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DTSTART;TZID=America/Chicago:20181111T140000
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UID:submissions.supercomputing.org_SC18_sess147_pec157@linklings.com
SUMMARY:Afternoon Keynote – Genomic Profiling of Normal, Premalignant, and
  Heterogeneous Tissues in Cancer Patients
DESCRIPTION:Workshop\nApplications, Deep Learning, Exascale, Workshop Reg 
 Pass\n\nAfternoon Keynote – Genomic Profiling of Normal, Premalignant, and
  Heterogeneous Tissues in Cancer Patients\n\nScheet\n\nNormal tissues adja
 cent 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 mali
 gnant phenotypes and/or targets for personalized prevention or precision m
 edicine. Yet, challenges to this objective include: 1) the small size of l
 esions or limited available tissue, often presenting “either or” choices f
 or molecular technologies (e.g. DNA or RNA, NGS or arrays); and 2) low mut
 ant cell fractions due to heterogeneous tissues and their corresponding ea
 rly stages of disease.  To address these, we have 1) conducted targeted ne
 xt-generation sequencing of DNA and RNA and, when possible, genome-wide DN
 A 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 meg
 abase-scale regions of allelic imbalance that reflect chromosomal deletion
 s, duplications and copy-neutral loss-of-heterozygosity.  We have applied 
 combinations of these strategies to normal appearing epithelial airway sam
 ples 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 annotat
 ed by alterations discovered in paired tumors, we identify key drivers, do
 cument two-hit models of tumorigenesis, highlight immune-related expressio
 n phenotypes in premalignant lesions and construct phylogenetic trees of i
 ntra-patient samples.  We also give examples of systematic errors in copy 
 number changes in TCGA that can be corrected by hapLOH.
URL:https://sc18.supercomputing.org/presentation/?id=pec157&sess=sess147
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