Chad Brown

A genome-wide association analysis of temozolomide response using lymphoblastoid cell lines shows a clinically relevant association with MGMT.

Objective Recently, lymphoblastoid cell lines (LCLs) have emerged as an innovative model system for mapping gene variants that predict the dose response to chemotherapy drugs. Methods In the current study, this strategy was expanded to the in-vitro genome-wide association approach, using 516 LCLs derived from a White cohort to assess the cytotoxic response to temozolomide. Results Genome-wide association analysis using ∼2.1 million quality-controlled single-nucleotide polymorphisms (SNPs) identified a statistically significant association (P<10−8) with SNPs in the O6-methylguanine-DNA methyltransferase (MGMT) gene. We also show that the primary SNP in this region is significantly associated with the differential gene expression of MGMT (P<10–26) in LCLs and differential methylation in glioblastoma samples from The Cancer Genome Atlas. Conclusion The previously documented clinical and functional relationships between MGMT and temozolomide response highlight the potential of well-powered genome-wide association studies of the LCL model system to identify meaningful genetic associations.

Population-Based in Vitro Hazard and Concentration–Response Assessment of Chemicals: The 1000 Genomes High-Throughput Screening Study

Background: Understanding of human variation in toxicity to environmental chemicals remains limited, so human health risk assessments still largely rely on a generic 10-fold factor (10½ each for toxicokinetics and toxicodynamics) to account for sensitive individuals or subpopulations. Objectives: We tested a hypothesis that population-wide in vitro cytotoxicity screening can rapidly inform both the magnitude of and molecular causes for interindividual toxicodynamic variability. Methods: We used 1,086 lymphoblastoid cell lines from the 1000 Genomes Project, representing nine populations from five continents, to assess variation in cytotoxic response to 179 chemicals. Analysis included assessments of population variation and heritability, and genome-wide association mapping, with attention to phenotypic relevance to human exposures. Results: For about half the tested compounds, cytotoxic response in the 1% most “sensitive” individual occurred at concentrations within a factor of 10½ (i.e., approximately 3) of that in the median individual; however, for some compounds, this factor was > 10. Genetic mapping suggested important roles for variation in membrane and transmembrane genes, with a number of chemicals showing association with SNP rs13120371 in the solute carrier SLC7A11, previously implicated in chemoresistance. Conclusions: This experimental approach fills critical gaps unaddressed by recent large-scale toxicity testing programs, providing quantitative, experimentally based estimates of human toxicodynamic variability, and also testable hypotheses about mechanisms contributing to interindividual variation. Citation: Abdo N, Xia M, Brown CC, Kosyk O, Huang R, Sakamuru S, Zhou YH, Jack JR, Gallins P, Xia K, Li Y, Chiu WA, Motsinger-Reif AA, Austin CP, Tice RR, Rusyn I, Wright FA. 2015. Population-based in vitro hazard and concentration–response assessment of chemicals: the 1000 Genomes high-throughput screening study. Environ Health Perspect 123:458–466; http://dx.doi.org/10.1289/ehp.1408775

Genome-wide association and pharmacological profiling of 29 anticancer agents using lymphoblastoid cell lines

