Predictability of progression free survival in ovarian cancer

ABSTRACT: Given the interrelated mechanisms by which changes to the tumor microenvironment (TME) may drive disease recurrence, we test a novel machine learning approach to identifying key genes that may be involved in the likelihood of recurrence. The ability to compare pre- and post- NACT samples from the same patient shows how chemotherapy drives changes in the TME. We also had the opportunity to employ machine learning methods to investigate whether patient progression-free survival can be prediced from the impact of chemotherapy on the tumor microenvironment.

ORGANISM(S): Homo sapiens

PROVIDER: GSE319500 | GEO | 2026/02/17

REPOSITORIES: GEO