrap at a resolution of 70,000 at 200 m/z. A targeted list of precursor ions with charge state ≥ 2 was isolated and fragmented using HCD fragmentation with 32% normalized collision energy and detected at a mass resolution of 30,000 at 200 m/z. The data were subsequently analyzed using Skyline .The PRM assay for a selected panel of 37 epithelial and mesenchymal proteins as represented in Additional file All PRM-MS raw files were processed in Skyline to generate XIC and perform peak integration . We asseAdditional file 1:Figure S1. Gene ontology-based annotation of proteins used for PRM-based proteomics analysis curated from literature. Figure S2. PRM transitions for 31 peptides precursors selected for analysis of 31 proteins mentioned in the figures across the cell lines. Figure S3. Calibration curve for 31 peptides precursors selected for monitoring protein expression across cell lines. Figure S4. Co-relation plots for mRNA-based gene expression and protein expression for values for depicted cell lines based on transcriptomics and reversed phase protein array (RPPA) proteomics data from CCLE database. Method S1. The overview of all the cell lines used in the study along with the tissue of origin and culture method.Additional file 2: Table S1. List of EMT-related proteins and peptides selected for PRM assays development.Additional file 3: Table S2. List of EMT-related proteins and limit of detection and limit of quantitation values.Additional file 4: Table S3. Relative expression of Epithelial and mesenchymal marker proteins across cell lines.


SNHG12 , also confirmed in an external dataset (The Cancer Genome Atlas). SNHG12 gene silencing increased the sensitivity to carboplatin, giving evidence that this lncRNA contributes to resistance to carboplatin in ovarian cancer cell lines. We also demonstrated that SNHG12 could control the expression of nearby genes probably by altering epigenetic markers and modifying the transcript levels.Epithelial ovarian cancer is a lethal malignancy in which recurrence and therapy resistance are the major causes of death. We investigated the transcriptome and DNA methylation profile of ovarian cancer cell lines sensitive and resistant to carboplatin, aiming to identify genes associated with therapy resistance. We focused on long non-coding RNAs (lncRNAs), known as epigenetic regulators of several cellular and biological processes. We found 11 lncRNAs associated with carboplatin resistance, including n = 111 OC samples) and internal datasets (n = 39 OC samples) to identify lncRNA candidates. A total of 4255 differentially expressed genes (DEGs) and 14529 differentially methylated CpG positions (DMPs) were identified comparing sensitive and resistant OC cell lines. The comparison of DEGs between OC cell lines and TCGA-OC dataset revealed 570 genes, including 50 lncRNAs, associated with carboplatin resistance. Eleven lncRNAs showed DMPs, including the SNHG12. Knockdown of SNHG12 in Ovc316 and OVCAR8 cells increased their sensitivity to carboplatin. The results suggest that the lncRNA SNHG12 contributes to carboplatin resistance in OC and is a potential therapeutic target. We demonstrated that SNHG12 is functionally related to epigenetic mechanisms.Genetic and epigenetic changes contribute to intratumor heterogeneity and chemotherapy resistance in several tumor types. LncRNAs have been implicated, directly or indirectly, in the epigenetic regulation of gene expression. We investigated lncRNAs that potentially mediate carboplatin-resistance of cell subpopulations, influencing the progression of ovarian cancer (OC). Four carboplatin-sensitive OC cell lines , their derivative resistant cells, and two inherently carboplatin-resistant cell lines (OVCAR8 and Ovc316) were subjected to RNA sequencing and global DNA methylation analysis. Integrative and cross-validation analyses were performed using external (The Cancer Genome Atlas, TCGA dataset,  Ovarian cancer (OC) is one of the most lethal malignancies in women and accounts for more than 200,000 deaths worldwide annually . OC is hPrimary care of OC patients is based on surgery and adjuvant chemotherapy. Despite the variety of OC subtypes, pointing in the direction that each subtype should potentially be treated differently, OC is treated as a single