Roman Yelensky

Efficiency and power in genetic association studies

We investigated selection and analysis of tag SNPs for genome-wide association studies by specifically examining the relationship between investment in genotyping and statistical power. Do pairwise or multimarker methods maximize efficiency and power? To what extent is power compromised when tags are selected from an incomplete resource such as HapMap? We addressed these questions using genotype data from the HapMap ENCODE project, association studies simulated under a realistic disease model, and empirical correction for multiple hypothesis testing. We demonstrate a haplotype-based tagging method that uniformly outperforms single-marker tests and methods for prioritization that markedly increase tagging efficiency. Examining all observed haplotypes for association, rather than just those that are proxies for known SNPs, increases power to detect rare causal alleles, at the cost of reduced power to detect common causal alleles. Power is robust to the completeness of the reference panel from which tags are selected. These findings have implications for prioritizing tag SNPs and interpreting association studies.

Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing

As more clinically relevant cancer genes are identified, comprehensive diagnostic approaches are needed to match patients to therapies, raising the challenge of optimization and analytical validation of assays that interrogate millions of bases of cancer genomes altered by multiple mechanisms. Here we describe a test based on massively parallel DNA sequencing to characterize base substitutions, short insertions and deletions (indels), copy number alterations and selected fusions across 287 cancer-related genes from routine formalin-fixed and paraffin-embedded (FFPE) clinical specimens. We implemented a practical validation strategy with reference samples of pooled cell lines that model key determinants of accuracy, including mutant allele frequency, indel length and amplitude of copy change. Test sensitivity achieved was 95–99% across alteration types, with high specificity (positive predictive value >99%). We confirmed accuracy using 249 FFPE cancer specimens characterized by established assays. Application of the test to 2,221 clinical cases revealed clinically actionable alterations in 76% of tumors, three times the number of actionable alterations detected by current diagnostic tests.

Identification of new ALK and RET gene fusions from colorectal and lung cancer biopsies

Applying a next-generation sequencing assay targeting 145 cancer-relevant genes in 40 colorectal cancer and 24 non–small cell lung cancer formalin-fixed paraffin-embedded tissue specimens identified at least one clinically relevant genomic alteration in 59% of the samples and revealed two gene fusions, C2orf44-ALK in a colorectal cancer sample and KIF5B-RET in a lung adenocarcinoma. Further screening of 561 lung adenocarcinomas identified 11 additional tumors with KIF5B-RET gene fusions (2.0%; 95% CI 0.8–3.1%). Cells expressing oncogenic KIF5B-RET are sensitive to multi-kinase inhibitors that inhibit RET.

Estimation of the multiple testing burden for genomewide association studies of nearly all common variants.

Genomewide association studies are an exciting strategy in genetics, recently becoming feasible and harvesting many novel genes linked to multiple phenotypes. Determining the significance of results in the face of testing a genomewide set of multiple hypotheses, most of which are producing noisy, null‐distributed association signals, presents a challenge to the wide community of association researchers. Rather than each study engaging in independent evaluation of significance standards, we have undertaken the task of developing such standards for genomewide significance, based on data collected by the International Haplotype Map Consortium. We report an estimated testing burden of a million independent tests genomewide in Europeans, and twice that number in Africans. We further identify the sensitivity of the testing burden to the required significance level, with implications to staged design of association studies. Genet. Epidemiol. 2008.

Evaluating and improving power in whole-genome association studies using fixed marker sets

Emerging technologies make it possible for the first time to genotype hundreds of thousands of SNPs simultaneously, enabling whole-genome association studies. Using empirical genotype data from the International HapMap Project, we evaluate the extent to which the sets of SNPs contained on three whole-genome genotyping arrays capture common SNPs across the genome, and we find that the majority of common SNPs are well captured by these products either directly or through linkage disequilibrium. We explore analytical strategies that use HapMap data to improve power of association studies conducted with these fixed sets of markers and show that limited inclusion of specific haplotype tests in association analysis can increase the fraction of common variants captured by 25–100%. Finally, we introduce a Bayesian approach to association analysis by weighting the likelihood of each statistical test to reflect the number of putative causal alleles to which it is correlated.

