The Cancer Genomics Hub (CGHub): overcoming cancer through the power of torrential data

Recombinant antibodies from clonally expanded cancer-associated plasma cells

Although the clinical significance of plasma cells within tumors has been recognized, studies on the development of plasma cells and the characteristics of the antibodies they secrete within the tumor microenvironment remain limited. We investigated the properties of plasma cells within cancer tissues using single-cell RNA and single cell B cell receptor sequencing. We characterized plasma cells exhibiting clonal expansion and synthesized the antibodies produced by these cells, confirming the clinical relevance of immunoglobulin H (IGH) isotypes. Plasma cells comprised approximately 5% of the total immune cell population within the tumor; clonal expansion was more prevalent in plasma cells than in B cells. Among plasma cells, the most frequent immunoglobulin isotype was IGHG1 and IGKC. We synthesized six recombinant antibodies, including those from the largest clonal plasma cells. Two antibodies that formed clones showed membranous staining in cancer cells. The cancer cells that metastasized to the lymph node showed a loss of expression as observed by immunohistochemistry. Analysis of bulk RNA sequencing data from 1078 patients with breast cancer revealed that tumor-infiltrating plasma cells expressing IGHG1 were associated with favorable prognoses. These tumors exhibited increased B cell receptor diversity, immunogenic mutation, and intratumoral heterogeneity. This study suggests the potential for discovering cancer-associated antibodies derived from intratumoral plasma cells.

Full-length transcript alterations in human bronchial epithelial cells with U2AF1 S34F mutations

U2AF1 is one of the most recurrently mutated splicing factors in lung adenocarcinoma and has been shown to cause transcriptome-wide pre-mRNA splicing alterations; however, the full-length altered mRNA isoforms associated with the mutation are largely unknown. To better understand the impact U2AF1 has on full-length isoform fate and function, we conducted high-throughput long-read cDNA sequencing from isogenic human bronchial epithelial cells with and without a U2AF1 S34F mutation. We identified 49,366 multi-exon transcript isoforms, more than half of which did not match GENCODE or short-read-assembled isoforms. We found 198 transcript isoforms with significant expression and usage changes relative to WT, only 68% of which were assembled by short reads. Expression of isoforms from immune-related genes is largely down-regulated in mutant cells and without observed splicing changes. Finally, we reveal that isoforms likely targeted by nonsense-mediated decay are down-regulated in U2AF1 S34F cells, suggesting that isoform changes may alter the translational output of those affected genes. Altogether, our work provides a resource of full-length isoforms associated with U2AF1 S34F in lung cells.

Particulate Matters Affecting lncRNA Dysregulation and Glioblastoma Invasiveness: In Silico Applications and Current Insights

With a reported rise in global air pollution, more than 50% of the population remains exposed to toxic air pollutants in the form of particulate matters (PMs). PMs, from various sources and of varying sizes, have a significant impact on health as long-time exposure to them has seen a correlation with various health hazards and have also been determined to be carcinogenic. In addition to disrupting known cellular pathways, PMs have also been associated with lncRNA dysregulation-a factor that increases predisposition towards the onset or progression of cancer. lncRNA dysregulation is further seen to mediate glioblastoma multiforme (GBM) progression. The vast array of information regarding cancer types including GBM and its various precursors can easily be obtained via innovative in silico approaches in the form of databases such as GEO and TCGA; however, a need to obtain selective and specific information correlating anthropogenic factors and disease progression-in the case of GBM-can serve as a critical tool to filter down and target specific PMs and lncRNAs responsible for regulating key cancer hallmarks in glioblastoma. The current review article proposes an in silico approach in the form of a database that reviews current updates on correlation of PMs with lncRNA dysregulation leading to GBM progression.

mirTarRnaSeq: An R/Bioconductor Statistical Package for miRNA-mRNA Target Identification and Interaction Analysis

We introduce mirTarRnaSeq, an R/Bioconductor package for quantitative assessment of miRNA-mRNA relationships within sample cohorts. mirTarRnaSeq is a statistical package to explore predicted or pre-hypothesized miRNA-mRNA relationships following target prediction.We present two use cases applying mirTarRnaSeq. First, to identify miRNA targets, we examined EBV miRNAs for interaction with human and virus transcriptomes of stomach adenocarcinoma. This revealed enrichment of mRNA targets highly expressed in CD105+ endothelial cells, monocytes, CD4+ T cells, NK cells, CD19+ B cells, and CD34 cells. Next, to investigate miRNA-mRNA relationships in SARS-CoV-2 (COVID-19) infection across time, we used paired miRNA and RNA sequenced datasets of SARS-CoV-2 infected lung epithelial cells across three time points (4, 12, and 24 hours post-infection). mirTarRnaSeq identified evidence for human miRNAs targeting cytokine signaling and neutrophil regulation immune pathways from 4 to 24 hours after SARS-CoV-2 infection. Confirming the clinical relevance of these predictions, three of the immune specific mRNA-miRNA relationships identified in human lung epithelial cells after SARS-CoV-2 infection were also observed to be differentially expressed in blood from patients with COVID-19. Overall, mirTarRnaSeq is a robust tool that can address a wide-range of biological questions providing improved prediction of miRNA-mRNA interactions.

Deficiency of replication-independent DNA mismatch repair drives a 5-methylcytosine deamination mutational signature in cancer

Multiple mutational signatures have been associated with DNA mismatch repair (MMR)–deficient cancers, but the mechanisms underlying their origin remain unclear. Here, using mutation data from cancer genomes, we identify a previously unknown function of MMR that is able to protect genomes from 5-methylcytosine (5mC) deamination–induced somatic mutations in a replication-independent manner. Cancers with deficiency of MMR proteins MSH2/MSH6 (MutSα) exhibit mutational signature contributions distinct from those that are deficient in MLH1/PMS2 (MutLα). This disparity arises from unrepaired 5mC deamination–induced mismatches rather than replicative DNA polymerase errors. In cancers with biallelic loss of MBD4 DNA glycosylase, repair of 5mC deamination damage is strongly associated with H3K36me3 chromatin, implicating MutSα as the essential factor in its repair. We thus uncover a noncanonical role of MMR in the protection against 5mC deamination–induced mutation in human cancers.

Using bioinformatics approaches to investigate driver genes and identify BCL7A as a prognostic gene in colorectal cancer

Colorectal cancer (CRC) results from the uncontrolled growth of cells in the colon, rectum, or appendix. The 5-year relative survival rate for patients with CRC is 65% and is correlated with the stage at diagnosis (being 91% for stage I at diagnosis versus 12% for stage IV). This study aimed to identify CRC driver genes to assist in the design of a cancer panel to detect gene mutations during clinical early-stage screening and identify genes for use in prognostic assessments and the evaluation of appropriate treatment options. First, we utilized bioinformatics approaches to analyze 354 paired sequencing profiles from The Cancer Genome Atlas (TCGA) to identify CRC driver genes and analyzed the sequencing profiles of 38 patients with >5 years of follow-up data to search for prognostic genes. The results revealed eight driver genes and ten prognostic genes. Next, the presence of the identified gene mutations was verified using tissue and blood samples from Taiwanese CRC patients. The results showed that the set identified gene mutations provide high coverage for driver gene screening, and APC, TP53, PIK3CA, and FAT4 could be detected in blood as ctDNA test targets. We further found that BCL7A gene mutation was correlated with prognosis in CRC (log-rank p-value = 0.02), and that mutations of BCL7A could be identified in ctDNA samples. These findings may be of value in clinical early cancer detection, disease monitoring, drug development, and treatment efforts in the future.

