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

Pathway-Based Drug-Repurposing Schemes in Cancer: The Role of Translational Bioinformatics

Cancer is a set of complex pathologies that has been recognized as a major public health problem worldwide for decades. A myriad of therapeutic strategies is indeed available. However, the wide variability in tumor physiology, response to therapy, added to multi-drug resistance poses enormous challenges in clinical oncology. The last years have witnessed a fast-paced development of novel experimental and translational approaches to therapeutics, that supplemented with computational and theoretical advances are opening promising avenues to cope with cancer defiances. At the core of these advances, there is a strong conceptual shift from gene-centric emphasis on driver mutations in specific oncogenes and tumor suppressors-let us call that the silver bullet approach to cancer therapeutics-to a systemic, semi-mechanistic approach based on pathway perturbations and global molecular and physiological regulatory patterns-we will call this the shrapnel approach. The silver bullet approach is still the best one to follow when clonal mutations in driver genes are present in the patient, and when there are targeted therapies to tackle those. Unfortunately, due to the heterogeneous nature of tumors this is not the common case. The wide molecular variability in the mutational level often is reduced to a much smaller set of pathway-based dysfunctions as evidenced by the well-known hallmarks of cancer. In such cases "shrapnel gunshots" may become more effective than "silver bullets". Here, we will briefly present both approaches and will abound on the discussion on the state of the art of pathway-based therapeutic designs from a translational bioinformatics and computational oncology perspective. Further development of these approaches depends on building collaborative, multidisciplinary teams to resort to the expertise of clinical oncologists, oncological surgeons, and molecular oncologists, but also of cancer cell biologists and pharmacologists, as well as bioinformaticians, computational biologists and data scientists. These teams will be capable of engaging on a cycle of analyzing high-throughput experiments, mining databases, researching on clinical data, validating the findings, and improving clinical outcomes for the benefits of the oncological patients.

An Integrative Computational Approach Based on Expression Similarity Signatures to Identify Protein-Protein Interaction Networks in Female-Specific Cancers

Breast, ovarian, and endometrial cancers have a major impact on mortality in women. These tumors share hormone-dependent mechanisms involved in female-specific cancers which support tumor growth in a different manner. Integrated computational approaches may allow us to better detect genomic similarities between these different female-specific cancers, helping us to deliver more sophisticated diagnosis and precise treatments. Recently, several initiatives of The Cancer Genome Atlas (TCGA) have encouraged integrated analyses of multiple cancers rather than individual tumors. These studies revealed common genetic alterations (driver genes) even in clinically distinct entities such as breast, ovarian, and endometrial cancers. In this study, we aimed to identify expression similarity signatures by extracting common genes among TCGA breast (BRCA), ovarian (OV), and uterine corpus endometrial carcinoma (UCEC) cohorts and infer co-regulatory protein-protein interaction networks that might have a relationship with the estrogen signaling pathway. Thus, we carried out an unsupervised principal component analysis (PCA)-based computational approach, using RNA sequencing data of 2,015 female cancer and 148 normal samples, in order to simultaneously capture the data heterogeneity of intertumors. Firstly, we identified tumor-associated genes from gene expression profiles. Secondly, we investigated the signaling pathways and co-regulatory protein-protein interaction networks underlying these three cancers by leveraging the Ingenuity Pathway Analysis software. In detail, we discovered 1,643 expression similarity signatures (638 downregulated and 1,005 upregulated genes, with respect to normal phenotype), denoted as tumor-associated genes. Through functional genomic analyses, we assessed that these genes were involved in the regulation of cell-cycle-dependent mechanisms, including metaphase kinetochore formation and estrogen-dependent S-phase entry. Furthermore, we generated putative co-regulatory protein-protein interaction networks, based on upstream regulators such as the ERBB2/HER2 gene. Moreover, we provided an ad-hoc bioinformatics workflow with a manageable list of intertumor expression similarity signatures for the three female-specific cancers. The expression similarity signatures identified in this study might uncover potential estrogen-dependent molecular mechanisms promoting carcinogenesis.

Global computational alignment of tumor and cell line transcriptional profiles

Cell lines are key tools for preclinical cancer research, but it remains unclear how well they represent patient tumor samples. Direct comparisons of tumor and cell line transcriptional profiles are complicated by several factors, including the variable presence of normal cells in tumor samples. We thus develop an unsupervised alignment method (Celligner) and apply it to integrate several large-scale cell line and tumor RNA-Seq datasets. Although our method aligns the majority of cell lines with tumor samples of the same cancer type, it also reveals large differences in tumor similarity across cell lines. Using this approach, we identify several hundred cell lines from diverse lineages that present a more mesenchymal and undifferentiated transcriptional state and that exhibit distinct chemical and genetic dependencies. Celligner could be used to guide the selection of cell lines that more closely resemble patient tumors and improve the clinical translation of insights gained from cell lines.

Long noncoding RNA SNHG20 promotes prostate cancer progression via upregulating DDX17

INTRODUCTION

Accumulating evidence has revealed the critical roles of long noncoding RNAs (lncRNAs) in various cancers. LncRNA SNHG20 has been shown to be a cancer-associated lncRNA in several cancers with diverse mechanisms. However, the clinical references, biological roles, and mechanisms of action of SNHG20 in prostate cancer (PCa) are still unclear.

MATERIAL AND METHODS

The expression of SNHG20 in PCa tissues and cell lines was detected by RT-qPCR. The correlations between SNHG20 expression and clinicopathological features were analyzed by χ² test. The roles of SNHG20 in PCa cell proliferation and migration were detected by CCK-8, EdU incorporation, and transwell assays. The regulatory mechanisms of SNHG20 on DDX17 were detected by dual luciferase reporter assay, RT-qPCR, and western blot.

RESULTS

SNHG20 is highly expressed in PCa tissues and cell lines. High expression of SNHG20 is positively correlated with high Gleason score and advanced tumor stage. Functional experiments revealed that overexpression of SNHG20 promotes PCa cell proliferation and migration. SNHG20 knockdown represses PCa cell proliferation and migration. Mechanistically, SNHG20 was verified to act as a competing endogenous RNA (ceRNA) to upregulate DDX17. DDX17 is also highly expressed and has oncogenic roles in PCa. Furthermore, the expression of DDX17 is significantly positively correlated with that of SNHG20 in PCa tissues. Depletion of DDX17 reverses the oncogenic roles of SNHG20 in PCa.

CONCLUSIONS

These data showed that SNHG20 promotes PCa cell proliferation and migration via acting as a ceRNA to upregulate DDX17. This study also suggested that SNHG20 may be a potential novel therapeutic target for PCa.

Cross-Cancer Genome-Wide Association Study of Endometrial Cancer and Epithelial Ovarian Cancer Identifies Genetic Risk Regions Associated with Risk of Both Cancers

BACKGROUND

Accumulating evidence suggests a relationship between endometrial cancer and ovarian cancer. Independent genome-wide association studies (GWAS) for endometrial cancer and ovarian cancer have identified 16 and 27 risk regions, respectively, four of which overlap between the two cancers. We aimed to identify joint endometrial and ovarian cancer risk loci by performing a meta-analysis of GWAS summary statistics from these two cancers.

METHODS

Using LDScore regression, we explored the genetic correlation between endometrial cancer and ovarian cancer. To identify loci associated with the risk of both cancers, we implemented a pipeline of statistical genetic analyses (i.e., inverse-variance meta-analysis, colocalization, and M-values) and performed analyses stratified by subtype. Candidate target genes were then prioritized using functional genomic data.

RESULTS

Genetic correlation analysis revealed significant genetic correlation between the two cancers (rG = 0.43, P = 2.66 × 10-5). We found seven loci associated with risk for both cancers (P Bonferroni < 2.4 × 10-9). In addition, four novel subgenome-wide regions at 7p22.2, 7q22.1, 9p12, and 11q13.3 were identified (P < 5 × 10-7). Promoter-associated HiChIP chromatin loops from immortalized endometrium and ovarian cell lines and expression quantitative trait loci data highlighted candidate target genes for further investigation.

CONCLUSIONS

Using cross-cancer GWAS meta-analysis, we have identified several joint endometrial and ovarian cancer risk loci and candidate target genes for future functional analysis.

IMPACT

Our research highlights the shared genetic relationship between endometrial cancer and ovarian cancer. Further studies in larger sample sets are required to confirm our findings.

SNHG1 represses the anti-cancer roles of baicalein in cervical cancer through regulating miR-3127-5p/FZD4/Wnt/β-catenin signaling

IMPACT STATEMENT

Baicalein exhibits anti-cancer roles in several cancers. However, the factors influencing the antitumorigenic efficiencies of baicalein in CC remain largely unclear. Here, we provide convincing evidences that lncRNA SNHG1 attenuates the tumor-suppressive roles of baicalein in CC cell viability, apoptosis, migration, and CC tumor growth. This study further demonstrates that the influences of SNHG1 in the antitumorigenic process of baicalein are achieved through modulating the miR-3127-5p/FZD4Wnt/β-catenin axis. SNHG1 attenuates the repressive role of baicalein on Wnt/β-catenin. Therefore, SNHG1 is a novel modulator of the tumor-suppressive roles of baicalein and SNHG1 represents a therapeutic intervention target to reinforce the tumor-suppressive roles of baicalein in CC.

