Ratio of the interferon-γ signature to the immunosuppression signature predicts anti-PD-1 therapy response in melanoma

Abstract

Immune checkpoint inhibitor (ICI) treatments produce clinical benefit in many patients. However, better pretreatment predictive biomarkers for ICI are still needed to help match individual patients to the treatment most likely to be of benefit. Existing gene expression profiling (GEP)-based biomarkers for ICI are primarily focused on measuring a T cell-inflamed tumor microenvironment that contributes positively to the response to ICI. Here, we identified an immunosuppression signature (IMS) through analyzing RNA sequencing data from a combined discovery cohort (n = 120) consisting of three publicly available melanoma datasets. Using the ratio of an established IFN-γ signature and IMS led to consistently better prediction of the ICI therapy outcome compared to a collection of nine published GEP signatures from the literature on a newly generated internal validation cohort (n = 55) and three published datasets of metastatic melanoma treated with anti-PD-1 (n = 54) and anti-CTLA-4 (n = 42), as well as in patients with gastric cancer treated with anti-PD-1 (n = 45), demonstrating the potential utility of IMS as a predictive biomarker that complements existing GEP signatures for immunotherapy.

PD-1 inhibition in patient derived tissue cultures of human gastric and gastroesophageal adenocarcinoma

“…Gene expression data were normalized to 15 housekeeping genes (ABCF1, DNAJC14, ERCC3, G6PD, GUSB, MRPL19, NRDE2, OAZ1, POLR2A, PSMC4, PUM1, SDHA, SF3A1, STK11IP, TBC1D10B, TBP, TFRC, TLK, TMUB2, UBB) and background thresholding was performed with NanoString nSolver software. IFNg signature 15 (CD27, CD274, CD276, CD8A, CMKLR1, CXCL9, CXCR6, HLA-DQA1, HLA-DRB1, HLA-E, IDO1, LAG3, NKG7, PDCD1LG2, PSMB10, STAT1, TIGIT) and immune suppression score 31 (IMS: CCL8, VCAN, CCL2, CD163, CCL13, COL6A3, BCAT1, ADAM12, AXL, ISG15, SIGLEC1, PDGFRB, IL10, STC1, OLFML2B, TWIST2, FAP, INHBA) were calculated according to publications. For each experiment, relative change of gene expression data in treated conditions (e.g., Nivolumab) related to the negative control was calculated.…”

“…In clinical trials of immunotherapy-specific t-lymphocyte inflammation, gene expression signatures have been established and have been proven of predictive value. 15,31 In future, cancer therapy may become more and more complex, and precision medicine will be part of daily practice. Novel biomarkers can guide oncologist in immunotherapy treatment decisions.…”

“…This t-lymphocyte inflammation based profile, still under investigation in current PD-1 inhibition trials, could be correlated to the obtained overall effects. 15 Further, the immune suppressor score investigated by Cui et al 31 for their predictive potency in clinical melanoma and gastric cancer specimens was aplied in the current investigation as well as a gene set of t-cell effector molecules that are involved in t-lymphocyte mediated cytotoxicity. [33][34][35][36] Though, t-cell effector gene set supported our classification of tissue response by immunohistochemical determination with statistical power.…”

“…[33][34][35][36] By comparing the relative change of expression of the t-cell effector gene set, responding tissue show a higher relative change of gene expression compared to non-responding tissue (∆0.54, p≤0.05; relative change: Nivo vs CTR; Figure 6a). Further, we analyzed two established gene expression scores, the interferon gamma score (IFNg) by Ayers et al 15 and immune suppression score (IMS) by Cui et al 31 were related to the t-cell effector gene set established in PDTC. A correlation between relative change of the t-cell effector gene set expression and the IFNg score were observed.…”

Overcoming Resistance to Immunotherapy in Advanced Cutaneous Squamous Cell Carcinoma

“…The 18-gene IFN-γ characterized by this group is better than PD-L1 immunohistochemistry at identifying patients who will respond to immunotherapy [48]. However, more experiments, currently being carried out [49,50], are needed to make clinical implementation possible.…”