Blood-based gene expression profiling to reveal potential response biomarkers for immunotherapy in advanced lung cancer.

Abstract

e15155 Background: Novel immunotherapies are becoming a viable option for advanced lung cancer treatment. High-throughput gene expression profiling (GEP) has enabled better understanding of patient responsiveness to targeted immunotherapy. However, non-invasive blood-based signatures are needed to monitor and predict response. Methods: To evaluate the predictive and prognostic role of blood-based GEP, we designed a longitudinal study by enrolling 15 patients with advanced lung cancers. A total of 65 samples were collected for RNA sequencing, ~4 blood specimens per patient, before and during anti-PD-1 treatment. We used multiple analyses, including time-course differential gene expression, principal component, lymphocyte compartment deconvolution, and genetic mutation, to search for and assess potential predictive and prognostic aspects. Results: Of 15 patients, 11 were classified as Responders (partial responders) and four were Non-Responders (one stable and three progressive diseases). Our analyses demonstrated: 1) By comparing baseline GEPs from Responders vs. Non-Responders before the first treatment, we identified potential markers (e.g., LY6E is significantly lower expressed in Responders, with Log2 Fold change = -3.44 and p = 1.83E-04) that can be used as predictors of responsiveness of the patients; 2) Immunoglobulin subunits- and T cell receptor complex-related genes were differentially expressed in Responders (DAVID analysis, p = 6.7E-3 and 2.1E-2, respectively), but not in Non-responders; 3) γδ T cells in the lymphocyte compartment were relatively increased in Responders; 4) Despite a different set of genes differentially expressed at different time points, the biggest GEP changes were at ~ week 6, after the second treatment. Additionally, we observed certain genes consistently up- or down-regulated through the whole course of treatment. Furthermore, after the first treatment, genes in the immune response pathway were regulated to different directions in Responders and Non-Responders. For example, interleukin receptor genes, such as IL18R1 and IL18RAP, and CD24 were down-regulated in Responders, but up-regulated in Non-Responders (p = 0.042, 0.023 and 0.044 respectively, t-test for the differential expression in these two groups). Conclusions: The utility of blood GEP to identify predictive and prognostic factors for precision immunotherapy is encouraging. Nevertheless, these results, predictive of the anti-PD-1 clinical response, are preliminary and need to be validated in a larger cohort.