CoronaPep: An Anti-Coronavirus Peptide Generation Tool

The novel coronavirus (COVID-19) infections have adopted the shape of a global pandemic now, demanding an urgent vaccine design. The current work reports contriving an anti-coronavirus peptide scanner tool to discern anti-coronavirus targets in the embodiment of peptides. The proffered CoronaPep tool features the fast fingerprinting of the anti-coronavirus target serving supreme prominence in the current bioinformatics research. The anti-coronavirus target protein sequences reported from the current outbreak are scanned against the anti-coronavirus target data-sets via CORONAPEP which provides precision-based anti-coronavirus peptides. This tool is specifically for the coronavirus data, which can predict peptides from the whole genome, or a gene or protein's list. Besides it is relatively fast, accurate, userfriendly and can generate maximum output from the limited information. The availability of tools like CORONAPEP will immeasurably perquisite researchers in the discipline of oncology and structure-based drug design.

Web tools to perform long non-coding RNAs analysis in oncology research

Accumulated evidence suggests that the widely expressed long-non-coding RNAs (lncRNAs) are involved in biogenesis. Some aberrant lncRNAs are closely related to pathological changes, for instance, in cancer. Both in tumorigenesis and cancer progression, depending on the interplay with cellular molecules, lncRNAs can modulate transcriptional interference, chromatin remodeling, post-translational regulation and protein modification, and further interfere with signaling pathways. Aiming to the diagnosis/ prognosis markers or potential therapeutical targets, it is important to figure out the specific mechanism and the tissue-specific expressing patterns of lncRNAs. Generally, the bioinformatics analysis is the first step. More and more in silico databases are increasing. But the existing integrative online platforms’ functions are not only having their unique features but also share some common features, which may lead to a waste of time for researchers. Here, we reviewed these web tools according to the functions. For each database, we clarified the data source, analysis method and the evidence that the analysis result is derived from. This review also illustrated examples in practical use for a specific lncRNA by these web tools. It will provide convenience for researchers to quickly choose the appropriate bioinformatics web tools in oncology studies.

Processing and Analysis of RNA-seq Data from Public Resources

Advances in next generation sequencing (NGS) technologies resulted in a broad array of large-scale gene expression studies and an unprecedented volume of whole messenger RNA (mRNA) sequencing data, or the transcriptome (also known as RNA sequencing, or RNA-seq). These include the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA), among others. Here we cover some of the commonly used datasets, provide an overview on how to begin the analysis pipeline, and how to explore and interpret the data provided by these publicly available resources.

Integrative analysis of long extracellular RNAs reveals a detection panel of noncoding RNAs for liver cancer

Rationale: Long extracellular RNAs (exRNAs) in plasma can be profiled by new sequencing technologies, even with low abundance. However, cancer-related exRNAs and their variations remain understudied. Methods: We investigated different variations (i.e. differential expression, alternative splicing, alternative polyadenylation, and differential editing) in diverse long exRNA species (e.g. long noncoding RNAs and circular RNAs) using 79 plasma exosomal RNA-seq (exoRNA-seq) datasets of multiple cancer types. We then integrated 53 exoRNA-seq datasets and 65 self-profiled cell-free RNA-seq (cfRNA-seq) datasets to identify recurrent variations in liver cancer patients. We further combined TCGA tissue RNA-seq datasets and validated biomarker candidates by RT-qPCR in an individual cohort of more than 100 plasma samples. Finally, we used machine learning models to identify a signature of 3 noncoding RNAs for the detection of liver cancer. Results: We found that different types of RNA variations identified from exoRNA-seq data were enriched in pathways related to tumorigenesis and metastasis, immune, and metabolism, suggesting that cancer signals can be detected from long exRNAs. Subsequently, we identified more than 100 recurrent variations in plasma from liver cancer patients by integrating exoRNA-seq and cfRNA-seq datasets. From these datasets, 5 significantly up-regulated long exRNAs were confirmed by TCGA data and validated by RT-qPCR in an independent cohort. When using machine learning models to combine two of these validated circular and structured RNAs (SNORD3B-1, circ-0080695) with a miRNA (miR-122) as a panel to classify liver cancer patients from healthy donors, the average AUROC of the cross-validation was 89.4%. The selected 3-RNA panel successfully detected 79.2% AFP-negative samples and 77.1% early-stage liver cancer samples in the testing and validation sets. Conclusions: Our study revealed that different types of RNA variations related to cancer can be detected in plasma and identified a 3-RNA detection panel for liver cancer, especially for AFP-negative and early-stage patients.

Single-cell analysis of AIMP2 splice variants informs on drug sensitivity and prognosis in hematologic cancer

Aminoacyl-tRNA synthetase-interacting multifunctional protein 2 (AIMP2) is a non-enzymatic component required for the multi-tRNA synthetase complex. While exon 2 skipping alternatively spliced variant of AIMP2 (AIMP2-DX2) compromises AIMP2 activity and is associated with carcinogenesis, its clinical potential awaits further validation. Here, we found that AIMP2-DX2/AIMP2 expression ratio is strongly correlated with major cancer signaling pathways and poor prognosis, particularly in acute myeloid leukemia (AML). Analysis of a clinical patient cohort revealed that AIMP2-DX2 positive AML patients show decreased overall survival and progression-free survival. We also developed targeted RNA-sequencing and single-molecule RNA-FISH tools to quantitatively analyze AIMP2-DX2/AIMP2 ratios at the single-cell level. By subclassifying hematologic cancer cells based on their AIMP2-DX2/AIMP2 ratios, we found that downregulating AIMP2-DX2 sensitizes cells to anticancer drugs only for a subgroup of cells while it has adverse effects on others. Collectively, our study establishes AIMP2-DX2 as a potential biomarker and a therapeutic target for hematologic cancer.

Ku, Kim et al develop a method to analyse the ratio of the alternatively spliced variant of AIMP2 to full length AIMP via single-molecule RNA-FISH. They can subclassify hematologic cancer based on AIMP2-DX2/AIMP2 ratio and find that cells with high AIMP2-DX2 ratio can be sensitized to chemotherapy drugs by depleting AIMP2-DX2.

Framework for quality assessment of whole genome cancer sequences

Bringing together cancer genomes from different projects increases power and allows the investigation of pan-cancer, molecular mechanisms. However, working with whole genomes sequenced over several years in different sequencing centres requires a framework to compare the quality of these sequences. We used the Pan-Cancer Analysis of Whole Genomes cohort as a test case to construct such a framework. This cohort contains whole cancer genomes of 2832 donors from 18 sequencing centres. We developed a non-redundant set of five quality control (QC) measurements to establish a star rating system. These QC measures reflect known differences in sequencing protocol and provide a guide to downstream analyses and allow for exclusion of samples of poor quality. We have found that this is an effective framework of quality measures. The implementation of the framework is available at: https://dockstore.org/containers/quay.io/jwerner_dkfz/pancanqc:1.2.2.