Drug design targeting active posttranslational modification protein isoforms

Modern drug design aims to discover novel lead compounds with attractable chemical profiles to enable further exploration of the intersection of chemical space and biological space. Identification of small molecules with good ligand efficiency, high activity, and selectivity is crucial toward developing effective and safe drugs. However, the intersection is one of the most challenging tasks in the pharmaceutical industry, as chemical space is almost infinity and continuous, whereas the biological space is very limited and discrete. This bottleneck potentially limits the discovery of molecules with desirable properties for lead optimization. Herein, we present a new direction leveraging posttranslational modification (PTM) protein isoforms target space to inspire drug design termed as "Post-translational Modification Inspired Drug Design (PTMI-DD)." PTMI-DD aims to extend the intersections of chemical space and biological space. We further rationalized and highlighted the importance of PTM protein isoforms and their roles in various diseases and biological functions. We then laid out a few directions to elaborate the PTMI-DD in drug design including discovering covalent binding inhibitors mimicking PTMs, targeting PTM protein isoforms with distinctive binding sites from that of wild-type counterpart, targeting protein-protein interactions involving PTMs, and hijacking protein degeneration by ubiquitination for PTM protein isoforms. These directions will lead to a significant expansion of the biological space and/or increase the tractability of compounds, primarily due to precisely targeting PTM protein isoforms or complexes which are highly relevant to biological functions. Importantly, this new avenue will further enrich the personalized treatment opportunity through precision medicine targeting PTM isoforms.

Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images

Histopathological images are a rich but incompletely explored data type for studying cancer. Manual inspection is time consuming, making it challenging to use for image data mining. Here we show that convolutional neural networks (CNNs) can be systematically applied across cancer types, enabling comparisons to reveal shared spatial behaviors. We develop CNN architectures to analyze 27,815 hematoxylin and eosin scanned images from The Cancer Genome Atlas for tumor/normal, cancer subtype, and mutation classification. Our CNNs are able to classify TCGA pathologist-annotated tumor/normal status of whole slide images (WSIs) in 19 cancer types with consistently high AUCs (0.995 ± 0.008), as well as subtypes with lower but significant accuracy (AUC 0.87 ± 0.1). Remarkably, tumor/normal CNNs trained on one tissue are effective in others (AUC 0.88 ± 0.11), with classifier relationships also recapitulating known adenocarcinoma, carcinoma, and developmental biology. Moreover, classifier comparisons reveal intra-slide spatial similarities, with an average tile-level correlation of 0.45 ± 0.16 between classifier pairs. Breast cancers, bladder cancers, and uterine cancers have spatial patterns that are particularly easy to detect, suggesting these cancers can be canonical types for image analysis. Patterns for TP53 mutations can also be detected, with WSI self- and cross-tissue AUCs ranging from 0.65-0.80. Finally, we comparatively evaluate CNNs on 170 breast and colon cancer images with pathologist-annotated nuclei, finding that both cellular and intercellular regions contribute to CNN accuracy. These results demonstrate the power of CNNs not only for histopathological classification, but also for cross-comparisons to reveal conserved spatial behaviors across tumors.

Comprehensive Genomic Profile of Heterogeneous Long Follow-Up Triple-Negative Breast Cancer and Its Clinical Characteristics Shows DNA Repair Deficiency Has Better Prognostic

Triple-negative breast cancer (TNBC) presents a marked diversity at the molecular level, which promotes a clinical heterogeneity that further complicates treatment. We performed a detailed whole exome sequencing profile of 29 Mexican patients with long follow-up TNBC to identify genomic alterations associated with overall survival (OS), disease-free survival (DFS), and pathologic complete response (PCR), with the aim to define their role as molecular predictive factors of treatment response and prognosis. We detected 31 driver genes with pathogenic mutations in TP53 (53%), BRCA1/2 (27%), CDKN1B (9%), PIK3CA (9%), and PTEN (9%), and 16 operative mutational signatures. Moreover, tumors with mutations in BRCA1/2 showed a trend of sensitivity to platinum salts. We found an association between deficiency in DNA repair and surveillance genes and DFS. Across all analyzed tumors we consistently found a heterogeneous molecular complexity in terms of allelic composition and operative mutational processes, which hampered the definition of molecular traits with clinical utility. This work contributes to the elucidation of the global molecular alterations of TNBC by providing accurate genomic data that may help forthcoming studies to improve treatment and survival. This is the first study that integrates genomic alterations with a long follow-up of clinical variables in a Latin American population that is an underrepresented ethnicity in most of the genomic studies.

Cancer classification based on chromatin accessibility profiles with deep adversarial learning model

Given the complexity and diversity of the cancer genomics profiles, it is challenging to identify distinct clusters from different cancer types. Numerous analyses have been conducted for this propose. Still, the methods they used always do not directly support the high-dimensional omics data across the whole genome (Such as ATAC-seq profiles). In this study, based on the deep adversarial learning, we present an end-to-end approach ClusterATAC to leverage high-dimensional features and explore the classification results. On the ATAC-seq dataset and RNA-seq dataset, ClusterATAC has achieved excellent performance. Since ATAC-seq data plays a crucial role in the study of the effects of non-coding regions on the molecular classification of cancers, we explore the clustering solution obtained by ClusterATAC on the pan-cancer ATAC dataset. In this solution, more than 70% of the clustering are single-tumor-type-dominant, and the vast majority of the remaining clusters are associated with similar tumor types. We explore the representative non-coding loci and their linked genes of each cluster and verify some results by the literature search. These results suggest that a large number of non-coding loci affect the development and progression of cancer through its linked genes, which can potentially advance cancer diagnosis and therapy.

CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence

BACKGROUND

Cancer of unknown primary (CUP), representing approximately 3-5% of all malignancies, is defined as metastatic cancer where a primary site of origin cannot be found despite a standard diagnostic workup. Because knowledge of a patient's primary cancer remains fundamental to their treatment, CUP patients are significantly disadvantaged and most have a poor survival outcome. Developing robust and accessible diagnostic methods for resolving cancer tissue of origin, therefore, has significant value for CUP patients.

METHODS

We developed an RNA-based classifier called CUP-AI-Dx that utilizes a 1D Inception convolutional neural network (1D-Inception) model to infer a tumor's primary tissue of origin. CUP-AI-Dx was trained using the transcriptional profiles of 18,217 primary tumours representing 32 cancer types from The Cancer Genome Atlas project (TCGA) and International Cancer Genome Consortium (ICGC). Gene expression data was ordered by gene chromosomal coordinates as input to the 1D-CNN model, and the model utilizes multiple convolutional kernels with different configurations simultaneously to improve generality. The model was optimized through extensive hyperparameter tuning, including different max-pooling layers and dropout settings. For 11 tumour types, we also developed a random forest model that can classify the tumour's molecular subtype according to prior TCGA studies. The optimised CUP-AI-Dx tissue of origin classifier was tested on 394 metastatic samples from 11 tumour types from TCGA and 92 formalin-fixed paraffin-embedded (FFPE) samples representing 18 cancer types from two clinical laboratories. The CUP-AI-Dx molecular subtype was also independently tested on independent ovarian and breast cancer microarray datasets FINDINGS: CUP-AI-Dx identifies the primary site with an overall top-1-accuracy of 98.54% in cross-validation and 96.70% on a test dataset. When applied to two independent clinical-grade RNA-seq datasets generated from two different institutes from the US and Australia, our model predicted the primary site with a top-1-accuracy of 86.96% and 72.46% respectively.

INTERPRETATION

The CUP-AI-Dx predicts tumour primary site and molecular subtype with high accuracy and therefore can be used to assist the diagnostic work-up of cancers of unknown primary or uncertain origin using a common and accessible genomics platform.

FUNDING

NIH R35 GM133562, NCI P30 CA034196, Victorian Cancer Agency Australia.

Reduced expression of ppGalNAc-T4 promotes proliferation of human breast cancer cells

Breast cancer, one of the most frequently diagnosed and aggressive malignancies, is the major cause of cancer-related death greatly threatening women health. Polypeptide N-acetylgalactosaminyltransferase 4 (ppGalNAc-T4), responsible for the initial step of mucin-type O-glycosylation, has been reported to be implicated in diverse types of human tumors. However, the biological role of ppGalNAc-T4 in breast cancer is still undetermined. In this study, we investigate the effects and mechanism of ppGalNAc-T4 to breast cancer cell proliferation. From analysis of high throughput RNA sequencing datasets of Gene Expression Omnibus and ArrayExpress, a positive correlation between ppGalNAc-T4 and the recurrence-free survival was observed in multigroup of human breast cancer datasets. Moreover, transcriptomes analysis using RNA-sequencing in MCF7 cells revealed that cell cycle-related genes induced the effects of ppGalNAc-T4 on breast cancer cell proliferation. Additionally, investigations showed that ppGalNAc-T4 impaired cell proliferation in MCF-7 and MDA-MB-231 breast cells. Furthermore, our results suggested that the ppGalNAc-T4 knockout activated Notch signaling pathway and enhanced cell proliferation. Collectively, our data indicated that ppGalNAc-T4 affected the proliferation of human breast cancer cells, which appears to be a novel target for understanding the underlying molecular mechanism of breast cancer.

