Abstract 3142: Enabling whole genome sequencing analysis from FFPE specimens in clinical oncology

Abstract

Abstract Whole genome sequencing (WGS) enables the identification of all cancer associated biomarkers in a patient’s tumor genome. Whilst fresh frozen (FF) derived WGS data provides optimal data quality, the majority of clinical biospecimens are from formalin fixed paraffin embedded (FFPE) tissue which results in DNA damage and an increase in artifactual mutation calls. Development of analytical frameworks tailored to FFPE derived WGS data can unlock the potential of genome profiling in clinical oncology. We performed comprehensive WGS analysis on 58 matched FF/FFPE specimens derived from 3 cancer centers. Consensus calling detected high-confidence somatic mutations across variant classes including: single nucleotide variants (SNVs), insertions/deletions (indels), structural variants (SVs) and copy number aberrations (CNAs). For each sample, genome-wide mutational patterns including tumor mutational burden (TMB), SNV/indel signatures, and homologous recombination deficiency (HRD) scores were estimated. We developed a random forest based framework using 33 features to learn mutation patterns associated with FFPE artifacts and implemented a filtration strategy for FFPE derived WGS data within a clinical prototype analytical workflow. We identified a high degree of concordance (~91%, n=192/210) for oncogenic variants between FF/FFPE WGS data. Comparison of small mutation calls presented an average 2-fold increase in FFPE samples with a range up to 152x for SNVs and 43x for indels. However, this was not the case for SVs: -0.4x (range -0.8-1.4). We demonstrate that genome-wide mutation patterns were significantly affected, impacting estimates for TMB, HRD and signature contributions. On average, TMB was overestimated in FFPE (median=10.3, range: 1.4-94) versus FF (median=3.4, range:0.04-29.6). For 7 patients with evidence of HRD in FF, HRD scores did not reach statistical significance in FFPE. Mutational signatures in FFPE were enriched for COSMIC signatures 37 and 5. Our artifact classification model achieved ROC AUC of 97.5% and precision-recall of 98.9%. Post artifact filtration, precision in SNV/indel calling was increased from 49.3% to 93.4% and 61.8% to 82.7% respectively with no effect on driver alterations. This increased global signal concordance drastically, with comparable TMB scores (median 2.4; range .03-26.1) and improved cosine similarity for SNV/indel signatures (median 0.98; range 0.40-1). HRD was successfully detected in 7/7 patients from FFPE derived data post filtering with probability scores ranging from 0.76-1. We demonstrate that FFPE specimens harbor a variable increase in artifactual mutation burden in SNV/indels but not in SVs. We propose an effective filtering procedure which successfully removes FFPE related artifacts enabling accurate profiling of clinically relevant driver mutations and genome-wide mutation patterns from readily available FFPE-derived tumor specimens. Citation Format: Dylan Domenico, Gunes Gundem, Max F. Levine, Juanes E. Arango-Ossa, Pauline Robbe, Georgios Asimomitis, Cassidy Cobbs, Emily Stockfisch, Janine Senz, Dawn Cochrane, Neeman Mohibullah, Neerav Shukla, Sohrab P. Shah, Andrew McPherson, Anna Schuh, Andrew L. Kung, Elli Papaemmanuil. Enabling whole genome sequencing analysis from FFPE specimens in clinical oncology [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 3142.