Working with cancer genomes from multiple projects can increase investigative power, but quality of sequences can vary. Here, the authors present a framework for comparing whole genome sequencing quality to help researchers guide downstream analyses and exclude poor quality samples.

Effects of germline and somatic events in candidate BRCA-like genes on breast-tumor signatures

Mutations in BRCA1 and BRCA2 cause deficiencies in homologous recombination repair (HR), resulting in repair of DNA double-strand breaks by the alternative non-homologous end-joining pathway, which is more error prone. HR deficiency of breast tumors is important because it is associated with better responses to platinum salt therapies and PARP inhibitors. Among other consequences of HR deficiency are characteristic somatic-mutation signatures and gene-expression patterns. The term "BRCA-like" (or "BRCAness") describes tumors that harbor an HR defect but have no detectable germline mutation in BRCA1 or BRCA2. A better understanding of the genes and molecular events associated with tumors being BRCA-like could provide mechanistic insights and guide development of targeted treatments. Using data from The Cancer Genome Atlas (TCGA) for 1101 breast-cancer patients, we identified individuals with a germline mutation, somatic mutation, homozygous deletion, and/or hypermethylation event in BRCA1, BRCA2, and 59 other cancer-predisposition genes. Based on the assumption that BRCA-like events would have similar downstream effects on tumor biology as BRCA1/BRCA2 germline mutations, we quantified these effects based on somatic-mutation signatures and gene-expression profiles. We reduced the dimensionality of the somatic-mutation signatures and expression data and used a statistical resampling approach to quantify similarities among patients who had a BRCA1/BRCA2 germline mutation, another type of aberration in BRCA1 or BRCA2, or any type of aberration in one of the other genes. Somatic-mutation signatures of tumors having a non-germline aberration in BRCA1/BRCA2 (n = 80) were generally similar to each other and to tumors from BRCA1/BRCA2 germline carriers (n = 44). Additionally, somatic-mutation signatures of tumors with germline or somatic events in ATR (n = 16) and BARD1 (n = 8) showed high similarity to tumors from BRCA1/BRCA2 carriers. Other genes (CDKN2A, CTNNA1, PALB2, PALLD, PRSS1, SDHC) also showed high similarity but only for a small number of events or for a single event type. Tumors with germline mutations or hypermethylation of BRCA1 had relatively similar gene-expression profiles and overlapped considerably with the Basal-like subtype; but the transcriptional effects of the other events lacked consistency. Our findings confirm previously known relationships between molecular signatures and germline or somatic events in BRCA1/BRCA2. Our methodology represents an objective way to identify genes that have similar downstream effects on molecular signatures when mutated, deleted, or hypermethylated.

Integrated Analysis of TP53 Gene and Pathway Alterations in The Cancer Genome Atlas

The TP53 tumor suppressor gene is frequently mutated in human cancers. An analysis of five data platforms in 10,225 patient samples from 32 cancers reported by The Cancer Genome Atlas (TCGA) enables comprehensive assessment of p53 pathway involvement in these cancers. More than 91% of TP53-mutant cancers exhibit second allele loss by mutation, chromosomal deletion, or copy-neutral loss of heterozygosity. TP53 mutations are associated with enhanced chromosomal instability, including increased amplification of oncogenes and deep deletion of tumor suppressor genes. Tumors with TP53 mutations differ from their non-mutated counterparts in RNA, miRNA, and protein expression patterns, with mutant TP53 tumors displaying enhanced expression of cell cycle progression genes and proteins. A mutant TP53 RNA expression signature shows significant correlation with reduced survival in 11 cancer types. Thus, TP53 mutation has profound effects on tumor cell genomic structure, expression, and clinical outlook.

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology

The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). Data curation poses a significant challenge as different parameters, instruments, and sample preparations approaches are employed for generating these big data sets. AI could reduce the fuzziness and randomness in data handling and build a platform for the data ecosystem, and thus serve as the primary choice for data mining and big data analysis to make informed decisions. However, AI implication remains intricate for researchers/clinicians lacking specific training in computational tools and informatics. Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites. Hence a comprehensive study, including genomics, epi-genomics, transcriptomics, proteomics, and metabolomics, along with the medical/mass-spectrometry imaging, patient clinical history, treatments provided, genetics, and disease endemicity, is essential. Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification of diagnostic and prognostic markers, and (d) monitor patient's response to drugs/treatments and recovery. AI enables precision disease management well beyond the prevalent disease stratification patterns, such as differential expression and supervised classification. This review highlights critical advances and challenges in omics data analysis, dealing with data variability from lab-to-lab, and data integration. We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from "big" data. In the future, AI could be expanded to achieve ground-breaking progress in disease management.

AnnoLnc2: the one-stop portal to systematically annotate novel lncRNAs for human and mouse

With the abundant mammalian lncRNAs identified recently, a comprehensive annotation resource for these novel lncRNAs is an urgent need. Since its first release in November 2016, AnnoLnc has been the only online server for comprehensively annotating novel human lncRNAs on-the-fly. Here, with significant updates to multiple annotation modules, backend datasets and the code base, AnnoLnc2 continues the effort to provide the scientific community with a one-stop online portal for systematically annotating novel human and mouse lncRNAs with a comprehensive functional spectrum covering sequences, structure, expression, regulation, genetic association and evolution. In response to numerous requests from multiple users, a standalone package is also provided for large-scale offline analysis. We believe that updated AnnoLnc2 (http://annolnc.gao-lab.org/) will help both computational and bench biologists identify lncRNA functions and investigate underlying mechanisms.

Mutational processes of distinct POLE exonuclease domain mutants drive an enrichment of a specific TP53 mutation in colorectal cancer

Cancer genomes with mutations in the exonuclease domain of Polymerase Epsilon (POLE) present with an extraordinarily high somatic mutation burden. In vitro studies have shown that distinct POLE mutants exhibit different polymerase activity. Yet, genome-wide mutation patterns and driver mutation formation arising from different POLE mutants remains unclear. Here, we curated somatic mutation calls from 7,345 colorectal cancer samples from published studies and publicly available databases. These include 44 POLE mutant samples including 9 with whole genome sequencing data available. The POLE mutant samples were categorized based on the specific POLE mutation present. Mutation spectrum, associations of somatic mutations with epigenomics features and co-occurrence with specific driver mutations were examined across different POLE mutants. We found that different POLE mutants exhibit distinct mutation spectrum with significantly higher relative frequency of C>T mutations in POLE V411L mutants. Our analysis showed that this increase frequency in C>T mutations is not dependent on DNA methylation and not associated with other genomic features and is thus specifically due to DNA sequence context alone. Notably, we found strong association of the TP53 R213* mutation specifically with POLE P286R mutants. This truncation mutation occurs within the TT[C>T]GA context. For C>T mutations, this sequence context is significantly more likely to be mutated in POLE P286R mutants compared with other POLE exonuclease domain mutants. This study refines our understanding of DNA polymerase fidelity and underscores genome-wide mutation spectrum and specific cancer driver mutation formation observed in POLE mutant cancers.

