Evaluating the evaluation of cancer driver genes.

Sequencing has identified millions of somatic mutations in human cancers, but distinguishing cancer driver genes remains a major challenge. Numerous methods have been developed to identify driver genes, but evaluation of the performance of these methods is hindered by the lack of a gold standard, that is, bona fide driver gene mutations. Here, we establish an evaluation framework that can be applied to driver gene prediction methods. We used this framework to compare the performance of eight such methods. One of these methods, described here, incorporated a machine-learning-based ratiometric approach. We show that the driver genes predicted by each of the eight methods vary widely. Moreover, the P values reported by several of the methods were inconsistent with the uniform values expected, thus calling into question the assumptions that were used to generate them. Finally, we evaluated the potential effects of unexplained variability in mutation rates on false-positive driver gene predictions. Our analysis points to the strengths and weaknesses of each of the currently available methods and offers guidance for improving them in the future.

Species-specific exon loss in human transcriptomes.

Changes in exon-intron structures and splicing patterns represent an important mechanism for the evolution of gene functions and species-specific regulatory networks. Although exon creation is widespread during primate and human evolution and has been studied extensively, much less is known about the scope and potential impact of human-specific exon loss events. Historically, transcriptome data and exon annotations are significantly biased toward humans over nonhuman primates. This ascertainment bias makes it challenging to discover human-specific exon loss events. We carried out a transcriptome-wide search of human-specific exon loss events, by taking advantage of RNA sequencing (RNA-seq) as a powerful and unbiased tool for exon discovery and annotation. Using RNA-seq data of humans, chimpanzees, and other primates, we reconstructed and compared transcript structures across the primate phylogeny. We discovered 33 candidate human-specific exon loss events, among which six exons passed stringent experimental filters for the complete loss of splicing activities in diverse human tissues. These events may result from human-specific deletion of genomic DNA, or small-scale sequence changes that inactivated splicing signals. The impact of human-specific exon loss events is predominantly regulatory. Three of the six events occurred in the 5' untranslated region (5'-UTR) and affected cis-regulatory elements of mRNA translation. In SLC7A6, a gene encoding an amino acid transporter, luciferase reporter assays suggested that both a human-specific exon loss event and an independent human-specific single nucleotide substitution in the 5'-UTR increased mRNA translational efficiency. Our study provides novel insights into the molecular mechanisms and evolutionary consequences of exon loss during human evolution.

Minimal functional driver gene heterogeneity among untreated metastases.

Metastases are responsible for the majority of cancer-related deaths. Although genomic heterogeneity within primary tumors is associated with relapse, heterogeneity among treatment-naïve metastases has not been comprehensively assessed. We analyzed sequencing data for 76 untreated metastases from 20 patients and inferred cancer phylogenies for breast, colorectal, endometrial, gastric, lung, melanoma, pancreatic, and prostate cancers. We found that within individual patients, a large majority of driver gene mutations are common to all metastases. Further analysis revealed that the driver gene mutations that were not shared by all metastases are unlikely to have functional consequences. A mathematical model of tumor evolution and metastasis formation provides an explanation for the observed driver gene homogeneity. Thus, single biopsies capture most of the functionally important mutations in metastases and therefore provide essential information for therapeutic decision-making.

Transcriptome-wide landscape of pre-mRNA alternative splicing associated with metastatic colonization.

UNLABELLED: Metastatic colonization is an ominous feature of cancer progression. Recent studies have established the importance of pre-mRNA alternative splicing (AS) in cancer biology. However, little is known about the transcriptome-wide landscape of AS associated with metastatic colonization. Both in vitro and in vivo models of metastatic colonization were utilized to study AS regulation associated with cancer metastasis. Transcriptome profiling of prostate cancer cells and derivatives crossing in vitro or in vivo barriers of metastasis revealed splicing factors with significant gene expression changes associated with metastatic colonization. These include splicing factors known to be differentially regulated in epithelial-mesenchymal transition (ESRP1, ESRP2, and RBFOX2), a cellular process critical for cancer metastasis, as well as novel findings (NOVA1 and MBNL3). Finally, RNA-seq indicated a large network of AS events regulated by multiple splicing factors with altered gene expression or protein activity. These AS events are enriched for pathways important for cell motility and signaling, and affect key regulators of the invasive phenotype such as CD44 and GRHL1. IMPLICATIONS: Transcriptome-wide remodeling of AS is an integral regulatory process underlying metastatic colonization, and AS events affect the metastatic behavior of cancer cells.