AIM Association mapping with lymphoblastoid cell lines (LCLs) is a promising approach in pharmacogenomics research, and in the current study we utilized LCLs to perform association mapping for 29 chemotherapy drugs. MATERIALS & METHODS Currently, we use LCLs to perform genome-wide association mapping of the cytotoxic response of 520 European-Americans to 29 different anticancer drugs; the largest LCL study to date. A novel association approach using a multivariate analysis of covariance design was employed with the software program MAGWAS, testing for differences in the dose-response profiles between genotypes without making assumptions about the response curve or the biologic mode of association. Additionally, by classifying 25 of the 29 drugs into eight families according to structural and mechanistic relationships, MAGWAS was used to test for associations that were shared across each drug family. Finally, a unique algorithm using multivariate responses and multiple linear regressions across pairs of response curves was used for unsupervised clustering of drugs. RESULTS Among the single-drug studies, suggestive associations were obtained for 18 loci, 12 within/near genes. Three of these, MED12L, CHN2 and MGMT, have been previously implicated in cancer pharmacogenomics. The drug family associations resulted in four additional suggestive loci (three contained within/near genes). One of these genes, HDAC4, associated with the DNA alkylating agents, shows possible clinical interactions with temozolomide. For the drug clustering analysis, 18 of 25 drugs clustered into the appropriate family. CONCLUSION This study demonstrates the utility of LCLs in identifying genes that have clinical importance in drug response and for assigning unclassified agents to specific drug families, and proposes new candidate genes for follow-up in a large number of chemotherapy drugs.

An adaptive permutation approach for genome-wide association study: evaluation and recommendations for use

BackgroundPermutation testing is a robust and popular approach for significance testing in genomic research, which has the broad advantage of estimating significance non-parametrically, thereby safe guarding against inflated type I error rates. However, the computational efficiency remains a challenging issue that limits its wide application, particularly in genome-wide association studies (GWAS). Because of this, adaptive permutation strategies can be employed to make permutation approaches feasible. While these approaches have been used in practice, there is little research into the statistical properties of these approaches, and little guidance into the proper application of such a strategy for accurate p-value estimation at the GWAS level.MethodsIn this work, we advocate an adaptive permutation procedure that is statistically valid as well as computationally feasible in GWAS. We perform extensive simulation experiments to evaluate the robustness of the approach to violations of modeling assumptions and compare the power of the adaptive approach versus standard approaches. We also evaluate the parameter choices in implementing the adaptive permutation approach to provide guidance on proper implementation in real studies. Additionally, we provide an example of the application of adaptive permutation testing on real data.ResultsThe results provide sufficient evidence that the adaptive test is robust to violations of modeling assumptions. In addition, even when modeling assumptions are correct, the power achieved by adaptive permutation is identical to the parametric approach over a range of significance thresholds and effect sizes under the alternative. A framework for proper implementation of the adaptive procedure is also generated.ConclusionsWhile the adaptive permutation approach presented here is not novel, the current study provides evidence of the validity of the approach, and importantly provides guidance on the proper implementation of such a strategy. Additionally, tools are made available to aid investigators in implementing these approaches.

Multivariate methods and software for association mapping in dose-response genome-wide association studies.

BackgroundThe large sample sizes, freedom of ethical restrictions and ease of repeated measurements make cytotoxicity assays of immortalized lymphoblastoid cell lines a powerful new in vitro method in pharmacogenomics research. However, previous studies may have over‐simplified the complex differences in dose‐response profiles between genotypes, resulting in a loss of power.MethodsThe current study investigates four previously studied methods, plus one new method based on a multivariate analysis of variance (MANOVA) design. A simulation study was performed using differences in cancer drug response between genotypes for biologically meaningful loci. These loci also showed significance in separate genome‐wide association studies. This manuscript builds upon a previous study, where differences in dose‐response curves between genotypes were constructed using the hill slope equation.ConclusionOverall, MANOVA was found to be the most powerful method for detecting real signals, and was also the most robust method for detection using alternatives generated with the previous simulation study. This method is also attractive because test statistics follow their expected distributions under the null hypothesis for both simulated and real data. The success of this method inspired the creation of the software program MAGWAS. MAGWAS is a computationally efficient, user‐friendly, open source software tool that works on most platforms and performs GWASs for individuals having multivariate responses using standard file formats.