disease. Standard first-line treatment includes primary cytoreductive surgery followed by adjuvant chemotherapy with platinum-based drugs, namely carboplatin or cisplatin, and a taxane, such as paclitaxel or docetaxel ,7. InitiBRCA1/BRCA2 variants or genomic instability [Among the drugs used in recurrent OC, PARP (poly- (adenosine diphosphate-ribose) polymerase) inhibitors have demonstrated prognostic impact in patients with pathogenic/likely pathogenic tability ,13. Aftetability . The anttability . ImmunolBRCA1/2 (BRCA1 and BRCA2 DNA repair associated) mutations, chromosomal rearrangements, and differential expression of non-coding RNAs (ncRNAs) [The heterogeneity of the disease and the molecular complexity of the genetic and epigenetic alterations promoted during tumor progression or due to the selective pressure caused by the treatment is rarely captured and taken into consideration in the clinical setting . The mol(ncRNAs) ,16,17,18SNHG10  overexpression significantly suppressed the tumorigenesis and epithelial-mesenchymal transition (EMT) of EOC [SNHG10/miR-200a-3p/BIN1 axis was reported as a predictive biomarker and therapeutic target in OC [HOTAIR has been described as altered in different tumor types. In EOC, overexpression of HOTAIR was associated with poor prognosis and pro-metastatic effects, which are at least partially exerted by regulating the expression of metalloproteinases and EMT-related genes [The ncRNAs, which are involved in transcriptional and post-transcriptional regulation, constitute an additional level of complexity in cancer drug resistance. Among them, long non-coding RNAs (lncRNA) have been drawing attention for their function as regulators of transcription and chromatin remodeling. LncRNAs include transcripts longer than 200 nucleotides that form complex structures that interact with DNA, RNA, or proteins to modulate gene expression and cellular signaling . LncRNAs) of EOC . SNHG10/et in OC . The lnced genes . Recent ed genes ,23.High-throughput transcriptome studies have identified differential expression patterns of lncRNAs associated with disease recurrence and platinum resistance ,25,26. FA well-known mechanism of transcriptional regulation is DNA methylation. The direct interaction between lncRNAs and proteins of the epigenetic machinery, such as chromatin modifiers and DNA methyltransferases, has emerged as an additional link between lncRNAs and tumorigenesis. Thus, knowledge of the interplay between lncRNAs with epigenetic modification and transcriptional regulation significantly contributes to understanding the mechanisms involved in gene regulation and unravels robust markers associated with resistance to therapy. Importantly, lncRNA targeting is one of the only therapeutic approaches that can upregulate tumor suppressor genes endogenously in a locus-specific manner . DespiteSNHG12  as a candidate biomarker of response to carboplatin in OC and investigated its functional mechanisms.Several studies have demonstrated that lncRNAs are associated with tumor progression and chemotherapy resistance. However, the implications of DNA methylation changes, lncRNAs, and their association with chemotherapy response in OC are poorly explored. Herein, using RNA sequencing (RNA-seq) and DNA methylation analyses in OC cell lines, we identified the lncRNA A panel of six OC cell lines  were included in this study. The cell lines OVCAR3, OVCAR5, and Ovc316 were kindly provided by Robert Strauss, Danish Cancer Society, Denmark, and the cell lines IGROV1, OVCAR4, and OVCAR8 were donated by Wan Lam, University of British Columbia, Vancouver, Canada. The cell lines were cultured in RPMI medium   or Dulbecco’s modified Eagle medium  , as recommended. The media were supplemented with 10% fetal bovine serum (FBS) , and 1% antibiotic-antimycotic . After confluence, cells were treated with 0.05% trypsin/0.02% EDTA (Sigma Aldrich) and replicated. We tested cell cultures for mycoplasma contamination using a PCR-based assay once per month. The mutation profile of cancer-related genes described in these cell lines was confirmed by Sanger sequencing .n = 39, FIGO stages III–IV) were obtained from patients who underwent surgical resection  at Horsens Hospital, DK, between 2005 and 2007. Follow-up and progression-free