Subclonal mutational signatures in breast cancer brain metastases

• Published in: Journal of clinical oncology Vol. 42; no. 16_suppl; p. e13108
• Main Authors: Varuzza, Muriele Bertagna, Winata, Helena, Patel, Yash, Mootor, Mohammed Faizal Eeman, Souza, Cristiano de Padua, Boutros, Paul Christopher, Silveira, Marcia Maria Chiquitelli Marques
• Format: Journal Article
• Language: English
• Published: 01.06.2024
• ISSN: 0732-183X; 1527-7755
• DOI: 10.1200/JCO.2024.42.16_suppl.e13108

e13108 Background: Breast cancer is the leading cause of cancer-related deaths among women in Brazil and the cancer type with highest incidence. When diagnosed at early stages, it has an overall five-year survival rate of up to 90%. However, in advanced stages, survival rate is reduced to about 24%, and 90% of women in stage IV die of metastatic complications. Brain is one of the most common metastatic sites in breast cancer patients, and even though it is a site with poor prognosis, the mutational profile of brain tumors is poorly described in the literature. Therefore, we understand that the identification of this profile may contribute to elucidate mechanisms associated with tumor progression. The aim of this study was to reconstruct the subclonal evolution of breast cancer metastases in the brain. Methods: To carry out this study, the whole exome was accessed through Next Generation Sequencing (NGS) using the Illumina platform in DNA samples extracted from buffy coat cryopreserved and paraffin material of breast tumors and paired brain metastases (n=10). The median read coverage was 68x. DNA was aligned to a Human Reference Genome using the Burrows–Wheeler Aligner, Genome Analysis Toolkit was used for germline short variants calling, and Mutect2 and MuSE were used for variant calling with somatic character (SNVs and INDELs). FACETS was used for assessing the somatic DNA copy number (Copy Number Aberrations). PyClone-VI tool was used for the subclonal reconstruction and to infer the phylogeny it was considered the mutation loci, Cancer Cell Fraction (CCF) and clustering assignments, using R Statistical Environment and Stats, Argparse, and Devtools packages. Results: We identified the mutational signature in these patients with subclonal and clonal mutations that segregated in approximately two clusters with a median between ten and forty variants related with tumor progression process. Conclusions: These findings show a set of variants with driver potential for breast cancer brain metastasis, bringing new perspectives in identifying mechanisms associated with disease progression.