A Comparison of Association Methods for Cytotoxicity Mapping in Pharmacogenomics

Cytotoxicity assays of immortalized lymphoblastoid cell lines (LCLs) represent a promising new in vitro approach in pharmacogenomics research. However, previous studies employing LCLs in gene mapping have used simple association methods, which may not adequately capture the true differences in non-linear response profiles between genotypes. Two common approaches summarize each dose-response curve with either the IC50 or the slope parameter estimates from a hill slope fit and treat these estimates as the response in a linear model. The current study investigates these two methods, as well as four novel methods, and compares their power to detect differences between the response profiles of genotypes under a variety of different alternatives. The four novel methods include two methods that summarize each dose-response by its area under the curve, one method based off of an analysis of variance (ANOVA) design, and one method that compares hill slope fits for all individuals of each genotype. The power of each method was found to depend not only on the choice of alternative, but also on the choice for the set of dosages used in cytotoxicity measurements. The ANOVA-based method was found to be the most robust across alternatives and dosage sets for power in detecting differences between genotypes.

Loss of Power in Two‐Stage Residual‐Outcome Regression Analysis in Genetic Association Studies

In the area of genetic epidemiology, investigating the association between genetic risk factors and phenotypic outcomes is an important challenge. Often in such studies, the analysis approach needs to control for potentially confounding factors, such as clinical, demographic, or environmental variables. Multiple linear regression (MLR) is often used for such analysis when the outcome of interest is a quantitative trait. Under this model, the effects of genetic factors are estimated effectively and covariates are adjusted correctly. In large-scale genome-wide studies with a relatively large number of genetic and environmental factors present, some alternative analytic strategies are being used to address the issues of covariate adjustment. One such approach is a two-stage residual-outcome regression analysis (2SR). At stage one, the outcome is regressed on all the covariates. At stage two, the residual-adjusted outcome is then regressed on genetic factors. 2SR has been employed in genetic association studies [Choy et al., 2008], as well as genetic linkage studies [Slager and Iturria, 2003]. Zeegers and colleagues have demonstrated that the 2SR has equal power with other covariate-adjusted methods in genetic linkage studies [Zeegers et al., 2004], but the use of such a two-stage approach in association analyses is less well understood. While there may be some potential advantages with the 2SR approach in both ease of use and computational and data management efficiency, some issues, such as the bias of estimates, power, and type I error, need to be thoroughly investigated.

Abstract 412: RNA-Seq provides cost-effective alternative for typing self-identifying antigens in immunotherapy patients

The excitement surrounding immunotherapy is being driven by results in the clinic. Currently, autoimmunity is an unfortunate side-effect for a large fraction of those treated. Consequently, understanding how self-antigens are recognized is nearly as important as characterizing the immune repertoire for efficacious delivery of these promising new therapies. One approach to measuring the tumor immune environment is with RNA-Seq assays. Here we show that the genes responsible for presenting self-antigens (HLA Class I and Class II) can also be ascertained from RNA-Seq on total RNA. We present the detailed results from standard RNA-Seq pipeline analysis for 40 lymphoblastoid cell lines to achieve 91.3% overall concordance with “gold standard” typings. These samples were sequenced with 50bp paired-ends and approximately 30M reads. We additionally present RNA-Seq analysis results from calling HLA alleles on 15 replicate pairs of FFPE hepatic tumor samples, finding 88.33% overall replicate concordance with 2-digit precision, and 90% for Class I alleles. Finally, we present HLA-types on triplicates from 2 common breast cancer cell lines, MCF7 and T47D for loci HLA-A,-B,-C,-DRB1,-DQB1, with 90% overall replicate concordance. Emerging targeted DNA-Seq assays aimed at high-throughput clinical trials require high quality whole-blood samples that yield larger amounts of assay input material. Alternatively, the analyses presented here do not require HLA-region enrichment and thus can also be performed on legacy RNA-Seq data. Notably, the data here are generated from sample types and amounts that are more typical to a clinical oncology setting, and because it does not rely on targeted capture as do DNA-Seq assays, these analyses hold greater promise for assembling rare alleles and fusions. Citation Format: Kimberly Robasky, Jason Powers, Donald Trapolsi, Jeff Jasper, Chad Brown. RNA-Seq provides cost-effective alternative for typing self-identifying antigens in immunotherapy patients. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 412.

