A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers

Pan-cancer characterization of immune-related lncRNAs identifies potential oncogenic biomarkers

Long noncoding RNAs (lncRNAs) are emerging as critical regulators of gene expression and they play fundamental roles in immune regulation. Here we introduce an integrated algorithm, ImmLnc, for identifying lncRNA regulators of immune-related pathways. We comprehensively chart the landscape of lncRNA regulation in the immunome across 33 cancer types and show that cancers with similar tissue origin are likely to share lncRNA immune regulators. Moreover, the immune-related lncRNAs are likely to show expression perturbation in cancer and are significantly correlated with immune cell infiltration. ImmLnc can help prioritize cancer-related lncRNAs and further identify three molecular subtypes (proliferative, intermediate, and immunological) of non-small cell lung cancer. These subtypes are characterized by differences in mutation burden, immune cell infiltration, expression of immunomodulatory genes, response to chemotherapy, and prognosis. In summary, the ImmLnc pipeline and the resulting data serve as a valuable resource for understanding lncRNA function and to advance identification of immunotherapy targets.

PIK3R1W624R Is an Actionable Mutation in High Grade Serous Ovarian Carcinoma

Identifying cancer drivers and actionable mutations is critical for precision oncology. In epithelial ovarian cancer (EOC) the majority of mutations lack biological or clinical validation. We fully characterized 43 lines of Patient-Derived Xenografts (PDXs) and performed copy number analysis and whole exome sequencing of 12 lines derived from naïve, high grade EOCs. Pyrosequencing allowed quantifying mutations in the source tumours. Drug response was assayed on PDX Derived Tumour Cells (PDTCs) and in vivo on PDXs. We identified a PIK3R1W624R variant in PDXs from a high grade serous EOC. Allele frequencies of PIK3R1W624R in all the passaged PDXs and in samples of the source tumour suggested that it was truncal and thus possibly a driver mutation. After inconclusive results in silico analyses, PDTCs and PDXs allowed the showing actionability of PIK3R1W624R and addiction of PIK3R1W624R carrying cells to inhibitors of the PI3K/AKT/mTOR pathway. It is noteworthy that PIK3R1 encodes the p85α regulatory subunit of PI3K, that is very rarely mutated in EOC. The PIK3R1W624R mutation is located in the cSH2 domain of the p85α that has never been involved in oncogenesis. These data show that patient-derived models are irreplaceable in their role of unveiling unpredicted driver and actionable variants in advanced ovarian cancer.

Mitogen-Activated Protein Kinase (MAPK) and Obesity-Related Cancer

Obesity is a major public health concern worldwide. The increased risk of certain types of cancer is now an established deleterious consequence of obesity, although the molecular mechanisms of this are not completely understood. In this review, we aim to explore the links between MAPK signalling and obesity-related cancer. We focus mostly on p38 and JNK MAPK, as the role of ERK remains unclear. These links are seen through the implication of MAPK in obesity-related immune paralysis as well as through effects on the endoplasmic reticulum stress response and activation of aromatase. By way of example, we highlight areas of interest and possibilities for future research in endometrioid endometrial cancer and hepatocellular carcinoma associated with non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH) and MAPK.

Long noncoding RNA DRAIC acts as a microRNA-122 sponge to facilitate nasopharyngeal carcinoma cell proliferation, migration and invasion via regulating SATB1

Increasing evidences have revealed that long noncoding RNAs (lncRNAs) are frequently involved in various cancers. However, the expression and function of lncRNA DRAIC in nasopharyngeal carcinoma (NPC) remain unknown. In this study, we found that DRAIC was significantly increased in NPC tissues. Increased expression of DRAIC was positively correlated with advanced clinical stages of NPC patients. Functional assays revealed that ectopic expression of DRAIC enhances NPC cell growth, migration and invasion. DRAIC knockdown represses NPC cell growth, migration and invasion. Mechanistically, we identified two miR-122 binding sites on DRAIC. RNA pull-down, RNA immunoprecipitation, and dual-luciferase reporter assays confirmed the binding of DRAIC to miR-122. Via binding of miR-122, DRAIC upregulated the expression of miR-122 target SATB1, which was abolished by the mutation of miR-122 binding sites on SATB1. Moreover, the oncogenic roles of DRAIC on NPC were reversed by the mutation of miR-122 binding sites on SATB1, simultaneous overexpression of miR-122, or depletion of SATB1. In addition, the expression of SATB1 was also increased and positively associated with that of DRAIC in NPC tissues. In conclusion, these findings revealed the important roles of DRAIC-miR-122-SATB1 axis in NPC and suggested that DRAIC may be a potential therapeutic target for NPC.

NExUS: Bayesian simultaneous network estimation across unequal sample sizes

MOTIVATION

Network-based analyses of high-throughput genomics data provide a holistic, systems-level understanding of various biological mechanisms for a common population. However, when estimating multiple networks across heterogeneous sub-populations, varying sample sizes pose a challenge in the estimation and inference, as network differences may be driven by differences in power. We are particularly interested in addressing this challenge in the context of proteomic networks for related cancers, as the number of subjects available for rare cancer (sub-)types is often limited.

RESULTS

We develop NExUS (Network Estimation across Unequal Sample sizes), a Bayesian method that enables joint learning of multiple networks while avoiding artefactual relationship between sample size and network sparsity. We demonstrate through simulations that NExUS outperforms existing network estimation methods in this context, and apply it to learn network similarity and shared pathway activity for groups of cancers with related origins represented in The Cancer Genome Atlas (TCGA) proteomic data.

AVAILABILITY AND IMPLEMENTATION

The NExUS source code is freely available for download at https://github.com/priyamdas2/NExUS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Long Non-coding RNAs: Emerging Roles in the Immunosuppressive Tumor Microenvironment

Tumor immunosuppression may assist the immune escape of cancer cells, which promotes tumor metastasis and resistance to chemo-radiotherapy. The therapeutic strategies against tumor immunosuppression mainly focus on blocking immune checkpoint receptors, enhancing T-cell recognition and neutralizing inhibitory molecules. Although immunotherapies based on these strategies have improved the clinical outcomes, immunological nonresponse and resistance are two barriers to tumor eradication. Therefore, there is an urgent need to identify new biomarkers for patient selection and therapeutic targets for the development of combination regimen with immunotherapy. Recent studies have reported that non-protein-coding modulators exhibit important functions in post-transcriptional gene regulation, which subsequently modulates multiple pathophysiological processes, including neoplastic transformation. Differentiated from microRNAs, long non-coding RNAs (lncRNAs) are reported to be involved in various processes of the immune response in the tumor microenvironment (TME) to promote tumor immunosuppression. Currently, studies on tumor immunity regulated by lncRNAs are mainly confined to certain types of cancer cells or stromal cells. Additionally, the majority of studies are focused on the events involved in T cells and myeloid-derived suppressor cells (MDSCs). Although the reported studies have indicated the significance of lncRNAs in immunotherapy, the lack of comprehensive studies prevents us from exploring useful lncRNAs. In the current review, we have summarized the roles of lncRNAs in tumor immune response, and highlighted major lncRNAs as potential biomarkers or therapeutic targets for clinical application of immunotherapy.

Investigating Chaperonin-Containing TCP-1 subunit 2 as an essential component of the chaperonin complex for tumorigenesis

Chaperonin-containing TCP-1 (CCT or TRiC) is a multi-subunit complex that folds many of the proteins essential for cancer development. CCT is expressed in diverse cancers and could be an ideal therapeutic target if not for the fact that the complex is encoded by eight distinct genes, complicating the development of inhibitors. Few definitive studies addressed the role of specific subunits in promoting the chaperonin’s function in cancer. To this end, we investigated the activity of CCT2 (CCTβ) by overexpressing or depleting the subunit in breast epithelial and breast cancer cells. We found that increasing total CCT2 in cells by 1.3-1.8-fold using a lentiviral system, also caused CCT3, CCT4, and CCT5 levels to increase. Likewise, silencing cct2 gene expression by ~50% caused other CCT subunits to decrease. Cells expressing CCT2 were more invasive and had a higher proliferative index. CCT2 depletion in a syngeneic murine model of triple negative breast cancer (TNBC) prevented tumor growth. These results indicate that the CCT2 subunit is integral to the activity of the chaperonin and is needed for tumorigenesis. Hence CCT2 could be a viable target for therapeutic development in breast and other cancers.

