New Classification System Tackling Recurrent Ovarian Cancer

By Bio-IT World Staff 

September 26, 2025 | A new classification system for recurrent ovarian cancer was developed by scientists at Lausanne University Hospital and the Ludwig Institute for Cancer Research. The system combines genomic sequencing with immune profiling to stratify patients into four immunologic subgroups, laying the groundwork for personalized therapies that could extend survival. 

This “immune classifier” was built using digital pathology and algorithmic modeling. Researchers analyzed nearly 700 ovarian cancer samples from 595 patients across five independent clinical cohorts, capturing variations in T cell density and spatial distribution within tumors. The classifier organizes tumors into four categories—purely inflamed, mixed-inflamed, excluded, and desert—each with distinct biological signatures that influence treatment response. 

When paired with genomic biomarkers such as BRCA1 mutation status, the classification points to tailored therapeutic strategies. BRCA1-mutated tumors, for example, tend to display an inflamed immune landscape and disrupted DNA repair machinery, making them more susceptible to novel combinations of drugs. Researchers demonstrated in preclinical models that combining PARP inhibitors with COX inhibitors and immune checkpoint blockade extended survival significantly, underscoring the potential of biotechnological synergy. 

The study also highlights how biotechnological tools can illuminate the hidden roles of the tumor microenvironment. By sequencing DNA and profiling immune cells, the team discovered that macrophages and dendritic cells function as key regulators of therapy resistance. In DNA repair–proficient tumors (desert type), macrophages suppress immune responses by expressing proteins like ApoE and Trem2, opening a potential avenue for antibody therapies targeting those pathways. In fact, mouse models treated with a Trem2 inhibitor showed enhanced responses to chemotherapy—an example of biotech-driven discovery pointing to translational opportunities. 

This multi-layered approach—integrating genomics, immunology, and digital pathology—reflects the direction of modern oncological biotechnology. Instead of one-size-fits-all regimens, researchers aim to parse the complexity of cancer biology into actionable, biomarker-driven categories. The study also underscores the role of biotechnology as a bridge between laboratory science and clinical translation. Advanced imaging and algorithmic analysis enabled the immune classifier; genomic sequencing linked mutational status to immune phenotypes; and molecular biology tools revealed vulnerabilities in macrophage and T cell dynamics.  

Denarda Dangaj Laniti, Ph.D., who led the study, and her team are hopeful that this new system will help clinicians finally bring precision medicine to a cancer type that has long resisted uniform treatment. In order to accomplish this, however, it will require partnerships with academic institutions, cancer centers, and industry collaborators, as well as substantial funding. 

To read the full article written by Deborah Borfitz, see Diagnostic World News.