Abstract 1487: Gene expression and linkage analysis implicates CBLB as a mediator of rituximab resistance

Drug resistance remains one of the largest challenges in the curative treatment of cancer. We investigated the problem of monoclonal antibody resistance in vitro in a high throughput, complement-dependent cytotoxic (CDC) assay using immortalized as well as cancerous cell lines. First, the heritability of rituximab and ofatumumab sensitivity was explored using Epstein Barr Virus-immortalized human lymphoblastoid cell lines from two independent sources: a collection of trios (95 samples, 13 trios) from the Centre d’Etude du Polymorphisme Humain and an unrelated collection (486 samples) from the Children9s Hospital Oakland Research Initiative. Cell viability in the presence or absence of 10ug/ml rituximab or 10ug/ml ofatumumab treatment was quantitated using AlamarBlue. Heritability was estimated using variance component analysis using MERLIN: H 2 = 33.11% (p 2 = 31.11% (p 6) result across all three analyses; this result for rituximab response occurred within the gene, SMOC2. Next, linkage analysis on the 95 related samples revealed one significant linkage peak on chromosome 12 for rituximab and two significant peaks for ofatumumab, on chromosome 12 and 3. Also, we identified genes whose mRNA expression level was correlated with the degree of either rituximab or ofatumumab sensitivity. Quantitative Significance Analysis of Microarrays revealed 13 genes whose expression was correlated with rituximab sensitivity and 25 genes whose expression was correlated with ofatumumab sensitivity (false discovery rate cutoff Citation Format: John Jack, George Small, Tammy Havener, Chad Brown, Howard McLeod, Alison Motsinger, Kristy L. Richards. Gene expression and linkage analysis implicates CBLB as a mediator of rituximab resistance. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1487.

Abstract 5276: Impact of duplicate removal on low frequency NGS somatic variant calling

Cancer genomic profiles created by analysis of targeted Next Generation Sequencing (NGS) panels is emerging as a powerful tool for making informed clinical decisions. Of the critical informatics challenges to address, accurate mutation calls and allele frequency estimations after accounting for PCR-mediated artifacts are debated. The process of sample preparation for NGS sequencing involves amplification by PCR. While PCR is relatively error-free, mistakes early in DNA synthesis can be compounded, driving detection of spurious mutations and having an adverse impact on clinical reporting. Previous reports have addressed the utility of detecting and removing PCR duplicate reads in Mendelian applications but have rarely examined its use with targeted NGS panels.1,2 We performed deduplication with 3 widely used tools (SAMtools rmdup,3 SAMBLASTER,4 and PICARD mark duplicates5) to understand sensitivity to call low frequency alleles and any impact on false positive/negative rates. Deduplication by Picard resulted in a greater decrease in the mean depth for the smaller panels (32-59%) compared to Exome (15%). Uniformity improved 6-18% after deduplication for the smaller panels, but only 1% for the Exome. Independent of panel size, about 32% of the total reads were marked as duplicates, reducing the power to call low frequency variants by 18%. Importantly, after added sequencing 95-96% of onco-specific variants were detected post-deduplication with a lower limit of detection of 3% compared to 2.5% pre-removal. For low-quality DNA samples we find no benefit in added sequencing for any panel. Molecular diversity also varies by sample type. High-quality DNA show higher molecular diversity and lower duplication rates than degraded FFPE samples. On average, deduplication cut the number of SNV calls by 17.4%, with the FFPE samples affected the most (288%). From our analysis we recommend performing deduplication during analysis of targeted panels. While we observed the most benefit for smaller panels with low uniformity and FFPE samples with lower false positive rates. Improved variant sensitivity was seen regardless of panel size. During experimental design, we advise a worksheet to guide deduplication decisions.