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While mutations in the KRAS oncogene are among the most prevalent in human cancer, there are few successful treatments to target these tumors. It is also likely that heterogeneity in KRAS -mutant tumor biology significantly contributes to the response to therapy. We hypothesized that the presence of commonly co-occurring mutations in STK11 and TP53 tumor suppressors may represent a significant source of heterogeneity in KRAS -mutant tumors. To address this, we utilized a large cohort of resected tumors from 442 lung adenocarcinoma patients with data including annotation of prevalent driver mutations ( KRAS and EGFR) and tumor suppressor mutations ( STK11 and TP53 ), microarray-based gene expression and clinical covariates, including overall survival (OS). Specifically, we determined impact of STK11 and TP53 mutations on a new KRAS mutation-associated gene expression signature as well as previously defined signatures of tumor cell proliferation and immune surveillance responses. Interestingly, STK11 , but not TP53 mutations, were associated with highly elevated expression of KRAS mutation-associated genes. Mutations in TP53 and STK11 also impacted tumor biology regardless of KRAS status, with TP53 strongly associated with enhanced proliferation and STK11 with suppression of immune surveillance. These findings illustrate the remarkably distinct ways through which tumor suppressor mutations may contribute to heterogeneity in KRAS -mutant tumor biology. In addition, these studies point to novel associations between gene mutations and immune surveillance that could impact the response to immunotherapy.

Background In recent years, increasing amounts of genomic and clinical cancer data have become publically available through large-scale collaborative projects such as The Cancer Genome Atlas (TCGA). However, as long as these datasets are difficult to access and interpret, they are essentially useless for a major part of the research community and their scientific potential will not be fully realized. To address these issues we developed MEXPRESS, a straightforward and easy-to-use web tool for the integration and visualization of the expression, DNA methylation and clinical TCGA data on a single-gene level ( http://mexpress.be ). Results In comparison to existing tools, MEXPRESS allows researchers to quickly visualize and interpret the different TCGA datasets and their relationships for a single gene, as demonstrated for GSTP1 in prostate adenocarcinoma. We also used MEXPRESS to reveal the differences in the DNA methylation status of the PAM50 marker gene MLPH between the breast cancer subtypes and how these differences were linked to the expression of MPLH . Conclusions We have created a user-friendly tool for the visualization and interpretation of TCGA data, offering clinical researchers a simple way to evaluate the TCGA data for their genes or candidate biomarkers of interest.

Although single base-pair resolution DNA methylation landscapes for embryonic and different somatic cell types provided important insights into epigenetic dynamics and cell-type specificity, such comprehensive profiling is incomplete across human cancer types. This prompted us to perform genome-wide DNA methylation profiling of 22 samples derived from normal tissues and associated neoplasms, including primary tumors and cancer cell lines. Unlike their invariant normal counterparts, cancer samples exhibited highly variable CpG methylation levels in a large proportion of the genome, involving progressive changes during tumor evolution. The whole-genome sequencing results from selected samples were replicated in a large cohort of 1112 primary tumors of various cancer types using genome-scale DNA methylation analysis. Specifically, we determined DNA hypermethylation of promoters and enhancers regulating tumor-suppressor genes, with potential cancer-driving effects. DNA hypermethylation events showed evidence of positive selection, mutual exclusivity and tissue specificity, suggesting their active participation in neoplastic transformation. Our data highlight the extensive changes in DNA methylation that occur in cancer onset, progression and dissemination.

The current histoclinical breast cancer classification is simple but imprecise. Several molecular classifications of breast cancers based on expression profiling have been proposed as alternatives. However, their reliability and clinical utility have been repeatedly questioned, notably because most of them were derived from relatively small initial patient populations. We analyzed the transcriptomes of 537 breast tumors using three unsupervised classification methods. A core subset of 355 tumors was assigned to six clusters by all three methods. These six subgroups overlapped with previously defined molecular classes of breast cancer, but also showed important differences, notably the absence of an ERBB2 subgroup and the division of the large luminal ER+ group into four subgroups, two of them being highly proliferative. Of the six subgroups, four were ER+/PR+/AR+, one was ER−/PR−/AR+ and one was triple negative (AR−/ER−/PR−). ERBB2-amplified tumors were split between the ER−/PR−/AR+ subgroup and the highly proliferative ER+ LumC subgroup. Importantly, each of these six molecular subgroups showed specific copy-number alterations. Gene expression changes were correlated to specific signaling pathways. Each of these six subgroups showed very significant differences in tumor grade, metastatic sites, relapse-free survival or response to chemotherapy. All these findings were validated on large external datasets including more than 3000 tumors. Our data thus indicate that these six molecular subgroups represent well-defined clinico-biological entities of breast cancer. Their identification should facilitate the detection of novel prognostic factors or therapeutical targets in breast cancer.

