Genomic Signatures of Immune Activation Predict Outcome in Advanced Stages of Ovarian Cancer and Basal-Like Breast Tumors - Alcaraz-Sanabria, Ana; Baliu-Piqué, Mariona; Saiz-Ladera, Cristina; Rojas, Katerin; Manzano, Aránzazu; Marquina, Gloria; Casado, Antonio; Cimas, Francisco J.; Pérez-Segura, Pedro; Pandiella, Atanasio; Gyorffy, Balázs; Ocana, Alberto (Frontiers in Oncology, 2020)
There is an unmet need for new therapies in metastatic ovarian cancer and basal-like breast cancer since no curative therapies are currently available. Immunotherapy has shown to be active in several solid tumors, but particularly more in those where a pre-activated immune state does exist. In this work, we aim to identify biomarkers that could distinguish immune-activated tumors and predict response to therapies in ovarian and basal-like breast cancer, as well as their association with the level of tumor immune infiltration. We found that the combined expression of IFNG, CD30, CXCL13, and PRF1 correlated with better overall survival (OS) in advanced stage ovarian cancer. This was confirmed using an independent dataset from TCGA. Interestingly, we observed that this gene combination also predicted for better prognosis in ovarian tumors with low mutational load, which typically respond less to immunotherapy. Expression of IFNG, CD30, CXCL13, and PRF1 was associated with increased level of immune infiltrates (CD8+ T cells, dendritic cells, and neutrophils) within the tumor. Moreover, we found that these gene signature also correlated with an increased OS and with a higher level of tumor immune infiltrates (B cells, CD8+ T cells, CD4+ T cells, neutrophils, and dendritic cells) in basal-like breast cancer. In conclusion, our analysis identifies genes signatures with potential to recognize immune activated ovarian and basal-like breast cancers with favorable prognosis and with a remarkable predictive capacity in tumors with low mutational burden. The presented results led to a hypothesis being formulated, but prospective clinical studies are needed to support a potential clinical application.