The impact of mutational clonality in predicting the response to immune checkpoint inhibitors in advanced urothelial cancer

Boll, Lilian Marie; Perera Bel, Júlia; Rodriguez-Vida, Alejo; Arpí Llucià, Oriol; Rovira, Ana; Juanpere, Nuria; Vázquez Montes de Oca, Sergio; Hernández Llodrà, Silvia; Lloreta, Josep, 1958-; Albà Soler, Mar; Bellmunt Molins, Joaquim, 1959-

doi:http://dx.doi.org/10.1038/s41598-023-42495-2

The impact of mutational clonality in predicting the response to immune checkpoint inhibitors in advanced urothelial cancer

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Boll LM, Perera-Bel J, Rodriguez-Vida A, Arpí O, Rovira A, Juanpere N, et al. The impact of mutational clonality in predicting the response to immune checkpoint inhibitors in advanced urothelial cancer. Sci Rep. 2023 Sep 15;13(1):15287. DOI: 10.1038/s41598-023-42495-2

Abstract

Immune checkpoint inhibitors (ICI) have revolutionized cancer treatment and can result in complete remissions even at advanced stages of the disease. However, only a small fraction of patients respond to the treatment. To better understand which factors drive clinical benefit, we have generated whole exome and RNA sequencing data from 27 advanced urothelial carcinoma patients treated with anti-PD-(L)1 monoclonal antibodies. We assessed the influence on the response of non-synonymous mutations (tumor mutational burden or TMB), clonal and subclonal mutations, neoantigen load and various gene expression markers. We found that although TMB is significantly associated with response, this effect can be mostly explained by clonal mutations, present in all cancer cells. This trend was validated in an additional cohort. Additionally, we found that responders with few clonal mutations had abnormally high levels of T and B cell immune markers, suggesting that a high immune cell infiltration signature could be a better predictive biomarker for this subset of patients. Our results support the idea that highly clonal cancers are more likely to respond to ICI and suggest that non-additive effects of different signatures should be considered for predictive models.