Identifying cancer driver genes in tumor genome sequencing studies

Motivation: Major tumor sequencing projects have been conducted in the past few years to identify genes that contain ‘driver’ somatic mutations in tumor samples. These genes have been defined as those for which the non-silent mutation rate is significantly greater than a background mutation rate estimated from silent mutations. Several methods have been used for estimating the background mutation rate

Computational Cancer Biology: An Evolutionary Perspective

ISSN:1553-734XISSN:1553-735

Integrated genomic and transcriptomic analyses of radiation-induced malignancies

Cancer is a genetic disease caused by an unregulated expansion of a clone of cells (Sompayrac, 2004). The genetic abnormalities in cancer are the consequences of defective DNA replication, repair, maintenance, and modification, genetic background, and exposure to mutagens (Alexandrov et al., 2013). Ionizing radiation (IR), a mutagen exposed to cancer patients during clinical radiotherapy (RT), can cause DNA damage, genomic instability, and mutagenesis (Sherborne et al., 2015). While RT has been effective in treating cancer, it increases the risk of second malignant neoplasm (SMN), a severe delayed complication associated with mainly pediatric cancer survivors many decades after the treatment of their first cancer (Robison & Hudson, 2014). As the mortality of patients with childhood cancer has been decreasing, cases of radiation-induced cancers has been increasing (Robison & Hudson, 2014). The considerable contribution by RT to SMN risk illustrate the need to characterize the genetic mechanism directly responsible for radiation-induced malignancies. To better our understanding of the mutational landscape of SMNs, our specific aims are to identify potential driver mutations implicated in radiation-induced malignancies through genome and transcriptome analysis and to assess whether genetic background, specifically germline polymorphisms and mutations in tumor suppressor gene TP53, has an impact on the formation of secondary malignancies

Network-based stratification of tumor mutations.

Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence

Comparison of TCGA and GENIE genomic datasets for the detection of clinically actionable alterations in breast cancer.

Whole exome sequencing (WES), targeted gene panel sequencing and single nucleotide polymorphism (SNP) arrays are increasingly used for the identification of actionable alterations that are critical to cancer care. Here, we compared The Cancer Genome Atlas (TCGA) and the Genomics Evidence Neoplasia Information Exchange (GENIE) breast cancer genomic datasets (array and next generation sequencing (NGS) data) in detecting genomic alterations in clinically relevant genes. We performed an in silico analysis to determine the concordance in the frequencies of actionable mutations and copy number alterations/aberrations (CNAs) in the two most common breast cancer histologies, invasive lobular and invasive ductal carcinoma. We found that targeted sequencing identified a larger number of mutational hotspots and clinically significant amplifications that would have been missed by WES and SNP arrays in many actionable genes such as PIK3CA, EGFR, AKT3, FGFR1, ERBB2, ERBB3 and ESR1. The striking differences between the number of mutational hotspots and CNAs generated from these platforms highlight a number of factors that should be considered in the interpretation of array and NGS-based genomic data for precision medicine. Targeted panel sequencing was preferable to WES to define the full spectrum of somatic mutations present in a tumor