Cells expressing PAX8 are the main source of homeostatic regeneration of adult mouse endometrial epithelium and give rise to serous endometrial carcinoma

Humans and mice have cyclical regeneration of the endometrial epithelium. It is expected that such regeneration is ensured by tissue stem cells, but their location and hierarchy remain debatable. A number of recent studies have suggested the presence of stem cells in the mouse endometrial epithelium. At the same time, it has been reported that this tissue can be regenerated by stem cells of stromal/mesenchymal or bone marrow cell origin. Here, we describe a single-cell transcriptomic atlas of the main cell types of the mouse uterus and epithelial subset transcriptome and evaluate the contribution of epithelial cells expressing the transcription factor PAX8 to the homeostatic regeneration and malignant transformation of adult endometrial epithelium. According to lineage tracing, PAX8+ epithelial cells are responsible for long-term maintenance of both luminal and glandular epithelium. Furthermore, multicolor tracing shows that individual glands and contiguous areas of luminal epithelium are formed by clonal cell expansion. Inactivation of the tumor suppressor genes Trp53 and Rb1 in PAX8+ cells, but not in FOXJ1+ cells, leads to the formation of neoplasms with features of serous endometrial carcinoma, one of the most aggressive types of human endometrial malignancies. Taken together, our results show that the progeny of single PAX8+ cells represents the main source of regeneration of the adult endometrial epithelium. They also provide direct experimental genetic evidence for the key roles of the P53 and RB pathways in the pathogenesis of serous endometrial carcinoma and suggest that PAX8+ cells represent the cell of origin of this neoplasm.

LncRNA-mediated posttranslational modifications and reprogramming of energy metabolism in cancer

Altered metabolism is a hallmark of cancer, and the reprogramming of energy metabolism has historically been considered a general phenomenon of tumors. It is well recognized that long noncoding RNAs (lncRNAs) regulate energy metabolism in cancer. However, lncRNA‐mediated posttranslational modifications and metabolic reprogramming are unclear at present. In this review, we summarized the current understanding of the interactions between the alterations in cancer‐associated energy metabolism and the lncRNA‐mediated posttranslational modifications of metabolic enzymes, transcription factors, and other proteins involved in metabolic pathways. In addition, we discuss the mechanisms through which these interactions contribute to tumor initiation and progression, and the key roles and clinical significance of functional lncRNAs. We believe that an in‐depth understanding of lncRNA‐mediated cancer metabolic reprogramming can help to identify cellular vulnerabilities that can be exploited for cancer diagnosis and therapy.

Molecular Profiling Reveals Common and Specific Development Processes in Different Types of Gynecologic Cancers

BACKGROUND

Gynecologic cancers have become a major threat to women's health. The molecular biology of gynecologic cancers is not as well understood as that of breast cancer, and precision targeting is still new. Although viewed collectively as a group of cancers within the female reproductive system, they are more often studied separately. A comprehensive within-group comparison on molecular profiles is lacking.

METHODS

We conducted a whole-exome sequencing study of cervical/endometrial/ovarian cancer samples from 209 Chinese patients. We combined our data with genomic and transcriptomic data from relevant TCGA cohorts to identify and verify common/exclusive molecular changes in cervical/endometrial/ovarian cancer.

RESULTS

We identified shared molecular features including a COSMIC signature of deficient mismatch repair (dMMR), four recurrent copy-number variation (CNV) events, and extensive alterations in PI3K-Akt-mTOR signaling and cilium component genes; we also identified transcription factors and pathways that are exclusively altered in cervical/endometrial/ovarian cancer. The functions of the commonly/exclusively altered genomic circuits suggest (1) a common reprogramming process during early tumor initiation, which involves PI3K activation, defects in mismatch repair and cilium organization, as well as disruption in interferon signaling and immune recognition; (2) a cell-type specific program at late-stage tumor development that eventually lead to tumor proliferation and migration.

CONCLUSION

This study describes, from a molecular point of view, how similar and how different gynecologic cancers are, and it provides a hypothesis about the causes of the observed similarities and differences.

Long noncoding RNA LINC01578 drives colon cancer metastasis through a positive feedback loop with the NF-κB/YY1 axis

Metastasis accounts for poor prognosis of cancers and related deaths. Accumulating evidence has shown that long noncoding RNAs (lncRNAs) play critical roles in several types of cancer. However, which lncRNAs contribute to metastasis of colon cancer is still largely unknown. In this study, we found that lncRNA LINC01578 was correlated with metastasis and poor prognosis of colon cancer. LINC01578 was upregulated in colon cancer, associated with metastasis, advanced clinical stages, poor overall survival, disease-specific survival, and disease-free survival. Gain-of-function and loss-of-function assays revealed that LINC01578 enhanced colon cancer cell viability and mobility in vitro and colon cancer liver metastasis in vivo. Mechanistically, nuclear factor kappa B (NF-κB) and Yin Yang 1 (YY1) directly bound to the LINC01578 promoter, enhanced its activity, and activated LINC01578 expression. LINC01578 was shown to be a chromatin-bound lncRNA, which directly bound NFKBIB promoter. Furthermore, LINC01578 interacted with and recruited EZH2 to NFKBIB promoter and further repressed NFKBIB expression, thereby activating NF-κB signaling. Through activation of NF-κB, LINC01578 further upregulated YY1 expression. Through activation of the NF-κB/YY1 axis, LINC01578 in turn enhanced its own promoter activity, suggesting that LINC01578 and NF-κB/YY1 formed a positive feedback loop. Blocking NF-κB signaling abolished the oncogenic roles of LINC01578 in colon cancer. Furthermore, the expression levels of LINC01578, NFKBIB, and YY1 were correlated in clinical tissues. Collectively, this study demonstrated that LINC01578 promoted colon cancer metastasis via forming a positive feedback loop with NF-κB/YY1 and suggested that LINC01578 represents a potential prognostic biomarker and therapeutic target for colon cancer metastasis.

A new metabolic signature contributes to disease progression and predicts worse survival in melanoma

Metabolic reprogramming is a common hallmark of tumor cells and is a crucial mediator of resistance toward anticancer therapies. The pattern of a metabolism-related signature in melanoma remains unknown. Here, we explored the role of a multi-metabolism-related gene signature in melanoma.We used the training and validation sets to develop a multi-metabolism-related gene signature. Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) method were used for constructing a model. The predictive role of the metabolic signature with clinicopathological features of melanoma was also analyzed. Functional analysis of this metabolic signature was also investigated.A ten metabolism-related gene signature was identified and can stratify melanoma into high- and low- risk groups. The signature was correlated with progressive T stage, Breslow thickness, Clark level, and worse survival (all Ps< 0.01). This metabolic signature was shown as an independent prognostic factor and was also a predictive indicator for worse survival in various clinical and molecular features of melanoma. Furthermore, the metabolic signature was implicated in immune responses such as the regulation of T cell activation and cytokine activity. The metabolic signaturewas associated with the progression and worse survival of melanoma. Our study offered a valuable metabolism-targeted therapy approach for melanoma.

Omics-Based Platforms: Current Status and Potential Use for Cholangiocarcinoma

Cholangiocarcinoma (CCA) has been identified as a highly malignant cancer that can be transformed from epithelial cells of the bile duct, including intrahepatic, perihilar and extrahepatic. High-resolution imaging tools (abdominal ultrasound, computed tomography and percutaneous transhepatic cholangial drainage) are recruited for diagnosis. However, the lack of early diagnostic biomarkers and treatment evaluation can lead to serious outcomes and poor prognosis (i.e., CA19-9, MUC5AC). In recent years, scientists have established a large number of omics profiles to reveal underlying mechanisms and networks (i.e., IL-6/STAT3, NOTCH). With these results, we achieved several genomic alteration events (i.e., TP53_mut, KRAS_mut) and epigenetic modifications (i.e., DNA methylation, histone modification) in CCA cells and clinical patients. Moreover, we reviewed candidate gene (such as NF-kB, YAP1) that drive gene transcription factors and canonical pathways through transcriptomics profiles (including microarrays and next-generation sequencing). In addition, the proteomics database also indicates which molecules and their directly binding status could trigger dysfunction signatures in tumorigenesis (carbohydrate antigen 19-9, mucins). Most importantly, we collected metabolomics datasets and pivotal metabolites. These results reflect the pharmacotherapeutic options and evaluate pharmacokinetic/pharmacodynamics in vitro and in vivo. We reversed the panels and selected many potentially small compounds from the connectivity map and L1000CDS² system. In this paper, we summarize the prognostic value of each candidate gene and correlate this information with clinical events in CCA. This review can serve as a reference for further research to clearly investigate the complex characteristics of CCA, which may lead to better prognosis, drug repurposing and treatment strategies.

