A whole-blood RNA transcript-based gene signature is associated with the development of CTLA-4 blockade-related diarrhea in patients with advanced melanoma treated with the checkpoint inhibitor tremelimumab.

BACKGROUND: Anti-CTLA-4 immune checkpoint blockade is associated with immune-related adverse events (irAEs). Grade 3-4 diarrhea/colitis is the most frequent irAE requiring treatment discontinuation. Predicting high-risk diarrhea/colitis patients may facilitate early intervention, limit irAE severity, and extend treatment duration. No biomarkers currently predict for anti-CTLA-4 immunotherapy related severe diarrhea. METHODS: Whole-blood was collected pre-treatment and 30 days post-treatment initiation from patients with stage III or IV unresectable melanoma who received 15 mg/kg tremelimumab at 90 day intervals in two clinical trials. The discovery dataset was a phase II study that enrolled 150 patients between December 2005 and November 2006. The validation dataset was a phase III study that enrolled 210 patients between March 2006 and July 2007. RT-PCR was performed for 169 genes associated with inflammation, immunity, CTLA-4 pathway and melanoma. Gene expression was correlated with grade 0-1 versus grade 2-4 diarrhea/colitis development. RESULTS: Pre-treatment blood obtained from the discovery dataset (N = 150) revealed no gene predictive of diarrhea/colitis development (p < 0.05). A 16-gene signature (CARD12, CCL3, CCR3, CXCL1, F5, FAM210B, GADD45A, IL18bp, IL2RA, IL5, IL8, MMP9, PTGS2, SOCS3, TLR9 and UBE2C) was identified from 30 days post-tremelimumab initiation blood that discriminated patients developing grade 0-1 from grade 2-4 diarrhea/colitis. The 16-gene signature demonstrated an AUC of 0.814 (95% CI 0.743 to 0.873, p < 0.0001), sensitivity 42.9%, specificity 99.2%, positive predictive value (PPV) 90.0%, and negative predictive value (NPV) 91.4%. In the validation dataset (N = 210), the 16-gene signature discriminated patients developing grade 0-1 from grade 2-4 diarrhea/colitis with an AUC 0.785 (95% CI 0.723 to 0.838, p < 0.0001), sensitivity 57.1%, specificity 84.4%, PPV 57.1% and NPV 84.4%. CONCLUSION: This study identifies a whole-blood mRNA signature predictive of a clinically relevant irAE in patients treated with immune checkpoint blockade. We hypothesize that immune system modulation induced by immune checkpoint blockade results in peripheral blood gene expression changes that are detectable prior to clinical onset of severe diarrhea. Assessment of peripheral blood gene expression changes in patients receiving anti-PD-1/PD-L1 immunotherapy, or combination anti-CTLA4 and anti-PD-1/PD-L1 immunotherapy, is warranted to provide early on-treatment mechanistic insights and identify clinically relevant predictive biomarkers. TRIAL REGISTRATION: Clinicaltrials.gov , NCT00257205 , registered 22 November 2005.

Whole-blood RNA transcript-based models can predict clinical response in two large independent clinical studies of patients with advanced melanoma treated with the checkpoint inhibitor, tremelimumab.

BACKGROUND: Tremelimumab is an antibody that blocks CTLA-4 and demonstrates clinical efficacy in a subset of advanced melanoma patients. An unmet clinical need exists for blood-based response-predictive gene signatures to facilitate clinically effective and cost-efficient use of such immunotherapeutic interventions. METHODS: Peripheral blood samples were collected in PAXgene® tubes from 210 treatment-naïve melanoma patients receiving tremelimumab in a worldwide, multicenter phase III study (discovery dataset). A central panel of radiologists determined objective response using RECIST criteria. Gene expression for 169 mRNA transcripts was measured using quantitative PCR. A 15-gene pre-treatment response-predictive classifier model was identified. An independent population (N = 150) of refractory melanoma patients receiving tremelimumab after chemotherapy enrolled in a worldwide phase II study (validation dataset). The classifier model, using the same genes, coefficients and constants for objective response and one-year survival after treatment, was applied to the validation dataset. RESULTS: A 15-gene pre-treatment classifier model (containing ADAM17, CDK2, CDKN2A, DPP4, ERBB2, HLA-DRA, ICOS, ITGA4, LARGE, MYC, NAB2, NRAS, RHOC, TGFB1, and TIMP1) achieved an area under the curve (AUC) of 0.86 (95% confidence interval 0.81 to 0.91, p < 0.0001) for objective response and 0.6 (95% confidence interval 0.54 to 0.67, p = 0.0066) for one-year survival in the discovery set. This model was validated in the validation set with AUCs of 0.62 (95% confidence interval 0.54 to 0.70 p = 0.0455) for objective response and 0.68 for one-year survival (95% confidence interval 0.59 to 0.75 p = 0.0002). CONCLUSIONS: To our knowledge, this is the largest blood-based biomarker study of a checkpoint inhibitor, tremelimumab, which demonstrates a validated pre-treatment mRNA classifier model that predicts clinical response. The data suggest that the model captures a biological signature representative of genes needed for a robust anti-cancer immune response. It also identifies non-responders to tremelimumab at baseline prior to treatment.