Abstract PD8-10: APOBEC mutation signature in breast cancer correlates with tumor mutation burden and poor responses to therapy

Abstract

Abstract Introduction Mutational processes can be characterized by unique combinations of mutation types in the form of mutational signatures and have been associated with age, known mutagenic exposures, defects in DNA maintenance, or the APOBEC family of cytidine deaminases. We asked whether mutation signatures could be extracted from DNA sequence information in a targeted 434 gene panel covering 297 breast cancer specimens. Materials and Methods Targeted whole exome sequencing (Illumina, 2x50bp) of a 434 gene panel was performed on a set of 297 primary and metastatic breast tumor samples. Tissue of origin included breast (56%), liver (15%), lymph node (10%), lung (3%) and others (16%). Alignment was done with BWA against the human reference hg19 and variant calling was performed using VarDict. Germline variants were filtered based on allele frequencies, cohort specific population frequencies, as well as using 1000 Genomes and ExAC population frequencies. For somatic signature inference, only single nucleotide variants were retained. Panel specific trinucleotide frequencies were computed and normalized towards whole genome frequencies and somatic signatures were inferred using deconstructSigs method. Results We identified a total of 26 signatures from the set of 30 known signatures in our patient samples. Due to the small panel size, there was only a limited number of mutations available per patient to infer somatic signatures. On average, we identified two somatic signatures per sample. Most common mutation signatures identified were: Signature 1 (90.8%) - result of an endogenous mutational process initiated by spontaneous deamination of 5-methylcytosine; Signature 6 (21.8%) - defective DNA mismatch repair; Signature 15 (15.6%) - defective DNA mismatch repair; Signatue 7 (9.9%) - ultraviolet light exposure; and Signature 10 (6.5%) - altered activity of POLE. An APOBEC specific signature was identified in 20 (7%) samples. APOBEC positive samples showed significantly higher tumor mutational burden (10.7 vs. 5.7 mutations/mb) as compared to APOBEC negative samples (p<=0.001). PIK3CA was found to be mutated in 80% of APOBEC positive samples, compared to 36% of APOBEC negative samples. In addition, we found higher rates of mutations in TP53 (70% vs. 50%), MLL3 (50% vs. 19%) and MLL2 (25% vs 14%) of APOBEC positive patients. Response rates of APOBEC positive patients were significantly worse than of APOBEC negative patients, with 50 percent of patients having progressive disease compared to 25 percent of APOBEC negative patients(p=0.07, borderline). Conclusions We demonstrate the feasibility of a targeted sequencing approach to extract somatic mutation signatures from breast tumor samples, and we highlight the potential of using the APOBEC signature to predict therapeutic responses. Citation Format: Meissner T, Amallraja A, Willis S, Harris R, Leyland-Jones B, Williams C. APOBEC mutation signature in breast cancer correlates with tumor mutation burden and poor responses to therapy [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr PD8-10.