Genomic amplification of long noncoding RNA HOTAIRM1 drives anaplastic thyroid cancer progression via repressing miR-144 biogenesis

Genomic aberrations are frequently found in anaplastic thyroid cancer (ATC). However, the functional genes in aberrantly genomic regions are largely unclear. In this study, we identified a long noncoding RNA (lncRNA) HOTAIRM1, whose encoding gene was amplified and expression was upregulated in ATC compared with papillary thyroid cancer and normal thyroid. Increased genomic copy number and expression of HOTAIRM1 were both correlated with poor survival of ATC patients. Functional assays revealed that HOTAIRM1 promoted proliferation, inhibited apoptosis, and promoted migration and invasion of ATC cells in vitro, and promoted ATC tumour growth and metastasis in vivo. HOTAIRM1 was found to bind ILF3, repress the binding between ILF3 and precursor miR-144 (pre-miR-144), block the effects of ILF3 on stabilizing pre-miR-144, and therefore downregulate pre-miR-144. Intriguingly, HOTAIRM1 was also found to directly bind primary miR-144 (pri-miR-144), repress the binding between pri-miR-144 and DROSHA, block the processing of pri-miR-144 by DROSHA, and therefore upregulate pri-miR-144 and downregulate pre-miR-144. Thus, HOTAIRM1 remarkably downregulated pre-miR-144 and further downregulated miR-144. Knockdown of ILF3 and DROSHA abolished the effects of HOTAIRM1 on pre-miR-144 and miR-144. The expression of miR-144 was downregulated and reversely correlated with HOTAIRM1 in ATC. Via repressing miR-144 biogenesis, HOTAIRM1 upregulated MET and activated AKT signalling. miR-144 overexpression reversed the oncogenic roles of HOTAIRM1 in ATC. Altogether, these findings identified a genomic copy number amplified and highly expressed lncRNA HOTAIRM1, which exerted oncogenic roles via repressing miR-144 biogenesis in ATC. Our data suggested HOTAIRM1 as a potential prognostic biomarker and therapeutic target for ATC.

Clinical implications of plasma circulating tumor DNA in gynecologic cancer patients

Molecular characterization of cancers is important in dictating prognostic factors and directing therapy. Next‐generation sequencing of plasma circulating tumor DNA (ctDNA) offers less invasive, more convenient collection, and a more real‐time representation of a tumor and its molecular heterogeneity than tissue. However, little is known about the clinical implications of ctDNA assessment in gynecologic cancer. We describe the molecular landscape identified on ctDNA, ctDNA concordance with tissue‐based analysis, and factors associated with overall survival (OS) in gynecologic cancer patients with ctDNA analysis. We reviewed clinicopathologic and genomic information for 105 consecutive gynecologic cancer patients with ctDNA analysis, including 78 with tissue‐based sequencing, enrolled in the Profile‐Related Evidence Determining Individualized Cancer Therapy (NCT02478931) trial at the University of California San Diego Moores Cancer Center starting July 2014. Tumors included ovarian (47.6%), uterine (35.2%), cervical (12.4%), vulvovaginal (2.9%), and unknown gynecologic primary (1.9%). Most ovarian and uterine cancers (86%) were high grade. 34% (N = 17) of ovarian cancers had BRCA alterations, and 22% (N = 11) were platinum sensitive. Patients received median 2 (range 0–13) lines of therapy prior to ctDNA collection. Most (75.2%) had at least one characterized alteration on ctDNA analysis, and the majority had unique genomic profiles on ctDNA. Most common alterations were TP53 (N = 59, 56.2% of patients), PIK3CA (N = 26, 24.8%), KRAS (N = 14, 13.3%), BRAF (N = 10, 9.5%), ERBB2 (N = 8, 7.6%), and MYC (N = 8, 7.6%). Higher ctDNA maximum mutation allele frequency was associated with worse OS [hazard ratio (HR): 1.91, P = 0.03], while therapy matched to ctDNA alterations (N = 33 patients) was independently associated with improved OS (HR: 0.34, P = 0.007) compared to unmatched therapy (N = 28 patients) in multivariate analysis. Tissue and ctDNA genomic results showed high concordance unaffected by temporal or spatial factors. This study provides evidence for the utility of ctDNA in determining outcome and individualizing cancer therapy in patients with gynecologic cancer.

The D2 and D3 Sublineages of Human Papilloma Virus 16-Positive Cervical Cancer in Guatemala Differ in Integration Rate and Age of Diagnosis

Human papillomavirus (HPV) 16 displays substantial sequence variation; four HPV16 lineages (A, B, C, and D) have been described as well as multiple sublineages. To identify molecular events associated with HPV16 carcinogenesis, we evaluated viral variation, the integration of HPV16, and somatic mutation in 96 cervical cancer samples from Guatemala. A total of 65% (62/96) of the samples had integrated HPV16 sequences and integration was associated with an earlier age of diagnosis and premenopausal disease. HPV16 integration sites were broadly distributed in the genome, but in one tumor, HPV16 integrated into the promoter of the IFN regulatory factor 4 (IRF4) gene, which plays an important role in the regulation of the IFN response to viral infection. The HPV16 D2 and D3 sublineages were found in 23% and 30% of the tumors, respectively, and were significantly associated with adenocarcinoma. D2-positive tumors had a higher rate of integration, earlier age of diagnosis, and a lower rate of somatic mutation, whereas D3-positive tumors were less likely to integrate, had later age of diagnosis, and exhibited a higher rate of somatic mutation. In conclusion, Guatemalan cervical tumors have a high frequency of very high-risk HPV16 D2 and D3 sublineages harboring distinct histology, which may help guide future therapeutic strategies to target the tumor and reduce recurrence. SIGNIFICANCE: This study details the biological and molecular properties of the most pathogenic forms of HPV16, the cause of the majority of cervical cancers.

Probability distribution of copy number alterations along the genome: an algorithm to distinguish different tumour profiles

Copy number alterations (CNAs) comprise deletions or amplifications of fragments of genomic material that are particularly common in cancer and play a major contribution in its development and progression. High resolution microarray-based genome-wide technologies have been widely used to detect CNAs, generating complex datasets that require further steps to allow for the determination of meaningful results. In this work, we propose a methodology to determine common regions of CNAs from these datasets, that in turn are used to infer the probability distribution of disease profiles in the population. This methodology was validated using simulated data and assessed using real data from Head and Neck Squamous Cell Carcinoma and Lung Adenocarcinoma, from the TCGA platform. Probability distribution profiles were produced allowing for the distinction between different phenotypic groups established within that cohort. This method may be used to distinguish between groups in the diseased population, within well-established degrees of confidence. The application of such methods may be of greater value in the clinical context both as a diagnostic or prognostic tool and, even as a useful way for helping to establish the most adequate treatment and care plans.

Markers of Angiogenesis, Lymphangiogenesis, and Epithelial-Mesenchymal Transition (Plasticity) in CIN and Early Invasive Carcinoma of the Cervix: Exploring Putative Molecular Mechanisms Involved in Early Tumor Invasion

The establishment of a proangiogenic phenotype and epithelial-to-mesenchymal transition (EMT) are considered as critical events that promote the induction of invasive growth in epithelial tumors, and stimulation of lymphangiogenesis is believed to confer the capacity for early dissemination to cancer cells. Recent research has revealed substantial interdependence between these processes at the molecular level as they rely on common signaling networks. Of great interest are the molecular mechanisms of (lymph-)angiogenesis and EMT associated with the earliest stages of transition from intraepithelial development to invasive growth, as they could provide the source of potentially valuable tools for targeting tumor metastasis. However, in the case of early-stage cervical cancer, the players of (lymph-)angiogenesis and EMT processes still remain substantially uncharacterized. In this study, we used RNA sequencing to compare transcriptomes of HPV(+) preinvasive neoplastic lesions and early-stage invasive carcinoma of the cervix and to identify (lymph-)angiogenesis- and EMT-related genes and pathways that may underlie early acquisition of invasive phenotype and metastatic properties by cervical cancer cells. Second, we applied flow cytometric analysis to evaluate the expression of three key lymphangiogenesis/EMT markers (VEGFR3, MET, and SLUG) in epithelial cells derived from enzymatically treated tissue specimens. Overall, among 201 differentially expressed genes, a considerable number of (lymph-)angiogenesis and EMT regulatory factors were identified, including genes encoding cytokines, growth factor receptors, transcription factors, and adhesion molecules. Pathway analysis confirmed enrichment for angiogenesis, epithelial differentiation, and cell guidance pathways at transition from intraepithelial neoplasia to invasive carcinoma and suggested immune-regulatory/inflammatory pathways to be implicated in initiation of invasive growth of cervical cancer. Flow cytometry showed cell phenotype-specific expression pattern for VEGFR3, MET, and SLUG and revealed correlation with the amount of tumor-infiltrating lymphocytes at the early stages of cervical cancer progression. Taken together, these results extend our understanding of driving forces of angiogenesis and metastasis in HPV-associated cervical cancer and may be useful for developing new treatments.

