IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade

Abstract

Programmed death-1-directed (PD-1-directed) immune checkpoint blockade results in durable antitumor activity in many advanced malignancies. Recent studies suggest that IFN-γ is a critical driver of programmed death ligand-1 (PD-L1) expression in cancer and host cells, and baseline intratumoral T cell infiltration may improve response likelihood to anti-PD-1 therapies, including pembrolizumab. However, whether quantifying T cell-inflamed microenvironment is a useful pan-tumor determinant of PD-1-directed therapy response has not been rigorously evaluated. Here, we analyzed gene expression profiles (GEPs) using RNA from baseline tumor samples of pembrolizumab-treated patients. We identified immune-related signatures correlating with clinical benefit using a learn-and-confirm paradigm based on data from different clinical studies of pembrolizumab, starting with a small pilot of 19 melanoma patients and eventually defining a pan-tumor T cell-inflamed GEP in 220 patients with 9 cancers. Predictive value was independently confirmed and compared with that of PD-L1 immunohistochemistry in 96 patients with head and neck squamous cell carcinoma. The T cell-inflamed GEP contained IFN-γ-responsive genes related to antigen presentation, chemokine expression, cytotoxic activity, and adaptive immune resistance, and these features were necessary, but not always sufficient, for clinical benefit. The T cell-inflamed GEP has been developed into a clinical-grade assay that is currently being evaluated in ongoing pembrolizumab trials.

Conflict of interest statement

Conflict of interest: M. Ayers, J. Lunceford, M. Nebozhyn, E. Murphy, A. Loboda, D.R. Kaufman, A. Albright, J.D. Cheng, S.P. Kang, J. Yearley, and T.K. McClanahan are employees of Merck & Co. Inc. S.P. Kang and T.K. McClanahan also disclose stock ownership in Merck & Co. Inc. M. Ayers, J. Lunceford, E. Murphy, A. Loboda, and T.K. McClanahan have a patent pending (system and methods for deriving gene signature biomarkers of response to PD-1 antagonists; PCT/US2015/064445), and S.P. Kang has a patent pending (MK-3475). V. Shankaran received a grant from Merck & Co. Inc. during the conduct of the study and has received grants from Amgen and Castle Biosciences. T.Y. Seiwert reports personal fees from Merck & Co. Inc. during the conduct of the study. A. Ribas has been a consultant for Merck & Co. Inc., with the honoraria paid to his institution.

Figures

Figure 1. Gene signature development in melanoma samples.

(A) Overall workflow for the development of immune-related gene signatures that predict response to anti–PD-1 therapy. (B) IFN-γ 10-gene signature evaluated in 19 patients with melanoma and association with response. (C) “Preliminary expanded immune” 28-gene signature with tight correlation to the IFN-γ 10-gene signature, validated in 62 patients with melanoma.

Figure 2. Box plots for the IFN-γ 10-gene and 28-gene expanded immune signatures and best overall response in 62 patients with melanoma with clinical outcomes under anti–PD-1 therapy.

Figure 3. Confirmatory testing and signature refinement across multiple cancer types.

(A and B) Confirmatory analyses of the IFN-γ and expanded immune signature scores for the HNSCC (43 total patients) (A) and gastric cancer (33 patients) (B) cohorts of KEYNOTE-012. (C and D) ROC curves of sensitivity and specificity for the HNSCC (C) and gastric cancer (D) cohorts of KEYNOTE-012.

Figure 4. Heatmap for the final 18-gene T cell–inflamed GEP for 216 tumors from patients in KEYNOTE-012 and KEYNOTE-028 considered evaluable for objective response.

Rows represent patients and columns genes. Expression levels have been standardized (centered and scaled) within columns for visualization. The “R” on the right side indicates whether the patient was a responder (by central imaging vendor in KEYNOTE-012 and by investigator assessment in KEYNOTE-028). The rows and columns have been grouped using unsupervised clustering.

Figure 5. Validation of the final 18-gene T cell–inflamed GEP.

(A) Heatmap of 18-gene T cell–inflamed GEP in 96 PD-L1–unselected patients with HNSCC from KEYNOTE-012. Expression levels have been standardized (centered and scaled) within columns for visualization. The “R” on the right side indicates whether the patient was a responder (by central imaging vendor). The rows and columns have been grouped using unsupervised clustering. (B) ROC curves comparing final 18-gene score with expression of PD-L1 as measured by IHC on tumor and inflammatory cells for a cohort of 96 PD-L1–unselected patients with HNSCC from KEYNOTE-012 considered evaluable for objective response by central imaging vendor.

Figure 6. Relationship between increases in IFN-γ immune-related signature score and PFS in response to anti–PD-1 therapy for the HNSCC and gastric cancer cohorts of KEYNOTE-012.

(A) Relationship in the HNSCC cohort (43 total patients). (B) Relationship in the gastric cancer cohort (33 patients). The cutoff associated with the Youden index is displayed in each figure.

Figure 7. PFS time versus T cell–inflamed GEP score in 244 patients from KEYNOTE-012 and KEYNOTE-028 for the 9 cancer cohorts used to determine the T cell–inflamed GEP.

Figure 8. The T cell-inflamed gene expression signature highlights the complex biology of the host immune microenvironment.