Generation of a multivariate stratification model to predict Trametinib-sensitive low-grade serous ovarian cancer tumours

Low-grade serous ovarian cancer (LGSOC) represents a distinctive subtype of ovarian cancer, accounting for approximately 4% of all epithelial ovarian cancer cases. What sets LGSOC apart is its characteristic display of reduced nuclear atypia and a lower mitotic index in comparison to high-grade serous carcinoma. At the molecular level, LGSOC is characterized by a notably low mutational burden, diminished p53 expression, and a relative state of chromosomal stability. One of its defining features is the prevalence of mutually exclusive activating mutations within the MAPK (Mitogen-Activated Protein Kinase) pathway.

In a clinical trial spearheaded by the Gourley group, the MEK inhibitor Trametinib has demonstrated remarkable effectiveness in enhancing disease-free survival for LGSOC patients. Intriguingly, the presence of activating mutations within the MAPK pathway exhibited a strong correlation with a positive response to Trametinib. However, some patients lacking these mutations also exhibited favorable responses to the treatment.

The principal objective of my study is to unravel the intricate molecular landscape of LGSOC by quantifying the activation of the MAPK pathway through a multivariate proteomic signature. By establishing associations between this pathway's activity, mutational status, and proteomic profiles, we aim to pinpoint common and rare genetic and proteomic characteristics linked to MAPK activation and Trametinib sensitivity in LGSOC. Ultimately, we will employ advanced Machine Learning techniques to predict Trametinib response in these patients.

My project unfolds in three interconnected phases: data generation, computational analysis, and interpretation. This comprehensive approach encompasses mass spectrometry-based proteomics, genomic data analysis, and the application of advanced statistical and Machine Learning methodologies.