Database of evidence for precision oncology portal

Pan-cancer proteogenomics connects oncogenic drivers to functional states

Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying their significant cis-effects and distal trans-effects quantified at the RNA, protein, and phosphoprotein levels. Salient observations include the association of point mutations and copy-number alterations with the rewiring of protein interaction networks, and notably, most cancer genes converge toward similar molecular states denoted by sequence-based kinase activity profiles. A correlation between predicted neoantigen burden and measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns of cancer hallmarks vary by polygenic protein abundance ranging from uniform to heterogeneous. Overall, our work demonstrates the value of comprehensive proteogenomics in understanding the functional states of oncogenic drivers and their links to cancer development, surpassing the limitations of studying individual cancer types.

Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness

Clear cell renal cell carcinomas (ccRCCs) represent ∼75% of RCC cases and account for most RCC-associated deaths. Inter- and intratumoral heterogeneity (ITH) results in varying prognosis and treatment outcomes. To obtain the most comprehensive profile of ccRCC, we perform integrative histopathologic, proteogenomic, and metabolomic analyses on 305 ccRCC tumor segments and 166 paired adjacent normal tissues from 213 cases. Combining histologic and molecular profiles reveals ITH in 90% of ccRCCs, with 50% demonstrating immune signature heterogeneity. High tumor grade, along with BAP1 mutation, genome instability, increased hypermethylation, and a specific protein glycosylation signature define a high-risk disease subset, where UCHL1 expression displays prognostic value. Single-nuclei RNA sequencing of the adverse sarcomatoid and rhabdoid phenotypes uncover gene signatures and potential insights into tumor evolution. In vitro cell line studies confirm the potential of inhibiting identified phosphoproteome targets. This study molecularly stratifies aggressive histopathologic subtypes that may inform more effective treatment strategies.

OncoPubMiner: a platform for mining oncology publications

Updated and expert-quality knowledge bases are fundamental to biomedical research. A knowledge base established with human participation and subject to multiple inspections is needed to support clinical decision making, especially in the growing field of precision oncology. The number of original publications in this field has risen dramatically with the advances in technology and the evolution of in-depth research. Consequently, the issue of how to gather and mine these articles accurately and efficiently now requires close consideration. In this study, we present OncoPubMiner (https://oncopubminer.chosenmedinfo.com), a free and powerful system that combines text mining, data structure customisation, publication search with online reading and project-centred and team-based data collection to form a one-stop 'keyword in-knowledge out' oncology publication mining platform. The platform was constructed by integrating all open-access abstracts from PubMed and full-text articles from PubMed Central, and it is updated daily. OncoPubMiner makes obtaining precision oncology knowledge from scientific articles straightforward and will assist researchers in efficiently developing structured knowledge base systems and bring us closer to achieving precision oncology goals.

Genomic Profiling Reveals the Molecular Landscape of Gastrointestinal Tract Cancers in Chinese Patients

Gastrointestinal tract cancers have high incidence and mortality in China, but their molecular characteristics have not been fully investigated. We sequenced 432 tumor samples from the colorectum, stomach, pancreas, gallbladder, and biliary tract to investigate cancer-related mutations and detail the landscape of microsatellite instability (MSI), tumor mutation burden (TMB), and chromosomal instability (CIN). We observed the highest TMB in colorectal and gastric cancers and the lowest TMB in gastrointestinal stromal tumors (GISTs). Twenty-four hyper-mutated tumors were identified only in colorectal and gastric cancers, with a significant enrichment of mutations in the polymerase genes (POLE, POLD1, and POLH) and mismatch repair (MMR) genes. Additionally, CIN preferentially occurred in colorectal and gastric cancers, while pancreatic, gallbladder, and biliary duct cancers had a much lower CIN. High CIN was correlated with a higher prevalence of malfunctions in chromosome segregation and cell cycle genes, including the copy number loss of WRN, NAT1, NF2, and BUB1B, and the copy number gain of MYC, ERBB2, EGFR, and CDK6. In addition, TP53 mutations were more abundant in high-CIN tumors, while PIK3CA mutations were more frequent in low-CIN tumors. In colorectal and gastric cancers, tumors with MSI demonstrated much fewer copy number changes than microsatellite stable (MSS) tumors. In colorectal and gastric cancers, the molecular characteristics of tumors revealed the mutational diversity between the different anatomical origins of tumors. This study provides novel insights into the molecular landscape of Chinese gastrointestinal cancers and the genetic differences between tumor locations, which could be useful for future clinical patient stratification and targeted interventions.

Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidatesfor targeted treatment

Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs' recapitulation of human tumors.

Clinical Interpretation Challenges of Germline-Shared Somatic Variants in Cancer

OBJECTIVE

To investigate the interpretation differences of germline-shared somatic variants.

METHODS

A total of 123,302 COSMIC variants associated with hematologic malignant neoplasms were used. The pathogenicity and actionability of shared variants were analyzed based on the standardized guidelines.

RESULTS

The overall frequency of variants shared in ClinVar/HGMD and COSMIC was 10%. The pathogenicity of 54 shared variants was pathogenic/likely pathogenic (P/LP; n = 30), variants of unknown significance (n = 3), and benign/likely benign (n = 21). In total, 30 P/LP variants were reclassified to tier I/tier II (83%) and tier III (17%) variants.

CONCLUSIONS

This is the first study about different clinical interpretations of shared variants based on the current standard guidelines. This study takes a meaningful step in bridging the interpretation gap between the somatic and germline variants.

CanDriS: posterior profiling of cancer-driving sites based on two-component evolutionary model

Current cancer genomics databases have accumulated millions of somatic mutations that remain to be further explored. Due to the over-excess mutations unrelated to cancer, the great challenge is to identify somatic mutations that are cancer-driven. Under the notion that carcinogenesis is a form of somatic-cell evolution, we developed a two-component mixture model: while the ground component corresponds to passenger mutations, the rapidly evolving component corresponds to driver mutations. Then, we implemented an empirical Bayesian procedure to calculate the posterior probability of a site being cancer-driven. Based on these, we developed a software CanDriS (Cancer Driver Sites) to profile the potential cancer-driving sites for thousands of tumor samples from the Cancer Genome Atlas and International Cancer Genome Consortium across tumor types and pan-cancer level. As a result, we identified that approximately 1% of the sites have posterior probabilities larger than 0.90 and listed potential cancer-wide and cancer-specific driver mutations. By comprehensively profiling all potential cancer-driving sites, CanDriS greatly enhances our ability to refine our knowledge of the genetic basis of cancer and might guide clinical medication in the upcoming era of precision medicine. The results were displayed in a database CandrisDB (http://biopharm.zju.edu.cn/candrisdb/).

Proteogenomic and metabolomic characterization of human glioblastoma

Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology. We identify key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation, as well as potential targets for EGFR-, TP53-, and RB1-altered tumors. Immune subtypes with distinct immune cell types are discovered using bulk omics methodologies, validated by snRNA-seq, and correlated with specific expression and histone acetylation patterns. Histone H2B acetylation in classical-like and immune-low GBM is driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identify specific lipid distributions across subtypes and distinct global metabolic changes in IDH-mutated tumors. This work highlights biological relationships that could contribute to stratification of GBM patients for more effective treatment.

The Fusion Gene Landscape in Taiwanese Patients with Non-Small Cell Lung Cancer

Simple Summary

Human cancer genomes show a variety of alterations, such as single base changes, deletions, insertions, copy number changes, and gene fusions. Analyzing fusion gene transcripts may yield a novel and effective approach for selecting cancer treatments. However, few comprehensive analyses of gene fusions in non-small cell lung cancer (NSCLC) patients have been performed. Here, we characterized the fusion gene landscape of NSCLC in a case study of Taiwanese lung cancer patients. We concluded that some fusion genes likely play driver roles in carcinogenesis, while others act as passengers. We demonstrated that by using RNA-sequencing to detect gene fusion events, putative therapeutic drug targets could be identified, potentially leading to more precise therapies for NSCLC.

