Network perturbation by recurrent regulatory variants in cancer

Recurrence-associated gene signature in patients with stage I non-small-cell lung cancer

Recurrent gene mutations and fusions in cancer patients are likely to be associated with cancer progression or recurrence by Vogelstein et al. (Science (80-) 340, 1546-1558 (2013)). In this study, we investigated gene mutations and fusions that recurrently occurred in early-stage cancer patients with stage I non-small-cell cancer (NSCLC). Targeted exome sequencing was performed to profile the variants and confirmed their fidelity at the gene and pathway levels through comparison with data for stage I lung cancer patients, which was obtained from The Cancer Genome Atlas (TCGA). Next, we identified prognostic gene mutations (ATR, ERBB3, KDR, and MUC6), fusions (GOPC-ROS1 and NTRK1-SH2D2A), and VEGF signaling pathway associated with cancer recurrence. To infer the functional implication of the recurrent variants in early-stage cancers, the extent of their selection pattern was investigated, and they were shown to be under positive selection, implying a selective advantage for cancer progression. Specifically, high selection scores were observed in the variants with significantly high risks for recurrence. Taken together, the results of this study enabled us to identify recurrent gene mutations and fusions in a stage I NSCLC cohort and to demonstrate positive selection, which had implications regarding cancer recurrence.

An integrative multi-omics network-based approach identifies key regulators for breast cancer

Although genome-wide association studies (GWASs) have successfully identified thousands of risk variants for human complex diseases, understanding the biological function and molecular mechanisms of the associated SNPs involved in complex diseases is challenging. Here we developed a framework named integrative multi-omics network-based approach (IMNA), aiming to identify potential key genes in regulatory networks by integrating molecular interactions across multiple biological scales, including GWAS signals, gene expression-based signatures, chromatin interactions and protein interactions from the network topology. We applied this approach to breast cancer, and prioritized key genes involved in regulatory networks. We also developed an abnormal gene expression score (AGES) signature based on the gene expression deviation of the top 20 rank-ordered genes in breast cancer. The AGES values are associated with genetic variants, tumor properties and patient survival outcomes. Among the top 20 genes, RNASEH2A was identified as a new candidate gene for breast cancer. Thus, our integrative network-based approach provides a genetic-driven framework to unveil tissue-specific interactions from multiple biological scales and reveal potential key regulatory genes for breast cancer. This approach can also be applied in other complex diseases such as ovarian cancer to unravel underlying mechanisms and help for developing therapeutic targets.

Computational inference of cancer-specific vulnerabilities in clinical samples

BACKGROUND

Systematic in vitro loss-of-function screens provide valuable resources that can facilitate the discovery of drugs targeting cancer vulnerabilities.

RESULTS

We develop a deep learning-based method to predict tumor-specific vulnerabilities in patient samples by leveraging a wealth of in vitro screening data. Acquired dependencies of tumors are inferred in cases in which one allele is disrupted by inactivating mutations or in association with oncogenic mutations. Nucleocytoplasmic transport by Ran GTPase is identified as a common vulnerability in Her2-positive breast cancers. Vulnerability to loss of Ku70/80 is predicted for tumors that are defective in homologous recombination and rely on nonhomologous end joining for DNA repair. Our experimental validation for Ran, Ku70/80, and a proteasome subunit using patient-derived cells shows that they can be targeted specifically in particular tumors that are predicted to be dependent on them.

CONCLUSION

This approach can be applied to facilitate the development of precision therapeutic targets for different tumors.

How to interpret and integrate multi-omics data at systems level

Current parallel sequencing technologies generate biological sequence data explosively and enable omics studies that analyze collective biological features. The more omics data that is accumulated, the more they show the regulatory complexity of biological phenotypes. This high order regulatory complexity needs systems-level approaches, including network analysis, to understand it. There are a series of layers in the omics field that are closely connected to each other as described in ‘central dogma.’ We, therefore, have to not only interpret each single omics layer but also to integrate multi-omics layers systematically to get a full picture of the regulatory landscape of the biological phenotype. Especially, individual omics data has their own adequate biological network to apply systematic analysis appropriately. A full regulatory landscape can only be obtained when multi-omics data are incorporated within adequate networks. In this review, we discuss how to interpret and integrate multi-omics data systematically using recent studies. We also propose an analysis framework for systematic multi-omics interpretation by centering on the transcriptional core regulator, which can be incorporated in all omics networks.

Protein ensembles link genotype to phenotype

Classically, phenotype is what is observed, and genotype is the genetic makeup. Statistical studies aim to project phenotypic likelihoods of genotypic patterns. The traditional genotype-to-phenotype theory embraces the view that the encoded protein shape together with gene expression level largely determines the resulting phenotypic trait. Here, we point out that the molecular biology revolution at the turn of the century explained that the gene encodes not one but ensembles of conformations, which in turn spell all possible gene-associated phenotypes. The significance of a dynamic ensemble view is in understanding the linkage between genetic change and the gained observable physical or biochemical characteristics. Thus, despite the transformative shift in our understanding of the basis of protein structure and function, the literature still commonly relates to the classical genotype-phenotype paradigm. This is important because an ensemble view clarifies how even seemingly small genetic alterations can lead to pleiotropic traits in adaptive evolution and in disease, why cellular pathways can be modified in monogenic and polygenic traits, and how the environment may tweak protein function.