PhD Scientific Days 2025

Poster Session II. - E: Pathological and Oncological Sciences

Large-scale integrative analysis reveals mutational signatures and their prognostic impact in breast cancer

Name of the presenter

Posta Máté

Institute/workplace of the presenter

Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary; Department of Bioinformatics, Semmelweis University, Budapest, Hungary

Authors

Máté Posta¹, Balázs Győrffy²

1: Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary; Department of Bioinformatics, Semmelweis University, Budapest, Hungary
2: Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary; Department of Bioinformatics, Semmelweis University, Budapest, Hungary; Department of Biophysics, Medical School, University of Pecs, Pecs, Hungary

Text of the abstract

Introduction: Advancing breast cancer treatment requires a deep understanding of the genetic and molecular mechanisms underlying disease progression. In this study, we investigated pathway-level mutational patterns to reveal molecular interactions that may influence survival outcomes.
Methods: We analyzed mutation data from three major breast cancer cohorts, encompassing 4,586 samples and over 25,000 genes. Focusing on disruptive mutations, we conducted survival analysis using Cox proportional hazards regression to identify associations between gene alterations, pathway disruptions, and patient outcomes.
Results: Our results revealed 17 genes significantly linked to relapse-free survival. Among these, TP53 (HR: 2.04, p = 4.65×10⁻³³), CARD11 (HR: 2.59, p = 1.54×10⁻⁵), and PIK3R1 (HR: 2.27, p = 3.66×10⁻⁵) had the strongest impact. At the pathway level, the most significant KEGG pathways were MicroRNAs in cancer (hsa05206, HR: 2.48, p = 2.69×10⁻³²), Hepatocellular carcinoma (hsa05225, HR: 2.39, p = 8.88×10⁻³⁰), and Breast cancer (hsa05224, HR: 2.38, p = 1.78×10⁻²⁸).
We also observed distinct patterns of mutual exclusivity and co-mutation among gene pairs. Notably, GATA3–TP53 (OR: 0.201, p = 1.42×10⁻³⁷), CDH1–TP53 (OR: 0.336, p = 3.66×10⁻²¹), and AKT1–PIK3CA (OR: 0.199, p = 4.97×10⁻¹³) were mutually exclusive, whereas MAP3K1–PIK3CA (OR: 2.83, p = 4.62×10⁻²³), CBFB–GATA3 (OR: 4.67, p = 3.37×10⁻¹⁸), and AHNAK2–MUC16 (OR: 3.39, p = 1.35×10⁻¹⁵) showed significant co-mutation.
To support future validation studies, we developed an online platform accessible at server2.kmplot.com/breast, enabling researchers to explore pathway-specific findings.
Conclusion: In conclusion, our large-scale integrative analysis identified key mutational patterns associated with relapse-free survival, offering valuable insights to inform future research and therapeutic strategies in breast cancer.
Funding: This project was supported by National Research, Development and Innovation Office (PharmaLab, RRF-2.3.1-21-2022-00015); the 2024-2-1-1-EKÖP-2024-00004 university research scholarship programme of the Ministryfor Culture and Innovation from the source of the National Research, Development and Innovation Fund.