Person:

MacConaill, Laura

It has been demonstrated for some cancers that the frequency of somatic oncogenic mutations may vary in ancestral populations. To determine whether key driver alterations might occur at different frequencies in colorectal cancer, we applied a high-throughput genotyping platform (OncoMap) to query 385 mutations across 33 known cancer genes in colorectal cancer DNA from 83 Asian, 149 Black and 195 White patients. We found that Asian patients had fewer canonical oncogenic mutations in the genes tested (60% vs Black 79% (P = 0.011) and White 77% (P = 0.015)), and that BRAF mutations occurred at a higher frequency in White patients (17% vs Asian 4% (P = 0.004) and Black 7% (P = 0.014)). These results suggest that the use of genomic approaches to elucidate the different ancestral determinants harbored by patient populations may help to more precisely and effectively treat colorectal cancer.

Background: Detection of critical cancer gene mutations in clinical tumor specimens may predict patient outcomes and inform treatment options; however, high-throughput mutation profiling remains underdeveloped as a diagnostic approach. We report the implementation of a genotyping and validation algorithm that enables robust tumor mutation profiling in the clinical setting. Methodology: We developed and implemented an optimized mutation profiling platform (“OncoMap”) to interrogate ∼400 mutations in 33 known oncogenes and tumor suppressors, many of which are known to predict response or resistance to targeted therapies. The performance of OncoMap was analyzed using DNA derived from both frozen and FFPE clinical material in a diverse set of cancer types. A subsequent in-depth analysis was conducted on histologically and clinically annotated pediatric gliomas. The sensitivity and specificity of OncoMap were 93.8% and 100% in fresh frozen tissue; and 89.3% and 99.4% in FFPE-derived DNA. We detected known mutations at the expected frequencies in common cancers, as well as novel mutations in adult and pediatric cancers that are likely to predict heightened response or resistance to existing or developmental cancer therapies. OncoMap profiles also support a new molecular stratification of pediatric low-grade gliomas based on BRAF mutations that may have immediate clinical impact. Conclusions: Our results demonstrate the clinical feasibility of high-throughput mutation profiling to query a large panel of “actionable” cancer gene mutations. In the future, this type of approach may be incorporated into both cancer epidemiologic studies and clinical decision making to specify the use of many targeted anticancer agents.

Genomic structural variation (SV), a common hallmark of cancer, has important predictive and therapeutic implications. However, accurately detecting SV using high-throughput sequencing data remains challenging, especially for ‘targeted’ resequencing efforts. This is critically important in the clinical setting where targeted resequencing is frequently being applied to rapidly assess clinically actionable mutations in tumor biopsies in a cost-effective manner. We present BreaKmer, a novel approach that uses a ‘kmer’ strategy to assemble misaligned sequence reads for predicting insertions, deletions, inversions, tandem duplications and translocations at base-pair resolution in targeted resequencing data. Variants are predicted by realigning an assembled consensus sequence created from sequence reads that were abnormally aligned to the reference genome. Using targeted resequencing data from tumor specimens with orthogonally validated SV, non-tumor samples and whole-genome sequencing data, BreaKmer had a 97.4% overall sensitivity for known events and predicted 17 positively validated, novel variants. Relative to four publically available algorithms, BreaKmer detected SV with increased sensitivity and limited calls in non-tumor samples, key features for variant analysis of tumor specimens in both the clinical and research settings.

Importance Lung cancer is the leading cause of cancer death in the United States in all ethnic and racial groups. The overall death rate from lung cancer is higher in black patients than in white patients.

Objective To compare the prevalence and types of somatic alterations between lung cancers from black patients and white patients. Differences in mutational frequencies could illuminate differences in prognosis and lead to the reduction of outcome disparities by more precisely targeting patients’ treatment.

Design, Setting, and Participants Tumor specimens were collected from Baptist Cancer Center (Memphis, Tennessee) over the course of 9 years (January 2004-December 2012). Genomic analysis by massively parallel sequencing of 504 cancer genes was performed at Dana-Farber Cancer Institute (Boston, Massachusetts). Overall, 509 lung cancer tumors specimens (319 adenocarcinomas; 142 squamous cell carcinomas) were profiled from 245 black patients and 264 white patients.

Main Outcomes and Measures The frequencies of genomic alterations were compared between tumors from black and white populations.

Results Overall, 509 lung cancers were collected and analyzed (273 women [129 black patients; 144 white patients] and 236 men [116 black patients; 120 white patients]). Using 313 adenocarcinomas and 138 squamous cell carcinomas with genetically supported ancestry, overall mutational frequencies and copy number changes were not significantly different between black and white populations in either tumor type after correcting for multiple hypothesis testing. Furthermore, specific activating alterations in members of the receptor tyrosine kinase/Ras/Raf pathway including EGFR and KRAS were not significantly different between populations in lung adenocarcinoma.

Conclusions and Relevance These results demonstrate that lung cancers from black patients are similar to cancers from white patients with respect to clinically actionable genomic alterations and suggest that clinical trials of targeted therapies could significantly benefit patients in both groups.

Background: Sample index cross-talk can result in false positive calls when massively parallel sequencing (MPS) is used for sensitive applications such as low-frequency somatic variant discovery, ancient DNA investigations, microbial detection in human samples, or circulating cell-free tumor DNA (ctDNA) variant detection. Therefore, the limit-of-detection of an MPS assay is directly related to the degree of index cross-talk. Results: Cross-talk rates up to 0.29% were observed when using standard, combinatorial adapters, resulting in 110,180 (0.1% cross-talk rate) or 1,121,074 (0.29% cross-talk rate) misassigned reads per lane in non-patterned and patterned Illumina flow cells, respectively. Here, we demonstrate that using unique, dual-matched indexed adapters dramatically reduces index cross-talk to ≤1 misassigned reads per flow cell lane. While the current study was performed using dual-matched indices, using unique, dual-unrelated indices would also be an effective alternative. Conclusions: For sensitive downstream analyses, the use of combinatorial indices for multiplexed hybrid capture and sequencing is inappropriate, as it results in an unacceptable number of misassigned reads. Cross-talk can be virtually eliminated using dual-matched indexed adapters. These results suggest that use of such adapters is critical to reduce false positive rates in assays that aim to identify low allele frequency events, and strongly indicate that dual-matched adapters be implemented for all sensitive MPS applications. Electronic supplementary material The online version of this article (10.1186/s12864-017-4428-5) contains supplementary material, which is available to authorized users.