Cancer Research

Head: Balázs Győrffy, MD, PhD

Purpose

The Cancer Research & Precision Oncology Program advances data-driven oncology focused on survival prediction, biomarker discovery, and translational strategies that improve treatment outcomes in aging populations. The program integrates large-scale multi-omics analysis with clinical oncology and population data to identify actionable targets and optimize precision medicine frameworks.

Why it matters

• Cancer survival increasingly depends on molecular stratification and early detection.
• Aging populations require oncology models that integrate biological heterogeneity with real-world outcomes.
• Translational biomarker discovery is essential for personalized therapy and prevention strategies.

What we do

• Large-scale transcriptomic and multi-omics platforms for tumor profiling
• Survival modeling using international clinical and genomic datasets
• AI-enabled biomarker discovery and validation pipelines
• Identification of prognostic and predictive molecular signatures
• Precision oncology frameworks for therapy stratification
• Integration of molecular data with population-level cancer epidemiology
• Translational research linking tumor biology with aging processes
• Development of reproducible analytic pipelines for clinical oncology
• Cross-consortium data harmonization and meta-analysis
• Collaboration with clinical oncology networks and biobanks

Key outputs

• Clinically validated prognostic biomarkers
• Survival prediction models
• Translational oncology datasets
• Contributions to international precision oncology consortia
• Evidence frameworks supporting personalized cancer care

Recent Publications

New Gene Signatures Could Improve Personalized Breast Cancer Care

Breast cancer is not a single disease but a collection of distinct subtypes that respond differently to treatment. This study investigated the role of sirtuins (SIRT1–SIRT7) - a family of genes involved in energy metabolism, DNA regulation, and healthy agingto determine whether their expression patterns could help predict patient outcomes. By analyzing gene expression data from more than 7,800 breast tumors, the researchers identified subtype-specific combinations of sirtuin genes that were more accurate in predicting recurrence-free survival than individual biomarkers. These findings sugges - t that molecular signatures reflecting both mitochondrial function and epigenetic regulation could support more precise risk assessment and treatment planning for breast cancer patients.

Researchers from the Institute for Translational Research contributed to this international collaboration, helping uncover how aging-related molecular pathways influence cancer progression. By linking longevity-associated genes with breast cancer biology, the study advances the development of precision medicine approaches that tailor diagnosis and therapy to the unique characteristics of each tumor. This research brings clinicians one step closer to more personalized and effective treatment strategies that improve long-term patient outcomes.

Sirtuin coexpression (SIRT1–SIRT7) quantified by Spearman rank correlations (ρ) using TNMplot (GEO datasets). A In normal breast tissue, sirtuins form a broadly coordinated network centered on SIRT6, with particularly strong coexpression with SIRT5 and SIRT2. B In breast cancer, the network is rewired: SIRT3, SIRT5, SIRT6, and SIRT2 remain tightly coexpressed, whereas SIRT1 shifts toward weak negative relationships, and SIRT4 becomes mainly uncoupled. Tiles report Spearman’s ρ; the color scale indicates the strength of correlation

Journal Reference

  1. Ungvari Z, Menyhárt O, Ocana A, Lehoczki A, Fekete M, Bianchini G, et al. Subtype-specific sirtuin expression signatures link mitochondrial-epigenetic networks to breast cancer survival. GeroScience. 2026. doi:10.1007/s11357-026-02143-9.