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

Abstract

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.

Keywords: Biomarker; Immune checkpoint inhibitors; Immunotherapy; NSCLC; TNBC; Tumor microenvironment.

Figure 1

Gene selection process for building the novel immuno-oncology algorithm. Gene set resulted from data set normalization, batch correction, gene set enrichment analysis, and elastic net modeling.

Figure 2

Box and Whisker plot displaying IO scores of TNBC samples from TCGA with high levels of TILs as compared to samples with increased neutrophil load. The IO score threshold is indicated at 0.09. The line within the box plots represents the median and the cross represents the mean.

Figure 3

Box and Whisker plot displaying IO scores from Responders (R) and Non-Responders (Non-R) from the combined NSCLC cohorts. The IO score threshold is indicated at 0.09. The line within the box plots represents the median and the cross represents the mean.

Figure 4

Overview of IO score as a measure of the quiescent or immunologically active state of the tumor microenvironment (TME). We hypothesized that a negative IO score may indicate a quiescent state, where the tumor cells are more actively promoting angiogenesis, inducing an inflammatory response, and stimulating cancer-associated fibroblasts which collectively is constructing extracellular matrix. By comparison, a positive IO score may indicate an immunologically active TME with reduced inflammatory characteristics combined with an increase in the innate and adaptive immune systems increasing tumor cell invasion. Whereas a biomarker such as PD-L1 may be present in both states, the immuno-oncology algorithm is able to distinguish a quiescent from an active TME.