Cancer arises through the accumulation of somatic mutations. The way that these somatic mutations form can vary greatly in different cancers. One of the most mutagenic processes that have been identified is caused by mutations within a replicative DNA polymerase known as Polymerase Epsilon (POLE). Cancers with such mutations present with hundreds of thousands of somatic mutations in their genome. Previous cancer genomics studies have identified a number of mutation hotspots in POLE, however how these different POLE mutants behave in affecting mutation distribution has not been studied. Here, we describe the genome-wide mutation profiles of distinct POLE mutant cancers. We find that different mutants indeed result in different mutation profiles and that this can be explained by the different fidelities of these mutants in replicating specific DNA sequences. Significantly, these differences have important implications in cancer formation as we found that a POLE mutation is strongly associated with a specific truncation of the TP53 cancer driver gene. This study furthers our understanding of the POLE mutagenic process in cancer and provide important insights into carcinogenesis in cancers with such mutations.

A-CaMP: a tool for anti-cancer and antimicrobial peptide generation

Anti-cancer peptides (ACPs) play a vital role in the cell signaling process. Antimicrobial peptides (AMPs) provide immunity against pathogenic microbes, AMPs present activity against pathogenic microbes. Some of them are known to possess both anticancer and antimicrobial activity. However, so far, no tools have been developed that could predict potential ACPs from wild and mutated cancerous protein sequences in the numerous public databases. In the present study, we developed a A-CaMP tool that allows rapid fingerprinting of the anti-cancer and antimicrobial peptides, which play a crucial role in current bioinformatics research. Besides, we compared the performance and functionality of our A-CaMP tool with those of other methods available online. A-CaMP scans the target protein sequences provided by the user against the datasets. It possesses a robust coding architecture, has been developed in PERL language and is scalable of therefore has extensive applications in bioinformatics. It was observed to achieve a prediction accuracy of 93.4%, which is much higher than that of any of the existing tools. Sequence alignment studies also highlight the potential use of A-CaMP as a tool for the identification of AMPs. A-CaMP is the first open source tool that uses clinical data and proposes final peptides along with the necessary information; this includes wild and mutant sequence and peptides, which lays the foundation for its application in therapies for cancer and bacterial infections. Communicated by Ramaswamy H. Sarma.

Tissue-specific transcription reprogramming promotes liver metastasis of colorectal cancer

Metastasis, the development of secondary malignant growths at a distance from a primary tumor, is the cause of death for 90% of cancer patients, but little is known about how metastatic cancer cells adapt to and colonize new tissue environments. Here, using clinical samples, patient-derived xenograft (PDX) samples, PDX cells, and primary/metastatic cell lines, we discovered that liver metastatic colorectal cancer (CRC) cells lose their colon-specific gene transcription program yet gain a liver-specific gene transcription program. We showed that this transcription reprogramming is driven by a reshaped epigenetic landscape of both typical enhancers and super-enhancers. Further, we identified that the liver-specific transcription factors FOXA2 and HNF1A can bind to the gained enhancers and activate the liver-specific gene transcription, thereby driving CRC liver metastasis. Importantly, similar transcription reprogramming can be observed in multiple cancer types. Our data suggest that reprogrammed tissue-specific transcription promotes metastasis and should be targeted therapeutically.

Differential network analysis depicts regulatory mechanisms for hepatocellular carcinoma from diverse backgrounds

Aim: To elucidate the integrative combinational gene regulatory network landscape of hepatocellular carcinoma (HCC) molecular carcinogenesis from diverse background. Materials & methods: Modified gene regulatory network analysis was used to prioritize differentially regulated genes and links. Integrative comparisons using bioinformatics methods were applied to identify potential critical molecules and pathways in HCC with different backgrounds. Results: E2F1 with its surrounding regulatory links were identified to play different key roles in the HCC risk factor dysregulation mechanisms. Hsa-mir-19a was identified as showed different effects in the three HCC differential regulation networks, and showed vital regulatory role in HBV-related HCC. Conclusion: We describe in detail the regulatory networks involved in HCC with different backgrounds. E2F1 may serve as a universal target for HCC treatment.

miR2Diabetes: A Literature-Curated Database of microRNA Expression Patterns, in Diabetic Microvascular Complications

microRNAs (miRNAs) have been established as critical regulators of the pathogenesis of diabetes mellitus (DM), and diabetes microvascular complications (DMCs). However, manually curated databases for miRNAs, and DM (including DMCs) association studies, have yet to be established. Here, we constructed a user-friendly database, “miR2Diabetes,” equipped with a graphical web interface for simple browsing or searching manually curated annotations. The annotations in our database cover 14 DM and DMC phenotypes, involving 156 miRNAs, by browsing diverse sample origins (e.g., blood, kidney, liver, and other tissues). Additionally, we provide miRNA annotations for disease-model organisms (including rats and mice), of DM and DMCs, for the purpose of improving knowledge of the biological complexity of these pathologies. We assert that our database will be a comprehensive resource for miRNA biomarker studies, as well as for prioritizing miRNAs for functional validation, in DM and DMCs, with likely extension to other diseases.

Network-based method for drug target discovery at the isoform level

Identification of primary targets associated with phenotypes can facilitate exploration of the underlying molecular mechanisms of compounds and optimization of the structures of promising drugs. However, the literature reports limited effort to identify the target major isoform of a single known target gene. The majority of genes generate multiple transcripts that are translated into proteins that may carry out distinct and even opposing biological functions through alternative splicing. In addition, isoform expression is dynamic and varies depending on the developmental stage and cell type. To identify target major isoforms, we integrated a breast cancer type-specific isoform coexpression network with gene perturbation signatures in the MCF7 cell line in the Connectivity Map database using the ‘shortest path’ drug target prioritization method. We used a leukemia cancer network and differential expression data for drugs in the HL-60 cell line to test the robustness of the detection algorithm for target major isoforms. We further analyzed the properties of target major isoforms for each multi-isoform gene using pharmacogenomic datasets, proteomic data and the principal isoforms defined by the APPRIS and STRING datasets. Then, we tested our predictions for the most promising target major protein isoforms of DNMT1, MGEA5 and P4HB4 based on expression data and topological features in the coexpression network. Interestingly, these isoforms are not annotated as principal isoforms in APPRIS. Lastly, we tested the affinity of the target major isoform of MGEA5 for streptozocin through in silico docking. Our findings will pave the way for more effective and targeted therapies via studies of drug targets at the isoform level.

Human cancer tissues exhibit reduced A-to-I editing of miRNAs coupled with elevated editing of their targets

A-to-I RNA editing is an important post-transcriptional modification, known to be altered in tumors. It targets dozens of sites within miRNAs, some of which impact miRNA biogenesis and function, as well as many miRNA recognition sites. However, the full extent of the effect of editing on regulation by miRNAs and its behavior in human cancers is still unknown. Here we systematically characterized miRNA editing in 10 593 human samples across 32 cancer types and normal controls. We find that the majority of previously reported sites show little to no evidence for editing in this dataset, compile a list of 58 reliable miRNA editing sites, and study them across normal and cancer samples. Edited miRNA versions tend to suppress expression of known oncogenes, and, consistently, we observe a clear global tendency for hypo-editing in tumors, in strike contrast to the behavior for mRNA editing, allowing an accurate classification of normal/tumor samples based on their miRNA editing profile. In many cancers this profile correlates with patients' survival. Finally, thousands of miRNA binding sites are differentially edited in cancer. Our study thus establishes the important effect of RNA editing on miRNA-regulation in the tumor cell, with prospects for diagnostic and prognostic applications.