Abstract

Background: Analyzing fusion gene transcripts may yield an effective approach for selecting cancer treatments. However, few comprehensive analyses of fusions in non-small cell lung cancer (NSCLC) patients have been performed. Methods: We enrolled 54 patients with NSCLC, and performed RNA-sequencing (RNA-Seq). STAR (Spliced Transcripts Alignment to a Reference)-Fusion was used to identify fusions. Results: Of the 218 fusions discovered, 24 had been reported and the rest were novel. Three fusions had the highest occurrence rates. After integrating our gene expression and fusion data, we found that samples harboring fusions containing ASXL1, CACNA1A, EEF1A1, and RET also exhibited increased expression of these genes. We then searched for mutations and fusions in cancer driver genes in each sample and found that nine patients carried both mutations and fusions in cancer driver genes. Furthermore, we found a trend for mutual exclusivity between gene fusions and mutations in the same gene, with the exception of DMD, and we found that EGFR mutations are associated with the number of fusion genes. Finally, we identified kinase gene fusions, and potentially druggable fusions, which may play roles in lung cancer therapy. Conclusion: The clinical use of RNA-Seq for detecting driver fusion genes may play an important role in the treatment of lung cancer.

HotSpot3D web server: an integrated resource for mutation analysis in protein 3D structures

MOTIVATION

HotSpot3D is a widely used software for identifying mutation hotspots on the 3D structures of proteins. To further assist users, we developed a new HotSpot3D web server to make this software more versatile, convenient and interactive.

RESULTS

The HotSpot3D web server performs data pre-processing, clustering, visualization and log-viewing on one stop. Users can interactively explore each cluster and easily re-visualize the mutational clusters within browsers. We also provide a database that allows users to search and visualize proximal mutations from 33 cancers in the Cancer Genome Atlas.

AVAILABILITY AND IMPLEMENTATION

http://niulab.scgrid.cn/HotSpot3D/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Methods and resources to access mutation-dependent effects on cancer drug treatment

In clinical cancer treatment, genomic alterations would often affect the response of patients to anticancer drugs. Studies have shown that molecular features of tumors could be biomarkers predictive of sensitivity or resistance to anticancer agents, but the identification of actionable mutations are often constrained by the incomplete understanding of cancer genomes. Recent progresses of next-generation sequencing technology greatly facilitate the extensive molecular characterization of tumors and promote precision medicine in cancers. More and more clinical studies, cancer cell lines studies, CRISPR screening studies as well as patient-derived model studies were performed to identify potential actionable mutations predictive of drug response, which provide rich resources of molecularly and pharmacologically profiled cancer samples at different levels. Such abundance of data also enables the development of various computational models and algorithms to solve the problem of drug sensitivity prediction, biomarker identification and in silico drug prioritization by the integration of multiomics data. Here, we review the recent development of methods and resources that identifies mutation-dependent effects for cancer treatment in clinical studies, functional genomics studies and computational studies and discuss the remaining gaps and future directions in this area.

Pan-cancer pharmacogenetics: targeted sequencing panels or exome sequencing?

Aim: This study provides clinicians and researchers with an informed choice between current commercially available targeted sequencing panels and exome sequencing panels in the context of pan-cancer pharmacogenetics. Materials & methods: Nine contemporary commercially available targeted pan-cancer panels and the xGen Exome Research Panel v2 were investigated to determine to what extent they cover the pharmacogenetic variant-drug interactions in five available cancer knowledgebases, and the driver mutations and fusion genes in the Cancer Genome Atlas. Results: xGen Exome Research Panel v2 and TrueSight Oncology 500 target 71.0 and 68.9% of the pharmacogenetic interactions in the available knowledgebases; and 93.7 and 86.0% of the driver mutations in the Cancer Genome Atlas, respectively. All other studied panels target lower percentages. Conclusion: Exome sequencing outperforms pan-cancer targeted sequencing panels in terms of covered cancer pharmacogenetic variant-drug interactions and pharmacogenetic cancer variants.

Digitizing the Pharma Neurons - A Technological Operation in Progress!

BACKGROUND

Digitization and automation are the buzzwords in clinical research and pharma companies are investigating heavily here. Right from drug discovery to personalized medicine, digital patients and patient engagement, there is great consideration of technology at each step.

METHODS

The published data and online information available is reviewed to give an overview of digitization in pharma, across the drug development cycle, industry collaborations and innovations. The regulatory guidelines, innovative collaborations across industry, academics and thought leadership are presented. Also included are some ideas, suggestions, way forwards while digitizing the pharma neurons, the regulatory stand, benefits and challenges.

RESULTS

The innovations range from discovering personalized medicine to conducting virtual clinical trials, and maximizing data collection from the real-world experience. To address the increasing demand for the real-world data and the needs of tech-savvy patients, the innovations are shaping up accordingly. Pharma companies are collaborating with academics and they are co-innovating the technology for example Massachusetts Institute of Technology's program. This focuses on the modernization of clinical trials, strategic use of artificial intelligence and machine learning using real-world evidence, assess the risk-benefit ratio of deploying digital analytics in medicine, and proactively identifying the solutions.

