Gene signatures of tumor inflammation and epithelial-to-mesenchymal transition (EMT) predict responses to immune checkpoint blockade in lung cancer with high accuracy

Abstract

Treatment of non-small cell lung cancer (NSCLC) with immune checkpoint blockade (ICB) has resulted in striking clinical responses, but only in a subset of patients. The goal of this study was to evaluate transcriptional signatures previously reported in the literature in an independent cohort of NSCLC patients receiving ICB. Materials and methods: This retrospective study analyzed transcriptional profiles from pre-treatment tumor samples of 52 chemotherapy-refractory advanced NSCLC patients treated with anti-PD1/PD-L1 therapy. Gene signatures based on published reports were created and examined for their association with response to therapy and progression-free and overall survival (PFS, OS). Results: Two signatures predicting response and outcomes were identified. One reflected the degree of immune infiltration and upregulation of interferon-gamma-induced genes. A second reflected the EMT status. Compared to those not responding to therapy, patients whose tumors responded to ICB had higher scores in an inflammatory gene signature (6.0 ± 2.9 vs -5.5 ± 3.4, p = 0.014) or a more epithelial phenotype (-1.7 ± 1.0 vs 2.1 ± 1.2, p = 0.016). Both signatures demonstrated a satisfactory predictive accuracy for response: AUC of 0.69 (95% CI: 0.54, 0.84) for the inflammatory and 0.70 (95% CI: 0.55, 0.85) for EMT signatures, respectively. A weighted score combining EMT and inflammatory signatures showed increased predictive value with AUC of 0.92 (95% CI: 0.85, 0.99). Kaplan-Meier curves for patients above and below the median combined score showed a significant separation for PFS and OS (all p < 0.01, log rank test). Conclusions: The EMT/Inflammation signature score may be useful in directing checkpoint inhibitor therapy in lung cancer and suggests that reversal of EMT might augment efficacy of ICB.

Gene signature of antigen processing and presentation machinery predicts response to checkpoint blockade in non-small cell lung cancer (NSCLC) and melanoma

“…We do have some data to suggest that our APMS is not simply another surrogate for certain type of leukocyte infiltration. First, in our previous study using these same samples 12 (Supplemental figures 2 and 4), we saw no associations of response to anti-PD1 therapy with lymphocyte genes (CD4, CD8A, and CD8B), with macrophage genes (CD68, CD14, CD163, and CSF1R), or to fibroblast genes (ACTA2 and Col4A1). We saw no correlation between the APM score and expression levels of the genes for CD45, CD8A, CD11b, CD68, CD14, and CD163 (online supplemental figure 2).…”

“…9 10 We previously showed that such an inflammatory gene signature was significantly associated with response to checkpoint blockade in patients with metastatic lung cancer with an AUC of 0.70 (figure 3A, gray line). 12 In the same NSCLC cohort, the eight-gene antigen processing score demonstrated an improved ability to predict response to checkpoint blockade relative to the inflammatory gene signature with an AUC of 0.84 (figure 3A, green line), even though there was a significant correlation between the APM score and inflammatory gene signature (Pearson's r=0.58, p<0.0001) ( figure 3B).…”

“…9 Our group demonstrated that the genes used in this signature were also predictive of response and survival to checkpoint blockade in patients with metastatic lung cancer. 12…”

Epithelial to Mesenchymal Transition: A Mechanism that Fuels Cancer Radio/Chemoresistance

“…On the other hand, these findings may suggest that cisplatin-resistant EMT tumor cells could display an increased sensitivity to anti-PD-1 or anti-PD-L1 antibody treatment [82]. However, recent studies investigating gene expression signatures, predicting the efficacy of PD-1/PD-L1 inhibitors in different cancer types, found that tumors with EMT or the mesenchymal phenotype showed a lower response rate [83][84][85]. Besides the potential application of the evaluation of the EMT status as a predictive marker for checkpoint agents, these results suggest that therapies aiming at the reversal of the EMT process may potentiate the efficacy of this type of cancer immunotherapy [86,87].…”

A gene expression signature of TREM2hi macrophages and γδ T cells predicts immunotherapy response

“…A prognostic signature based on 11 immune-related genes (IRGs) for predicting CC (cervical cancer) patients' response to immune checkpoint inhibitors (ICIs) 28 LRRC15.CAF.Sig A signature of 14 marker genes of a specific type of carcinoma-associated fibroblasts (CAF) -"LRRC15 + CAFs" that correlated with poor response to anti-PD-L1 therapy 29 T.cell.inflamed.Sig An 18 gene "T-cell-inflamed gene expression signature" that can predict clinical benefit of anti-PD-1 in various cancer types (melanoma, head and neck squamous cell carcinomas, digestive cancers, ovarian and triple negative breast cancers) 24,30 IPRES.Sig IPRES (innate anti-PD-1 resistance) that included 16 genes involved in cell adhesion, extracellular matrix remodeling, angiogenesis, wound healing, and mesenchymal transition that predicted response to anti-PD-1 antibody therapy in melanoma 9,31 Inflammatory.Sig A gene expression signature of 27 inflammation related genes that predicted response to immune checkpoint blockade in lung cancer 31…”