Cell Line Models for Genome Wide Association Mapping in Cancer Drug Response

Transcription

1 Cell Line Models for Genome Wide Association Mapping in Cancer Drug Response Alison Motsinger-Reif, PhD Bioinformatics Research Center Department of Statistics

2 Introduction Understanding variability in individual response to drug/chemical exposure is a key goal of pharmacogenomics and toxicogenomics Goals of gene mapping: Find efficient predictors of response (efficacy, toxicity,potency, etc.) Dissect the underlying mechanisms of differential response

3 Challenges in Dose Response Genetics Study design limitations Clinical trials Rarely have family data Limited sample size Limited replication opportunities. Limits ability to test basic genetic assumptions Are these traits heritable? Is this actually a genetics problem?

4 Challenges in Dose Response Genetics Study design limitations Clinical trials Limited number of Rarely have family data Limited sample size, replication opportunities. Limits ability to test basic genetic assumptions Are these traits heritable? Is this actually a genetics problem?

5 Challenges in Dose Response Genetics Study design limitations Clinical trials Limited number of Rarely High-throughput have family data in vitro assays Limited of dose sample response size, replication can help opportunities. assess the heritability of dose response and perform wellpowered linkage and association analysis. Limits ability to test basic genetic assumptions Are these traits heritable? Is this actually a genetics problem?

6 Current Uses of the Model Cytotoxicity mapping for chemotherapy Cytotoxics Monoclonal antibodies Evaluation of methods for capturing dose response associations Use of high throughput methodology for chemical exposure

7 Assay Methodology Alamar blue viability assays 6 point dose response curves Immortalized lymphoblastoid cell lines

8 Use of the Model We are using this model to interrogate genetic predictors of drug response for 45 chemotherapy drugs Heritability assessed with family-based samples CEPH cell lines Mapping in unrelated cohorts CHORI cohort 1000 Genomes 44

9 Use of the Model We are using this model to interrogate genetic predictors of drug response for 45 chemotherapy drugs Heritability assessed with family-based samples Lots of methods challenges in here along the way CEPH cell lines Mapping in unrelated cohorts CHORI cohort 1000 Genomes 44

10 Variation in Cellular Sensitivity Typical dose response curves

11 Heritability Calculations Variance components analysis as implemented in MERLIN sg/abecasis/merlin/index.ht ml h 2 of the growth rate for each vehicle was calculated h 2 adjusted for the growth rate for the appropriate vehicle by using growth rate as a covariate

12 GWAS Study Genome-wide association (GWAS) studies for highly heritable drugs Children s Hospital of Oakland Research Institute (CHORI) population based cohorts used to generate cell line 520 samples 650K SNP-chip data available for mapping Simulation experiments to prepare for association mapping Imputed to ~2 million variants

13 Association Mapping Previous studies have looked at fitting curves and then doing simple association tests on genotypes versus these values: EC/IC50 Hillslope These choices make LOTS of assumptions Assumptions about how associations may be happening Need methods that don t make these assumptions

14 Complicated Response Curves Differences between phenotypes could be manifested in many ways.

15 Modeling Robustly The vector or responses across concentration were modeled jointly using multivariate analysis of covariance (MANCOVA): I where E ij iid N(0, ), Minimal modeling assumptions y ij = + µ i + X ij + E ij, I y ij is the vector of responses for the j th LCL with genotype i, I X ij contains confounding covariates, I and µ i is the vector of e ects due to genotype i. No assumptions made about the form of dose response curves or how these curves vary between genotypes The assumptions of multivairate normality seems reasonable in real data

16 Simulation Study Results MANCOVA has most power to detect real signals (top) and is most robust for hill slope alternatives (bottom)

17 MAGWAS Multivariate Analysis of covariance Genome-Wide Analysis Association Software Designed for GWAS having multivariate responses Allows for incorporation of covariates Command line based, platform independent Accepts data in PLINK format Computationally efficient typical GWAS in 2-20 minutes

18 Association Results

19 Drug Families

20 Association by Drug Family Each dose response curve was summarized by the mean viability across drug concentrations MANCOVA was used to jointly model the mean viabilities across drug families Information is combined across drug family Small differences can become detectable, even if not present for each drug individually

21 Association by Drug Family Drug Class Chrom. rsid log 10 (p) Gene(s) nearby 1 DNA Alkylating Agents 2 rs u HDAC4 2 DNA Alkylating Agents 16 rs d NOB1 / d WWP2 3 u NQO1 / u NFAT5 4 Platinum Agents 10 rs C10orf107 5 TK Inhibitors 10 rs None Locus rs is associated (p < 10 6 ) with response to the alkylating class (temozolomide and mitomycin), and is located upstream of NIN1/RPN12 binding protein 1 homolog (NOB1) Polymorphisms on NOB1 have been found to be associated with myelotoxicity in malignant glioma patients treated with temozolomide

22 Drug Clustering Can LCLs be used to predict drug families? Distance metrics between each pair of drugs were calculated from their vectors of viabilities Y ai = Y bi + X i, Y ai and Y bi are viabilities for the i th LCL for drugs a and b X i is the matrix of covariates Distance between drugs a and b was estimated as one minus the average partial r-squared for a regressed on b and b regressed on a.

