Archival tumor samples present a rich resource of annotated specimens for genomics research. However, standard variant calling approaches require a matched normal sample from the same individual, which is often not available with archival tissue. Without a matched normal sample, it is very difficult to distinguish between true somatic variants and germline variants that are private to the individual. Archival FFPE sections often contain adjacent normal tissue, but this normal tissue is often contaminated with infiltrating tumor cells. Comparative somatic variant callers are designed to exclude variants present in the normal sample, so a novel approach is required to leverage sequencing of adjacent normal tissue. Here we present LumosVar 2.0, a software package designed to jointly analyze multiple samples from the same patient. The approach is based on the concept that the allelic fraction of somatic variants, but not germline variants, would be diluted by the presence of normal tissue. Given the allele specific copy number state and expected tumor fraction of each sample, one can model the expected allelic fractions of somatic and germline variants. LumosVar estimates allele specific copy number and tumor sample fractions from the data, and uses the model to call somatic and germline variants. LumosVar is also able to detect sub clonal events and spatial heterogeneity between samples. To evaluate this approach, we used a glioblastoma dataset, where enhancing (high tumor content) and non-enhancing (low tumor content) samples were available for each patient, as well as the matched normal sample to define the true somatic variants. We show that both sensitivity and positive predictive value are improved by analyzing the high tumor and low tumor jointly compared to analyzing the samples individually or in silico pooling of the two samples. Finally, we apply this approach to a set of breast and prostate archival FFPE tumor samples where normal samples were not available for germline sequencing.

Citation Format: Rebecca F. Halperin, Winnie S. Liang, Erica Tassone, Jonathan Adkins, Megan Russell, Nhan Tran, Nicole Hank, James Newell, Sandeep Gupta, Michael Berens, Ronald Korn, David W. Craig, Sara A. Byron. Leveraging spatial heterogeneity in tumor purity for improved somatic variant calling of archival tumor-only samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-007. doi:10.1158/1538-7445.AM2017-LB-007