CONCLUSION

With unfolding data on the impact of science and technology amalgamation, we need shared mindset between data scientists and medical professionals to maximize the utility of enormous health and medical data. To tackle this efficiently, there is a need of cross-collaboration and education, and align with ethical and regulatory requirements. A perfect blend of industry, regulatory, and academia will ensure successful digitization of pharma neurons.

Chemogenomic profiling of breast cancer patient-derived xenografts reveals targetable vulnerabilities for difficult-to-treat tumors

Subsets of breast tumors present major clinical challenges, including triple-negative, metastatic/recurrent disease and rare histologies. Here, we developed 37 patient-derived xenografts (PDX) from these difficult-to-treat cancers to interrogate their molecular composition and functional biology. Whole-genome and transcriptome sequencing and reverse-phase protein arrays revealed that PDXs conserve the molecular landscape of their corresponding patient tumors. Metastatic potential varied between PDXs, where low-penetrance lung micrometastases were most common, though a subset of models displayed high rates of dissemination in organotropic or diffuse patterns consistent with what was observed clinically. Chemosensitivity profiling was performed in vivo with standard-of-care agents, where multi-drug chemoresistance was retained upon xenotransplantation. Consolidating chemogenomic data identified actionable features in the majority of PDXs, and marked regressions were observed in a subset that was evaluated in vivo. Together, this clinically-annotated PDX library with comprehensive molecular and phenotypic profiling serves as a resource for preclinical studies on difficult-to-treat breast tumors.

Evolution and structure of clinically relevant gene fusions in multiple myeloma

Multiple myeloma is a plasma cell blood cancer with frequent chromosomal translocations leading to gene fusions. To determine the clinical relevance of fusion events, we detect gene fusions from a cohort of 742 patients from the Multiple Myeloma Research Foundation CoMMpass Study. Patients with multiple clinic visits enable us to track tumor and fusion evolution, and cases with matching peripheral blood and bone marrow samples allow us to evaluate the concordance of fusion calls in patients with high tumor burden. We examine the joint upregulation of WHSC1 and FGFR3 in samples with t(4;14)-related fusions, and we illustrate a method for detecting fusions from single cell RNA-seq. We report fusions at MYC and a neighboring gene, PVT1, which are related to MYC translocations and associated with divergent progression-free survival patterns. Finally, we find that 4% of patients may be eligible for targeted fusion therapies, including three with an NTRK1 fusion.

Multiple myeloma is characterised by frequent gene fusions. Here, the authors use data from the Multiple Myeloma Research Foundation CoMMpass Study to further investigate fusion genes in this disease and their clinical relevance.

Pan-Cancer Analysis Reveals the Diverse Landscape of Novel Sense and Antisense Fusion Transcripts

Gene fusions that contribute to oncogenicity can be explored for identifying cancer biomarkers and potential drug targets. To investigate the nature and distribution of fusion transcripts in cancer, we examined the transcriptome data of about 9,000 primary tumors from 33 different cancers in TCGA (The Cancer Genome Atlas) along with cell line data from CCLE (Cancer Cell Line Encyclopedia) using ChimeRScope, a novel fusion detection algorithm. We identified several fusions with sense (canonical, 39%) or antisense (non-canonical, 61%) transcripts recurrent across cancers. The majority of the recurrent non-canonical fusions found in our study are novel, unexplored, and exhibited highly variable profiles across cancers, with breast cancer and glioblastoma having the highest and lowest rates, respectively. Overall, 4,344 recurrent fusions were identified from TCGA in this study, of which 70% were novel. Additional analysis of 802 tumor-derived cell line transcriptome data across 20 cancers revealed significant variability in recurrent fusion profiles between primary tumors and corresponding cell lines. A subset of canonical and non-canonical fusions was validated by examining the structural variation evidence in whole-genome sequencing (WGS) data or by Sanger sequencing of fusion junctions. Several recurrent fusion genes identified in our study show promise for drug repurposing in basket trials and present opportunities for mechanistic studies.

