A novel immuno-oncology algorithm measuring tumor microenvironment to predict response to immunotherapies.

Immune checkpoint inhibitor (ICI) therapies can improve clinical outcomes for patients with solid tumors, but relatively few patients respond. Because ICI therapies support an adaptive immune response, patients with an active tumor microenvironment (TME) may be more likely to respond, and thus biomarkers capable of discerning an active from a quiescent TME may be useful in patient selection. We developed an algorithm optimized for genes expressed in the mesenchymal and immunomodulatory subtypes of a 101-gene triple negative breast cancer model (Ring, BMC Cancer, 2016, 16:143) as a means to capture the immunological state of the TME. We compared the outcome of the algorithm (IO score) with the 101-gene model and found 88% concordance, indicating the models are correlated but not identical, and may be measuring different TME features. We found 92.5% correlation between IO scores of matched tumor epithelial and adjacent stromal tissues, indicating the IO score is not specific to these tissues, but reflects the TME as a whole. We observed a significant difference in IO score (p = 0.0092) between samples with high tumor-infiltrating lymphocytes and samples with increased neutrophil load, demonstrating agreement between IO score and these two prognostic markers. Finally, among non-small cell lung cancer patients receiving immunotherapy, we observed a significant difference in IO score (p = 0.0035) between responders and non-responders, and a significant odds ratio (OR = 5.76, 95% CI 1.30-25.51, p = 0.021), indicating the IO score can predict patient response. The immuno-oncology algorithm may offer independent and incremental predictive value over current biomarkers in the clinic.