End-to-End Security for Local and Remote Human Genetic Data Applications at the EGA

Sensitive genomic data should remain secure - whether on disk for storage, or analysis, or in transport. However, secure storage, delivery, and usage of genomic data is complicated by the size of files and diversity of workflows. This paper presents solutions developed by GA4GH and EGA to use custom-ized encryption, encrypted file formats, toolchain integration, and intelligent APIs to help solve this problem.

Events of alternative splicing in head and neck cancer via RNA sequencing - an update

BACKGROUND

Alternative splicing (AS) is a regulatory mechanism used to create many forms of mature messengers RNAs (mRNAs) from the same gene. Sequencing of RNA (RNA-Seq) is an advanced technology, which has been utilized by different studies to find AS mechanisms in head and neck cancer (HNC). Hitherto, there is no available review that could inform us of the major findings from these studies. Hence, we aim to perform a systematic literature search following PRISMA guidelines to study AS events in HNC identified through RNA-Seq studies.

RESULTS

A total of five records were identified that utilized RNA-Seq data for identifying AS events in HNC. Five software was used in these records to identify AS events. Two genes influenced by AS i.e. MLL3 and RPS9 were found to be common in 4 out of 5 records. Likewise, 38 genes were identified to be similar in at least 3 records.

CONCLUSIONS

Alternative splicing in HNC is a multifaceted regulatory mechanism of gene expression. It can be studied via RNA-Seq using different bioinformatics tools. Genes MLL3, as well as RPS9, were repeatedly found to be associated with HNC, however needs further functional validation.

Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

Background

Long non-coding RNAs (lncRNAs) are emerging as crucial regulators of cellular processes in diseases such as cancer, although the functions of most remain poorly understood. To address this, here we apply a novel strategy to integrate gene expression profiles across 32 cancer types, and cluster human lncRNAs based on their pan-cancer protein-coding gene associations. By doing so, we derive 16 lncRNA modules whose unique properties allow simultaneous inference of function, disease specificity and regulation for over 800 lncRNAs.

Results

Remarkably, modules could be grouped into just four functional themes: transcription regulation, immunological, extracellular, and neurological, with module generation frequently driven by lncRNA tissue specificity. Notably, three modules associated with the extracellular matrix represented potential networks of lncRNAs regulating key events in tumour progression. These included a tumour-specific signature of 33 lncRNAs that may play a role in inducing epithelial-mesenchymal transition through modulation of TGFβ signalling, and two stromal-specific modules comprising 26 lncRNAs linked to a tumour suppressive microenvironment and 12 lncRNAs related to cancer-associated fibroblasts. One member of the 12-lncRNA signature was experimentally supported by siRNA knockdown, which resulted in attenuated differentiation of quiescent fibroblasts to a cancer-associated phenotype.

Conclusions

Overall, the study provides a unique pan-cancer perspective on the lncRNA functional landscape, acting as a global source of novel hypotheses on lncRNA contribution to tumour progression.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5850-7) contains supplementary material, which is available to authorized users.

Insights into BRCA Cancer Predisposition from Integrated Germline and Somatic Analyses in 7632 Cancers

BACKGROUND

It is often assumed any cancer in a germline BRCA1 or BRCA2 (collectively termed BRCA) mutation carrier was caused by that mutation. It is also often assumed the occurrence of breast or ovarian cancer in an individual with a variant of uncertain significance (VUS) suggests the VUS is pathogenic. These assumptions have profound management implications for cancer patients and healthy individuals.

METHODS

We compared the frequency of BRCA mutations, allele loss, and Signature 3 in 7632 individuals with 28 cancers and 1000 population controls. Because only increased frequency was the focus of the study, all statistical tests were one-sided.

RESULTS

Individuals with breast or ovarian cancer had increased germline BRCA pathogenic mutation frequencies compared to controls (P = 1.0x10-10 and P = 1.4x10-34, respectively). There was no increase in other cancer types. Wild-type allele loss and Signature 3 were statistically significantly higher in breast and ovarian cancers with BRCA mutations compared with other cancers with BRCA mutations (P = 5.1x10-10 and P = 3.7x10-9) and cancers without BRCA mutations (P = 2.8x10-53 and P = 1.0x10-134). There was no difference between non-breast and non-ovarian cancers with BRCA mutations and cancers without BRCA mutations. Allele loss and Signature 3 were statistically significantly higher in breast and ovarian cancers in individuals with BRCA pathogenic mutations compared to those with VUS (P = 3.8x10-17 and P = 1.6x10-8) or benign variants (P = 1.2x10-28 and P = 2.2x10-10). There was no difference between individuals with BRCA VUS and those with benign variants.

CONCLUSIONS

These data show that non-breast and non-ovarian cancers in individuals with germline BRCA pathogenic mutations are often not causally related to the mutation and that BRCA VUS are highly unlikely to be pathogenic. These results should reduce inappropriate management of germline BRCA information.

RNA Sequencing of the NCI-60: Integration into CellMiner and CellMiner CDB

CellMiner (http://discover.nci.nih.gov/cellminer) and CellMinerCDB (https://discover.nci.nih.gov/cellminercdb/) are web-based applications for mining publicly available genomic, molecular, and pharmacologic datasets of human cancer cell lines including the NCI-60, Cancer Cell Line Encyclopedia, Genomics of Drug Sensitivity in Cancer, Cancer Therapeutics Response Portal, NCI/DTP small cell lung cancer, and NCI Almanac cell line sets. Here, we introduce our RNA sequencing (RNA-seq) data for the NCI-60 and their access and integration with the other databases. Correlation to transcript microarray expression levels for identical genes and identical cell lines across CellMinerCDB demonstrates the high quality of these new RNA-seq data. We provide composite and isoform transcript expression data and demonstrate diversity in isoform composition for individual cancer- and pharmacologically relevant genes, including HRAS, PTEN, EGFR, RAD51, ALKBH2, BRCA1, ERBB2, TP53, FGFR2, and CTNND1. We reveal cell-specific differences in the overall levels of isoforms and show their linkage to expression of RNA processing and splicing genes as well as resultant alterations in cancer and pharmacologic gene sets. Gene-drug pairings linked by pathways or functions show specific correlations to isoforms compared with composite gene expression, including ALKBH2-benzaldehyde, AKT3-vandetanib, BCR-imatinib, CDK1 and 20-palbociclib, CASP1-imexon, and FGFR3-pazopanib. Loss of MUC1 20 amino acid variable number tandem repeats, which is used to elicit immune response, and the presence of the androgen receptor AR-V4 and -V7 isoforms in all NCI-60 tissue of origin types demonstrate translational relevance. In summary, we introduce RNA-seq data to our CellMiner and CellMinerCDB web applications, allowing their exploration for both research and translational purposes. SIGNIFICANCE: The current study provides RNA sequencing data for the NCI-60 cell lines made accessible through both CellMiner and CellMinerCDB and is an important pharmacogenomics resource for the field.