Pancreatic cancer (PC) is an aggressive disease with a high mortality and poor prognosis. The human microbiome is a key factor in many malignancies, having the ability to alter host metabolism and immune responses and participate in tumorigenesis. Gut microbes have an influence on physiological functions of the healthy pancreas and are themselves controlled by pancreatic secretions. An altered oral microbiota may colonize the pancreas and cause local inflammation by the action of its metabolites, which may lead to carcinogenesis. The mechanisms behind dysbiosis and PC development are not completely clear. Herein, we review the complex interactions between PC tumorigenesis and the microbiota, and especially the question, whether and how an altered microbiota induces oncogenomic changes, or vice versa, whether cancer mutations have an impact on microbiota composition. In addition, the role of the microbiota in drug efficacy in PC chemo- and immunotherapies is discussed. Possible future scenarios are the intentional manipulation of the gut microbiota in combination with therapy or the utilization of microbial profiles for the noninvasive screening and monitoring of PC.

Multiple DNA methylation changes in the cancer methylome are associated with the acquisition of drug resistance; however it remains uncertain how many represent critical DNA methylation drivers of chemoresistance. Using isogenic, cisplatin-sensitive/resistant ovarian cancer cell lines and inducing resensitizaton with demethylating agents, we aimed to identify consistent methylation and expression changes associated with chemoresistance. Using genome-wide DNA methylation profiling across 27 578 CpG sites, we identified loci at 4092 genes becoming hypermethylated in chemoresistant A2780/cp70 compared with the parental-sensitive A2780 cell line. Hypermethylation at gene promoter regions is often associated with transcriptional silencing; however, expression of only 245 of these hypermethylated genes becomes downregulated in A2780/cp70 as measured by microarray expression profiling. Treatment of A2780/cp70 with the demethylating agent 2-deoxy-5′-azacytidine induces resensitization to cisplatin and re-expression of 41 of the downregulated genes. A total of 13/41 genes were consistently hypermethylated in further independent cisplatin-resistant A2780 cell derivatives. CpG sites at 9 of the 13 genes ( ARHGDIB , ARMCX2 , COL1A, FLNA , FLNC , MEST , MLH1 , NTS and PSMB9 ) acquired methylation in ovarian tumours at relapse following chemotherapy or chemoresistant cell lines derived at the time of patient relapse. Furthermore, 5/13 genes ( ARMCX2 , COL1A1 , MDK , MEST and MLH1 ) acquired methylation in drug-resistant ovarian cancer-sustaining (side population) cells. MLH1 has a direct role in conferring cisplatin sensitivity when reintroduced into cells in vitro . This combined genomics approach has identified further potential key drivers of chemoresistance whose expression is silenced by DNA methylation that should be further evaluated as clinical biomarkers of drug resistance.

Computation-based drug-repurposing/repositioning approaches can greatly speed up the traditional drug discovery process. To date, systematic and comprehensive computation-based approaches to identify and validate drug-repositioning candidates for epithelial ovarian cancer (EOC) have not been undertaken. Here, we present a novel drug discovery strategy that combines a computational drug-repositioning system (DrugPredict) with biological testing in cell lines in order to rapidly identify novel drug candidates for EOC. DrugPredict exploited unique repositioning opportunities rendered by a vast amount of disease genomics, phenomics, drug treatment, and genetic pathway and uniquely revealed that non-steroidal anti-inflammatories (NSAIDs) rank just as high as currently used ovarian cancer drugs. As epidemiological studies have reported decreased incidence of ovarian cancer associated with regular intake of NSAIDs, we assessed whether NSAIDs could have chemoadjuvant applications in EOC and found that (i) NSAID Indomethacin induces robust cell death in primary patient-derived platinum-sensitive and platinum- resistant ovarian cancer cells and ovarian cancer stem cells and (ii) downregulation of β-catenin is partially driving effects of Indomethacin in cisplatin-resistant cells. In summary, we demonstrate that DrugPredict represents an innovative computational drug- discovery strategy to uncover drugs that are routinely used for other indications that could be effective in treating various cancers, thus introducing a potentially rapid and cost-effective translational opportunity. As NSAIDs are already in routine use in gynecological treatment regimens and have acceptable safety profile, our results will provide with a rationale for testing NSAIDs as potential chemoadjuvants in EOC patient trials.