Molecular profiling of gynecologic cancers for treatment and management of disease - demonstrating clinical significance using the AMP/ASCO/CAP guidelines for interpretation and reporting of somatic variants

Molecular features of gynecologic cancers have been investigated in comprehensive studies, but correlation of these molecular signatures with clinical significance for precision medicine is yet to be established. Towards this end, we evaluated 95 gynecologic cancer cases submitted for testing using The JAX ActionSeq™ NGS panel. Molecular profiles were studied and compared to TCGA datasets to identify similarities and distinguishing features among subtypes. We identified 146 unique clinically significant variants (Tier I and II) across 45 of the 212 genes (21%), in 87% (83/95) of cases. TP53, PTEN, ARID1A, PIK3CA and ATM were the most commonly mutated genes; CCNE1 and ERBB2 amplifications were the most frequently detected copy-number alterations. PARP inhibitors were among the most commonly reported drug class with clinical trials, consistent with the frequency of DNA damage-response pathway mutations in our cohort. Overall, our study provides additional insight into the molecular profiles of gynecologic cancers, highlighting regulatory pathways involved, raising the potential implications for targeted therapeutic options currently available.

A multiomics comparison between endometrial cancer and serous ovarian cancer

BACKGROUND

Endometrial carcinoma (EC) and serous ovarian carcinoma (OvCa) are both among the common cancer types in women. EC can be divided into two subtypes, endometroid EC and serous-like EC, with distinct histological characterizations and molecular phenotypes. There is an increasing awareness that serous-like EC resembles serous OvCa in genetic landscape, but a clear relationship between them is still lacking.

METHODS

Here, we took advantage of the large-scale molecular profiling of The Cancer Genome Atlas(TCGA) to compare the two EC subtypes and serous OvCa. We used bioinformatics data analytic methods to systematically examine the somatic mutation (SM) and copy number alteration (SCNA), gene expression, pathway activities, survival gene signatures and immune infiltration. Based on these quantifiable molecular characterizations, we asked whether serous-like EC should be grouped more closely to serous OvCa, based on the context of being serous-like; or if should be grouped more closely to endometroid EC, based on the same organ origin.

RESULTS

We found that although serous-like EC and serous OvCa share some common genotypes, including mutation and copy number alteration, they differ in molecular phenotypes such as gene expression and signaling pathway activity. Moreover, no shared prognostic gene signature was found, indicating that they use unique genes governing tumor progression. Finally, although the endometrioid EC and serous OvCa are both highly immune infiltrated, the immune cell composition in serous OvCa is mostly immune suppressive, whereas endometrioid EC has a higher level of cytotoxic immune cells. Overall, our genetic aberration and molecular phenotype characterizations indicated that serous-like EC and serous OvCa cannot be simply treated as a simple "serous" cancer type. In particular, additional attention should be paid to their unique gene activities and tumor microenvironments for novel targeted therapy development.

ResMarkerDB: a database of biomarkers of response to antibody therapy in breast and colorectal cancer

The clinical efficacy of therapeutic monoclonal antibodies for breast and colorectal cancer has greatly contributed to the improvement of patients' outcomes by individualizing their treatments according to their genomic background. However, primary or acquired resistance to treatment reduces its efficacy. In this context, the identification of biomarkers predictive of drug response would support research and development of new alternative treatments. Biomarkers play a major role in the genomic revolution, supporting disease diagnosis and treatment decision-making. Currently, several molecular biomarkers of treatment response for breast and colorectal cancer have been described. However, information on these biomarkers is scattered across several resources, and needs to be identified, collected and properly integrated to be fully exploited to inform monitoring of drug response in patients. Therefore, there is a need of resources that offer biomarker data in a harmonized manner to the user to support the identification of actionable biomarkers of response to treatment in cancer. ResMarkerDB was developed as a comprehensive resource of biomarkers of drug response in colorectal and breast cancer. It integrates data of biomarkers of drug response from existing repositories, and new data extracted and curated from the literature (referred as ResCur). ResMarkerDB currently features 266 biomarkers of diverse nature. Twenty-five percent of these biomarkers are exclusive of ResMarkerDB. Furthermore, ResMarkerDB is one of the few resources offering non-coding DNA data in response to drug treatment. The database contains more than 500 biomarker-drug-tumour associations, covering more than 100 genes. ResMarkerDB provides a web interface to facilitate the exploration of the current knowledge of biomarkers of response in breast and colorectal cancer. It aims to enhance translational research efforts in identifying actionable biomarkers of drug response in cancer.

Interpreting pathways to discover cancer driver genes with Moonlight

Cancer driver gene alterations influence cancer development, occurring in oncogenes, tumor suppressors, and dual role genes. Discovering dual role cancer genes is difficult because of their elusive context-dependent behavior. We define oncogenic mediators as genes controlling biological processes. With them, we classify cancer driver genes, unveiling their roles in cancer mechanisms. To this end, we present Moonlight, a tool that incorporates multiple -omics data to identify critical cancer driver genes. With Moonlight, we analyze 8000+ tumor samples from 18 cancer types, discovering 3310 oncogenic mediators, 151 having dual roles. By incorporating additional data (amplification, mutation, DNA methylation, chromatin accessibility), we reveal 1000+ cancer driver genes, corroborating known molecular mechanisms. Additionally, we confirm critical cancer driver genes by analysing cell-line datasets. We discover inactivation of tumor suppressors in intron regions and that tissue type and subtype indicate dual role status. These findings help explain tumor heterogeneity and could guide therapeutic decisions.

High-resolution copy number analysis of clear cell endometrial carcinoma

Uterine cancer is the 6th leading cause of cancer death amongst American women. Most uterine cancers are endometrial carcinomas (ECs), which are classified into histological subtypes including endometrioid, serous, and clear cell ECs. Somatic copy number alterations (SCNAs) are frequent in serous EC, infrequent in endometrioid ECs, and poorly defined in clear cell ECs. The purpose of this study was to evaluate the occurrence of SCNAs in clinically diagnosed clear cell ECs. Paired tumor-normal DNAs for 51 ECs were hybridized to Illumina Infinium HumanHap650Y or Human660W-Quad Beadchips. Copy number calls were made using the Hidden Markov Model based SNP-FASST2 segmentation algorithm within Nexus Copy Number software (v.6.1). High-level SCNAs were defined as gain of ≥5 copies or homozygous deletion, both <10Mb. GISTIC 1.0, in Nexus, was used to identify statistically significant SCNAs, corrected for multiple testing. One or more high-level SCNAs were detected in 50% of 6 clear cell ECs, 78.6% of 28 serous ECs, and 17.6% of 17 endometrioid ECs. A positive association was found between high-level SCNAs and TP53 mutation across ECs (two-tailed p value<0.0001). Classifying tumors according to POLE, MSI, and TP53 status yielded four molecular subgroups; copy number altered tumors were more frequent in the TP53-mutated subgroup (95.8%) than in the unspecified subgroup (22.2%), and absent from the POLE and MSI subgroups. In conclusion, our study provides evidence of inter-tumor heterogeneity in the extent to which SCNAs occur in clinically diagnosed clear cell EC, and across molecular subgroups of EC. The co-occurrence of high-level SCNAs and TP53 mutations in some clear cell ECs is consistent with the view that a subset of clinically diagnosed clear cell ECs have molecular similarities to serous ECs.

