Pan cancer patterns of allelic imbalance from chromosomal alterations in 33 tumor types. Issue 1 (11th January 2021)

• Record Type: Journal Article
• Authors: Sivakumar, Smruthy; San Lucas, F Anthony; Jakubek, Yasminka A; Ozcan, Zuhal; Fowler, Jerry; Scheet, Paul
• Editors: Risch, N
• Is Part Of: Genetics. Volume 217:Issue 1(2021)
• Publication Date: 2021-01-11
• Subjects: allelic imbalance -- genomic instability -- copy number alterations -- cancer aneuploidy; Genetics -- Periodicals; 576.5
• DOI: 10.1093/genetics/iyaa021
• Languages: English
• ISSNs: 0016-6731

Abstract

Abstract: Somatic copy number alterations (SCNAs) serve as hallmarks of tumorigenesis and often result in deviations from one-to-one allelic ratios at heterozygous loci, leading to allelic imbalance (AI). The Cancer Genome Atlas (TCGA) reports SCNAs identified using a circular binary segmentation algorithm, providing segment mean copy number estimates from single-nucleotide polymorphism DNA microarray total intensities (log R ratio), but not allele-specific intensities ("B allele" frequencies) that inform of AI. Our approach provides more sensitive identification of SCNAs by modeling the "B allele" frequencies jointly, thereby bolstering the catalog of chromosomal alterations in this widely utilized resource. Here we present AI summaries for all 33 tumor sites in TCGA, including those induced by SCNAs and copy-neutral loss-of-heterozygosity (cnLOH). We identified AI in 94% of the tumors, higher than in previous reports. Recurrent events included deletions of 17p, 9q, 3p, amplifications of 8q, 1q, 7p, as well as mixed event types on 8p and 13q. We also observed both site-specific and pan-cancer (spanning 17p) cnLOH, patterns which have not been comprehensively characterized. The identification of such cnLOH events elucidates tumor suppressors and multi-hit pathways to carcinogenesis. We also contrast the landscapes inferred from AI- and total intensity-derived SCNAs and propose an automated procedure to improve and adjust SCNAs in TCGA for cases where high levels of aneuploidy obscured baseline intensity identification. Our findings support the exploration of additional methods for robust automated inference procedures and to aid empirical discoveries across TCGA.