A method to comprehensively identify germline SNVs, INDELs and CNVs from whole exome sequencing data of BRCA1/2 negative breast cancer patients

Author:

Bianchi Andrea1ORCID,Zelli Veronica2ORCID,D’Angelo Andrea1ORCID,Di Matteo Alessandro1ORCID,Scoccia Giulia1,Cannita Katia3,Dimas Antigone S4,Glentis Stavros45,Zazzeroni Francesca2,Alesse Edoardo2,Di Marco Antinisca1ORCID,Tessitore Alessandra2

Affiliation:

1. Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila , L’Aquila 67100, Italy

2. Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila , L’Aquila 67100, Italy

3. Oncology Division, Mazzini Hospital, ASL Teramo , Teramo 64100, Italy

4. Institute for Bioinnovation, Biomedical Sciences Research Center, Alexander Fleming , Vari 16672, Greece

5. Pediatric Hematology/Oncology Unit (POHemU), First Department of Pediatrics, University of Athens, Aghia Sophia Children’s Hospital , Athens 11527, Grece

Abstract

Abstract In the rapidly evolving field of genomics, understanding the genetic basis of complex diseases like breast cancer, particularly its familial/hereditary forms, is crucial. Current methods often examine genomic variants—such as Single Nucleotide Variants (SNVs), insertions/deletions (Indels), and Copy Number Variations (CNVs)—separately, lacking an integrated approach. Here, we introduced a robust, flexible methodology for a comprehensive variants’ analysis using Whole Exome Sequencing (WES) data. Our approach uniquely combines meticulous validation with an effective variant filtering strategy. By reanalyzing two germline WES datasets from BRCA1/2 negative breast cancer patients, we demonstrated our tool’s efficiency and adaptability, uncovering both known and novel variants. This contributed new insights for potential diagnostic, preventive, and therapeutic strategies. Our method stands out for its comprehensive inclusion of key genomic variants in a unified analysis, and its practical resolution of technical challenges, offering a pioneering solution in genomic research. This tool presents a breakthrough in providing detailed insights into the genetic alterations in genomes, with significant implications for understanding and managing hereditary breast cancer.

Funder

Italian RI for Social Mining and Big Data Analytics

Italian Ministry of University and Research (MUR) National Innovation Ecosystem

Publisher

Oxford University Press (OUP)

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