A Review of Precision Oncology Knowledgebases for Determining the Clinical Actionability of Genetic Variants

The increased availability of tumor genetic testing and targeted cancer therapies contributes to the advancement of precision medicine in the field of oncology. Precision oncology knowledgebases provide a way of organizing clinically relevant genetic information in a way that is easily accessible for both oncologists and patients, facilitating the genetic-based clinical decision making. Many organizations and companies have built precision oncology knowledgebases, intended for multiple users. In general, these knowledgebases offer information on cancer-related genetic variants as well as their associated diagnostic, prognostic, and therapeutic implications, but they often differ in their information curations, designs, and user experiences. It is advisable that oncologists use multiple knowledgebases during their practice to have them complement each other. In the future, convergence toward common standards and formats is needed to ensure that the comprehensive knowledge across all sources can be unified to bring the oncology community closer to the achievement of the goal of precision oncology.

Japanese version of The Cancer Genome Atlas, JCGA, established using fresh frozen tumors obtained from 5143 cancer patients

This study aimed to establish the Japanese Cancer Genome Atlas (JCGA) using data from fresh frozen tumor tissues obtained from 5143 Japanese cancer patients, including those with colorectal cancer (31.6%), lung cancer (16.5%), gastric cancer (10.8%) and other cancers (41.1%). The results are part of a single-center study called "High-tech Omics-based Patient Evaluation" or "Project HOPE" conducted at the Shizuoka Cancer Center, Japan. All DNA samples and most RNA samples were analyzed using whole-exome sequencing, cancer gene panel sequencing, fusion gene panel sequencing and microarray gene expression profiling, and the results were annotated using an analysis pipeline termed "Shizuoka Multi-omics Analysis Protocol" developed in-house. Somatic driver alterations were identified in 72.2% of samples in 362 genes (average, 2.3 driver events per sample). Actionable information on drugs that is applicable in the current clinical setting was associated with 11.3% of samples. When including those drugs that are used for investigative purposes, actionable information was assigned to 55.0% of samples. Germline analysis revealed pathogenic mutations in hereditary cancer genes in 9.2% of samples, among which 12.2% were confirmed as pathogenic mutations by confirmatory test. Pathogenic mutations associated with non-cancerous hereditary diseases were detected in 0.4% of samples. Tumor mutation burden (TMB) analysis revealed 5.4% of samples as having the hypermutator phenotype (TMB ≥ 20). Clonal hematopoiesis was observed in 8.4% of samples. Thus, the JCGA dataset and the analytical procedures constitute a fundamental resource for genomic medicine for Japanese cancer patients.

ChiTaRS 5.0: the comprehensive database of chimeric transcripts matched with druggable fusions and 3D chromatin maps

Chimeric RNA transcripts are formed when exons from two genes fuse together, often due to chromosomal translocations, transcriptional errors or trans-splicing effect. While these chimeric RNAs produce functional proteins only in certain cases, they play a significant role in disease phenotyping and progression. ChiTaRS 5.0 (http://chitars.md.biu.ac.il/) is the latest and most comprehensive chimeric transcript repository, with 111 582 annotated entries from eight species, including 23 167 known human cancer breakpoints. The database includes unique information correlating chimeric breakpoints with 3D chromatin contact maps, generated from public datasets of chromosome conformation capture techniques (Hi-C). In this update, we have added curated information on druggable fusion targets matched with chimeric breakpoints, which are applicable to precision medicine in cancers. The introduction of a new section that lists chimeric RNAs in various cell-lines is another salient feature. Finally, using text-mining techniques, novel chimeras in Alzheimer's disease, schizophrenia, dyslexia and other diseases were collected in ChiTaRS. Thus, this improved version is an extensive catalogue of chimeras from multiple species. It extends our understanding of the evolution of chimeric transcripts in eukaryotes and contributes to the analysis of 3D genome conformational changes and the functional role of chimeras in the etiopathogenesis of cancers and other complex diseases.

Evidence-Based Network Approach to Recommending Targeted Cancer Therapies

PURPOSE

In this work, we introduce CDGnet (Cancer-Drug-Gene Network), an evidence-based network approach for recommending targeted cancer therapies. CDGnet represents a user-friendly informatics tool that expands the range of targeted therapy options for patients with cancer who undergo molecular profiling by including the biologic context via pathway information.

