Abstract 2476: RECUR: Algorithm for directional allelic imbalance profiling and visualization from multi-sample data
Authors: Yasminka A. Jakubek, F. Anthony San Lucas, Paul Scheet
Journal: Cancer Research (2019)

Abstract Somatic chromosomal alterations play an important role in the development and progression of cancer. Allelic imbalance (AI) resulting from such changes (gain, loss, or copy-neutral loss-of-heterozygosity; cnLOH) is defined as a deviation from the 1:1 ratio of inherited parental haplotypes. Observed “B allele” frequencies (BAFs) at germline heterozygous loci, from SNP array or next-generation sequencing data, are used to detect AI. In addition, log R ratio or read-depth data, can be analyzed alone or jointly with BAFs to detect gains and losses. When AI is detected in multiple intra-individual samples, spanning similar loci, it is natural to assess whether these signals reflect the same underlying mutation. One way to do this would be to note if the observed AI differs in mutation type (gain, loss), an approach that falls short for events of the same type and subtle AI events that cannot be classified as gains, losses, or cnLOH. In such a case, or with two mutations of the same type, samples may differ in their maternal/paternal haplotype balance and consequently their alleles will shift in opposite directions. This phenomenon is indicative of a recurrent or independent mutation, or an error in chromosome segregation that generates two “mirrored” clones, one with a gain and the other with a loss. Recent studies have used such a directional AI analysis to yield important insights into cancer initiation and progression (Jakubek et al. Cancer Research 2016, Jamal-Hanjani et al. New England Journal of Medicine 2017, Turajlic et al. Cell 2018). We developed REpeat Chromosomal changes Uncovered by Reflection (RECUR) to explicitly test directionality of AI from multiple samples with overlapping AI segments through the comparative analysis of AI profiles derived from SNP array and next-generation sequencing data. The algorithm accepts genotype calls and BAFs from at least 2 samples derived from the same individual. For a predefined set of genomic regions with AI, RECUR compares BAF values among samples. In the presence of AI, the expected value of a BAF can shift in two possible directions, reflecting an increased or decreased abundance of the maternal haplotype, relative to the paternal. RECUR detects such genomic segments of opposite haplotypes in imbalance and plots BAF values for all samples, using a two-color scheme for intuitive visualization. It can help identify genomic regions under selective pressure for example recurrent deletions/gains in the same genomic loci and/or regions with generalized genomic instability. Integration of directional AI, copy number, and somatic mutation data can help build more accurate phylogenetic trees and further illuminate the timing and distribution of somatic chromosomal aberrations to offer insights into cancer initiation and progression. RECUR is available at scheet.org Citation Format: Yasminka A. Jakubek, F. Anthony San Lucas, Paul Scheet. RECUR: Algorithm for directional allelic imbalance profiling and visualization from multi-sample data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2476.