Abstract 3400: Characterizing genomic variation and tumor heterogeneity in cancer (2018)

Abstract Cancer genomes are highly unstable with new genetic variations emerging even within a single metastatic site, making it difficult to track the causal changes that drive metastasis and treatment resistance. Here we present a two-pronged approach for analyzing the full spectrum of genetic variations present in cancer samples. The first approach allows for comprehensive and high-resolution characterization of a broad range of variant types on bulk tumor sample. While the second approach characterizes structural variation at the level of the single cell, allowing for the exploration of tumor clonality and heterogeneity. At the core of our approaches is a microfluidics platform that enables the production of hundreds of thousands to millions of partitioned barcoded reactions. This platform can partition high-molecular weight DNA or single cells. Together, these complementary approaches provide a more complete picture of the genomic variation and clonal structure present in a tumor. For bulk tumor analysis, we obtained high molecular weight DNA from known cancer cell lines and used the 10x Chromium Genome solution to produce Illumina-ready sequencing libraries. In this workflow, partitioning of a limited amount of genomic DNA allows for haplotype-level dilution of genome equivalents, which are then barcoded to create a novel data type referred to as “Linked-Reads”. These molecular barcodes are used to identify reads originating from the same input molecule providing long range information on highly accurate short reads. In addition to highly accurate SNP calling, this further enables identification of complex structural rearrangements in tumor genomes. To gain insight into tumor heterogeneity and clonal structure, we performed single cell DNA sequencing and analysis using 10x Chromium scDNA solution. This platform integrates single cell encapsulation, cell lysis and DNA barcoding into a streamlined workflow. Molecular barcodes are used to associate reads with individual cells allowing for copy number variant (CNV) detection. We applied our scDNA sequencing method to a variety of cancer cell lines revealing their clonal structure, as identified by CNVs, with the capability to identify as few as 10 cells in a sample size of one thousand cells. Using cluster analyses we were able to detect 100kb scale events and by aggregating reads in large clones we were able to confidently identify smaller CNV events down to tens of kilobases. Using whole genome bulk sequencing we identified more than 500 large structural variants in HCC1954, including balanced and unbalanced events. In this presentation, we will integrate this Linked-Read data with single cell genome analysis on the same samples, and compare the genetic variation revealed by these two approaches. We will further explore the power of combining these data types for a more complete picture of tumor genome dynamic Citation Format: Claudia Catalanotti, Sarah Garcia, Kamila Belhocine, Vijay Kumar, Zeljko Dzakula, Andrew Price, Shamoni Maheshwar, Yifeng Yin, Michael Schnall-Levin, Rajiv Bharadwaj, Sara Agee Le, Deanna M. Church. Characterizing genomic variation and tumor heterogeneity in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3400.