survival (PFS) data were retrieved from the Danish Health Registries and research databases of the Department of Oncology, University Hospital of Southern Denmark, Vejle, DK. The samples were classified according to the World Health Organization (WHO) classification of female genital tumors [High-grade serous ovarian tumor tissue samples  and the Danish Data Protection Agency (19/48213). Informed consent was obtained from all patients prior to sample collection.50 measurements before and after this period using a crystal violet assay for determining the viability of cultured cells as previously described [Carboplatin-sensitive cells   were expescribed ,33.Total RNA was isolated with the RNeasy mini kit  according to the manufacturer’s recommendations. The RNA purity and quantity were measured with a nanodrop spectrophotometer , and the integrity was assessed with RNA screen tape on a 2200 TapeStation .Libraries were generated using 500 ng of total RNA and prepared using the TruSeq Stranded Total RNA Library Prep Gold kit , following the manufacturer’s protocol. Integrated DNA Technology (IDT) for TruSeq RNA UD Indexes was added for multiplexing, and samples were paired-end sequenced with 2 × 100 bp on the NovaSeq 6000 system (Illumina) using the S1 Reagent Kit v1.5 (200 cycles), as recommended by the supplier. RNA-seq data quality control was performed using FastQC and MultiQC ,35. ReadWe performed enrichment analysis using our list of DEGs to identify overrepresented pathways unique to sensitive and resistant OC cell lines. The analysis was performed using the Kyoto Encyclopedia of Genes and Genomes 2021 Human database via the Enrichr analysis tool .p-value for all probes, background noise detection, and adjustment were performed using the same package. Differences Type-I and Type-II probes adjustment and normalization of β values were performed using the Beta Mixture Quantile dilation (BMIQ) model [p-value > 0.05), mapped in X/Y chromosomes, and cross-reactive were filtered out, and those mapped to SNPs were removed (package minfi) [p-value < 0.01 and |∆β| > 0.1 were considered significant and annotated using the Illumina manifest file.Genomic DNA was extracted using DNeasy Blood and Tissue Kit . A total of 210 ng of DNA was sodium bisulfite converted with the EZ DNA Methylation-Gold™ Kit , according to the manufacturer’s recommendations. The genome-wide methylation status of each sample was assessed using the Infinium MethylationEPIC 850 K BeadChip . The DNA methylation data is available at the GEO database (GSE198077). The microarrays were scanned using the Illumina HiScan system, and the data were processed using R language and analyzed as previously described . For metQ) model . Batch eQ) model . Probes e minfi) ,44. Diffe minfi) . Probes p < 0.05) was applied to identify probes and genes/lncRNAs with positive (r+) or negative (r-) correlation between DNA methylation and gene expression.Only lncRNAs showing differential expression and DNA methylation were selected for further analysis. Pearson correlation test  or resistant  to carboplatin. Data normalization and differential expression analysis between sensitive and resistant samples were performed in the Galaxy bioinformatics platform using EdgeR [External RNA-seq data from The Cancer Genome Atlas (TCGA) was used as a validation dataset. A total of 111 RNA-seq gene count files from patients with high-grade serous ovarian cancer (HGSC) were downloaded from the Genomic Data Commons Data Portal (TCGA-OV project). Only patients who received treatment with carboplatin were selected from this data set. The data files were created using a similar bioinformatics pipeline to the one applied in the analysis of our experimental data (STAR-HTseq count). Clinical information was collected from a published study . Based ong EdgeR ,38.SNHG12, was further investigated by determining its expression levels in the internal cohort of 39 HGSC samples. Next, siRNA experiments were performed in selected OC cell lines. Bioinformatics analyses were also carried out using online lncRNA databases for investigating SNHG12 subcellular localization , regulatory relationships , and co-expressed genes  [We selected lncRNAs overexpressed in resistant compared to sensitive cell lines. We filtered out