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Addressing genetic tumor heterogeneity through computationally predictive combination therapy.

ABSTRACT: Recent tumor sequencing data suggest an urgent need to develop a methodology to directly address intratumoral heterogeneity in the design of anticancer treatment regimens. We use RNA interference to model heterogeneous tumors, and demonstrate successful validation of computational predictions for how optimized drug combinations can yield superior effects on these tumors both in vitro and in vivo. Importantly, we discover here that for many such tumors knowledge of the predominant subpopulation is insufficient for determining the best drug combination. Surprisingly, in some cases, the optimal drug combination does not include drugs that would treat any particular subpopulation most effectively, challenging straightforward intuition. We confirm examples of such a case with survival studies in a murine preclinical lymphoma model. Altogether, our approach provides new insights about design principles for combination therapy in the context of intratumoral diversity, data that should inform the development of drug regimens superior for complex tumors.This study provides the first example of how combination drug regimens, using existing chemotherapies, can be rationally designed to maximize tumor cell death, while minimizing the outgrowth of clonal subpopulations.

SUBMITTER: Zhao B

PROVIDER: S-EPMC3975231 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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Addressing genetic tumor heterogeneity through computationally predictive combination therapy.

Zhao Boyang B Pritchard Justin R JR Lauffenburger Douglas A DA Hemann Michael T MT

Cancer discovery 20131206 2

<h4>Unlabelled</h4>Recent tumor sequencing data suggest an urgent need to develop a methodology to directly address intratumoral heterogeneity in the design of anticancer treatment regimens. We use RNA interference to model heterogeneous tumors, and demonstrate successful validation of computational predictions for how optimized drug combinations can yield superior effects on these tumors both in vitro and in vivo. Importantly, we discover here that for many such tumors knowledge of the predomin ...[more]

PMID: 24318931

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Project description:Colorectal cancer arises in part from the cumulative effects of multiple gene lesions. Recent studies in selected cancer types have revealed significant intra-tumor genetic heterogeneity and highlighted its potential role in disease progression and resistance to therapy. We hypothesized the existence of significant intra-tumor genetic heterogeneity in rectal cancers involving variations in localized somatic mutations and copy number abnormalities. Two or three spatially disparate regions from each of six rectal tumors were dissected and subjected to the next-generation whole-exome DNA sequencing, Oncoscan SNP arrays, and targeted confirmatory sequencing and analysis. The resulting data were integrated to define subclones using SciClone. Mutant-allele tumor heterogeneity (MATH) scores, mutant allele frequency correlation, and mutation percent concordance were calculated, and copy number analysis including measurement of correlation between samples was performed. Somatic mutations profiles in individual cancers were similar to prior studies, with some variants found in previously reported significantly mutated genes and many patient-specific mutations in each tumor. Significant intra-tumor heterogeneity was identified in the spatially disparate regions of individual cancers. All tumors had some heterogeneity but the degree of heterogeneity was quite variable in the samples studied. We found that 67-97% of exonic somatic mutations were shared among all regions of an individual's tumor. The SciClone computational method identified 2-8 shared and unshared subclones in the spatially disparate areas in each tumor. MATH scores ranged from 7 to 41. Allele frequency correlation scores ranged from R(2)=0.69-0.96. Measurements of correlation between samples for copy number changes varied from R(2)=0.74-0.93. All tumors had some heterogeneity, but the degree was highly variable in the samples studied. The occurrence of significant intra-tumor heterogeneity may allow selected tumors to have a genetic reservoir to draw from in their evolutionary response to therapy and other challenges.

Dataset Information

Addressing genetic tumor heterogeneity through computationally predictive combination therapy.

Publications

Addressing genetic tumor heterogeneity through computationally predictive combination therapy.

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