Noncoding RNAs Serve as Diagnosis and Prognosis Biomarkers for Hepatocellular Carcinoma

BACKGROUND

Reliable noninvasive biomarkers for hepatocellular carcinoma (HCC) diagnosis and prognosis are urgently needed. We explored the potential of not only microRNAs (miRNAs) but other types of noncoding RNAs (ncRNAs) as HCC biomarkers.

METHODS

Peripheral blood samples were collected from 77 individuals; among them, 57 plasma cell-free RNA transcriptomes and 20 exosomal RNA transcriptomes were profiled. Significantly upregulated ncRNAs and published potential HCC biomarkers were validated with reverse transcription (RT)-qPCR in an independent validation cohort (60-150 samples). We particularly investigated the diagnosis and prognosis performance and biological function for 1 ncRNA biomarker, RN7SL1, and its S fragment.

RESULTS

We identified certain circulating ncRNAs escaping from RNase degradation, possibly through binding with RNA-binding proteins: 899 ncRNAs were highly upregulated in HCC patients. Among them, 337 genes were fragmented long noncoding RNAs, 252 genes were small nucleolar RNAs, and 134 genes were piwi-interacting RNAs. Forty-eight candidates were selected and validated with RT-qPCR, of which, 16 ncRNAs were verified to be significantly upregulated in HCC, including RN7SL1, SNHG1, ZFAS1, and LINC01359. Particularly, the abundance of RN7SL1 S fragment discriminated HCC samples from negative controls (area under the curve, 0.87; 95% CI, 0.817-0.920). HCC patients with higher concentrations of RN7SL1 S fragment had lower survival rates. Furthermore, RN7SL1 S fragment alone promoted cancer cell proliferation and clonogenic growth.

CONCLUSIONS

Our results show that various ncRNA species, not only miRNAs, identified in the small RNA sequencing of plasma are also able to serve as noninvasive biomarkers. Particularly, we identified a domain of srpRNA RN7SL1 with reliable clinical performance for HCC diagnosis and prognosis.

Data Lakes, Clouds, and Commons: A Review of Platforms for Analyzing and Sharing Genomic Data

Data commons collate data with cloud computing infrastructure and commonly used software services, tools, and applications to create biomedical resources for the large-scale management, analysis, harmonization, and sharing of biomedical data. Over the past few years, data commons have been used to analyze, harmonize, and share large-scale genomics datasets. Data ecosystems can be built by interoperating multiple data commons. It can be quite labor intensive to curate, import, and analyze the data in a data commons. Data lakes provide an alternative to data commons and simply provide access to data, with the data curation and analysis deferred until later and delegated to those that access the data. We review software platforms for managing, analyzing, and sharing genomic data, with an emphasis on data commons, but also cover data ecosystems and data lakes.

Network-based approach to identify principal isoforms among four cancer types

Protein isoforms are structurally similar proteins produced by alternative splicing of a single gene or genes from the same family. Isoforms of a protein can perform the same, similar, or even opposite biological functions. A previous study identified principal isoforms of proteins based on the extent of interactions per isoform in a functional relationship network, focusing on data from normal tissues. Additionally, the expression levels of specific isoforms of various genes associated with tumorigenesis and prognosis are frequently altered in tumors compared with those in normal tissues. In this study, we aimed to identify higher degree isoforms (HDIs) of multi-isoform genes (MIGs) in cancer by applying a meta-analytical framework to calculate co-expression between each pair of isoforms in two large datasets of RNA-seq profiles from breast cancer, lung cancer, leukemia, and colon cancer cell lines. Then, we compared HDIs with isoforms identified by proteomic data and prognostic and predictive evidence in various cancers. In addition, we separately analyzed the associations between HDIs and non-HDIs (nHDIs) of the same genes according to transcript expression and drug responses in various cancer type cell lines. Collectively, these results indicated the complex properties of HDIs per gene identified by cancer type-based isoform-isoform co-expression networks and showed the potential of HDIs as novel therapeutic targets for cancer treatment.

Investigating Cell Signaling with Gene Expression Datasets

Modern molecular biology is a data- and computationally-intensive field with few instructional resources for introducing undergraduate students to the requisite skills and techniques for analyzing large data sets. This Lesson helps students: (i) build an understanding of the role of signal transduction in the control of gene expression; (ii) improve written scientific communication skills through engagement in literature searches, data analysis, and writing reports; and (iii) develop an awareness of the procedures and protocols for analyzing and making inferences from high-content quantitative molecular biology data. The Lesson is most suited to upper level biology courses because it requires foundational knowledge on cellular organization, protein structure and function, and the tenets of information flow from DNA to proteins. The first step lays the foundation for understanding cell signaling, which can be accomplished through assigned readings and presentations. In subsequent active learning sessions, data analysis is integrated with exercises that provide insight into the structure of scientific papers. The Lesson emphasizes the role of quantitative methods in research and helps students gain experience with functional genomics databases and data analysis, which are important skills for molecular biologists. Assessment is conducted through mini-reports designed to gauge students' perceptions of the purpose of each step, their awareness of the possible limitations of the methods utilized, and the ability to identify opportunities for further investigation. Summative assessment is conducted through a final report. The modules are suitable for complementing wet-laboratory experiments and can be adapted for different courses that use molecular biology data.

Using Semantic Web Technologies to Enable Cancer Genomics Discovery at Petabyte Scale

Increased efforts in cancer genomics research and bioinformatics are producing tremendous amounts of data. These data are diverse in origin, format, and content. As the amount of available sequencing data increase, technologies that make them discoverable and usable are critically needed. In response, we have developed a Semantic Web-based Data Browser, a tool allowing users to visually build and execute ontology-driven queries. This approach simplifies access to available data and improves the process of using them in analyses on the Seven Bridges Cancer Genomics Cloud (CGC; www.cancergenomicscloud.org). The Data Browser makes large data sets easily explorable and simplifies the retrieval of specific data of interest. Although initially implemented on top of The Cancer Genome Atlas (TCGA) data set, the Data Browser's architecture allows for seamless integration of other data sets. By deploying it on the CGC, we have enabled remote researchers to access data and perform collaborative investigations.

RNA editing derived epitopes function as cancer antigens to elicit immune responses

In addition to genomic mutations, RNA editing is another major mechanism creating sequence variations in proteins by introducing nucleotide changes in mRNA sequences. Deregulated RNA editing contributes to different types of human diseases, including cancers. Here we report that peptides generated as a consequence of RNA editing are indeed naturally presented by human leukocyte antigen (HLA) molecules. We provide evidence that effector CD8⁺ T cells specific for edited peptides derived from cyclin I are present in human tumours and attack tumour cells that are presenting these epitopes. We show that subpopulations of cancer patients have increased peptide levels and that levels of edited RNA correlate with peptide copy numbers. These findings demonstrate that RNA editing extends the classes of HLA presented self-antigens and that these antigens can be recognised by the immune system.

RNA editing is a biological process that creates sequence variation. Here the authors show that peptides generated as a consequence of RNA editing are naturally presented by human leukocyte antigen (HLA) and serve as antigens to elicit anti-tumour immune responses.