lncRNAs not associated with differential DNA methylation and lncRNAs that were downregulated in the resistant OC samples from the TCGA cohort. Among the remaining lncRNAs with the highest expression, we investigated their roles in cancer and response to therapy available in the literature. The final candidate, ry 2022) ,48,49.GUSB and assay ID: Hs00414754 for the target gene SNHG12). The RT-qPCR amplifications were performed in a QuantStudio 12K Flex Real-Time PCR System  with the following parameters: 50 °C for 2 min, 95 °C for 2 min, 40 cycles at 95 °C for 1 s, and 60 °C for 20 s. The relative expression in each sample was calculated using the 2−ΔΔCt method [Total RNA was isolated from frozen ovarian tissues (39 tumor and 8 non-tumor samples) and OC cell lines using the RNeasy mini kit . Reverse transcription of 1 µg RNA was carried out with the High-Capacity RNA to cDNA kit  according to the manufacturer’s instructions. Quantitative PCR was performed using TaqMan Fast Advanced Master Mix and TaqMan probes  approach. Cells were seeded into flat-bottom 6-well (3 × 105 cells/well) and 96-well (1 × 104 cells/well) tissue culture plates and maintained at 37 °C under 5% CO2. Transfections were carried out with ~60% confluency in each of the four cell lines using Opti-MEM I Reduced Serum Medium, GlutaMAX Supplement, and Lipofectamine RNAiMAX Transfection Reagent , according to the manufacturer’s recommendations. The Silencer Select Negative Control siRNA #1 (cat# 4390843) and SNHG12 siRNA  were used at 1 pmol (96-well plate) and 25 pmol (6-well plate) per well, respectively . Transfection efficiency was determined using BLOCK-iT Alexa Fluor Red Fluorescent Oligo . After 48 h incubation, the medium was replaced with the growth medium only or medium containing two different concentrations of carboplatin (10 µM or 50 µM). Triplicates of each condition were analyzed in two independent experiments. The step-by-step workflow used to evaluate the SNHG12 silencing is shown in Four OC cell lines  were selected for knockdown experiments of the lncRNA Following carboplatin treatment, cell viability was measured using the standard protocol of CellTiter Blue Viability Assay  at 24 h, 72 h, and 96 h time points. Fluorescence was recorded at 530/590 nm using the Synergy HT plate reader . Reading corrections were performed using wells with no cells treated under the same conditions.p-values ≤ 0.05.Data were generated from at least two independent experiments with triplicates. Graphical and statistical analyses were performed using Graph Pad Prism 9.0  and Galaxy bioinformatics platform . Cluster50 values above 32 µM). Carboplatin acquired resistance was observed in derived subpopulations from IGROV1 (IGROV1-R1), OVCAR3 (OVCAR3-R1 and OVCAR3-R2), and OVCAR5 (OVCAR5-R1). OVCAR4 presented high sensitivity to carboplatin and, therefore, this cell line was not used in the functional assays. The resistance to carboplatin was validated by an increase in the IC50 from 4–17 µM up to 28–44 µM , and two (OVCAR8 and Ovc316) were inherently resistant (IC28–44 µM . Two res28–44 µM . OVCAR3-p < 0.05), among which 1913 were upregulated and 2342 downregulated in carboplatin-resistant OC cells. The normalized read counts considering FDR < 0.05 and logFC < −1.5/> 1.5 revealed 86 differentially expressed genes comparing cell lines sensitive and resistant to carboplatin. Supervised clustering analysis of the significant genes between resistant and sensitive OC cells is depicted in To assess differential gene expression between the sensitive and resistant OC cells, RNA sequencing was performed in the four cell lines with acquired in vitro resistance to carboplatin , four sensitive cell lines , and the two inherently carboplatin-resistant OC cell lines (Ovc316 and OVCAR8). Genes differentially expressed were determined by comparing the sensitive with the resistant cell lines. This comparison revealed 4255 differentially expressed genes . Interestingly, several of the downregulated genes were involved in epigenetic regulation of gene expression, including DNA methylation and histone post-translational modifications  and -resistant (n = 42) patients, identifying 8246 DEGs (p < 0.05)  C. The co < 0.05) C, with 3p < 0.05), 13,023 hypermethylated and 1506 hypomethylated