BRD4 modulates vulnerability of triple-negative breast cancer to targeting of integrin-dependent signaling pathways

PURPOSE

Stemming from a myriad of genetic and epigenetic alterations, triple-negative breast cancer (TNBC) is tied to poor clinical outcomes and aspires for individualized therapies. Here we investigated the therapeutic potential of co-inhibiting integrin-dependent signaling pathway and BRD4, a transcriptional and epigenetic mediator, for TNBC.

METHODS

Two independent patient cohorts were subjected to bioinformatic and IHC examination for clinical association of candidate cancer drivers. The efficacy and biological bases for co-targeting these drivers were interrogated using cancer cell lines, a protein kinase array, chemical inhibitors, RNAi/CRISPR/Cas9 approaches, and a 4 T1-Balb/c xenograft model.

RESULTS

We found that amplification of the chromosome 8q24 region occurred in nearly 20% of TNBC tumors, and that it coincided with co-upregulation or amplification of c-Myc and FAK, a key effector of integrin-dependent signaling. This co-upregulation at the mRNA or protein level correlated with a poor patient survival (p < 0.0109 or p < 0.0402, respectively). Furthermore, we found that 14 TNBC cell lines exhibited high vulnerabilities to the combination of JQ1 and VS-6063, potent pharmacological antagonists of the BRD4/c-Myc and integrin/FAK-dependent pathways, respectively. We also observed a cooperative inhibitory effect of JQ1 and VS-6063 on tumor growth and infiltration of Ly6G⁺ myeloid-derived suppressor cells in vivo. Finally, we found that JQ1 and VS-6063 cooperatively induced apoptotic cell death by altering XIAP, Bcl2/Bcl-xl and Bim levels, impairing c-Src/p130Cas-, PI3K/Akt- and RelA-associated signaling, and were linked to EMT-inducing transcription factor Snail- and Slug-dependent regulation.

CONCLUSION

Based on our results, we conclude that the BRD4/c-Myc- and integrin/FAK-dependent pathways act in concert to promote breast cancer cell survival and poor clinical outcomes. As such, they represent promising targets for a synthetic lethal-type of therapy against TNBC.

Long noncoding RNA LINC00662 promotes M2 macrophage polarization and hepatocellular carcinoma progression via activating Wnt/β-catenin signaling

Tumor-associated macrophages have important roles in hepatocellular carcinoma (HCC) initiation and progression. Long noncoding RNAs (lncRNAs) have also been reported to be involved in HCC. In this study, we explored how lncRNA LINC00662 may influence HCC progression through both tumor cell-dependent and macrophage-dependent mechanisms. LINC00662 was found to be upregulated in HCC, and high LINC00662 levels correlated with poor survival of HCC patients. LINC00662 upregulated WNT3A expression and secretion via competitively binding miR-15a, miR-16, and miR-107. Through inducing WNT3A secretion, LINC00662 activated Wnt/β-catenin signaling in HCC cells in an autocrine manner and further promoted HCC cell proliferation, cell cycle, and tumor cell invasion, while repressing HCC cell apoptosis. In addition, acting through WNT3A secretion, LINC00662 activated Wnt/β-catenin signaling in macrophages in a paracrine manner and further promoted M2 macrophage polarization. Via activating Wnt/β-catenin signaling and M2 macrophages polarization, LINC00662 significantly promoted HCC tumor growth and metastasis in vivo. Hence, targeting LINC00662 may provide novel therapeutic strategy against HCC.

A deep neural network approach to predicting clinical outcomes of neuroblastoma patients

BACKGROUND

The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also important to better understand the biological mechanisms underlying disease etiology and development, as well as treatment responses. Recently, different predictive models, relying on distinct algorithms (including Support Vector Machines and Random Forests) have been investigated. In this context, deep learning strategies are of special interest due to their demonstrated superior performance over a wide range of problems and datasets. One of the main challenges of such strategies is the "small n large p" problem. Indeed, omics datasets typically consist of small numbers of samples and large numbers of features relative to typical deep learning datasets. Neural networks usually tackle this problem through feature selection or by including additional constraints during the learning process.

METHODS

We propose to tackle this problem with a novel strategy that relies on a graph-based method for feature extraction, coupled with a deep neural network for clinical outcome prediction. The omics data are first represented as graphs whose nodes represent patients, and edges represent correlations between the patients' omics profiles. Topological features, such as centralities, are then extracted from these graphs for every node. Lastly, these features are used as input to train and test various classifiers.

RESULTS

We apply this strategy to four neuroblastoma datasets and observe that models based on neural networks are more accurate than state of the art models (DNN: 85%-87%, SVM/RF: 75%-82%). We explore how different parameters and configurations are selected in order to overcome the effects of the small data problem as well as the curse of dimensionality.

CONCLUSIONS

Our results indicate that the deep neural networks capture complex features in the data that help predicting patient clinical outcomes.

Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics

The Dirichlet Process (DP) mixture model has become a popular choice for model-based clustering, largely because it allows the number of clusters to be inferred. The sequential updating and greedy search (SUGS) algorithm (Wang & Dunson, 2011) was proposed as a fast method for performing approximate Bayesian inference in DP mixture models, by posing clustering as a Bayesian model selection (BMS) problem and avoiding the use of computationally costly Markov chain Monte Carlo methods. Here we consider how this approach may be extended to permit variable selection for clustering, and also demonstrate the benefits of Bayesian model averaging (BMA) in place of BMS. Through an array of simulation examples and well-studied examples from cancer transcriptomics, we show that our method performs competitively with the current state-of-the-art, while also offering computational benefits. We apply our approach to reverse-phase protein array (RPPA) data from The Cancer Genome Atlas (TCGA) in order to perform a pan-cancer proteomic characterisation of 5157 tumour samples. We have implemented our approach, together with the original SUGS algorithm, in an open-source R package named sugsvarsel, which accelerates analysis by performing intensive computations in C++ and provides automated parallel processing. The R package is freely available from: https://github.com/ococrook/sugsvarsel.

Multimodal Long Noncoding RNA Interaction Networks: Control Panels for Cell Fate Specification

Lineage specification in early development is the basis for the exquisitely precise body plan of multicellular organisms. It is therefore critical to understand cell fate decisions in early development. Moreover, for regenerative medicine, the accurate specification of cell types to replace damaged/diseased tissue is strongly dependent on identifying determinants of cell identity. Long noncoding RNAs (lncRNAs) have been shown to regulate cellular plasticity, including pluripotency establishment and maintenance, differentiation and development, yet broad phenotypic analysis and the mechanistic basis of their function remains lacking. As components of molecular condensates, lncRNAs interact with almost all classes of cellular biomolecules, including proteins, DNA, mRNAs, and microRNAs. With functions ranging from controlling alternative splicing of mRNAs, to providing scaffolding upon which chromatin modifiers are assembled, it is clear that at least a subset of lncRNAs are far from the transcriptional noise they were once deemed. This review highlights the diversity of lncRNA interactions in the context of cell fate specification, and provides examples of each type of interaction in relevant developmental contexts. Also highlighted are experimental and computational approaches to study lncRNAs.

Comprehensive analysis of the regulatory network of differentially expressed mRNAs, lncRNAs and circRNAs in gastric cancer

Gastric cancer (GC) is one of the most common types of human cancers. However, the mechanisms underlying GC remained largely unclear. To determine whether the differentially expressed mRNAs, lncRNAs and circRNAs in GC, we screened conducted SBC-ceRNA microarray analysis in 3 pairs of GC and normal tissues. Furthermore, differentially expressed mRNAs mediated protein protein interaction (PPI) networks, lncRNAs mediated cis-regulatory network, and circRNA mediated ceRNA network were for the first time constructed to reveal their potential functions and mechanisms in GC. Quantitative real-time polymerase chain reaction analysis (qRT-PCR) was conducted to validate the microarray analysis. A total of 922 mRNAs, 2112 lncRNAs and 2896 circRNAs were observed to be dysregulated in GC samples. Bioinformatics analysis showed these differentially expressed genes (DEGs) were significantly associated with regulating branched - chain amino acid catabolic process, Glycolysis/Gluconeogenesis and ARF protein signal transduction. Moreover, we found the dysregulation of key mRNAs and lncRNAs were associated with the overall survival time in GC patients. We believe this study provides useful information for understanding the mechanism underlying GC progression and exploring potential therapeutic and prognostic targets for GC.

