Multi-omics data integration reveals novel drug targets in hepatocellular carcinoma.

BACKGROUND: Genetic aberrations in hepatocellular carcinoma (HCC) are well known, but the functional consequences of such aberrations remain poorly understood. RESULTS: Here, we explored the effect of defined genetic changes on the transcriptome, proteome and phosphoproteome in twelve tumors from an mTOR-driven hepatocellular carcinoma mouse model. Using Network-based Integration of multi-omiCS data (NetICS), we detected 74 'mediators' that relay via molecular interactions the effects of genetic and miRNA expression changes. The detected mediators account for the effects of oncogenic mTOR signaling on the transcriptome, proteome and phosphoproteome. We confirmed the dysregulation of the mediators YAP1, GRB2, SIRT1, HDAC4 and LIS1 in human HCC. CONCLUSIONS: This study suggests that targeting pathways such as YAP1 or GRB2 signaling and pathways regulating global histone acetylation could be beneficial in treating HCC with hyperactive mTOR signaling.

Identification and Validation of a Biomarker Signature in Patients With Resectable Pancreatic Cancer via Genome-Wide Screening for Functional Genetic Variants.

Importance: Surgery currently offers the only chance for a cure in pancreatic ductal adenocarcinoma (PDAC), but it carries a significant morbidity and mortality risk and results in varying oncologic outcomes. At present, to our knowledge, there are no tests available before surgical resection to identify tumors with an aggressive biological phenotype that could guide personalized treatment strategies. Objective: Identification of noninvasive genetic biomarkers that could direct therapy in patients whose cases are amenable to pancreatic cancer resection. Design, Setting, and Participants: This multicenter study combined a prospective European cohort of patients with PDAC who underwent pancreatic resection (from University Hospital of Zurich, Zurich, Switzerland; Cantonal Hospital of Winterthur, Winterthur, Switzerland; and University Clinic of Ulm, Ulm, Germany) with data from the Cancer Genome Atlas database in the United States, which includes prospectively registered patients with PDAC. A genome-wide screening for functional single-nucleotide polymorphisms (SNPs) that affect PDAC survival was conducted using the European cohort for identification and the Cancer Genome Atlas cohort for validation. We used Cox proportional hazards models to screen for high-frequency polymorphic variants that are associated with allelic differences in tumor-associated survival and either result in an altered protein structure and function or reside in known regulatory noncoding genomic regions. The false-discovery rate method was applied for multiple hypothesis-testing corrections. Data analysis occurred from November 2017 to May 2018. Exposures: Pancreatic resection. Main Outcomes and Measures: Tumor-associated survival. Results: A total of 195 patients in the European cohort were included, as well as 136 patients in the Cancer Genome Atlas cohort (overall median [range] age, 66 [19-87] years; 156 [47.1%] were women, and 175 [52.9%] were men). Two SNPs in noncoding, functional regions of genes that regulate cancer progression, invasion, and metastasis were identified (CHI3L2 SNP rs684559 and CD44 SNP rs353630). These were associated with survival after PDAC resection; patients who carry the risk alleles at 1 of both SNP loci had a 2.63-fold increased risk for tumor-associated death compared with those with protective genotypes (hazard ratio for survival, 0.38 [95% CI, 0.27-0.53]; P = 1.0 × 10-8). Conclusions and Relevance: The identified polymorphisms may serve as a noninvasive biomarker signature of prospective survival after pancreatic resection that is readily available at the time of PDAC diagnosis. This signature can be used to identify a subset of high-risk patients with PDAC with very low survival probability who might be eligible for inclusion in clinical trials of new therapeutic strategies, including neoadjuvant chemotherapy protocols. In addition, the biological knowledge about these SNPs could help guide the development of individualized genomic strategies for PDAC therapies.

Network-based integration of multi-omics data for prioritizing cancer genes.

Motivation: Several molecular events are known to be cancer-related, including genomic aberrations, hypermethylation of gene promoter regions and differential expression of microRNAs. These aberration events are very heterogeneous across tumors and it is poorly understood how they affect the molecular makeup of the cell, including the transcriptome and proteome. Protein interaction networks can help decode the functional relationship between aberration events and changes in gene and protein expression. Results: We developed NetICS (Network-based Integration of Multi-omics Data), a new graph diffusion-based method for prioritizing cancer genes by integrating diverse molecular data types on a directed functional interaction network. NetICS prioritizes genes by their mediator effect, defined as the proximity of the gene to upstream aberration events and to downstream differentially expressed genes and proteins in an interaction network. Genes are prioritized for individual samples separately and integrated using a robust rank aggregation technique. NetICS provides a comprehensive computational framework that can aid in explaining the heterogeneity of aberration events by their functional convergence to common differentially expressed genes and proteins. We demonstrate NetICS' competitive performance in predicting known cancer genes and in generating robust gene lists using TCGA data from five cancer types. Availability and implementation: NetICS is available at https://github.com/cbg-ethz/netics. Supplementary information: Supplementary data are available at Bioinformatics online.

Computational approaches for the identification of cancer genes and pathways.

High-throughput DNA sequencing techniques enable large-scale measurement of somatic mutations in tumors. Cancer genomics research aims at identifying all cancer-related genes and solid interpretation of their contribution to cancer initiation and development. However, this venture is characterized by various challenges, such as the high number of neutral passenger mutations and the complexity of the biological networks affected by driver mutations. Based on biological pathway and network information, sophisticated computational methods have been developed to facilitate the detection of cancer driver mutations and pathways. They can be categorized into (1) methods using known pathways from public databases, (2) network-based methods, and (3) methods learning cancer pathways de novo. Methods in the first two categories use and integrate different types of data, such as biological pathways, protein interaction networks, and gene expression measurements. The third category consists of de novo methods that detect combinatorial patterns of somatic mutations across tumor samples, such as mutual exclusivity and co-occurrence. In this review, we discuss recent advances, current limitations, and future challenges of these approaches for detecting cancer genes and pathways. We also discuss the most important current resources of cancer-related genes. WIREs Syst Biol Med 2017, 9:e1364. doi: 10.1002/wsbm.1364 For further resources related to this article, please visit the WIREs website.