Biology and bioinformatics oriented projects
Breast cancer remains the leading cause of cancer-related death among women, primarily due to treatment resistance in metastatic cases. While some patients are diagnosed with de novo metastatic breast cancer, the majority of metastatic cases arise from progression of the initial disease following treatment failure.
Heterogeneity in components forming tumor microenvironment (TME) is now considered as one of the major causes of treatment resistance. Indeed, TME is inextricably involved in cancer progression - from carcinoma in situ to local invasion and dissemination - and variation in the expression of several of its components have been associated with breast cancer recurrence. Given the strong inter-patient variability in TME composition, there is a growing need for personalized treatment approaches, which in turn require the identification of novel biomarkers to better characterize tumor biology.
In our first project - PredAlgoBC, funded by the European Commission Horizon 2020 program /Marie Skłodowska-Curie Action - we focused on identifying biomarkers associated with response to hormone therapy in patients with early-stage breast cancer. We developed supervised machine learning models using integrated transcriptomic datasets selected from public repositories. Using supervised machine learning models applied to integrated transcriptomic datasets from public repositories, we discovered that neuronal progenitor markers and perineural invasion were associated with poor outcomes in patients treated with hormone therapy (Basseville et al., Cancer Research Communications, 2022) . The study also resulted in the development of two nervous system-related gene expression which demonstrated strong predictive and prognostic performance across multiple cohorts. These findings underscore the potential of neuronal components as novel biomarkers and therapeutic targets in breast cancer.
We are now expanding this research through the INTRICATE project - funded by La Ligue Contre Le Cancer and Perseverance - which investigates the biological interactions between tumoral nerves, cancer cells, and other components of the TME using spatial transcriptomics.
PredAlgoBC also highlighted the need for more advanced mathematical and computational approaches tailored to high-dimensional tabular data—an essential aspect of developing precision medicine strategies. For more details, see the Mathematics and computer science -oriented projects section.
Ultimately, our goal is to identify biomarkers that can serve either as therapeutic targets or as clinical decision tools to guide the selection of optimal treatment strategies.