METHODS

CDGnet considers biologic pathway information specifically by looking at targets or biomarkers downstream of oncogenes and is personalized for individual patients via user-inputted molecular alterations and cancer type. It integrates a number of different sources of knowledge: patient-specific inputs (molecular alterations and cancer type), US Food and Drug Administration-approved therapies and biomarkers (curated from DailyMed), pathways for specific cancer types (from Kyoto Encyclopedia of Genes and Genomes [KEGG]), gene-drug connections (from DrugBank), and oncogene information (from KEGG). We consider 4 different evidence-based categories for therapy recommendations. Our tool is delivered via an R/Shiny Web application. For the 2 categories that use pathway information, we include an interactive Sankey visualization built on top of d3.js that also provides links to PubChem.

RESULTS

We present a scenario for a patient who has estrogen receptor (ER)-positive breast cancer with FGFR1 amplification. Although many therapies exist for patients with ER-positive breast cancer, FGFR1 amplifications may confer resistance to such treatments. CDGnet provides therapy recommendations, including PIK3CA, MAPK, and RAF inhibitors, by considering targets or biomarkers downstream of FGFR1.

CONCLUSION

CDGnet provides results in a number of easily accessible and usable forms, separating targeted cancer therapies into categories in an evidence-based manner that incorporates biologic pathway information.

Contributions from the 2018 Literature on Bioinformatics and Translational Informatics

Objectives : To summarize recent research and select the best papers published in 2018 in the field of Bioinformatics and Translational Informatics (BTI) for the corresponding section of the International Medical Informatics Association (IMIA) Yearbook.

Methods : A literature review was performed for retrieving from PubMed papers indexed with keywords and free terms related to BTI. Independent review allowed the two section editors to select a list of 14 candidate best papers which were subsequently peer-reviewed. A final consensus meeting gathering the whole IMIA Yearbook editorial committee was organized to finally decide on the selection of the best papers.

Results : Among the 636 retrieved papers published in 2018 in the various subareas of BTI, the review process selected four best papers. The first paper presents a computational method to identify molecular markers for targeted treatment of acute myeloid leukemia using multi-omics data (genome-wide gene expression profiles) and in vitro sensitivity to 160 chemotherapy drugs. The second paper describes a deep neural network approach to predict the survival of patients suffering from glioma on the basis of digitalised pathology images and genomics biomarkers. The authors of the third paper adopt a pan-cancer approach to take benefit of multi-omics data for drug repurposing. The fourth paper presents a graph-based semi-supervised method to accurate phenotype classification applied to ovarian cancer.

Conclusions : Thanks to the normalization of open data and open science practices, research in BTI continues to develop and mature. Noteworthy achievements are sophisticated applications of leading edge machine-learning methods dedicated to personalized medicine.

Integrative omics analyses broaden treatment targets in human cancer

Background

Although large-scale, next-generation sequencing (NGS) studies of cancers hold promise for enabling precision oncology, challenges remain in integrating NGS with clinically validated biomarkers.

Methods

To overcome such challenges, we utilized the Database of Evidence for Precision Oncology (DEPO) to link druggability to genomic, transcriptomic, and proteomic biomarkers. Using a pan-cancer cohort of 6570 tumors, we identified tumors with potentially druggable biomarkers consisting of drug-associated mutations, mRNA expression outliers, and protein/phosphoprotein expression outliers identified by DEPO.

Results

Within the pan-cancer cohort of 6570 tumors, we found that 3% are druggable based on FDA-approved drug-mutation interactions in specific cancer types. However, mRNA/phosphoprotein/protein expression outliers and drug repurposing across cancer types suggest potential druggability in up to 16% of tumors. The percentage of potential drug-associated tumors can increase to 48% if we consider preclinical evidence. Further, our analyses showed co-occurring potentially druggable multi-omics alterations in 32% of tumors, indicating a role for individualized combinational therapy, with evidence supporting mTOR/PI3K/ESR1 co-inhibition and BRAF/AKT co-inhibition in 1.6 and 0.8% of tumors, respectively. We experimentally validated a subset of putative druggable mutations in BRAF identified by a protein structure-based computational tool. Finally, analysis of a large-scale drug screening dataset lent further evidence supporting repurposing of drugs across cancer types and the use of expression outliers for inferring druggability.

Conclusions

Our results suggest that an integrated analysis platform can nominate multi-omics alterations as biomarkers of druggability and aid ongoing efforts to bring precision oncology to patients.

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0564-z) contains supplementary material, which is available to authorized users.