A stroma-corrected ZEB1 transcriptional signature is inversely associated with antitumor immune activity in breast cancer

The epithelial-to-mesenchymal transition (EMT) is an essential developmental process which can be hijacked by cancer cells, leading to enhanced metastasis and chemoresistance in experimental models. Recent studies have linked gene expression of EMT-associated gene signatures to increased inflammatory immune response in multiple cancer types. However, these studies did not account for the potential confounding effects of gene expression by tumor-infiltrating mesenchymal stromal cells. In this study, we comprehensively dissect the associations between multiple EMT transcription factors and EMT markers with stromal and immune tumor infiltration. We find that EMT-related genes are highly correlated with intratumoral stromal cell abundance and identify a specific relationship between stroma-corrected ZEB1 expression and decreased immune activity in multiple cancer types. We derive a stroma-corrected ZEB1-activated transcriptional signature and demonstrate that this signature includes several known inhibitors of inflammation, including BMPR2. Finally, multivariate survival analysis reveals that ZEB1 and its expression signature are significantly associated with reduced overall survival in breast cancer patients. In conclusion, this study identifies a novel association between stroma-adjusted ZEB1 expression and tumor immune activity and addresses the critical issue of confounding between EMT-associated genes and tumor stromal content.

Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

Background

Gynecologic malignancy is one of the leading causes of mortality in female adults worldwide. Comprehensive genomic analysis has revealed a list of molecular aberrations that are essential to tumorigenesis, progression, and metastasis of gynecologic tumors. However, targeting such alterations has frequently led to treatment failures due to underlying genomic complexity and simultaneous activation of various tumor cell survival pathway molecules. A compilation of molecular characterization of tumors with pharmacological drug response is the next step toward clinical application of patient-tailored treatment regimens.

Results

Toward this goal, we establish a library of 139 gynecologic tumors including epithelial ovarian cancers (EOCs), cervical, endometrial tumors, and uterine sarcomas that are genomically and/or pharmacologically annotated and explore dynamic pharmacogenomic associations against 37 molecularly targeted drugs. We discover lineage-specific drug sensitivities based on subcategorization of gynecologic tumors and identify TP53 mutation as a molecular determinant that elicits therapeutic response to poly (ADP-Ribose) polymerase (PARP) inhibitor. We further identify transcriptome expression of inhibitor of DNA biding 2 (ID2) as a potential predictive biomarker for treatment response to olaparib.

Conclusions

Together, our results demonstrate the potential utility of rapid drug screening combined with genomic profiling for precision treatment of gynecologic cancers.

Expression profile analysis identifies IER3 to predict overall survival and promote lymph node metastasis in tongue cancer

Background

Lymph node metastasis is one of the most important factors affecting the prognosis of tongue cancer, and the molecular mechanism regulating lymph node metastasis of tongue cancer is poorly known.

Methods

The gene expression dataset GSE2280 and The Cancer Genome Atlas (TCGA) tongue cancer dataset were downloaded. R software was used to identify the differentially expressed hallmark gene sets and individual genes between metastatic lymph node tissues and primary tongue cancer tissues, and the Kaplan-Meier method was used to evaluate the association with overall survival. The screening and validation of functional genes was performed using western blot, q-PCR, CCK-8, migration and invasion assays, and lymphangiogenesis was examined by using a tube formation assay.

Results

Thirteen common hallmark gene sets were found based on Gene Set Variation Analysis (GSVA) and then subjected to differential gene expression analysis, by which 76 deregulated genes were found. Gene coexpression network analysis and survival analysis further confirmed that IER3 was the key gene associated with the prognosis and lymph node metastasis of tongue cancer patients. Knockdown of IER3 with siRNA inhibited the proliferation, colony formation, migration and invasion of Tca-8113 cells in vitro and it also inhibited the secretion and expression of VEGF-C in these cells. The culture supernatant of Tca-8113 cells could promote lymphangiogenesis and migration of lymphatic endothelial cells, and knockdown of IER3 in Tca-8113 cells suppressed these processes.

Conclusion

Our study demonstrated that IER3 plays important roles in lymphangiogenesis regulation and prognosis in tongue cancer and might be a potential therapeutic target.

Molecular Profiles of Matched Primary and Metastatic Tumor Samples Support a Linear Evolutionary Model of Breast Cancer

The interpretation of accumulating genomic data with respect to tumor evolution and cancer progression requires integrated models. We developed a computational approach that enables the construction of disease progression models using static sample data. Application to breast cancer data revealed a linear, branching evolutionary model with two distinct trajectories for malignant progression. Here, we used the progression model as a foundation to investigate the relationships between matched primary and metastasis breast tumor samples. Mapping paired data onto the model confirmed that molecular breast cancer subtypes can shift during progression and supported directional tumor evolution through luminal subtypes to increasingly malignant states. Cancer progression modeling through the analysis of available static samples represents a promising breakthrough. Further refinement of a roadmap of breast cancer progression will facilitate the development of improved cancer diagnostics, prognostics, and targeted therapeutics. SIGNIFICANCE: Analysis of matched primary and metastatic tumor samples supports a unidirectional, linear cancer evolution process and sheds light on longstanding issues regarding the origins of molecular subtypes and their progression relationships.

Multi-omics analysis reveals epithelial-mesenchymal transition-related gene FOXM1 as a novel prognostic biomarker in clear cell renal carcinoma

Identification of novel clinical biomarker in clear cell renal carcinoma (ccRCC) is warranted. Integrating transcriptome (n=1669), DNA methylation (n=577) and copy number data (n=832), we developed a method to identify driver biomarkers by analyzing the omics-level dynamics of Epithelial-Mesenchymal Transition (EMT)-related genes in ccRCC. We first identified 504 expression dynamic changed genes involved in ccRCC-associated key pathways such as EMT, cell cycle, EGFR and PI3K/AKT signaling. Further analysis identified 229 (90 gene promoters) aberrant expression quantitative trait methylation (eQTM) and 256 genes with expression quantitative trait copy number (eQTCN) alterations. Among them, FOXM1 was affected by both eQTM and eQTCN. FOXM1 copy number amplification (115/500, 23% of patients), occurred in an amplified peak in chromosome 12q13.3, was enriched in late-stage ccRCC samples and was associated with worse survival. FOXM1-overexpressed pT3 patients with distant metastasis showed ~25% shorter overall survival in both training (log-rank P=0.006) and validation (log-rank P=0.018) cohorts. The eQTM-gene hybrid signature (cg00044170 and FOXM1), superior to either gene expression or DNA methylation alone, showed great potential in diagnosing localized ccRCC in training (area under curve = 0.958) and validation datasets. FOXM1 could be a novel prognostic biomarker and shed light for early diagnosis at molecular level in ccRCC.

Long Non-coding RNA LINC01787 Drives Breast Cancer Progression via Disrupting miR-125b Generation

Breast cancer is still the most common and leading cause of cancer-related deaths in women worldwide. Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) have shown key regulator roles in various cancers. Previous reports have identified miR-125b as a critical tumor suppressor in breast cancer. However, the role of lncRNAs in breast cancer is far from well-characterized. In this study, we identified a novel lncRNA LINC01787, which specifically binds pre-miR-125b, inhibits the binding between DICER and pre-miR-125b, represses the processing of pre-miR-125b by DICER, and therefore induces pre-miR-125b accumulation and represses mature miR-125b generation. Functional assays showed that LINC01787 promotes breast cancer cell proliferation and migration and breast cancer xenograft growth in vivo, which is abolished by the mutation of pre-miR-125b binding sites on LINC01787 or overexpression of miR-125b. Furthermore, LINC01787 is up-regulated in breast cancer tissues and is associated with advanced stages and poor survival. The expression of LINC01787 is inversely associated with that of miR-125b in breast cancer tissues. In conclusion, our findings identified a novel up-regulated and oncogenic lncRNA LINC01787 in breast cancer, which binds pre-miR-125b and represses mature miR-125b generation. Our data suggests LINC01787 as a potential prognostic biomarker and therapeutic target for breast cancer.