23 Empirical support for Drug Clustering

24 MGMT and Temozolomide Proof of concept that LCLs can identify clinically significant genes in cancer drug efficacy. Manhattan plot for Temozolomide The large red peak is for locus rs477693, located in the gene coding for MGMT (O 6 -methylguanine DNA methyltransferase), a protein known to be associated with Temozolomide efficacy [Hegi et al., 2005].

25 Gene Expression and MGMT MGMT repairs DNA that has been damages (methylated), helping prevent cell death MGMT expression is also associated with rs531572

26 Clinical Validation Moffitt Cancer Center clinical trial SOC 437 patients with high grade glioma 318 on standard of care (SOC) Resection plus radiation plus temozolomide Evaluated 7 SNPs in linkage disequilibrium with this hit Looked at overall survival Rs top hit Additive Genotypic Group N deaths HR (95% CI)* p- value HR (95% CI)* p- value all patients (0.80, 1.09) (0.70, 1.11) SOC, male (0.63, 0.99) (0.52, 1.01) SOC, female (0.85, 1.53) (0.75, 1.95) 0.448

27 Monoclonal Antibodies Used the LCL model for testing new class of drugs (anti-cd20): Rituximab Ofitumumab Used the C EPH Pedigrees for linkage analysis Found 2 large peaks for followup Chr 3, Chr 12 Overlapped for both drugs 50

28 Monoclonal Antibodies To narrow down genes Gene expression data 57 C EPH cell lines with available expression For genes in the region, One Gene: CBLB CBLB encodes an E3 ubitquitin ligase Involved in T-Cell and B-Cell receptor downregulation CBLB loss provokes autoimmunity via loss of autoregulatory mechanisms 51

29 Functional Validation Rituximab s target is CD20 Tested whether knocking down CBLB changes CD20 expression Immunofluorescence assay showing CD20 localization CD20 Gene Expression is not altered by CBLB Knockdown 52

30 Lessons from the LCL models LCLs are a promising approach for dose response mapping: Allow for research that is not possible with human subjects High throughput means that QC, both genotypic and phenotypic, is important Typical association methods may not capture the full array of potential differential response Support for known drug/chemical classes Dose response models seem as complex as complex trait mapping always is

31 Other Methods Development Challenges Along the Way Dose response modeling EADRM Beam A, Motsinger-Reif A. Beyond IC50s: Towards Robust Statistical Methods for in vitro Association Studies. J Pharmacogenomics Pharmacoproteomics Mar 1;5(1): Beam AL, Motsinger-Reif AA. Optimization of nonlinear dose- and concentration-response models utilizing evolutionary computation. Dose Response. 2011;9(3): Extending approaches for accurate permutation testing Che R, Jack JR, Motsinger-Reif AA, Brown CC. An adaptive permutation approach for genome-wide association study: evaluation and recommendations for use. BioData Min Jun 14;7:9. doi: / ecollection 2014.

32 Current Work Tyrosine Kinase Inhibitors Continued follow up of top hits Evaluating the model for PD1K inhibitors Exploring analysis methods to combine results across drugs Pathway analysis Cross-heritability Additional methods to build more complex models Gene-gene, gene-drug interactions Genomic prediction approaches and Bayesian variable selection Advances in permutation testing implementations

33 Current Work Drug combinations Chemotherapies are rarely given alone Modeling mixtures is a real challenge Evaluating methods for quantifying synergy Add inference to Chou-Talalay method

34 Synergy in the LCL Model Pilot Study 8 different drugs/ 6 concentrations 7 combinations of mixtures tested 123 LCLs Contains 45 trios (a set of parents and single child) Hypothesis: synergy/antagonism is quantifiable in vitro and heritable

35 Synergy in the LCL Model

36 Synergy in the LCL Model

37 Genetic Etiology of Synergy

38 Summary In vitro assays can be used to assess the genetic component of dose response traits and to perform well-powered GWAS. Such new models take careful consideration and experimentation with new statistical approaches to answer biological questions. Biology Methods Development

39 Acknowledgments NCSU Daniel Rotroff Kyle Roell John Jack Chad Brown Fred Wright Paul Gallins Yihiu Zhou David Reif UNC Chapel Hill Tammy Havener Tim Wiltshire Eric Peters Michael Wagner Kristy Richards Paul Gallins Nour Abdo Moffitt Howard McLeod Kathleen Egan CHORI Ron Krauss Marisa Wong-Medina Funding: National Cancer Institute: R01 CA161608

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