Distribution-based measures of tumor heterogeneity are sensitive to mutation calling and lack strong clinical predictive power

Mutant allele frequency distributions in cancer samples have been used to estimate intratumoral heterogeneity and its implications for patient survival. However, mutation calls are sensitive to the calling algorithm. It remains unknown whether the relationship of heterogeneity and clinical outcome is robust to these variations. To resolve this question, we studied the robustness of allele frequency distributions to the mutation callers MuTect, SomaticSniper, and VarScan in 4722 cancer samples from The Cancer Genome Atlas. We observed discrepancies among the results, particularly a pronounced difference between allele frequency distributions called by VarScan and SomaticSniper. Survival analysis showed little robust predictive power for heterogeneity as measured by Mutant-Allele Tumor Heterogeneity (MATH) score, with the exception of uterine corpus endometrial carcinoma. However, we found that variations in mutant allele frequencies were mediated by variations in copy number. Our results indicate that the clinical predictions associated with MATH score are primarily caused by copy number aberrations that alter mutant allele frequencies. Finally, we present a mathematical model of linear tumor evolution demonstrating why MATH score is insufficient for distinguishing different scenarios of tumor growth. Our findings elucidate the importance of allele frequency distributions as a measure for tumor heterogeneity and their prognostic role.

Systematic discovery of germline cancer predisposition genes through the identification of somatic second hits

The genetic causes of cancer include both somatic mutations and inherited germline variants. Large-scale tumor sequencing has revolutionized the identification of somatic driver alterations but has had limited impact on the identification of cancer predisposition genes (CPGs). Here we present a statistical method, ALFRED, that tests Knudson's two-hit hypothesis to systematically identify CPGs from cancer genome data. Applied to ~10,000 tumor exomes the approach identifies known and putative CPGs - including the chromatin modifier NSD1 - that contribute to cancer through a combination of rare germline variants and somatic loss-of-heterozygosity (LOH). Rare germline variants in these genes contribute substantially to cancer risk, including to ~14% of ovarian carcinomas, ~7% of breast tumors, ~4% of uterine corpus endometrial carcinomas, and to a median of 2% of tumors across 17 cancer types.

The somatic mutation landscape of premalignant colorectal adenoma

OBJECTIVE

There are few studies which characterised the molecular alterations in premalignant colorectal adenomas. Our major goal was to establish colorectal adenoma genome atlas and identify molecular markers of progression from colorectal adenoma to adenocarcinoma.

DESIGN

Whole-exome sequencing and targeted sequencing were carried out in 149 adenoma samples and paired blood from patients with conventional adenoma or sessile serrated adenoma to characterise the somatic mutation landscape for premalignant colorectal lesions. The identified somatic mutations were compared with those in colorectal cancer (CRC) samples from The Cancer Genome Atlas. A supervised random forest model was employed to identify gene panels differentiating adenoma from CRC.

RESULTS

Similar somatic mutation frequencies, but distinctive driver mutations, were observed in sessile serrated adenomas and conventional adenomas. The final model included 20 genes and was able to separate the somatic mutation profile of colorectal adenoma and adenocarcinoma with an area under the curve of 0.941.

CONCLUSION

The findings of this project hold potential to better identify patients with adenoma who may be candidates for targeted surveillance programmes and preventive interventions to reduce the incidence of CRC.

Co-fuse: a new class discovery analysis tool to identify and prioritize recurrent fusion genes from RNA-sequencing data

Recurrent oncogenic fusion genes play a critical role in the development of various cancers and diseases and provide, in some cases, excellent therapeutic targets. To date, analysis tools that can identify and compare recurrent fusion genes across multiple samples have not been available to researchers. To address this deficiency, we developed Co-occurrence Fusion (Co-fuse), a new and easy to use software tool that enables biologists to merge RNA-seq information, allowing them to identify recurrent fusion genes, without the need for exhaustive data processing. Notably, Co-fuse is based on pattern mining and statistical analysis which enables the identification of hidden patterns of recurrent fusion genes. In this report, we show that Co-fuse can be used to identify 2 distinct groups within a set of 49 leukemic cell lines based on their recurrent fusion genes: a multiple myeloma (MM) samples-enriched cluster and an acute myeloid leukemia (AML) samples-enriched cluster. Our experimental results further demonstrate that Co-fuse can identify known driver fusion genes (e.g., IGH-MYC, IGH-WHSC1) in MM, when compared to AML samples, indicating the potential of Co-fuse to aid the discovery of yet unknown driver fusion genes through cohort comparisons. Additionally, using a 272 primary glioma sample RNA-seq dataset, Co-fuse was able to validate recurrent fusion genes, further demonstrating the power of this analysis tool to identify recurrent fusion genes. Taken together, Co-fuse is a powerful new analysis tool that can be readily applied to large RNA-seq datasets, and may lead to the discovery of new disease subgroups and potentially new driver genes, for which, targeted therapies could be developed. The Co-fuse R source code is publicly available at https://github.com/sakrapee/co-fuse .

MYC Drives a Subset of High-Risk Pediatric Neuroblastomas and Is Activated through Mechanisms Including Enhancer Hijacking and Focal Enhancer Amplification

The amplified MYCN gene serves as an oncogenic driver in approximately 20% of high-risk pediatric neuroblastomas. Here, we show that the family member MYC is a potent transforming gene in a separate subset of high-risk neuroblastoma cases (∼10%), based on (i) its upregulation by focal enhancer amplification or genomic rearrangements leading to enhancer hijacking, and (ii) its ability to transform neuroblastoma precursor cells in a transgenic animal model. The aberrant regulatory elements associated with oncogenic MYC activation include focally amplified distal enhancers and translocation of highly active enhancers from other genes to within topologically associating domains containing the MYC gene locus. The clinical outcome for patients with high levels of MYC expression is virtually identical to that of patients with amplification of the MYCN gene, a known high-risk feature of this disease. Together, these findings establish MYC as a bona fide oncogene in a clinically significant group of high-risk childhood neuroblastomas.Significance: Amplification of the MYCN oncogene is a recognized hallmark of high-risk pediatric neuroblastoma. Here, we demonstrate that MYC is also activated as a potent oncogene in a distinct subset of neuroblastoma cases through either focal amplification of distal enhancers or enhancer hijacking mediated by chromosomal translocation. Cancer Discov; 8(3); 320-35. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 253.

The Integrated Genomic Landscape of Thymic Epithelial Tumors

Thymic epithelial tumors (TETs) are one of the rarest adult malignancies. Among TETs, thymoma is the most predominant, characterized by a unique association with autoimmune diseases, followed by thymic carcinoma, which is less common but more clinically aggressive. Using multi-platform omics analyses on 117 TETs, we define four subtypes of these tumors defined by genomic hallmarks and an association with survival and World Health Organization histological subtype. We further demonstrate a marked prevalence of a thymoma-specific mutated oncogene, GTF2I, and explore its biological effects on multi-platform analysis. We further observe enrichment of mutations in HRAS, NRAS, and TP53. Last, we identify a molecular link between thymoma and the autoimmune disease myasthenia gravis, characterized by tumoral overexpression of muscle autoantigens, and increased aneuploidy.