Deconvolution of the Genomic and Epigenomic Interaction Landscape of Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most aggressive form of breast cancer. Emerging evidenced suggests that both genetics and epigenetic factors play a role in the pathogenesis of TNBC. However, oncogenic interactions and cooperation between genomic and epigenomic variation have not been characterized. The objective of this study was to deconvolute the genomic and epigenomic interaction landscape in TNBC using an integrative genomics approach, which integrates information on germline, somatic, epigenomic and gene expression variation. We hypothesized that TNBC originates from a complex interplay between genomic (both germline and somatic variation) and epigenomic variation. We further hypothesized that these complex arrays of interacting genomic and epigenomic factors affect entire molecular networks and signaling pathways which, in turn, drive TNBC. We addressed these hypotheses using germline variation from genome-wide association studies and somatic, epigenomic and gene expression variation from The Cancer Genome Atlas (TCGA). The investigation revealed signatures of functionally related genes containing germline, somatic and epigenetic variations. DNA methylation had an effect on gene expression. Network and pathway analysis revealed molecule networks and signaling pathways enriched for germline, somatic and epigenomic variation, among them: Role of BRCA1 in DNA Damage Response, Hereditary Breast Cancer Signaling, Molecular Mechanisms of Cancer, Estrogen-Dependent Breast Cancer, p53, MYC Mediated Apoptosis, and PTEN Signaling pathways. The investigation revealed that integrative genomics is a powerful approach for deconvoluting the genomic-epigenomic interaction landscape in TNBC. Further studies are needed to understand the biological mechanisms underlying oncogenic interactions between genomic and epigenomic factors in TNBC.

Identification of driver genes regulating immune cell infiltration in cervical cancer by multiple omics integration

Cervical cancer (CC) is one of the most common cancers in women. However, copy number alteration (CNA)-driven dysregulated genes and their functions in CC are still rarely investigated. In the present study, we conducted integrative analysis of CNA and gene expression data from The Cancer Genome Atlas (TCGA) cervical cancer to identify dysregulated genes triggered by CNAs. The integration of copy number status and RNA expression revealed 763 amplified and 1,391 deleted genes significantly dysregulated by the CNAs (P-value < 1e-8). Among these CNA genes, five driver genes, including PI3KCA, PI3KCB, DVL3, WWTR1, and ERBB2, exhibited a strong association with immune cell infiltration, suggesting that the pathways that they participate in may be involved in regulating immune cell infiltration. Moreover, we also observed that the genes of immunotherapeutic targets were abundantly expressed in the wild-type samples, suggesting that immunotherapy based on these immunotherapeutic targets may be applied to wild-type samples. In addition, the two CNA driver genes, DVL3 and ERBB2, might be sensitive and resistant biomarkers for examining the tumor's response to chemoradiotherapy, respectively. Particularly, the expression of ERBB2 was also observed to be higher in responders of chemotherapy than non-responders. Furthermore, a subset of CNA genes was identified to predict the prognosis of cervical cancer. In summary, our systematic data analysis of these CNA genes not only improved our understanding of the veiled mechanism behind immune cell infiltration, but also provided the potential clinical application of these CNA genes in cervical cancer.

The Many Faces of Gene Regulation in Cancer: A Computational Oncogenomics Outlook

Cancer is a complex disease at many different levels. The molecular phenomenology of cancer is also quite rich. The mutational and genomic origins of cancer and their downstream effects on processes such as the reprogramming of the gene regulatory control and the molecular pathways depending on such control have been recognized as central to the characterization of the disease. More important though is the understanding of their causes, prognosis, and therapeutics. There is a multitude of factors associated with anomalous control of gene expression in cancer. Many of these factors are now amenable to be studied comprehensively by means of experiments based on diverse omic technologies. However, characterizing each dimension of the phenomenon individually has proven to fall short in presenting a clear picture of expression regulation as a whole. In this review article, we discuss some of the more relevant factors affecting gene expression control both, under normal conditions and in tumor settings. We describe the different omic approaches that we can use as well as the computational genomic analysis needed to track down these factors. Then we present theoretical and computational frameworks developed to integrate the amount of diverse information provided by such single-omic analyses. We contextualize this within a systems biology-based multi-omic regulation setting, aimed at better understanding the complex interplay of gene expression deregulation in cancer.

Detecting TP53 mutations in diagnostic and archival liquid-based Pap samples from ovarian cancer patients using an ultra-sensitive ddPCR method

High-grade serous ovarian cancer (HGSOC) is the most common subtype of epithelial ovarian cancer and early detection is challenging. TP53 mutations are a hallmark of HGSOC and detection of these mutations in liquid-based Pap samples could provide a method for early diagnosis. Here we evaluate the use of IBSAFE, an ultra-sensitive droplet digital PCR (ddPCR) method, for detecting TP53 mutations in liquid-based Pap samples collected from fifteen women at the time of diagnosis (diagnostic samples) and/or up to seven years prior to diagnosis (archival samples). We analysed tumours for somatic TP53 mutations with next generation sequencing and were able to detect the corresponding mutations in diagnostic samples from six of eight women, while one patient harboured a germline mutation. We further detected a mutation in an archival sample obtained 20 months prior to the ovarian cancer diagnosis. The custom designed IBSAFE assays detected minor allele frequencies (MAFs) with very high assay sensitivity (MAF = 0.0068%) and were successful despite low DNA abundance (0.17–206.14 ng, median: 17.27 ng). These results provide support for further evaluation of archival liquid-based Pap samples for diagnostic purposes and demonstrate that ultra-sensitive ddPCR should be evaluated for ovarian cancer screening in high-risk groups or in the recurrent setting.

Identification of hub genes and key pathways associated with the progression of gynecological cancer

Gynecological cancer is the leading cause of cancer mortality in women. However, the mechanisms underlying gynecological cancer progression have remained largely unclear. In the present study, 799 dysregulated genes were identified in ovarian serous cystadenocarcinoma (OV), 488 dysregulated genes in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), and 621 dysregulated genes in uterine corpus endometrial carcinoma (UCEC). Bioinformatics analysis revealed that mRNA splicing and cell proliferation-associated biological processes served important roles in OV progression. Metabolism-associated biological processes played important roles in CESC progression, and protein phosphorylation and small GTPase-mediated signal transduction served important roles in UCEC progression. The present study also constructed OV, CESC and UCEC progression-associated protein-protein interaction networks to reveal the associations among these genes. Furthermore, Kaplan-Meier curve analysis showed that progression-related genes were associated with the duration of overall survival. Finally, NARS2 and TPT1 in OV, SMYD2, EGLN1, TNFRSF10D, FUT11, SYTL3, MMP8 and EREG in CESC, and SLC5A1, TXN, KDM4B, TXNDC11, HSDL2, COX16, MGAT4A, DAGLA, ELOVL7, THRB and PCOLCE2 in UCEC were identified as hub genes in cancer progression. Therefore, this study may assist in the identification of novel mechanisms underlying cancer progression and new biomarkers for gynecological cancer prognosis and therapy.