KAT6A amplifications are associated with shorter progression-free survival and overall survival in patients with endometrial serous carcinoma

Somatic copy number alterations (CNA) are common in endometrial serous carcinoma (ESC). We used the Tumor Cancer Genome Atlas Pan Cancer dataset (TCGA Pan Can) to explore the impact of somatic CNA and gene expression levels (mRNA) of cancer-related genes in ESC. Results were correlated with clinico-pathologic parameters such as age of onset, disease stage, progression-free survival (PFS) and overall survival (OS) (n = 108). 1,449 genes with recurrent somatic CNA were identified, observed in 10% or more tumor samples. Somatic CNA and mRNA expression levels were highly correlated (r> = 0.6) for 383 genes. Among these, 45 genes were classified in the Tier 1 category of Cancer Genome Census-Catalogue of Somatic Mutations in Cancer. Eighteen of 45 Tier 1 genes had highly correlated somatic CNA and mRNA expression levels including ARNT, PIK3CA, TBLXR1, ASXL1, EIF4A2, HOOK3, IKBKB, KAT6A, TCEA1, KAT6B, ERBB2, BRD4, KEAP1, PRKACA, DNM2, SMARCA4, AKT2, SS18L1. Our results are in agreement with previously reported somatic CNA for ERBB2, BRD4 and PIK3C in ESC. In addition, AKT2 (p = 0.002) and KAT6A (p = 0.015) amplifications were more frequent in tumor samples from younger patients (<60), and CEBPA (p = 0.028) and MYC (p = 0.023) amplifications were more common with advanced (stage III and IV) disease stage. Patients with tumors carrying KAT6A and MYC amplifications had shorter PFS and OS. The hazard ratio (HR) of KAT6A was 2.82 [95 CI 1.12-7.07] for PFS and 3.87 [95 CI 1.28-11.68] for OS. The HR of MYC was 2.25 [95 CI 1.05-4.81] and 2.62[95 CI 1.07-6.41] for PFS and OS, respectively.

High expression of MKK3 is associated with worse clinical outcomes in African American breast cancer patients

BACKGROUND

African American women experience a twofold higher incidence of triple-negative breast cancer (TNBC) and are 40% more likely to die from breast cancer than women of other ethnicities. However, the molecular bases for the survival disparity in breast cancer remain unclear, and no race-specific therapeutic targets have been proposed. To address this knowledge gap, we performed a systematic analysis of the relationship between gene mRNA expression and clinical outcomes determined for The Cancer Genome Atlas (TCGA) breast cancer patient cohort.

METHODS

The systematic differential analysis of mRNA expression integrated with the analysis of clinical outcomes was performed for 1055 samples from the breast invasive carcinoma TCGA PanCancer cohorts. A deep learning fully-convolutional model was used to determine the association between gene expression and tumor features based on breast cancer patient histopathological images.

RESULTS

We found that more than 30% of all protein-coding genes are differentially expressed in White and African American breast cancer patients. We have determined a set of 32 genes whose overexpression in African American patients strongly correlates with decreased survival of African American but not White breast cancer patients. Among those genes, the overexpression of mitogen-activated protein kinase kinase 3 (MKK3) has one of the most dramatic and race-specific negative impacts on the survival of African American patients, specifically with triple-negative breast cancer. We found that MKK3 can promote the TNBC tumorigenesis in African American patients in part by activating of the epithelial-to-mesenchymal transition induced by master regulator MYC.

CONCLUSIONS

The poor clinical outcomes in African American women with breast cancer can be associated with the abnormal elevation of individual gene expression. Such genes, including those identified and prioritized in this study, could represent new targets for therapeutic intervention. A strong correlation between MKK3 overexpression, activation of its binding partner and major oncogene MYC, and worsened clinical outcomes suggests the MKK3-MYC protein-protein interaction as a new promising target to reduce racial disparity in breast cancer survival.

Human Endogenous Retrovirus Expression Is Associated with Head and Neck Cancer and Differential Survival

Human endogenous retroviruses (HERVs) have been implicated in a variety of human diseases including cancers. However, technical challenges in analyzing HERV sequence data have limited locus-specific characterization of HERV expression. Here, we use the software Telescope (developed to identify expressed transposable elements from metatranscriptomic data) on 43 paired tumor and adjacent normal tissue samples from The Cancer Genome Atlas Program to produce the first locus-specific retrotranscriptome of head and neck cancer. Telescope identified over 3000 expressed HERVs in tumor and adjacent normal tissue, and 1078 HERVs were differentially expressed between the two tissue types. The majority of differentially expressed HERVs were expressed at a higher level in tumor tissue. Differentially expressed HERVs were enriched in members of the HERVH family. Hierarchical clustering based on HERV expression in tumor-adjacent normal tissue resulted in two distinct clusters with significantly different survival probability. Together, these results highlight the importance of future work on the role of HERVs across a range of cancers.

Utilization and survival outcomes of sequential, concurrent and sandwich therapies for advanced stage endometrial cancers by histology

OBJECTIVE

To determine the impact on overall survival (OS) of different modalities of adjuvant therapy for the treatment of stage III endometrial cancer (EC), by histology.

METHODS

Stage 3 endometrioid (EAC), serous (SER), clear cell (CC), and carcinosarcoma (CS) patients who underwent primary surgical staging from 2000 to 2013 were identified in SEER-Medicare. Adjuvant therapy was defined by a 4-arm comparator grouping (none; RT only; CT only; combination RT), as well as by an 8-arm comparator grouping (none; RT only; CT only; concurrent CT-RT; concurrent CT-RT then CT; Serial CT-RT; serial RT-CT; sandwich). Modality of RT and CT were analyzed using Kaplan-Meier estimates, log rank tests, and multivariable cox modeling.

RESULTS

Of 2870 cases identified (1798 EAC, 606 SER, 118 CC, 348 CS), 31.5% received no adjuvant therapy. The remainder received RT or CT alone, concurrent RT-CT, serial or sandwich modalities. OS differed by adjuvant therapy in adjusted and unadjusted models, when combining all histologies, and when stratifying by histology using both the 4-arm, and 8-arm comparator analyses (log rank p < .05, all). By histology, in adjusted analyses, sandwich modality had the greatest improvement in OS for endometrioid, but pairwise comparisons did not identify a superior chemotherapy-based regimen. For serous and clear cell, the greatest improvement in OS was seen with concurrent RT-CT, and for carcinosarcoma, CT alone.

CONCLUSIONS

OS for advanced EC significantly differs by histology and mode of adjuvant therapy. Future studies should evaluate the efficacy of combination-based adjuvant therapy versus chemotherapy alone, by histologic subtype and molecular signature.

Molecular and Cellular Factors Associated with Racial Disparity in Breast Cancer

Recent studies have demonstrated that racial differences can influence breast cancer incidence and survival rate. African American (AA) women are at two to three fold higher risk for breast cancer than other ethnic groups. AA women with aggressive breast cancers show worse prognoses and higher mortality rates relative to Caucasian (CA) women. Over the last few years, effective treatment strategies have reduced mortality from breast cancer. Unfortunately, the breast cancer mortality rate among AA women remains higher compared to their CA counterparts. The focus of this review is to underscore the racial differences and differential regulation/expression of genetic signatures in CA and AA women with breast cancer. Moreover, immune cell infiltration significantly affects the clinical outcome of breast cancer. Here, we have reviewed recent findings on immune cell recruitment in the tumor microenvironment (TME) and documented its association with breast cancer racial disparity. In addition, we have extensively discussed the role of cytokines, chemokines, and other cell signaling molecules among AA and CA breast cancer patients. Furthermore, we have also reviewed the distinct genetic and epigenetic changes in AA and CA patients. Overall, this review article encompasses various molecular and cellular factors associated with breast cancer disparity that affects mortality and clinical outcome.

Exploring the classification of cancer cell lines from multiple omic views

BACKGROUND

Cancer classification is of great importance to understanding its pathogenesis, making diagnosis and developing treatment. The accumulation of extensive omics data of abundant cancer cell line provide basis for large scale classification of cancer with low cost. However, the reliability of cell lines as in vitro models of cancer has been controversial.

METHODS

In this study, we explore the classification on pan-cancer cell line with single and integrated multiple omics data from the Cancer Cell Line Encyclopedia (CCLE) database. The representative omics data of cancer, mRNA data, miRNA data, copy number variation data, DNA methylation data and reverse-phase protein array data were taken into the analysis. TumorMap web tool was used to illustrate the landscape of molecular classification.The molecular classification of patient samples was compared with cancer cell lines.

RESULTS

Eighteen molecular clusters were identified using integrated multiple omics clustering. Three pan-cancer clusters were found in integrated multiple omics clustering. By comparing with single omics clustering, we found that integrated clustering could capture both shared and complementary information from each omics data. Omics contribution analysis for clustering indicated that, although all the five omics data were of value, mRNA and proteomics data were particular important. While the classifications were generally consistent, samples from cancer patients were more diverse than cancer cell lines.

CONCLUSIONS

The clustering analysis based on integrated omics data provides a novel multi-dimensional map of cancer cell lines that can reflect the extent to pan-cancer cell lines represent primary tumors, and an approach to evaluate the importance of omic features in cancer classification.

