Abstract 6539: Application of optical genome mapping to identify samples with homologous recombination deficiency

Abstract

Abstract Certain cancer treatments, such as the poly (ADP-ribos) polymerase (PARP) inhibitors, have been shown to be effective in killing cancer cells exhibiting genome instability signatures indicative of homologous recombination deficiency (HRD). Hence, these signatures are used as biomarkers to inform treatment decisions and prognosis. There are three measurements of HRD signatures commonly employed: loss of heterozygosity (HRD-LOH), telomeric allelic imbalance (TAI) and large-scale state transition (LST). It has been shown that combining all three scores can better determine the HRD phenotype, leading to a higher clinical impact. Yet current HRD signature tests, used to estimate HRD, have low negative predictive value, and one possible reason is that current genomic technologies lack sensitivity to capture the full extent of somatic genomic rearrangements. Here, optical genome mapping (OGM) was used to detect large structural variants (SVs) and calculate a HRD score. OGM captures high molecular weight DNA to call SVs by aligning these molecules to the public reference genome. OGM can comprehensively detect insertions and deletions >5kbp, inversions, interchromosomal translocations, plus large interstitial copy number variation (CNV) and aneusomies. Subsequently, an automated script was developed to compute the HRD score, which is the summation of the three HRD signatures: HRD-LOH, the number of regions with a loss >15 Mbp but shorter than the whole chromosome; TAI, the number of regions of gain and loss >10Mbp that extend to a subtelomere but do not cross the centromere; and LST, the number of chromosomal breakpoints whose SV size >10Mb but not the whole chromosome. We applied this script on 20 samples of solid tumors and myeloid neoplasms, and the automated scores are concordant with expertly curated scores, confirming the validity of the calculation. We believe that OGM data combined with this automated analysis of HRD signatures is much more sensitive and accurate for detection of HRD signatures and that this will enable more precise prediction of drug response for multiple tumor types. Citation Format: Andy Wing Chun Pang, Kelsea Chang, Nikhil Sahajpal, Daniel Saul, Ravindra Kolhe, Alka Chaubey, Alex Hastie. Application of optical genome mapping to identify samples with homologous recombination deficiency [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6539.