Comprehensive analysis of the lncRNA‑associated competing endogenous RNA network in breast cancer

Long noncoding RNAs (lncRNAs) have been confirmed to be potential prognostic markers in a variety of cancers and to interact with microRNAs (miRNAs) as competing endogenous RNAs (ceRNAs) to regulate target gene expression. However, the role of lncRNA‑mediated ceRNAs in breast cancer (BC) remains unclear. In the present study, a ceRNA network was generated to explore their role in BC. The expression profiles of mRNAs, miRNAs and lncRNAs in 1,109 BC tissues and 113 normal breast tissues were obtained from The Cancer Genome Atlas database (TCGA). A total of 3,198 differentially expressed (DE) mRNAs, 150 differentially DEmiRNAs and 1,043 DElncRNAs were identified between BC and normal tissues. A lncRNA‑miRNA‑mRNA network associated with BC was successfully constructed based on the combined data obtained from RNA databases, and comprised 97 lncRNA nodes, 24 miRNA nodes and 74 mRNA nodes. The biological functions of the 74 DEmRNAs were further investigated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The results demonstrated that the DEmRNAs were significantly enriched in two GO biological process categories; the main biological process enriched term was 'positive regulation of GTPase activity'. By KEGG analysis, four key enriched pathways were obtained, including the 'MAPK signaling pathway', the 'Ras signaling pathway', 'prostate cancer', and the 'FoxO signaling pathway'. Kaplan‑Meier survival analysis revealed that six DElncRNAs (INC AC112721.1, LINC00536, MIR7‑3HG, ADAMTS9‑AS1, AL356479.1 and LINC00466), nine DEmRNAs (KPNA2, RACGAP1, SHCBP1, ZNF367, NTRK2, ORS1, PTGS2, RASGRP1 and SFRP1) and two DEmiRNAs (hsa‑miR‑301b and hsa‑miR‑204) had significant effects on overall survival in BC. The present results demonstrated the aberrant expression of INC AC112721.1, AL356479.1, LINC00466 and MIR7‑3HG in BC, indicating their potential prognostic role in patients with BC.

Genomics of gynaecological carcinosarcomas and future treatment options

Gynaecological carcinosarcomas are the most lethal gynaecological malignancies that are often highly resistant to standard chemotherapy. They are composed of both carcinomatous and sarcomatous components and are associated with high rates of metastatic disease. Due to their rarity, molecular studies have been carried out on relatively few tumours, revealing a broad spectrum of heterogeneity. In this review, we have collated the gene mutations, gene expression, epigenetic regulation and protein expression reported by a number of studies on gynaecological carcinosarcomas. Based on these results, we describe potential therapeutics that may demonstrate efficacy and present any pre-clinical studies that have been carried out. We also describe the pre-clinical models currently available for future research to assess the potential of molecularly matched therapies. Interestingly, over-expression of many biomarkers in carcinosarcoma tumours often doesn't correlate with a worse prognosis. Therefore, we propose that profiling the mutational landscape, gene expression, and gene amplification/deletion may better indicate potential treatment strategies and predict response, thus improving outcomes for women with this rare, aggressive disease.

Proteomics advances for precision therapy in ovarian cancer

Introduction: Due to the relatively low mutation rate and high frequency of copy number variation, finding actionable genetic drivers of high-grade serous carcinoma (HGSC) is a challenging task. Furthermore, emerging studies show that genetic alterations are frequently poorly represented at the protein level adding a layer of complexity. With improvements in large-scale proteomic technologies, proteomics studies have the potential to provide robust analysis of the pathways driving high HGSC behavior. Areas covered: This review summarizes recent large-scale proteomics findings across adequately sized ovarian cancer sample sets. Key words combined with 'ovarian cancer' including 'proteomics', 'proteogenomic', 'reverse-phase protein array', 'mass spectrometry', and 'adaptive response', were used to search PubMed. Expert opinion: Proteomics analysis of HGSC as well as their adaptive responses to therapy can uncover new therapeutic liabilities, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is a pressing need to better understand how the genomic and epigenomic heterogeneity intrinsic to ovarian cancer is reflected at the protein level and how this information could be used to improve patient outcomes.

The molecular genetic make-up of male breast cancer

Male breast cancer (MBC) is extremely rare and accounts for less than 1% of all breast malignancies. Therefore, clinical management of MBC is currently guided by research on the disease in females. In this study, DNA obtained from 45 formalin-fixed paraffin-embedded (FFPE) MBCs with and 90 MBCs (52 FFPE and 38 fresh-frozen) without matched normal tissues was subjected to massively parallel sequencing targeting all exons of 1943 cancer-related genes. The landscape of mutations and copy number alterations was compared to that of publicly available estrogen receptor (ER)-positive female breast cancers (smFBCs) and correlated to prognosis. From the 135 MBCs, 90% showed ductal histology, 96% were ER-positive, 66% were progesterone receptor (PR)-positive, and 2% HER2-positive, resulting in 50, 46 and 4% luminal A-like, luminal B-like and basal-like cases, respectively. Five patients had Klinefelter syndrome (4%) and 11% of patients harbored pathogenic BRCA2 germline mutations. The genomic landscape of MBC to some extent recapitulated that of smFBC, with recurrent PIK3CA (36%) and GATA3 (15%) somatic mutations, and with 40% of the most frequently amplified genes overlapping between both sexes. TP53 (3%) somatic mutations were significantly less frequent in MBC compared to smFBC, whereas somatic mutations in genes regulating chromatin function and homologous recombination deficiency-related signatures were more prevalent. MDM2 amplifications were frequent (13%), correlated with protein overexpression (P = 0.001) and predicted poor outcome (P = 0.007). In conclusion, despite similarities in the genomic landscape between MBC and smFBC, MBC is a molecularly unique and heterogeneous disease requiring its own clinical trials and treatment guidelines.

Chromatin-informed inference of transcriptional programs in gynecologic and basal breast cancers

Chromatin accessibility data can elucidate the developmental origin of cancer cells and reveal the enhancer landscape of key oncogenic transcriptional regulators. We develop a computational strategy called PSIONIC (patient-specific inference of networks informed by chromatin) to combine chromatin accessibility data with large tumor expression data and model the effect of enhancers on transcriptional programs in multiple cancers. We generate a new ATAC-seq data profiling chromatin accessibility in gynecologic and basal breast cancer cell lines and apply PSIONIC to 723 patient and 96 cell line RNA-seq profiles from ovarian, uterine, and basal breast cancers. Our computational framework enables us to share information across tumors to learn patient-specific TF activities, revealing regulatory differences between and within tumor types. PSIONIC-predicted activity for MTF1 in cell line models correlates with sensitivity to MTF1 inhibition, showing the potential of our approach for personalized therapy. Many identified TFs are significantly associated with survival outcome. To validate PSIONIC-derived prognostic TFs, we perform immunohistochemical analyses in 31 uterine serous tumors for ETV6 and 45 basal breast tumors for MITF and confirm that the corresponding protein expression patterns are also significantly associated with prognosis.

Identification of lncRNAs associated with lung squamous cell carcinoma prognosis in the competitive endogenous RNA network

Background

Long noncoding RNAs (lncRNAs) play a role in the formation, development, and prognosis of various cancers. Our study aimed to identify prognostic-related lncRNAs in lung squamous cell carcinoma (LUSC), which may provide new perspectives for individualized treatment of patients.

Materials and Methods

The RNA sequencing (lncRNA, microRNA (miRNA), mRNA) data and clinical information related to LUSC were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed RNA sequences were used to construct the competitive endogenous RNA (ceRNA) network. In present study, we mainly used two prognostic verification methods, Cox analysis and survival analysis, to identify the prognostic relevance of specific lncRNAs and construct prognostic model of lncRNA.

