From cancer genomes to oncogenic drivers, tumour dependencies and therapeutic targets

Abstract

The analysis of human cancer by genome sequencing and various types of arrays has proved that many tumours harbour hundreds of genes that are mutated or substantially altered by copy number changes. But how many of these changes are meaningful? And how can we exploit these massive data sets to yield new targets for cancer treatment? In this Opinion article, we describe emerging approaches that aim to determine which altered genes are actually contributing to cancer, as well as their potential as therapeutic targets.

Targeted genetic dependency screen facilitates identification of actionable mutations in FGFR4, MAP3K9, and PAK5 in lung cancer

“…More recently, several approaches are coming to the forefront to more efficiently identify genetic dependencies from cancer cells for which whole genome or exome sequencing has been performed (10). These include cross-species comparisons, insertional mutagenesis screens, and RNAi screening (10). There are benefits and deficiencies with many of these approaches, as highlighted by Eifert et al (10).…”

“…These include cross-species comparisons, insertional mutagenesis screens, and RNAi screening (10). There are benefits and deficiencies with many of these approaches, as highlighted by Eifert et al (10). Our targeted approach benefits from a small screening strategy, a custom ontarget siRNA library for efficient knockdown of all genes harboring nonsynonymous somatic mutations, and a defined endpoint.…”

Integrative clustering methods of multi‐omics data for molecule‐based cancer classifications

“…Another important source is functional genomics data. High-content screening techniques using RNAi [53] or CRISPR/Cas9 [55]generated many genome-scale genetic interaction and gene-phenotype association data. These data can help describe causal mechanisms between genetic variations and gene activity, and then help obtain new subtypes.…”

“…Recurrent epigenetic alterations, especially DNA methylation [52], and large-scale functional screens [53][54][55][56] can also be used to identified novel candidate drivers. Then, these alterations or perturbations are integrative analyzed with gene expression variations (either cis-or trans-acting effects), the functional indications of genetic alterations [57][58][59][60][61].…”

Understanding pancreatic cancer genomes

“…The intersection of these datasets can identify high-priority driver genes and pathways. (Reprinted and adapted with permission from Macmillan Publishers Ltd: Nature Reviews Cancer [23], copyright 2012) potential functional relevance to other significantly mutated genes in our study-MLL3, TGFBR2, SF3B1, EPC1, ARID1A, ARID2, MAP2K4, ATM, NALCN, ZIM2, SLC16A4-and many other genes mutated at low frequency.…”