SNVMix: predicting single nucleotide variants from next-generation sequencing of tumors

ARID1A mutations in endometriosis-associated ovarian carcinomas

BACKGROUND

Ovarian clear-cell and endometrioid carcinomas may arise from endometriosis, but the molecular events involved in this transformation have not been described.

METHODS

We sequenced the whole transcriptomes of 18 ovarian clear-cell carcinomas and 1 ovarian clear-cell carcinoma cell line and found somatic mutations in ARID1A (the AT-rich interactive domain 1A [SWI-like] gene) in 6 of the samples. ARID1A encodes BAF250a, a key component of the SWI–SNF chromatin remodeling complex. We sequenced ARID1A in an additional 210 ovarian carcinomas and a second ovarian clear-cell carcinoma cell line and measured BAF250a expression by means of immunohistochemical analysis in an additional 455 ovarian carcinomas.

RESULTS

ARID1A mutations were seen in 55 of 119 ovarian clear-cell carcinomas (46%), 10 of 33 endometrioid carcinomas (30%), and none of the 76 high-grade serous ovarian carcinomas. Seventeen carcinomas had two somatic mutations each. Loss of the BAF250a protein correlated strongly with the ovarian clear-cell carcinoma and endometrioid carcinoma subtypes and the presence of ARID1A mutations. In two patients, ARID1A mutations and loss of BAF250a expression were evident in the tumor and contiguous atypical endometriosis but not in distant endometriotic lesions.

CONCLUSIONS

These data implicate ARID1A as a tumor-suppressor gene frequently disrupted in ovarian clear-cell and endometrioid carcinomas. Since ARID1A mutation and loss of BAF250a can be seen in the preneoplastic lesions, we speculate that this is an early event in the transformation of endometriosis into cancer. (Funded by the British Columbia Cancer Foundation and the Vancouver General Hospital–University of British Columbia Hospital Foundation.).

Analysis of next-generation genomic data in cancer: accomplishments and challenges

The application of next-generation sequencing technology has produced a transformation in cancer genomics, generating large data sets that can be analyzed in different ways to answer a multitude of questions about the genomic alterations associated with the disease. Analytical approaches can discover focused mutations such as substitutions and small insertion/deletions, large structural alterations and copy number events. As our capacity to produce such data for multiple cancers of the same type is improving, so are the demands to analyze multiple tumor genomes simultaneously growing. For example, pathway-based analyses that provide the full mutational impact on cellular protein networks and correlation analyses aimed at revealing causal relationships between genomic alterations and clinical presentations are both enabled. As the repertoire of data grows to include mRNA-seq, non-coding RNA-seq and methylation for multiple genomes, our challenge will be to intelligently integrate data types and genomes to produce a coherent picture of the genetic basis of cancer.

Statistical Analyses of Next Generation Sequence Data: A Partial Overview

Next generation sequencing has revolutionized the status of biological research. For a long time, the gold standard of DNA sequencing was considered to be the Sanger method. However, in 2005, commercial launching of next generation sequencing has made it possible to generate massively parallel and high resolution DNA sequence data. Its usefulness in various genomic applications such as genome-wide detection of SNPs, DNA methylation profiling, mRNA expression profiling, whole-genome re-sequencing and so on are now well recognized. There are several platforms for generating next generation sequencing (NGS) data which we briefly discuss in this mini overview. With new technologies come new challenges for the data analysts. This mini review attempts to present a collection of selected topics in the current development of statistical methods dealing with these novel data types. We believe that knowing the advances and bottlenecks of this technology will help the researchers to benchmark the analytical tools dealing with these data and will pave the path for its proper application into clinical diagnostics.