Abstract PR07: Complex sub-clonal populations in colorectal cancer lymph node metastasis
Authors: Karin M. Hardiman, Peter J. Ulintz, Joel K. Greenson, Rong Wu, Eric R. Fearon
Journal: Cancer Research (2017)

Abstract A number of studies have highlighted the role of sub-clones in tumor progression across multiple cancer types including colorectal cancer (CRC). However, the genetic origin of lymph node (LN) metastasis is not well understood. Given that the prognosis for CRC patients worsens once metastasis occurs, we aimed to better understand the relationship between detected sub-clones in the primary tumor and the LN metastasis. Two or three spatially disparate regions from each of 5 colon cancers were dissected and subjected to next-generation whole exome DNA sequencing (average read depth of 50x) followed by targeted confirmatory deep sequencing (average read depth of 420x). The number of mutations per tumor ranged from 95 to 334 in the 4 non-hypermutated tumors and was 1208 in the single hypermutated tumor. DNA was then isolated from the metastatic areas of two to four matched metastatic LN's from each tumor. Deep-targeted sequencing was also performed on the LN samples targeting the same somatic variant locations identified in the primary tumors. Affymetrix OncoScan SNP arrays were performed on all primary tumor and LN samples to assess copy number (CN) variation and to permit more accurate inference of the cellular frequency of each somatic variant using the computational method PyClone. The PyClone software implements a probabilistic model to estimate the cellular frequency of each mutation in a population of cells using observed alternate allele frequencies as well as copy number and loss of heterozygosity (LOH) information, resulting in a clustering of variants based on the cellular frequency patterns of variants in each sample. Sub-clones are inferred corresponding to the distinct groupings of variants across all tumor and LN samples from a single patient. Additionally, the Clomial software was utilized to complement and validate PyClone results. Clomial implements an expectation-maximization algorithm to infer, given a count of distinct clones expected in a sample, profiles of the variant genotypes of each sample and sample clonal compositions. Generating Clomial profiles at several possible expected clone counts provides an orthogonal measure of clonal genotypes, as well as generating tumor purity estimates and facilitating phylogenetic profiling of variants and clones. CN and LOH differences were identified between different spatial locations in some individual tumors as well as between different LN's from the same tumor. Some LN's contained the same CN changes as a particular spatial region of the matched primary tumor and others contained CN variants that were distinct from the tested regions of the primary tumor. As defined by PyClone, each region in the primary tumor contained multiple (2–13, average 6.1) distinct sub-clones and each LN contained 3 - 13 sub-clones (average 5.9). We demonstrate evidence that individual LN metastasis are comprised of multiple sub-clones from the primary tumor. When there are multiple independent LN metastases in an individual patient, they do not all have the same composition of sub-clones. Some metastatic sub-clones were shared among all of the LN's from the same tumor but there were also sub-clones that were private to a portion of the tumor and to an individual LN. This may be evidence of individual sub-clones metastasizing at different times to different LN's. Further study of the origins of metastasis may help elucidate how to better target it therapeutically. This abstract is also being presented as Poster A10. Citation Format: Karin M. Hardiman, Peter J. Ulintz, Joel K. Greenson, Rong Wu, Eric R. Fearon. Complex sub-clonal populations in colorectal cancer lymph node metastasis. [abstract]. In: Proceedings of the AACR Special Conference on Colorectal Cancer: From Initiation to Outcomes; 2016 Sep 17-20; Tampa, FL. Philadelphia (PA): AACR; Cancer Res 2017;77(3 Suppl):Abstract nr PR07.