Multi-omics and phenotype integration for cancer molecular subtyping and patient stratification

We are interested in joining consortia developing multi-omics approaches for molecular characterization of cancer, biomarker discovery, patient stratification and precision treatment selection.

Our main interest is the integration of genomic, transcriptomic, single-cell transcriptomic and other molecular data with clinical and treatment information to identify biologically and clinically meaningful cancer subtypes. Particular emphasis will be placed on discovering molecular signatures associated with disease progression, therapeutic response and resistance, and on translating these signatures into clinically applicable biomarkers and potential therapeutic targets.

We can contribute expertise in next-generation sequencing, bulk and single-cell RNA sequencing, genomic data analysis, bioinformatics, functional genomics and integrative analysis of high-dimensional molecular datasets. We are also interested in applying machine-learning approaches to identify multimodal signatures that improve patient classification and prediction of treatment response.

We are seeking partners with complementary expertise in cancer biology, clinical oncology, pathology, functional validation, proteomics/metabolomics, AI and machine learning, biostatistics, biomarker validation and access to well-characterized clinical cohorts.

The ultimate objective is to develop robust and clinically interpretable molecular stratification frameworks that connect tumor biology with therapeutic vulnerabilities and support individualized treatment decisions.