Characterization of Neoantigen Load Subgroups in Gynecologic and Breast Cancers

Objective

Although gynecologic and breast (Pan-Gyn) cancers share a variety of similar characteristics, their response to immunotherapy is different. Immune checkpoint inhibitor therapy is not effective in all patients, while neoantigen load (NAL) may be a predictive biomarker. However, the selection of a NAL cutoff point and its predictive effect remain to be elucidated.

Methods

We divided 812 Pan-Gyn cancer samples from The Cancer Genome Atlas into three groups based on 60 and 80% of their load percentile. We then correlated the identified NAL subgroups with gene expression, somatic mutation, DNA methylation, and clinicopathological information. We also characterized each subgroup by distinct immune cell enrichment, PD-1 signaling, and cytolytic activity. Finally, we predicted the response of each subgroup to chemotherapy and immunotherapy.

Results

Across Pan-Gyn cancers, we identified three distinct NAL subgroups. These subgroups showed differences in biological function, genetic information, clinical variables, and immune infiltration. Eighty percent was identified as a meaningful cutoff point for NAL. In all patients, a higher NAL (top 20%) was associated with better overall survival as well as high immune infiltration and low intra-tumor heterogeneity. Furthermore, an interesting lncRNA named AC092580.4 was found, which was associated with two significantly different immune genes (CXCL9 and CXCL13).

Conclusions

Our novel findings provide further insights into the NAL of Pan-Gyn cancers and may open up novel opportunities for their exploitation toward personalized treatment with immunotherapy.

An RNA-sequencing-based transcriptome for a significantly prognostic novel driver signature identification in bladder urothelial carcinoma

Bladder cancer (BC) is the ninth most common malignancy worldwide. Bladder urothelial carcinoma (BLCA) constitutes more than 90% of bladder cancer (BC). The five-year survival rate is 5–70%, and patients with BLCA have a poor clinical outcome. The identification of novel clinical molecular markers in BLCA is still urgent to allow for predicting clinical outcomes. This study aimed to identify a novel signature integrating the three-dimension transcriptome of protein coding genes, long non-coding RNAs, microRNAs that is related to the overall survival of patients with BLCA, contributing to earlier prediction and effective treatment selection, as well as to the verification of the established model in the subtypes identified. Gene expression profiling and the clinical information of 400 patients diagnosed with BLCA were retrieved from The Cancer Genome Atlas (TCGA) database. A univariate Cox regression analysis, robust likelihood-based survival modelling analysis and random forests for survival regression and classification algorithms were used to identify the critical biomarkers. A multivariate Cox regression analysis was utilized to construct a risk score formula with a maximum area under the curve (AUC = 0.7669 in the training set). The significant signature could classify patients into high-risk and low-risk groups with significant differences in overall survival time. Similar results were confirmed in the test set (AUC = 0.645) and in the entire set (AUC = 0.710). The multivariate Cox regression analysis indicated that the five-RNA signature was an independent predictive factor for patients with BLCA. Non-negative matrix factorization and a similarity network fusion algorithm were applied for identifying three molecular subtypes. The signature could separate patients in every subtype into high- and low- groups with a distinct difference. Gene set variation analysis of protein-coding genes associated with the five prognostic RNAs demonstrated that the co-expressed protein-coding genes were involved in the pathways and biological process of tumourigenesis. The five-RNA signature could serve as to some degree a reliable independent signature for predicting outcome in patients with BLCA.

Pan-cancer image-based detection of clinically actionable genetic alterations

Molecular alterations in cancer can cause phenotypic changes in tumor cells and their micro-environment. Routine histopathology tissue slides - which are ubiquitously available - can reflect such morphological changes. Here, we show that deep learning can consistently infer a wide range of genetic mutations, molecular tumor subtypes, gene expression signatures and standard pathology biomarkers directly from routine histology. We developed, optimized, validated and publicly released a one-stop-shop workflow and applied it to tissue slides of more than 5000 patients across multiple solid tumors. Our findings show that a single deep learning algorithm can be trained to predict a wide range of molecular alterations from routine, paraffin-embedded histology slides stained with hematoxylin and eosin. These predictions generalize to other populations and are spatially resolved. Our method can be implemented on mobile hardware, potentially enabling point-of-care diagnostics for personalized cancer treatment. More generally, this approach could elucidate and quantify genotype-phenotype links in cancer.

TCGA Pan-Cancer Genomic Analysis of Alternative Lengthening of Telomeres (ALT) Related Genes

Telomere maintenance mechanisms (TMM) are used by cancer cells to avoid apoptosis, 85–90% reactivate telomerase, while 10–15% use the alternative lengthening of telomeres (ALT). Due to anti-telomerase-based treatments, some tumors switch from a telomerase-dependent mechanism to ALT; in fact, the co-existence between both mechanisms has been observed in some cancers. Although different elements in the ALT pathway are uncovered, some molecular mechanisms are still poorly understood. Therefore, with the aim to identify potential molecular markers for the study of ALT, we combined in silico approaches in a 411 telomere maintenance gene set. As a consequence, we conducted a genomic analysis of these genes in 31 Pan-Cancer Atlas studies from The Cancer Genome Atlas and found 325,936 genomic alterations; from which, we identified 20 genes highly mutated in the cancer studies. Finally, we made a protein-protein interaction network and enrichment analysis to observe the main pathways of these genes and discuss their role in ALT-related processes, like homologous recombination and homology directed repair. Overall, due to the lack of understanding of the molecular mechanisms of ALT cancers, we proposed a group of genes, which after ex vivo validations, could represent new potential therapeutic markers in the study of ALT.

Kinome capture sequencing of high-grade serous ovarian carcinoma reveals novel mutations in the JAK3 gene

High-grade serous ovarian carcinoma (HGSOC) remains the deadliest form of epithelial ovarian cancer and despite major efforts little improvement in overall survival has been achieved. Identification of recurring "driver" genetic lesions has the potential to enable design of novel therapies for cancer. Here, we report on a study to find such new therapeutic targets for HGSOC using exome-capture sequencing approach targeting all kinase genes in 127 patient samples. Consistent with previous reports, the most frequently mutated gene was TP53 (97% mutation frequency) followed by BRCA1 (10% mutation frequency). The average mutation frequency of the kinase genes mutated from our panel was 1.5%. Intriguingly, after BRCA1, JAK3 was the most frequently mutated gene (4% mutation frequency). We tested the transforming properties of JAK3 mutants using the Ba/F3 cell-based in vitro functional assay and identified a novel gain-of-function mutation in the kinase domain of JAK3 (p.T1022I). Importantly, p.T1022I JAK3 mutants displayed higher sensitivity to the JAK3-selective inhibitor Tofacitinib compared to controls. For independent validation, we re-sequenced the entire JAK3 coding sequence using tagged amplicon sequencing (TAm-Seq) in 463 HGSOCs resulting in an overall somatic mutation frequency of 1%. TAm-Seq screening of CDK12 in the same population revealed a 7% mutation frequency. Our data confirms that the frequency of mutations in kinase genes in HGSOC is low and provides accurate estimates for the frequency of JAK3 and CDK12 mutations in a large well characterized cohort. Although p.T1022I JAK3 mutations are rare, our functional validation shows that if detected they should be considered as potentially actionable for therapy. The observation of CDK12 mutations in 7% of HGSOC cases provides a strong rationale for routine somatic testing, although more functional and clinical characterization is required to understand which nonsynonymous mutations alterations are associated with homologous recombination deficiency.

From human genome epidemiology to systems epidemiology: current progress and future perspective

The recent progress in human genome epidemiology (HuGE) is already having a profound impact on the practice of medicine and public health. First, the success of genome-wide association studies has greatly expanded the direction and content of epidemiological researches, including revealing new genetic mechanisms of complex diseases, identifying new targets for therapeutic interventions, and improving application in early screening of high-risk populations. At the same time, large-scale genomic studies make it possible to efficiently explore the gene-environment interactions, which will help better understand the biological pathways of complex diseases and identify individuals who may be more susceptible to diseases. Additionally, the emergence of systems epidemiology aims to integrate multi-omics together with epidemiological data to create a systems network that can comprehensively characterize the diverse range of factors contributing to disease development. These progress will help to apply HuGE findings into practice to improve the health of individuals and populations.

Artificial intelligence and omics in cancer

Cancer is a major public health problem worldwide. Current predictions suggest that 13 million people will die each year from cancer by 2030. Thus, new ideas are urgently needed to change paradigms in the global fight against cancer. Over the last decades, artificial intelligence (AI) emerged in the field of cancer research as a new and promising discipline. Although emerging, a great potential is appreciated in AI to improve cancer diagnosis and prognosis, as well as to identify relevant therapeutics in the current era of personalized medicine. Developing pipelines connecting patient-generated health data easily translatable into clinical practice to assist clinicians in decision making represents a challenging but fascinating task. AI algorithms are mainly fueled by multi omics data which, in the case of cancer research, have been largely derived from international cancer programs, including The Cancer Genome Atlas (TCGA). Here, I briefly review some examples of supervised and unsupervised big data derived from TCGA programs and comment on how AI algorithms have been applied to improve the management of patients with cancer. In this context, Artificial Intelligence in Cancer journal was specifically launched to promote the development of this discipline, by serving as a forum to publish high-quality basic and clinical research articles in various fields of AI in oncology.