Introduction Understanding changes in cell identity in cancer and ageing is of great importance. In this work, we analyzed how gene expression changes in human tissues are associated with tissue specificity during cancer and ageing using transcriptome data from TCGA and GTEx. Results We found significant downregulation of tissue-specific genes during ageing in 40% of the tissues analyzed, which suggests loss of tissue identity with age. For most cancer types, we have noted a consistent pattern of downregulation in genes that are specific to the tissue from which the tumor originated. Moreover, we observed in cancer an activation of genes not usually expressed in the tissue of origin as well as an upregulation of genes specific to other tissues. These patterns in cancer were associated with patient survival. The age of the patient, however, did not influence these patterns. Conclusion We identified loss of cellular identity in 40% of the tissues analysed during human ageing, and a clear pattern in cancer, where during tumorigenesis cells express genes specific to other organs while suppressing the expression of genes from their original tissue. The loss of cellular identity observed in cancer is associated with prognosis and is not influenced by age, suggesting that it is a crucial stage in carcinogenesis.

Herein, we reported mutations in five DNA Damage Repair (DDR) i.e., TP53 , ATR , ATM , CHEK1 and CHEK2 involved in OSCC using NG-WES and their analysis using bioinformatics tools. Out of 42 identified mutations, 16.7% are reported for the 1st time. A total of 28 nonsynonymous SNVs are identified. TP53 harbored the highest number of mutations followed by ATM , ATR , CHEK1 and CHEK2 . Nine mutations ( TP53 p.R43H , TP53 p.L125Q , TP53 p.R116Q , TP53 p.C110Y , TP53 p.L62F , ATR p.H120Y , ATM p.P1054R , ATM p.D1853V , ATM p.T2934N ) were predicted highly pathogenic. SAAFEQ-SEQ predicted destabilizing effects for all mutations, while ISPRED-SEQ identified 09 IS mutations, 07 on TP53 , 01 in ATR and 01 in CHEK1 with no IS mutations predicted for ATM and CHEK2 . Among the IS mutations, only SNVs were used in MDS simulations. The gyration radius for all IS SNVs was larger for mutant as compared to the wild type indicating perturbed folding behavior of the mutant proteins. Structural deviations across the carbon back bone were noted by RMSD for mutant and wild type. The TP53 IS mutations include TP53 p.R116Q , TP53 p.C110Y , TP53 p.R43H , TP53 p.E214X , TP53 p.R210X , TP53 p.C110Afs*5 and TP53 p,S108Ffs*23 whereas ATR and CHEK1 IS mutations consist of ATR p.M1932T and CHEK1 p.E76Kfs*21 . ConSurf analysis revealed four SNVs with a high conservation score (9) on TP53 and ATM. TP53 p.P33R was predominantly associated with moderately differentiated tumors (84.60%), naswar users (86.60%) and positive family history of cancer (91.60%). The TP53 p.P33R , ATR p.M211T and CHEK1 p.I437V mutations were found recurrently in 21/27 (77.7%), 20/27 (74.04%), and 27/27 (100%) patients, suggesting its potential biomarker applications in local screening.

Little is known of the underlying biology of estrogen receptor-negative, progesterone receptor-negative (ER(−)/PR(−)) breast cancer (BC), and few targeted therapies are available. Clinical heterogeneity of ER(−)/PR(−) tumors suggests that molecular subsets exist. We performed genome-wide expression analysis of 99 primary BC samples and eight BC cell lines in an effort to reveal distinct subsets, provide insight into their biology and potentially identify new therapeutic targets. We identified a subset of ER(−)/PR(−) tumors with paradoxical expression of genes known to be either direct targets of ER, responsive to estrogen, or typically expressed in ER(+) BC. Differentially expressed genes included SPDEF, FOXA1, XBP1, CYB5, TFF3, NAT1, APOD, ALCAM and AR ( P <0.001). A classification model based on the expression signature of this tumor class identified molecularly similar BCs in an independent human BC data set and among BC cell lines (MDA-MB-453). This cell line demonstrated a proliferative response to androgen in an androgen receptor-dependent and ER-independent manner. In addition, the androgen-induced transcriptional program of MDA-MB-453 significantly overlapped the molecular signature of the unique ER(−)/PR(−) subclass of human tumors. This subset of BCs, characterized by a hormonally regulated transcriptional program and response to androgen, suggests the potential for therapeutic strategies targeting the androgen signaling pathway.