Publication date: Oct 11, 2024

Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, providing significant benefit to patients across various tumour types, including melanoma. However, around 40% of melanoma patients do not benefit from ICI treatment, and accurately predicting ICI response remains challenging. We now describe a novel and simple approach that integrates immune-associated transcriptome signatures and tumour volume burden to better predict ICI response in melanoma patients. RNA sequencing was performed on pre-treatment (PRE) tumour specimens derived from 32 patients with advanced melanoma treated with combination PD1 and CTLA4 inhibitors. Of these 32 patients, 11 also had early during treatment (EDT, 5-15 days after treatment start) tumour samples. Tumour volume was assessed at PRE for all 32 patients, and at first computed tomography (CT) imaging for the 11 patients with EDT samples. Analysis of the Hallmark IFNγ gene set revealed no association with ICI response at PRE (AUC ROC curve = 0. 6404, p = 0. 24, 63% sensitivity, 71% specificity). When IFNg activity was evaluated with tumour volume (ratio of gene set expression to tumour volume) using logistic regression to predict ICI response, we observed high discriminative power in separating ICI responders from non-responders (AUC = 0. 7760, p = 0. 02, 88% sensitivity, 67% specificity); this approach was reproduced with other immune-associated transcriptomic gene sets. These findings were further replicated in an independent cohort of 23 melanoma patients treated with PD1 inhibitor. Hence, integrating tumour volume with immune-associated transcriptomic signatures improves the prediction of ICI response, and suggest that higher levels of immune activation relative to tumour burden are required for durable ICI response.

Open Access PDF

Semantics

Original Article