in resistant OC cells. Clustering of the most significant DMPs (5721 positions with p ≤ 0.01 and |∆β| ≥ 0.1) clearly showed that resistant and sensitive cells have a distinct methylation profile  for the ten cell lines subjected to RNA sequencing. The comparison of resistant and sensitive cell lines revealed 14,529 differentially methylated CpG positions  recently curated by Huang et al. . We founSNHG12 was also quantified in an independent cohort of 39 HGSC and 7 normal ovarian tissues . We found no significant differences between patients sensitive and resistant to carboplatin, possibly due to the limited number of resistant patients . However, no significant difference was observed for OS in this cohort and for PFS (6 or 12 months) and OS in our internal dataset.The expression of the lncRNA patients C. To evaSNHG12 on the carboplatin resistance in OC cells. The inherently resistant Ovc316 and OVCAR8 and the pair IGROV1 (sensitive) and IGROV1-R1 (IGROV1-derived carboplatin-resistant subpopulation) were transfected with si-SNHG12. Carboplatin dose-response curves were confirmed by cell viability assay before knockdown experiments  and RUNX3 (RUNX family transcription factor 3) methylation in tumor specimens was associated with shorter OS and PFS [A repertoire of molecular and functional studies in OC has been useful for tumor stratification predicting different clinical outcomes ,52,53,54sistance ,52. In a and PFS . Additio and PFS ,59,60.SNHG12, EMX2OS (EMX2 opposite strand/antisense RNA), and AC010894.3 had their roles previously investigated in OC, but the mechanisms involved in the transcriptional regulation were barely explored [EMX2OS exhibited a trend towards hypermethylation in resistant OC cells, while AC010894.3 was hypermethylated in the same cell lines. EMX2OS was previously reported as upregulated in OC tissues and cell lines, enhancing proliferation, invasion, and sphere formation in vitro and tumor growth in vivo [AC010894.3 was included in a four-lncRNA signature that accurately predicted the OS of OC patients, and its increased expression was associated with better survival [AC244502.1, AP002807.1, and AP002518.2, were integrated into prognostic signatures for breast cancer, clear cell renal cell carcinoma, and Wilms tumor, respectively [SNHG12 was selected as a candidate due to its inverse correlation between high expression levels and DNA hypomethylation in carboplatin-resistant OC cell lines.In this study, we evaluated the interplay between the DNA methylome and transcriptome profiles in carboplatin-resistant OC cell lines and validated our findings with functional assays and gene expression in OC tissues from HGSC patients. We focused on identifying lncRNA genes both differentially expressed and affected by methylation changes associated with the development of platinum-resistance in OC. An integrative analysis using an external cross-validation set of selected OC samples from TCGA revealed 570 DEGs, including 50 lncRNAs. In this last group, 11 lncRNAs were also associated with at least 1 DMP. Among these 11 lncRNAs, explored ,62,63,64ectively ,66,67. TSNHG12 is a lincRNA (long intergenic non-coding RNA) and a member of non-coding genes that host small nucleolar RNAs (snoRNA). Approximately 30 SNHG genes were identified in the human genome, and some of them  have been reported as overexpressed in different types of human cancers [SNHG genes are processed and contain exons. Additionally, SNHG RNAs differ from snoRNAs by their subcellular location both in the nucleus and the cytoplasm. Specifically, interactions between SNHG12 and microRNAs have been implicated in several cancer-related cellular processes and signaling pathways. Experimental analysis showed that SNHG12 is an endogenous sponge for microRNAs in cervical, prostate, gastric, breast, lung, colorectal cancer, gliomas, osteosarcoma, hepatocellular, papillary thyroid, and clear cell renal cell carcinomas . TheSNHG12 were described in hepatocellular, papillary thyroid, and cervical carcinomas compared to normal tissues, among other cancer types [SNHG12 expression level with advanced FIGO stage, vascular involvement, and lymph node metastasis in cervical cancers evidenced its potential clinical significance [SNHG12 overexpression predicted poor