Abstract P5-13-01: TUMOR PROGRESSION MODEL IN BREAST CANCER WITH BRAIN METASTASIS

Abstract

INTRODUCTION: Breast cancer are the main cause of related deaths cancer among women, corresponding to 25% of new cases each year. When diagnosed at early stages, it has an overall five-year survival rate of up to 90%. However, in more advanced stages, survival is reduced to about 24%, with 90% of women in stage IV dying as a result of complications related to metastases. Considering that brain metastasis is an unfavorable prognostic site, and the identification of genetic-molecular profiles in primary tumors and in metastatic sites are a subject poorly described in the literature, we understand that the identification of mutational profiles may contribute to elucidate the genetic-molecular mechanisms associated with tumor progression. AIM: The aim of this study was to identify clonal and subclonal driver mutations that lead to evolution of metastatic clones from a breast cancer progression model. MATERIAL AND METHODS: For tumor progression model, automated extraction of DNA from buffy coat and paraffin samples of breast tumors and paired brain metastases (n=9) was performed. In the present work, we used a subclonal reconstruction model based on the combination of machine learning and population genetics concepts. This proposal is based on the frequency spectrum of each somatic mutation (SNVs or indels), considering VAF (Variant Allele Frequency - ratio of mapped reads of the mutant allele) in relation to the coverage of variant locus, as known as CCF (Cancer Cell Fraction). CCF is defined as the proportion of neoplastic cells that have a certain set of mutations and then is normalized considering the sample purity and the segments with changes in number of DNA copies (Copy Number Alterations). Then, a statistical model based on finite Dirichlet mixtures with mixed distributions is applied. In this model, Beta components capture clonal expansions and population genetics concepts were applied to mutant alleles in each population considering principles of cancer evolution. Finally, confidence was computed using both parametric and non-parametric bootstraps. The functions for building the model are implemented at https://caravagnalab.github.io/mobster/, and for visualization and construction of graphs, packages in R statistical-mathematical environment were used, such as ggplot2, sads, cli, clisymbols, cowplot, crayon, ctree, dndscv, dplyr, magrittr, reshape2, and tidyr. RESULTS: With this model was possible to identify the distribution of clones according to somatic alterations in patients with breast cancer and brain metastasis. It was possible to observe common patterns, such as alterations of the SF3B1 gene as a common ancestral clone in both conditions and the frequency of the AKT1 gene in a subclonal condition. Other genes relevant to breast cancer carcinogenesis, such as PIK3CA and TP53, are found in a different clonal hierarchical pattern between the two conditions. CONCLUSION: This data suggests a model of clonal evolution capable to identify which drivers clones and subclones are involved in the metastatic process. Citation Format: Muriele B. Varuzza, Adriane F. Evangelista, Cristiano P. Souza, Márcia C. Marques. TUMOR PROGRESSION MODEL IN BREAST CANCER WITH BRAIN METASTASIS [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-13-01.