<span class="var-sub_title">Afternoon Keynote – Genomic Profiling of Normal, Premalignant, and Heterogeneous Tissues in Cancer Patients</span> SC18 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Fourth Computational Approaches for Cancer Workshop (CAFCW18)


Afternoon Keynote – Genomic Profiling of Normal, Premalignant, and Heterogeneous Tissues in Cancer Patients

Authors: Paul Scheet (MD Anderson Cancer Center)

Abstract: Normal tissues adjacent to tumor and premalignant lesions present an opportunity for in vivo human models of early disease pathology. Genomic studies of such “at risk” tissues may identify molecular pathways involved in a transition to malignant phenotypes and/or targets for personalized prevention or precision medicine. Yet, challenges to this objective include: 1) the small size of lesions or limited available tissue, often presenting “either or” choices for molecular technologies (e.g. DNA or RNA, NGS or arrays); and 2) low mutant cell fractions due to heterogeneous tissues and their corresponding early stages of disease. To address these, we have 1) conducted targeted next-generation sequencing of DNA and RNA and, when possible, genome-wide DNA SNP arrays, 2) considered various ensemble strategies for off-the-shelf single-nucleotide variant calling algorithms to determine mutations, and 3) developed sensitive haplotype-based techniques (hapLOH) to determine megabase-scale regions of allelic imbalance that reflect chromosomal deletions, duplications and copy-neutral loss-of-heterozygosity. We have applied combinations of these strategies to normal appearing epithelial airway samples adjacent to non-small cell lung cancers, premalignant tissues, and to public data from paired normal and tumor samples from 11,000 patients of The Cancer Genome Atlas (TCGA). In mutational analyses of tissues annotated by alterations discovered in paired tumors, we identify key drivers, document two-hit models of tumorigenesis, highlight immune-related expression phenotypes in premalignant lesions and construct phylogenetic trees of intra-patient samples. We also give examples of systematic errors in copy number changes in TCGA that can be corrected by hapLOH.




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