Targeted Next-generation Sequencing of Advanced Prostate Cancer Identifies Potential Therapeutic Targets and Disease Heterogeneity

BACKGROUND Most personalized cancer care strategies involving DNA sequencing are highly reliant on acquiring sufficient fresh or frozen tissue. It has been challenging to comprehensively evaluate the genome of advanced prostate cancer (PCa) because of limited access to metastatic tissue. OBJECTIVE To demonstrate the feasibility of a novel next-generation sequencing (NGS)-based platform that can be used with archival formalin-fixed paraffin-embedded (FFPE) biopsy tissue to evaluate the spectrum of DNA alterations seen in advanced PCa. DESIGN, SETTING, AND PARTICIPANTS FFPE samples (including archival prostatectomies and prostate needle biopsies) were obtained from 45 patients representing the spectrum of disease: localized PCa, metastatic hormone-naive PCa, and metastatic castration-resistant PCa (CRPC). We also assessed paired primaries and metastases to understand disease heterogeneity and disease progression. INTERVENTION At least 50 ng of tumor DNA was extracted from FFPE samples and used for hybridization capture and NGS using the Illumina HiSeq 2000 platform. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS A total of 3320 exons of 182 cancer-associated genes and 37 introns of 14 commonly rearranged genes were evaluated for genomic alterations. RESULTS AND LIMITATIONS We obtained an average sequencing depth of >900X. Overall, 44% of CRPCs harbored genomic alterations involving the androgen receptor gene (AR), including AR copy number gain (24% of CRPCs) or AR point mutation (20% of CRPCs). Other recurrent mutations included transmembrane protease, serine 2 gene (TMPRSS2):v-ets erythroblastosis virus E26 oncogene homolog (avian) gene (ERG) fusion (44%); phosphatase and tensin homolog gene (PTEN) loss (44%); tumor protein p53 gene (TP53) mutation (40%); retinoblastoma gene (RB) loss (28%); v-myc myelocytomatosis viral oncogene homolog (avian) gene (MYC) gain (12%); and phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit α gene (PIK3CA) mutation (4%). There was a high incidence of genomic alterations involving key genes important for DNA repair, including breast cancer 2, early onset gene (BRCA2) loss (12%) and ataxia telangiectasia mutated gene (ATM) mutations (8%); these alterations are potentially targetable with poly(adenosine diphosphate-ribose)polymerase inhibitors. A novel and actionable rearrangement involving the v-raf murine sarcoma viral oncogene homolog B1 gene (BRAF) was also detected. CONCLUSIONS This first-in-principle study demonstrates the feasibility of performing in-depth DNA analyses using FFPE tissue and brings new insight toward understanding the genomic landscape within advanced PCa.

Transferability of tag SNPs in genetic association studies in multiple populations

A general question for linkage disequilibrium–based association studies is how power to detect an association is compromised when tag SNPs are chosen from data in one population sample and then deployed in another sample. Specifically, it is important to know how well tags picked from the HapMap DNA samples capture the variation in other samples. To address this, we collected dense data uniformly across the four HapMap population samples and eleven other population samples. We picked tag SNPs using genotype data we collected in the HapMap samples and then evaluated the effective coverage of these tags in comparison to the entire set of common variants observed in the other samples. We simulated case-control association studies in the non-HapMap samples under a disease model of modest risk, and we observed little loss in power. These results demonstrate that the HapMap DNA samples can be used to select tags for genome-wide association studies in many samples around the world.