Results

Datasets on 551 samples of lncRNA and mRNA and 523 miRNA samples were retrieved from the TCGA database. Analysis of the normal and LUSC samples identified 170 DElncRNAs, 331 DEmiRNAs, and 417 DEmRNAs differentially expressed RNAs. The ceRNA network contained 27 lncRNAs, 43 miRNAs, and 11 mRNAs. Furthermore, we identified seven specific lncRNAs (ERVH48-1, HCG9, SEC62-AS1, AC022148.1, LINC00460, C5orf17, LINC00261) as potential prognostic factors after correlation analysis, and five of the seven lncRNAs (AC022148.1, HCG9, LINC00460, C5orf17, LINC00261) constructed a prognostic model of LUSC.

Conclusion

In present study, we identified seven lncRNAs in the ceRNA network that are associated with potential prognosis in LUSC patients, and constructed a prognostic model of LUSC which can be used to assess the prognosis risk of clinical patients. Further biological experiments are needed to elucidate the specific molecular mechanisms underlying them.

Dysregulated expression of repetitive DNA in ER+/HER2- breast cancer

Limited studies on breast cancer indicated pathogenic changes in the expressions of some repeat elements. A global analysis was much needed within this context to distinguish the most significant repeats from more than a thousand repeat motifs. Utilising a previously presented RNA-seq dataset, we studied expression changes of all repeats in ER+/HER2- human breast tumour samples obtained from 22 patients in comparison to matched normal tissues. Fifty six (56) repeat subtypes including satellites and transposons were found to be differentially expressed and most of them were novel for breast cancer. HERVKC4-int and HERV1_LTRc, whose expressions correlated well with that of the estrogen receptor gene ESR1, were upregulated at the highest level. REP522 and D20S16 satellites were also significantly upregulated along with insignificant increases in the expressions of other satellites including HSATI and BSR/beta. Interestingly, expressions of REP522 and D20S16 correlated with many key breast cancer pathway (e.g. BRCA1, BRCA2, AKT1, MTOR, KRAS) and survival genes; possibly highlighting their importance in the carcinogenesis of breast. Additional differentially expressed elements such as L1P and various MER transposons also exhibited a similar pattern. Finally, our repeat enrichment analysis on the promoters of differentially expressed genes revealed further links between additional repeats and nearby genes.

Increased RNA Expression of von Willebrand Factor Gene Is Associated With Infiltrating Lobular Breast Cancer and Normal PAM50 Subtype

BACKGROUND

Infiltrating lobular carcinoma (ILC) is the second most common histologicaI subtype of breast cancer, accounting for 10% of all cases. ILC has a characteristic genomic profile. ILC shows a high frequency of cadherin 1 (CDH1) mutations, along with loss of phosphatase and tensin homolog (PTEN), activation of alpha serine/threonine kinase (AKT), and mutations in T-box transcription factor (TBX3) and forkhead box protein A1 (FOXA1). We suspected that another gene, von Willebrand factor (VWF), might also be part of the profile, since coagulation tests reveal significant differences in patients with breast cancer.

MATERIALS AND METHODS

For newly-diagnosed breast cancer, the association between VWF and histology in the GDC Breast Cancer dataset in The Cancer Genome Atlas (TCGA) was evaluated. The following were used to access and analyze the data: Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov/); Xena browser (https://xenabrowser.net); cBioportal (http://cbioportal.org); Oncomine (https://oncomine.org); and Prediction Analysis of Microarray 50 (PAM50).

RESULTS

Patients with ILC had higher VWF RNA expression than patients with infiltrating ductal carcinoma and other histology. The difference of expression was present to the same degree in both pre-menopausal and post-menopausal patients. Nine alterations in VWF and PTEN were significantly co-occurrent. Considering all histologies in 843 samples, Tukey's honest significant difference post hoc test showed that VWF RNA expression of the normal subtype was significantly greater than that of the other subtypes (p<0.001).

CONCLUSION

Our finding of significantly higher VWF RNA expression in the PAM50 normal (non-basal-like) breast cancer subtype suggests that VWF protein measurement might complement or corroborate PAM50 results. VWF and PAM50 results both suggesting a low risk of recurrence might make the decision whether to give chemotherapy easier, especially if VWF protein were an independent predictor.

Clinical actionability of molecular targets in endometrial cancer

Endometrial cancer accounts for ~76,000 deaths among women each year worldwide. Disease mortality and the increasing number of new diagnoses make endometrial cancer an important consideration in women's health, particularly in industrialized countries, where the incidence of this tumour type is highest. Most endometrial cancers are carcinomas, with the remainder being sarcomas. Endometrial carcinomas can be classified into several histological subtypes, including endometrioid, serous and clear cell carcinomas. Histological subtyping is currently used routinely to guide prognosis and treatment decisions for endometrial cancer patients, while ongoing studies are evaluating the potential clinical utility of molecular subtyping. In this Review, we summarize the overarching molecular features of endometrial cancers and highlight recent studies assessing the potential clinical utility of specific molecular features for early detection, disease risk stratification and directing targeted therapies.

Pooled Genomic Screens Identify Anti-apoptotic Genes as Targetable Mediators of Chemotherapy Resistance in Ovarian Cancer

High-grade serous ovarian cancer (HGSOC) is often sensitive to initial treatment with platinum and taxane combination chemotherapy, but most patients relapse with chemotherapy-resistant disease. To systematically identify genes modulating chemotherapy response, we performed pooled functional genomic screens in HGSOC cell lines treated with cisplatin, paclitaxel, or cisplatin plus paclitaxel. Genes in the intrinsic pathway of apoptosis were among the top candidate resistance genes in both gain-of-function and loss-of-function screens. In an open reading frame overexpression screen, followed by a mini-pool secondary screen, anti-apoptotic genes including BCL2L1 (BCL-XL) and BCL2L2 (BCL-W) were associated with chemotherapy resistance. In a CRISPR-Cas9 knockout screen, loss of BCL2L1 decreased cell survival whereas loss of proapoptotic genes promoted resistance. To dissect the role of individual anti-apoptotic proteins in HGSOC chemotherapy response, we evaluated overexpression or inhibition of BCL-2, BCL-XL, BCL-W, and MCL1 in HGSOC cell lines. Overexpression of anti-apoptotic proteins decreased apoptosis and modestly increased cell viability upon cisplatin or paclitaxel treatment. Conversely, specific inhibitors of BCL-XL, MCL1, or BCL-XL/BCL-2, but not BCL-2 alone, enhanced cell death when combined with cisplatin or paclitaxel. Anti-apoptotic protein inhibitors also sensitized HGSOC cells to the poly (ADP-ribose) polymerase inhibitor olaparib. These unbiased screens highlight anti-apoptotic proteins as mediators of chemotherapy resistance in HGSOC, and support inhibition of BCL-XL and MCL1, alone or combined with chemotherapy or targeted agents, in treatment of primary and recurrent HGSOC. IMPLICATIONS: Anti-apoptotic proteins modulate drug resistance in ovarian cancer, and inhibitors of BCL-XL or MCL1 promote cell death in combination with chemotherapy.

An NF90/long noncoding RNA-LET/miR-548k feedback amplification loop controls esophageal squamous cell carcinoma progression

In our previous study we have found that miR-548k has oncogenic roles in esophageal squamous cell carcinoma (ESCC) via repressing long noncoding RNA (lncRNA)-LET and further upregulating nuclear factor 90 (NF90). However, the upstream factors controlling miR-548k expression are still unknown. In this study, we found NF90 directly binds pri-miR-548k, increases the stability of pri-miR-548k, and upregulates the expression of pri-miR-548k and miR-548k. Therefore, NF90, miR-548k and lncRNA-LET forms a feedback loop. Gain-of-function and loss-of-function assays demonstrated that in accordance with the roles of miR-548k, NF90 also promotes ESCC cell proliferation and migration. Furthermore, we verified the regulatory feedback loop between NF90, miR-548k, and lncRNA-LET. We found NF90 upregulated miR-548k and downregulated lncRNA-LET. miR-548k downregulated lncRNA-LET and upregulated NF90. lncRNA-LET downregulated NF90 and miR-548k. Through the reciprocal regulations between each other, the NF90/miR-548k/lncRNA-LET feedback loop controls the expressions of NF90 targets (HIF-1α and VEGF), miR-548k targets (KLF10 and EGFR), and lncRNA-LET target (p53). Further functional assays demonstrated that activation of the NF90/miR-548k/lncRNA-LET feedback loop via simultaneously overexpressing NF90 and miR-548k and simultaneously depleting lncRNA-LET significantly promotes ESCC cell proliferation and migration in vitro and ESCC tumor growth in vivo. Targeting the NF90/miR-548k/lncRNA-LET feedback loop via simultaneously depleting NF90 and miR-548k and simultaneously overexpressing lncRNA-LET significantly inhibits ESCC cell proliferation and migration in vitro and ESCC tumor growth in vivo. In summary, our findings identified a crucial oncogenic NF90/lncRNA-LET/miR-548k feedback amplification loop, which may be promising therapeutic targets for ESCC.