Targeting the Hippo pathway in cancer, fibrosis, wound healing and regenerative medicine

The Hippo pathway is an evolutionarily conserved signalling pathway with key roles in organ development, epithelial homeostasis, tissue regeneration, wound healing and immune modulation. Many of these roles are mediated by the transcriptional effectors YAP and TAZ, which direct gene expression via control of the TEAD family of transcription factors. Dysregulated Hippo pathway and YAP/TAZ-TEAD activity is associated with various diseases, most notably cancer, making this pathway an attractive target for therapeutic intervention. This Review highlights the key findings from studies of Hippo pathway signalling across biological processes and diseases, and discusses new strategies and therapeutic implications of targeting this pathway.

Toll-Like Receptor Characterization Correlates with Asthma and Is Predictive of Diagnosis

Toll-like receptors (TLRs) play crucial roles in the recognition of invading pathogens and the immune system. However, the effect of TLRs in asthma is still not fully known. This study was performed to better understand the role of TLR signatures in asthma. Blood samples from case-control studies (study 1: 348 asthmas and 39 normal controls and validation study 2: 411 asthmas and 87 normal controls) were enrolled. The single-sample gene set enrichment analysis method was performed to quantify the abundance of 21 TLR signatures. Gene ontology analysis and pathway function analysis were conducted for functional analysis, and a protein-protein interaction network was constructed. The area under the curve (AUC) value was used to assess the diagnostic capacity. In this study, TLR2/TLR3/TLR4 pathway, MyD88-dependent/independent TLR pathway, positive regulation of TLR4 pathway, and TLR binding signatures were significantly higher in asthma. Functional analysis showed that biological processes and pathways were still involved in TLR cascades and TLR signaling pathway. Eleven hub TLR-related genes were identified, and further validation demonstrated that the combination of TLR-related genes was a good diagnostic biomarker for asthma (AUC = 0.8). Our study provided more insight into the underlying immune mechanism of how TLR signatures affected asthma. The use of the easy-to-apply TLR-related genes might represent a promising blood-based biomarker for early detection of asthma.

Long Noncoding RNA LINC01134 Promotes Hepatocellular Carcinoma Metastasis via Activating AKT1S1 and NF-κB Signaling

Hepatocellular carcinoma (HCC) is one of the most common malignancies with poor outcomes. The main causes of HCC-related deaths are recurrence and metastasis. Long noncoding RNAs (lncRNAs) are recently identified as critical regulators in cancers. However, the lncRNAs involved in HCC recurrence and metastasis are poorly understood. In this study, via analyzing The Cancer Genome Atlas Liver Hepatocellular Carcinoma dataset, we identified a novel lncRNA LINC01134, which is highly expressed in HCC tissues and correlated with microvascular invasion, macrovascular invasion, recurrence, and poor overall survival of HCC patients. Functional experiments revealed that ectopic expression of LINC01134 promotes HCC cell migration and invasion in vitro and HCC liver metastasis and lung metastasis in vivo. Knockdown of LINC01134 represses HCC cell migration and invasion in vitro and HCC liver metastasis and lung metastasis in vivo. Mechanistically, we found that LINC01134 directly binds the promoter of AKT1S1 and activates AKT1S1 expression. Via activating AKT1S1, LINC01134 further activates NF-κB signaling. The expression of LINC01134 is significantly positively correlated with that of AKT1S1 in HCC tissues. In line with LINC01134, AKT1S1 is also highly expressed in HCC tissues and correlated with poor survival of HCC patients. Functional rescue experiments showed that repressing AKT1S1 or NF-κB signaling abrogates the roles of LINC01134 in HCC. Taken together, these findings recognized LINC01134 as a novel oncogenic lncRNA, which indicates vascular invasion, recurrence, and poor overall survival of HCC patients. LINC01134 promotes HCC metastasis via activating AKT1S1 expression and subsequently activating NF-κB signaling. This study suggested LINC01134 as a potential prognostic biomarker and therapeutic target for HCC.

Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets

The significance of pan-cancer categories has recently been recognized as widespread in cancer research. Pan-cancer categorizes a cancer based on its molecular pathology rather than an organ. The molecular similarities among multi-omics data found in different cancer types can play several roles in both biological processes and therapeutic developments. Therefore, an integrated analysis for various genomic data is frequently used to reveal novel genetic and molecular mechanisms. However, a variety of algorithms for multi-omics clustering have been proposed in different fields. The comparison of different computational clustering methods in pan-cancer analysis performance remains unclear. To increase the utilization of current integrative methods in pan-cancer analysis, we first provide an overview of five popular computational integrative tools: similarity network fusion, integrative clustering of multiple genomic data types (iCluster), cancer integration via multi-kernel learning (CIMLR), perturbation clustering for data integration and disease subtyping (PINS) and low-rank clustering (LRACluster). Then, a priori interactions in multi-omics data were incorporated to detect prominent molecular patterns in pan-cancer data sets. Finally, we present comparative assessments of these methods, with discussion over key issues in applying these algorithms. We found that all five methods can identify distinct tumor compositions. The pan-cancer samples can be reclassified into several groups by different proportions. Interestingly, each method can classify the tumors into categories that are different from original cancer types or subtypes, especially for ovarian serous cystadenocarcinoma (OV) and breast invasive carcinoma (BRCA) tumors. In addition, all clusters of the five computational methods show notable prognostic values. Furthermore, both the 9 recurrent differential genes and the 15 common pathway characteristics were identified across all the methods. The results and discussion can help the community select appropriate integrative tools according to different research tasks or aims in pan-cancer analysis.

Comprehensive analysis of prognostic alternative splicing signature in cervical cancer

Background

Alternative splicing (AS) is a key factor in protein-coding gene diversity, and is associated with the development and progression of malignant tumours. However, the role of AS in cervical cancer is unclear.

Methods

The AS data for cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) were downloaded from The Cancer Genome Atlas (TCGA) SpliceSeq website. Few prognostic AS events were identified through univariate Cox analysis. We further identified the prognostic prediction models of the seven subtypes of AS events and assessed their predictive power. We constructed a clinical prediction model through global analysis of prognostic AS events and established a nomogram using the risk score calculated from the prognostic model and relevant clinical information. Unsupervised cluster analysis was used to explore the relationship between prognostic AS events in the model and clinical features.

Results

A total of 2860 prognostic AS events in cervical cancer were identified. The best predictive effect was shown by a single alternate acceptor subtype with an area under the curve of 0.96. Our clinical prognostic model included a nine-AS event signature, and the c-index of the predicted nomogram model was 0.764. SNRPA and CCDC12 were hub genes for prognosis-associated splicing factors. Unsupervised cluster analysis through the nine prognostic AS events revealed three clusters with different survival patterns.

Conclusions

AS events affect the prognosis and biological progression of cervical cancer. The identified prognostic AS events and splicing regulatory networks can increase our understanding of the underlying mechanisms of cervical cancer, providing new therapeutic strategies.

Identification of the Mutational Landscape of Gynecological Malignancies

Background: Cancer is a complex disease that arises from the accumulation of multiple genetic and non-genetic changes. Advances in sequencing technologies have allowed unbiased and global analysis of patient-derived tumor samples and the discovery of genetic and transcriptional changes in key genes and oncogenic pathways. That in turn has facilitated a better understanding of the underlying causes of cancer initiation and progression, resulting in new therapeutic targets. Methods: In our study, we have analyzed the mutational landscape of gynecological malignancies using datasets from The Cancer Genome Atlas (TCGA). We have also analyzed Oncomine datasets to establish the impact of their alteration on disease recurrence and survival of patients. Results: In this study, we analyzed a series of different gynecological malignancies for commonly occurring genetic and non-genetic alterations. These studies show that white women have higher incidence of gynecological malignancies. Furthermore, our study identified 16 genes that are altered at a frequency >10% among all of the gynecological malignancies and tumor suppressor TP53 is the most altered gene in these malignancies (>50% of the cases). The top 16 genes fall into the categories of either tumor suppressor or oncogenes and a subset of these genes are associated with poor prognosis, some affecting recurrence and survival of ovarian cancer patients. Conclusion: In sum, our study identified 16 major genes that are broadly mutated in a large majority of gynecological malignancies and in some cases predict survival and recurrence in patients with gynecological malignancies. We predict that the functional studies will determine their relative role in the initiation and progression of gynecological malignancies and also establish if some of them represents drug targets for anti-cancer therapy.

Biomarkers in ovarian cancer: To be or not to be

Biomarkers are becoming increasingly important in the treatment of epithelial ovarian cancer. Recent work from many laboratories has begun to provide clinically meaningful biomarkers. This review summarizes the state of the science regarding biomarkers for stratifying early-stage patients into those who benefit from adjuvant treatment, primary debulking versus interval debulking, and specific targeted therapy. In addition, new molecular imaging technologies have been developed to allow the surgeon to resect subvisible tumor deposits. These efforts should increase clinical effectiveness while minimizing toxicities for patients.