Emergence of Constitutively Active Estrogen Receptor-α Mutations in Pretreated Advanced Estrogen Receptor–Positive Breast Cancer

Purpose: We undertook this study to determine the prevalence of estrogen receptor (ER) α (ESR1) mutations throughout the natural history of hormone-dependent breast cancer and to delineate the functional roles of the most commonly detected alterations. Experimental Design: We studied a total of 249 tumor specimens from 208 patients. The specimens include 134 ER-positive (ER+/HER2−) and, as controls, 115 ER-negative (ER−) tumors. The ER+ samples consist of 58 primary breast cancers and 76 metastatic samples. All tumors were sequenced to high unique coverage using next-generation sequencing targeting the coding sequence of the estrogen receptor and an additional 182 cancer-related genes. Results: Recurring somatic mutations in codons 537 and 538 within the ligand-binding domain of ER were detected in ER+ metastatic disease. Overall, the frequency of these mutations was 12% [9/76; 95% confidence interval (CI), 6%–21%] in metastatic tumors and in a subgroup of patients who received an average of 7 lines of treatment the frequency was 20% (5/25; 95% CI, 7%–41%). These mutations were not detected in primary or treatment-naïve ER+ cancer or in any stage of ER− disease. Functional studies in cell line models demonstrate that these mutations render estrogen receptor constitutive activity and confer partial resistance to currently available endocrine treatments. Conclusions: In this study, we show evidence for the temporal selection of functional ESR1 mutations as potential drivers of endocrine resistance during the progression of ER+ breast cancer. Clin Cancer Res; 20(7); 1757–67. ©2014 AACR.

Kinase fusions are frequent in Spitz tumours and spitzoid melanomas

Spitzoid neoplasms are a group of melanocytic tumours with distinctive histopathological features. They include benign tumours (Spitz naevi), malignant tumours (spitzoid melanomas) and tumours with borderline histopathological features and uncertain clinical outcome (atypical Spitz tumours). Their genetic underpinnings are poorly understood, and alterations in common melanoma-associated oncogenes are typically absent. Here we show that spitzoid neoplasms harbour kinase fusions of ROS1 (17%), NTRK1 (16%), ALK (10%), BRAF (5%) and RET (3%) in a mutually exclusive pattern. The chimeric proteins are constitutively active, stimulate oncogenic signalling pathways, are tumourigenic and are found in the entire biologic spectrum of spitzoid neoplasms, including 55% of Spitz naevi, 56% of atypical Spitz tumours and 39% of spitzoid melanomas. Kinase inhibitors suppress the oncogenic signalling of the fusion proteins in vitro. In summary, kinase fusions account for the majority of oncogenic aberrations in spitzoid neoplasms and may serve as therapeutic targets for metastatic spitzoid melanomas.

Genetic Analysis of Human Traits In Vitro: Drug Response and Gene Expression in Lymphoblastoid Cell Lines

Lymphoblastoid cell lines (LCLs), originally collected as renewable sources of DNA, are now being used as a model system to study genotype–phenotype relationships in human cells, including searches for QTLs influencing levels of individual mRNAs and responses to drugs and radiation. In the course of attempting to map genes for drug response using 269 LCLs from the International HapMap Project, we evaluated the extent to which biological noise and non-genetic confounders contribute to trait variability in LCLs. While drug responses could be technically well measured on a given day, we observed significant day-to-day variability and substantial correlation to non-genetic confounders, such as baseline growth rates and metabolic state in culture. After correcting for these confounders, we were unable to detect any QTLs with genome-wide significance for drug response. A much higher proportion of variance in mRNA levels may be attributed to non-genetic factors (intra-individual variance—i.e., biological noise, levels of the EBV virus used to transform the cells, ATP levels) than to detectable eQTLs. Finally, in an attempt to improve power, we focused analysis on those genes that had both detectable eQTLs and correlation to drug response; we were unable to detect evidence that eQTL SNPs are convincingly associated with drug response in the model. While LCLs are a promising model for pharmacogenetic experiments, biological noise and in vitro artifacts may reduce power and have the potential to create spurious association due to confounding.