Long non-coding RNA CASC19 is associated with the progression and prognosis of advanced gastric cancer

Evidence indicates that aberrantly expressed long non-coding RNAs (lncRNAs) are involved in the development and progression of advanced gastric cancer (AGC). Using RNA sequencing data and clinical information obtained from The Cancer Gene Atlas, we combined differential lncRNA expression profiling and weighted gene co-expression network analysis to identify key lncRNAs associated with AGC progression and prognosis. Cancer susceptibility 19 (CASC19) was the top hub lncRNA among the lncRNAs included in the gene module most significantly correlated with AGC’s pathological variables. CASC19 was upregulated in AGC clinical samples and was significantly associated with higher pathologic TNM stage, pathologic T stage, lymph node metastasis, and poor overall survival. Multivariable Cox analysis confirmed that CASC19 overexpression is an independent prognostic factor for overall survival. Furthermore, quantitative real-time PCR assay confirmed that CASC19 expression in four human gastric cancer cells (AGS, BGC-823, MGC-803, and HGC-27) was significantly upregulated compared with human normal gastric mucosal epithelial cell line (GES-1). Functionally, CASC19 knockdown inhibited GC cell proliferation and migration in vitro. These findings suggest that CASC19 may be a novel prognostic biomarker and a potential therapeutic target for AGC.

Insights into Impact of DNA Copy Number Alteration and Methylation on the Proteogenomic Landscape of Human Ovarian Cancer via a Multi-omics Integrative Analysis

In this work, we propose iProFun, an integrative analysis tool to screen for proteogenomic functional traits perturbed by DNA copy number alterations (CNAs) and DNA methylations. The goal is to characterize functional consequences of DNA copy number and methylation alterations in tumors and to facilitate screening for cancer drivers contributing to tumor initiation and progression. Specifically, we consider three functional molecular quantitative traits: mRNA expression levels, global protein abundances, and phosphoprotein abundances. We aim to identify those genes whose CNAs and/or DNA methylations have cis-associations with either some or all three types of molecular traits. Compared with analyzing each molecular trait separately, the joint modeling of multi-omics data enjoys several benefits: iProFun experienced enhanced power for detecting significant cis-associations shared across different omics data types, and it also achieved better accuracy in inferring cis-associations unique to certain type(s) of molecular trait(s). For example, unique associations of CNAs/methylations to global/phospho protein abundances may imply posttranslational regulations.We applied iProFun to ovarian high-grade serous carcinoma tumor data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium and identified CNAs and methylations of 500 and 121 genes, respectively, affecting the cis-functional molecular quantitative traits of the corresponding genes. We observed substantial power gain via the joint analysis of iProFun. For example, iProFun identified 117 genes whose CNAs were associated with phosphoprotein abundances by leveraging mRNA expression levels and global protein abundances. By comparison, analyses based on phosphoprotein data alone identified none. A network analysis of these 117 genes revealed the known oncogene AKT1 as a key hub node interacting with many of the rest. In addition, iProFun identified one gene, BIN2, whose DNA methylation has cis-associations with its mRNA expression, global protein, and phosphoprotein abundances. These and other genes identified by iProFun could serve as potential drug targets for ovarian cancer.

The Cytoplasmic DNA Sensor cGAS Promotes Mitotic Cell Death

Pathogenic and other cytoplasmic DNAs activate the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway to induce inflammation via transcriptional activation by IRF3 and nuclear factor κB (NF-κB), but the functional consequences of exposing cGAS to chromosomes upon mitotic nuclear envelope breakdown are unknown. Here, we show that nucleosomes competitively inhibit DNA-dependent cGAS activation and that the cGAS-STING pathway is not effectively activated during normal mitosis. However, during mitotic arrest, low level cGAS-dependent IRF3 phosphorylation slowly accumulates without triggering inflammation. Phosphorylated IRF3, independently of its DNA-binding domain, stimulates apoptosis through alleviating Bcl-xL-dependent suppression of mitochondrial outer membrane permeabilization. We propose that slow accumulation of phosphorylated IRF3, normally not sufficient for inducing inflammation, can trigger transcription-independent induction of apoptosis upon mitotic aberrations. Accordingly, expression of cGAS and IRF3 in cancer cells makes mouse xenograft tumors responsive to the anti-mitotic agent Taxol. The Cancer Genome Atlas (TCGA) datasets for non-small cell lung cancer patients also suggest an effect of cGAS expression on taxane response.

Molecular Basis of Tumor Heterogeneity in Endometrial Carcinosarcoma

Endometrial carcinosarcoma (ECS) represents one of the most extreme examples of tumor heterogeneity among human cancers. ECS is a clinically aggressive, high-grade, metaplastic carcinoma. At the morphological level, intratumor heterogeneity in ECS is due to an admixture of epithelial (carcinoma) and mesenchymal (sarcoma) components that can include heterologous tissues, such as skeletal muscle, cartilage, or bone. Most ECSs belong to the copy-number high serous-like molecular subtype of endometrial carcinoma, characterized by the TP53 mutation and the frequently accompanied by a large number of gene copy-number alterations, including the amplification of important oncogenes, such as CCNE1 and c-MYC. However, a proportion of cases (20%) probably represent the progression of tumors initially belonging to the copy-number low endometrioid-like molecular subtype (characterized by mutations in genes such as PTEN, PI3KCA, or ARID1A), after the acquisition of the TP53 mutations. Only a few ECS belong to the microsatellite-unstable hypermutated molecular type and the POLE-mutated, ultramutated molecular type. A common characteristic of all ECSs is the modulation of genes involved in the epithelial to mesenchymal process. Thus, the acquisition of a mesenchymal phenotype is associated with a switch from E- to N-cadherin, the up-regulation of transcriptional repressors of E-cadherin, such as Snail Family Transcriptional Repressor 1 and 2 (SNAI1 and SNAI2), Zinc Finger E-Box Binding Homeobox 1 and 2 (ZEB1 and ZEB2), and the down-regulation, among others, of members of the miR-200 family involved in the maintenance of an epithelial phenotype. Subsequent differentiation to different types of mesenchymal tissues increases tumor heterogeneity and probably modulates clinical behavior and therapy response.

An integrative sparse boosting analysis of cancer genomic commonality and difference

In cancer research, high-throughput profiling has been extensively conducted. In recent studies, the integrative analysis of data on multiple cancer patient groups/subgroups has been conducted. Such analysis has the potential to reveal the genomic commonality as well as difference across groups/subgroups. However, in the existing literature, methods with a special attention to the genomic commonality and difference are very limited. In this study, a novel estimation and marker selection method based on the sparse boosting technique is developed to address the commonality/difference problem. In terms of technical innovation, a new penalty and computation of increments are introduced. The proposed method can also effectively accommodate the grouping structure of covariates. Simulation shows that it can outperform direct competitors under a wide spectrum of settings. The analysis of two TCGA (The Cancer Genome Atlas) datasets is conducted, showing that the proposed analysis can identify markers with important biological implications and have satisfactory prediction and stability.