Biomarkers: Delivering on the expectation of molecularly driven, quantitative health

Biomarkers are the pillars of precision medicine and are delivering on expectations of molecular, quantitative health. These features have made clinical decisions more precise and personalized, but require a high bar for validation. Biomarkers have improved health outcomes in a few areas such as cancer, pharmacogenetics, and safety. Burgeoning big data research infrastructure, the internet of things, and increased patient participation will accelerate discovery in the many areas that have not yet realized the full potential of biomarkers for precision health. Here we review themes of biomarker discovery, current implementations of biomarkers for precision health, and future opportunities and challenges for biomarker discovery. Impact statement Precision medicine evolved because of the understanding that human disease is molecularly driven and is highly variable across patients. This understanding has made biomarkers, a diverse class of biological measurements, more relevant for disease diagnosis, monitoring, and selection of treatment strategy. Biomarkers' impact on precision medicine can be seen in cancer, pharmacogenomics, and safety. The successes in these cases suggest many more applications for biomarkers and a greater impact for precision medicine across the spectrum of human disease. The authors assess the status of biomarker-guided medical practice by analyzing themes for biomarker discovery, reviewing the impact of these markers in the clinic, and highlight future and ongoing challenges for biomarker discovery. This work is timely and relevant, as the molecular, quantitative approach of precision medicine is spreading to many disease indications.

H3B-6527 Is a Potent and Selective Inhibitor of FGFR4 in FGF19-Driven Hepatocellular Carcinoma

Activation of the fibroblast growth factor receptor FGFR4 by FGF19 drives hepatocellular carcinoma (HCC), a disease with few, if any, effective treatment options. While a number of pan-FGFR inhibitors are being clinically evaluated, their application to FGF19-driven HCC may be limited by dose-limiting toxicities mediated by FGFR1-3 receptors. To evade the potential limitations of pan-FGFR inhibitors, we generated H3B-6527, a highly selective covalent FGFR4 inhibitor, through structure-guided drug design. Studies in a panel of 40 HCC cell lines and 30 HCC PDX models showed that FGF19 expression is a predictive biomarker for H3B-6527 response. Moreover, coadministration of the CDK4/6 inhibitor palbociclib in combination with H3B-6527 could effectively trigger tumor regression in a xenograft model of HCC. Overall, our results offer preclinical proof of concept for H3B-6527 as a candidate therapeutic agent for HCC cases that exhibit increased expression of FGF19. Cancer Res; 77(24); 6999-7013. ©2017 AACR.

RMEL3, a novel BRAFV600E-associated long noncoding RNA, is required for MAPK and PI3K signaling in melanoma

Previous work identified RMEL3 as a lncRNA with enriched expression in melanoma. Analysis of The Cancer Genome Atlas (TCGA) data confirmed RMEL3 enriched expression in melanoma and demonstrated its association with the presence of BRAF^V600E. RMEL3 siRNA-mediated silencing markedly reduced (95%) colony formation in different BRAF^V600E melanoma cell lines. Multiple genes of the MAPK and PI3K pathways found to be correlated with RMEL3 in TCGA samples were experimentally confirmed. RMEL3 knockdown led to downregulation of activators or effectors of these pathways, including FGF2, FGF3, DUSP6, ITGB3 and GNG2. RMEL3 knockdown induces gain of protein levels of tumor suppressor PTEN and the G1/S cyclin-Cdk inhibitors p21 and p27, as well as a decrease of pAKT (T308), BRAF, pRB (S807, S811) and cyclin B1. Consistently, knockdown resulted in an accumulation of cells in G1 phase and subG0/G1 in an asynchronously growing population. Thus, TCGA data and functional experiments demonstrate that RMEL3 is required for MAPK and PI3K signaling, and its knockdown decrease BRAF^V600E melanoma cell survival and proliferation.

A comprehensive analysis of breast cancer microbiota and host gene expression

The inflammatory tumoral-immune response alters the physiology of the tumor microenvironment, which may attenuate genomic instability. In addition to inducing inflammatory immune responses, several pathogenic bacteria produce genotoxins. However the extent of microbial contribution to the tumor microenvironment biology remains unknown. We utilized The Cancer Genome Atlas, (TCGA) breast cancer data to perform a novel experiment utilizing unmapped and mapped RNA sequencing read evidence to minimize laboratory costs and effort. Our objective was to characterize the microbiota and associate the microbiota with the tumor expression profiles, for 668 breast tumor tissues and 72 non-cancerous adjacent tissues. The prominent presence of Proteobacteria was increased in the tumor tissues and conversely Actinobacteria abundance increase in non-cancerous adjacent tissues. Further, geneset enrichment suggests Listeria spp to be associated with the expression profiles of genes involved with epithelial to mesenchymal transitions. Moreover, evidence suggests H. influenza may reside in the surrounding stromal material and was significantly associated with the proliferative pathways: G2M checkpoint, E2F transcription factors, and mitotic spindle assembly. In summary, further unraveling this complicated interplay should enable us to better diagnose and treat breast cancer patients.

Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas

Sarcomas are a broad family of mesenchymal malignancies exhibiting remarkable histologic diversity. We describe the multi-platform molecular landscape of 206 adult soft tissue sarcomas representing 6 major types. Along with novel insights into the biology of individual sarcoma types, we report three overarching findings: (1) unlike most epithelial malignancies, these sarcomas (excepting synovial sarcoma) are characterized predominantly by copy-number changes, with low mutational loads and only a few genes (TP53, ATRX, RB1) highly recurrently mutated across sarcoma types; (2) within sarcoma types, genomic and regulomic diversity of driver pathways defines molecular subtypes associated with patient outcome; and (3) the immune microenvironment, inferred from DNA methylation and mRNA profiles, associates with outcome and may inform clinical trials of immune checkpoint inhibitors. Overall, this large-scale analysis reveals previously unappreciated sarcoma-type-specific changes in copy number, methylation, RNA, and protein, providing insights into refining sarcoma therapy and relationships to other cancer types.

3D collagen architecture induces a conserved migratory and transcriptional response linked to vasculogenic mimicry

The topographical organization of collagen within the tumor microenvironment has been implicated in modulating cancer cell migration and independently predicts progression to metastasis. Here, we show that collagen matrices with small pores and short fibers, but not Matrigel, trigger a conserved transcriptional response and subsequent motility switch in cancer cells resulting in the formation of multicellular network structures. The response is not mediated by hypoxia, matrix stiffness, or bulk matrix density, but rather by matrix architecture-induced β1-integrin upregulation. The transcriptional module associated with network formation is enriched for migration and vasculogenesis-associated genes that predict survival in patient data across nine distinct tumor types. Evidence of this gene module at the protein level is found in patient tumor slices displaying a vasculogenic mimicry (VM) phenotype. Our findings link a collagen-induced migration program to VM and suggest that this process may be broadly relevant to metastatic progression in solid human cancers.

Extracellular matrix plays a central role in driving cancer development. Here the authors using an in vitro approach show that confining collagen architectures induce fast and persistent cell migration and the formation of multicellular network structures linked to vascular mimicry observed in tumours from patients.