prognosis in prostate cancer [n = 39) showed higher SNHG12 expression levels compared to normal ovarian samples (n = 5); however, no statistical significance was detected with histopathological and clinical parameters, probably due to the limited number of cases. Sun and Fan evaluated the expression levels of SNHG12 by RT-qPCR in 24 matched OC and adjacent normal tissues and reported that SNHG12 levels were higher for patients with stage III-IV than for those with stage I-II, as well as for metastatic patients [SNHG12 expression. Using the OC dataset from TGCA, we found high SNHG12 expression levels associated with decreased PFS  mRNA. In vitro studies also showed the effect of SNHG12 repression in inhibiting proliferation, migration and invasion, and metastasis in osteosarcomas and gastric cancer [SNHG12 also induced apoptosis and cell cycle arrest, while the opposite effects were observed after the forced expression of SNHG12 [SNHG12 is a downstream target gene of the oncoprotein MYC and its upregulation mediates cell proliferation and migration in triple-negative breast cancer [We used a siRNA strategy to inhibit epressed . The autc cancer . The supf SNHG12 . Similart cancer .SNHG12 mediates the multi-drug chemoresistance is yet unclear. However, it is recognized that the SNHG12 knockdown can reverse the resistance to cisplatin, paclitaxel, and gefitinib in cell lines derived from lung cancer [SNHG12 mediated doxorubicin resistance in osteosarcoma, while its knockdown contributed to the upregulation of miR-320a and improved the sensitivity to this drug [SNHG12 was correlated with the transcription factor SP1, leading to its up-regulation in temozolomide-resistant cell lines [SNHG12 was involved in the regulation of the CDCA3 (cell division cycle associated 3) gene mediated by SP1, promoting resistance to sunitinib [SNHG12 dysregulation is involved in tumorigenesis, impacts diverse cellular processes, and promotes resistance to chemotherapy.The mechanism by which g cancer . SNHG12 his drug . In glioll lines . In renaunitinib . These dSNHG12 was also detected in the nucleus, nucleoplasm, nucleolus, and chromatin [SNHG12 locus and includes six genes mapped upstream  and downstream  [SNHG12 expression correlates with genes mapped within this region in the 111 OC samples. Our RNA-seq data showed that while the SNHG12 was upregulated in carboplatin-resistant OC cell lines, the neighboring genes  were downregulated in those cells , MYC , and STAT2  in OC and predict interactions between SNHG12 and HDACI9 protein by Cross-Linking and ImmunoPrecipitation (CLIP) experiments [HDAC9 (histone deacetylase 9) gene is overexpressed in cancer cells and is a member of a family of enzymes responsible for the deacetylation of lysine residues, a key event in the aberrant epigenetic repression in cancer [SNHG genes, including DANCR (former SNHG13), SNHG14, and SNHG15, act synergistically with EZH2  to inhibit the expression of downstream target genes in endocrine-related cancers [HDAC1 (histone deacetylase 1) and DNA demethylase TET1 (tet methylcytosine dioxygenase 1) were DEGs, and the EZH2 (enhancer of zeste 2 polycomb repressive complex 2 subunit) gene associated with DMPs was also DEG in carboplatin-resistant cell lines. Previous studies reported that EZH2 was generally overexpressed and implicated in the advanced stage, platinum resistance, and poor patient survival in OC [In addition to the cytoplasm, hromatin . Importad GMEB1) . SNHG12 se cells . This fieriments ,85,86. Tn cancer . Other S cancers . Recentl cancers . The genal in OC . Taken tSNHG12 is differentially expressed and methylated in OC cells. SNHG12 modulates carboplatin-response in the cell lines tested, although the mechanism involved in this process should be deeper studied. The plethora of cancer-related features associated with the disrupted expression of SNHG12 can be a result of the complex network interactions of this lncRNA with miRNAs and proteins. While DNA methylation changes explain the aberrant expression of SNHG12 in cancer cells, emerging data indicate that SNHG12 may control the expression of neighboring genes probably by changing epigenetic marks on histones and affecting their transcription.The integrative transcriptomic and epigenomic approach revealed that