Mots-Clés
Bioinformatique
Intelligence Artificielle (IA) / AI for Health
Oncologie
Immunologie
Description
Computational methods for decoding immune aging as determinant of response to cancer immunotherapy
Immunotherapy has transformed cancer treatment, yet who benefits remains largely unpredictable. In non-small cell lung cancer, PD-1/PD-L1 blockade fails in approximately 60% of patients (Bray et al. 2024). Similarly, in relapsed/refractory B-cell malignancies, CD19 CAR T-cell therapy achieves durable remission only in roughly 40% of patients (Neelapu et al. 2023). This underscores the need to better understand the determinants of immune response and resistance.
Immunotherapy relies on a host immune system, with the functional state of patients immune system as a plausible determinant of outcome. As cancer is predominantly a disease of ageing, immunosenescence (immune system ageing) is increasingly suspected as a barrier to effective immunotherapy. Up to date, studies have associated immunotherapy outcome with individual ageing-related markers in isolated compartments, such as senescent CD8 T-cell phenotypes, telomere length or loss of naive-like T cells, rather than assessing ageing across the immune system as a whole (Ferrara et al., 2021; Bruins et al., 2025). This is suprising as myeloid-derived signals shape T-cell mediated immune responses in immunotherapy. It therefore remains unknown which ageing immune populations limit response to immunotherapy, and whether a measure of immune age could serve as a predictive biomarker.
Several single-cell immune ageing clocks have recently become available (sc-ImmuAging, IMMClock, immAge), alongside IMM-AGE and transcriptional senescence signatures such as SenMayo. These were built and validated in healthy populations, in infection and in vaccination, and have never been applied to immunotherapy-treated patients. Notably, sc-ImmuAging resolves ageing in myeloid as well as lymphoid populations, and its application to COVID-19 revealed pronounced age acceleration specifically in monocytes. Whether myeloid immune ageing shapes immunotherapy outcome has nonetheless never been examined, despite accumulating evidence that myeloid-derived signals govern CAR T-cell expansion and resistance to checkpoint blockade.
This project will apply immune ageing clocks, together with a senescence score, to published single-cell datasets of peripheral blood from patients treated with chemotherapy, checkpoint blockade, their combination, or CD19 CAR T cells. It asks three questions: Is biological immune age, measured before treatment, predictive of clinical response? Which immune compartments carry that predictive signal, with particular attention to the myeloid lineage. How does treatment itself, and especially prior chemotherapy exposure, reshape immune age and the activation states associated with response? The student will start by applying multi-modal statistical data analysis to decipher correlation and potential causal links between patient characteristics and biological and clinical readouts. In the long term, this work will establish whether immune age is a usable stratification tool and identify which ageing immune populations limit therapeutic efficacy, laying a molecular framework for patient stratification and subsequently for mechanistic and functional validation.
This internship is particularly suited for a Master 2 student in bioinformatics, biostatistics, data science, AI for health or computational biology, with strong interest in oncology and immunology. Skills in Python or R are required, and prior experience with longitudinal data analysis, machine learning, survival analysis or clinical data would be an advantage. The project will provide training in the analysis of real-world datasets and contribute to the development of quantitative tools supporting future clinical trials.
References to aging clocks/ immune ageing:
• sc-ImmuAging: Nat Aging. 2025. 10.1038/s43587-025-00819-z
• immAge: Ping et al. Immunity. 2026. 10.1016/j.immuni.2026.02.007
• IMMClock: bioRxiv. 2024. doi:10.1101/2024.11.13.623449
• Alpert A, Pickman Y, Leipold M, et al. A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring. Nat Med. 2019;25(3):487-495.
• Saul D, Kosinsky RL, Atkinson EJ, et al. A new gene set identifies senescent cells and predicts senescence-associated pathways across tissues. Nat Commun. 2022;13:4827.
• Gong, Q., Sharma, M., Glass, M.C. et al. Multi-omic profiling reveals age-related immune dynamics in healthy adults. Nature 648, 696–706 (2025).