Ancestry-specific predisposing germline variants in cancer

BACKGROUND

Distinct prevalence of inherited genetic predisposition may partially explain the difference of cancer risks across ancestries. Ancestry-specific analyses of germline genomes are required to inform cancer genetic risk and prognosis of diverse populations.

METHODS

We conducted analyses using germline and somatic sequencing data generated by The Cancer Genome Atlas. Collapsing pathogenic and likely pathogenic variants to cancer predisposition genes (CPG), we analyzed the association between CPGs and cancer types within ancestral groups. We also identified the predisposition-associated two-hit events and gene expression effects in tumors.

RESULTS

Genetic ancestry analysis classified the cohort of 9899 cancer cases into individuals of primarily European (N = 8184, 82.7%), African (N = 966, 9.8%), East Asian (N = 649, 6.6%), South Asian (N = 48, 0.5%), Native/Latin American (N = 41, 0.4%), and admixed (N = 11, 0.1%) ancestries. In the African ancestry, we discovered a potentially novel association of BRCA2 in lung squamous cell carcinoma (OR = 41.4 [95% CI, 6.1-275.6]; FDR = 0.002) previously identified in Europeans, along with a known association of BRCA2 in ovarian serous cystadenocarcinoma (OR = 8.5 [95% CI, 1.5-47.4]; FDR = 0.045). In the East Asian ancestry, we discovered one previously known association of BRIP1 in stomach adenocarcinoma (OR = 12.8 [95% CI, 1.8-90.8]; FDR = 0.038). Rare variant burden analysis further identified 7 suggestive associations in African ancestry individuals previously described in European ancestry, including SDHB in pheochromocytoma and paraganglioma, ATM in prostate adenocarcinoma, VHL in kidney renal clear cell carcinoma, FH in kidney renal papillary cell carcinoma, and PTEN in uterine corpus endometrial carcinoma. Most predisposing variants were found exclusively in one ancestry in the TCGA and gnomAD datasets. Loss of heterozygosity was identified for 7 out of the 15 African ancestry carriers of predisposing variants. Further, tumors from the SDHB or BRCA2 carriers showed simultaneous allelic-specific expression and low gene expression of their respective affected genes, and FH splice-site variant carriers showed mis-splicing of FH.

CONCLUSIONS

While several CPGs are shared across patients, many pathogenic variants are found to be ancestry-specific and trigger somatic effects. Studies using larger cohorts of diverse ancestries are required to pinpoint ancestry-specific genetic predisposition and inform genetic screening strategies.

Prediction of breast cancer proteins involved in immunotherapy, metastasis, and RNA-binding using molecular descriptors and artificial neural networks

Breast cancer (BC) is a heterogeneous disease where genomic alterations, protein expression deregulation, signaling pathway alterations, hormone disruption, ethnicity and environmental determinants are involved. Due to the complexity of BC, the prediction of proteins involved in this disease is a trending topic in drug design. This work is proposing accurate prediction classifier for BC proteins using six sets of protein sequence descriptors and 13 machine-learning methods. After using a univariate feature selection for the mix of five descriptor families, the best classifier was obtained using multilayer perceptron method (artificial neural network) and 300 features. The performance of the model is demonstrated by the area under the receiver operating characteristics (AUROC) of 0.980 ± 0.0037, and accuracy of 0.936 ± 0.0056 (3-fold cross-validation). Regarding the prediction of 4,504 cancer-associated proteins using this model, the best ranked cancer immunotherapy proteins related to BC were RPS27, SUPT4H1, CLPSL2, POLR2K, RPL38, AKT3, CDK3, RPS20, RASL11A and UBTD1; the best ranked metastasis driver proteins related to BC were S100A9, DDA1, TXN, PRNP, RPS27, S100A14, S100A7, MAPK1, AGR3 and NDUFA13; and the best ranked RNA-binding proteins related to BC were S100A9, TXN, RPS27L, RPS27, RPS27A, RPL38, MRPL54, PPAN, RPS20 and CSRP1. This powerful model predicts several BC-related proteins that should be deeply studied to find new biomarkers and better therapeutic targets. Scripts can be downloaded at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins.

B-cell lymphoma 2 family genes show a molecular pattern of spatiotemporal heterogeneity in gynaecologic and breast cancer

Objectives

BCL2 family proteins have been widely studied over the past decade due to their essential roles in apoptosis, oncogenesis and anti‐cancer therapy. However, the similarities and differences in the spatial pattern of the BCL2 gene family within the context of chromatin have not been well characterized. We sought to fill this knowledge gap by assessing correlations between gene alteration, gene expression, chromatin accessibility, and clinical outcomes in gynaecologic and breast cancer.

Materials and methods

In this study, the molecular characteristics of the BCL2 gene family in gynaecologic cancer were systematically analysed by integrating multi‐omics datasets, including transcriptomics, chromatin accessibility, copy number variation, methylomics and clinical outcome.

Results

We evaluated spatiotemporal associations between long‐range regulation peaks and tumour heterogeneity. Differential expression of the BCL2 family was coupled with widespread chromatin accessibility changes in gynaecologic cancer, accompanied by highly heterogeneous distal non‐coding accessibility surrounding the BCL2L1 gene loci. A relationship was also identified between gene expression, gene amplification, enhancer signatures, DNA methylation and overall patient survival. Prognostic analysis implied clinical correlations with BAD, BIK and BAK1. A shared protein regulatory network was established in which the co‐mutation signature of TP53 and PIK3CA was linked to the BCL2L1 gene.

Conclusions

Our results provide the first systematic identification of the molecular features of the BCL2 family under the spatial pattern of chromatin in gynaecologic and breast cancer. These findings broaden the therapeutic scope of the BCL2 family to the non‐coding region by including a significantly conserved distal region overlaying an enhancer.

Non-coding somatic mutations converge on the PAX8 pathway in ovarian cancer

The functional consequences of somatic non-coding mutations in ovarian cancer (OC) are unknown. To identify regulatory elements (RE) and genes perturbed by acquired non-coding variants, here we establish epigenomic and transcriptomic landscapes of primary OCs using H3K27ac ChIP-seq and RNA-seq, and then integrate these with whole genome sequencing data from 232 OCs. We identify 25 frequently mutated regulatory elements, including an enhancer at 6p22.1 which associates with differential expression of ZSCAN16 (P = 6.6 × 10-4) and ZSCAN12 (P = 0.02). CRISPR/Cas9 knockout of this enhancer induces downregulation of both genes. Globally, there is an enrichment of single nucleotide variants in active binding sites for TEAD4 (P = 6 × 10-11) and its binding partner PAX8 (P = 2×10-10), a known lineage-specific transcription factor in OC. In addition, the collection of cis REs associated with PAX8 comprise the most frequently mutated set of enhancers in OC (P = 0.003). These data indicate that non-coding somatic mutations disrupt the PAX8 transcriptional network during OC development.

Chemoresistance-associated alternative splicing signatures in serous ovarian cancer

Primary platinum-based chemoresistance occurs in ~30% of patients with serous ovarian cancer. Chemoresistance is the main cause of disease recurrence, and accurate predictors to identify these patients with chemoresistance are required. Alternative splicing (AS) is a post-transcriptional modification process that is altered in cancer. A possible association between AS and chemoresistance is unclear and needs to be studied comprehensively in ovarian cancer. In the present study, RNA-sequencing data and clinical information for 320 patients with ovarian serous cystadenocarcinoma (OV) were downloaded from The Cancer Genome Atlas (TCGA) database. Splicing events were determined using the TCGA SpliceSeq tool. Seven types of AS events were identified. Univariate and multivariate logistic analyses were performed, and predictive models for OV chemoresistance were established, as well as a splicing network. A total of 22,036 AS events were identified in 7,404 genes, with 915 AS events detected in 677 genes that were significantly associated with chemoresistance in patients with OV. A receiver operating characteristic (ROC) curve was constructed for resistance predictive models composed of the most significant AS events. The area under the ROC curve was 0.931, indicating strong and efficient prediction of chemoresistance. Additionally, the high-risk score was associated with shorter overall survival. The splicing correlation network suggested a potential role of splicing factors in chemoresistance. In summary, the present study created a powerful predictor for primary platinum-based chemoresistance in patients with OV, identified splicing networks that could be involved in potential mechanisms of chemoresistance and provided potential targets to overcome chemoresistance.

Detection and Comparative Analysis of Methylomic Biomarkers of Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a common autoimmune disorder influenced by both genetic and environmental factors. To investigate possible contributions of DNA methylation to the etiology of RA with minimum confounding genetic heterogeneity, we investigated genome-wide DNA methylation in disease-discordant monozygotic twin pairs. This study hypothesized that methylomic biomarkers might facilitate accurate RA detection. A comprehensive series of biomarker detection algorithms were utilized to find the best methylomic biomarkers for detecting RA patients using the methylomic data of the peripheral blood samples. The best model achieved 100.00% in accuracy (Acc) with 81 methylomic biomarkers and a 10-fold cross-validation (10FCV) strategy. Some of the methylomic biomarkers were experimentally confirmed to be associated with the onset or development of RA. It is also interesting to observe that many of the detected biomarkers were from chromosome Y, supporting the knowledge that RA has a significant gender discrepancy.