Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics.

The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing

A computational search for mutational drivers of cancer

The notion that DNA changes could drive the growth of cancer was first speculated more than a century ago, and has acquired overwhelming evidence in the past several decades. The recent decrease in cost of next-generation sequencing has spurred the growth of cancer sequencing studies that catalog mutations observed in cancer. However, the vast majority of mutations in cancer do not increase the fitness of cancer cells. As a consequence, computational methods have become essential to distinguish the specific driver mutations implicated in cancer by leveraging patterns of genetic variation observed across many cancer samples. Here, I introduce several new computational methods to analyze cancer drivers at different levels of resolution -- including at the gene (20/20+), protein region (HotMAPS), and mutation (CHASMplus) level. I use these methods to interrogate fundamental questions regarding cancer driver mutations, such as their cancer type specificity, commonness or rarity, and the characteristics of oncogenes and tumor suppressor genes. Different types of cancer varied substantially on the precise cancer driver genes and the balance of oncogenes versus tumor suppressor genes, but shared clusters of cancer driver genes were seen in cancer types with a common cell of origin. Results also indicate a prominent emerging role for rare driver mutations, suggesting interpretation of a cancer genome will need to be increasingly personalized, as a patient's driver mutation may have not been previously observed. I also probe the efficacy of computational methods, which is difficult because there is no accepted gold-standard. I first analyze consequences expected analytically, and then compare existing methods on newly developed benchmarks. I found many prior computational methods do not appropriately model the heterogeneity of mutations expected by chance. The recent completion of The Cancer Genome Atlas has provided a unique capability to understand cancer at an unprecedented scale. I comprehensively discover both cancer driver genes and mutations across nearly 10,000 cancers from 33 cancer types. This revealed 299 cancer driver genes and >3,000 driver mutations. Although this expansive analysis found 59 novel genes not previously associated as cancer drivers, some evidence points to diminishing returns for future driver discovery

PrimerSeq: Design and visualization of RT-PCR primers for alternative splicing using RNA-seq data.

The vast majority of multi-exon genes in higher eukaryotes are alternatively spliced and changes in alternative splicing (AS) can impact gene function or cause disease. High-throughput RNA sequencing (RNA-seq) has become a powerful technology for transcriptome-wide analysis of AS, but RT-PCR still remains the gold-standard approach for quantifying and validating exon splicing levels. We have developed PrimerSeq, a user-friendly software for systematic design and visualization of RT-PCR primers using RNA-seq data. PrimerSeq incorporates user-provided transcriptome profiles (i.e., RNA-seq data) in the design process, and is particularly useful for large-scale quantitative analysis of AS events discovered from RNA-seq experiments. PrimerSeq features a graphical user interface (GUI) that displays the RNA-seq data juxtaposed with the expected RT-PCR results. To enable primer design and visualization on user-provided RNA-seq data and transcript annotations, we have developed PrimerSeq as a stand-alone software that runs on local computers. PrimerSeq is freely available for Windows and Mac OS X along with source code at http://primerseq.sourceforge.net/. With the growing popularity of RNA-seq for transcriptome studies, we expect PrimerSeq to help bridge the gap between high-throughput RNA-seq discovery of AS events and molecular analysis of candidate events by RT-PCR

PrimerSeq: Design and visualization of RT-PCR primers for alternative splicing using RNA-seq data.

The vast majority of multi-exon genes in higher eukaryotes are alternatively spliced and changes in alternative splicing (AS) can impact gene function or cause disease. High-throughput RNA sequencing (RNA-seq) has become a powerful technology for transcriptome-wide analysis of AS, but RT-PCR still remains the gold-standard approach for quantifying and validating exon splicing levels. We have developed PrimerSeq, a user-friendly software for systematic design and visualization of RT-PCR primers using RNA-seq data. PrimerSeq incorporates user-provided transcriptome profiles (i.e., RNA-seq data) in the design process, and is particularly useful for large-scale quantitative analysis of AS events discovered from RNA-seq experiments. PrimerSeq features a graphical user interface (GUI) that displays the RNA-seq data juxtaposed with the expected RT-PCR results. To enable primer design and visualization on user-provided RNA-seq data and transcript annotations, we have developed PrimerSeq as a stand-alone software that runs on local computers. PrimerSeq is freely available for Windows and Mac OS X along with source code at http://primerseq.sourceforge.net/. With the growing popularity of RNA-seq for transcriptome studies, we expect PrimerSeq to help bridge the gap between high-throughput RNA-seq discovery of AS events and molecular analysis of candidate events by RT-PCR

Systematic characterization of mutations altering protein degradation in human cancers.

The ubiquitin-proteasome system (UPS) is the primary route for selective protein degradation in human cells. The UPS is an attractive target for novel cancer therapies, but the precise UPS genes and substrates important for cancer growth are incompletely understood. Leveraging multi-omics data across more than 9,000 human tumors and 33 cancer types, we found that over 19% of all cancer driver genes affect UPS function. We implicate transcription factors as important substrates and show that c-Myc stability is modulated by CUL3. Moreover, we developed a deep learning model (deepDegron) to identify mutations that result in degron loss and experimentally validated the prediction that gain-of-function truncating mutations in GATA3 and PPM1D result in increased protein stability. Last, we identified UPS driver genes associated with prognosis and the tumor microenvironment. This study demonstrates the important role of UPS dysregulation in human cancer and underscores the potential therapeutic utility of targeting the UPS