Abstract 2420: A population sampling approach for validating deconvolution of high throughput sequencing data to describe clonality in heterogeneous tumor samples
Authors: Amy L. Paguirigan, Weston D. Christensen, Zaneta J. Holman, Jordan L. Smith, Emily A. Stevens, Brent Wood, Jerald P. Radich
Journal: Cancer Research (2016)

Abstract This abstract describes an in silico model of a heterogeneous population sampling methodology that elucidates how to best determine which genetic variants in a population are likely to occur in the same or different clones along with an experimental system to validate the approach. While the role of clonal evolution during leukemia development and therapy has been a focus for a number of avenues of research, our abilities to deduce the clonal composition of individual samples have been limited by the use of bulk samples. Leukemia populations of unknown heterogeneity are typically described by extracting nucleic acids from the entire sample, including cells of unrelated lineages, any residual normal cells, and those cells constituting the leukemic population. Bioinformatic approaches are employed to reconstruct the identities of the clones involved using clustering of feature frequencies in sequence data, but by definition this type of approach relies on a basic set of assumptions regarding the underlying biology which may or may not be accurate at all loci for all samples. We have generated an in silico model of how to use random population sampling with either targeted high throughput sequencing (54 genes) or targeted pyrosequencing (4 genes) to better determine which assumptions are not accurate for a given patient. In addition, we have an in vitro leukemia system on which we can test the in silico model. Flow cytometric validation of three AML cell lines along with the genetic characterization of each cell type and verification of comparable growth rates in a normalized medium in vitro has been performed. Subsequent mixing of these cell lines, random sampling of cells (10-2,000) isolated via flow cytometry and DNA sequencing can be used to test our ability to deconvolve the mixture based on a bulk measurement with the knowledge of the true population composition obtained via flow cytometry. Using bulk sequencing data, we can identify potential clonal structures likely to exist in a given sample via variant allele frequency densities and k-means clustering. Given the possible clonal models, we can select variants that appear to be in the same clone. The in silico sampling model then aids in determining the ideal replicate number and sample size to best discriminate if proposed variants are truly in the same clones or not. Using our experimental system, we then verified if the predicted variants can be deconvolved using specific sampling methods. With this sampling approach in hand and a framework for testing the assumptions, we can perform a prospective analysis of genetic variants within a heterogeneous leukemia sample via high throughput sequencing, then selectively validate selected mutations for concurrence in individual cells via targeted pyrosequencing. These results suggest that we can better describe leukemia heterogeneity without the requirement for extensive single cell analyses. Citation Format: Amy L. Paguirigan, Weston D. Christensen, Zaneta J. Holman, Jordan L. Smith, Emily A. Stevens, Brent Wood, Jerald P. Radich. A population sampling approach for validating deconvolution of high throughput sequencing data to describe clonality in heterogeneous tumor samples. [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 2420.