PhD Scientific Days 2024

Poster Session J - Pathological and Oncological Sciences 1.

Predicting Immune Checkpoint Inhibitor Therapy Efficacy in Advanced Urothelial Carcinoma: Integrated Analysis of Molecular Patterns and Clinical Factors

Text of the abstract

Introduction: In recent years, immune checkpoint inhibitors (ICIs) have emerged as standard systemic treatment option for advanced urothelial carcinoma (aUC). However, patients exhibit considerable variability in their responses to these treatments. As the therapeutic landscape of aUC is expanding, predicting the efficacy of ICI therapies has become increasingly essential in clinical practice.
Aims: We aimed to identify new molecular markers associated with ICI sensitivity and to develop a model that combines clinical and molecular data to predict the efficacy of ICI therapy.
Material and methods: Clinical and follow-up data as well as paraffinized tissue samples from aUC patients treated with ICI have been analysed. To identify ICI predictive gene expression patterns, we assessed the expression 770 immune-related genes using the NanoString nCounter© technology. In addition, we determined molecular subtypes (according to the MDA, TCGA, Lund and Consensus classification systems) based on the expression of 48 genes covering 8 signatures. PD-L1 immunohistochemistry (IHC) (using antibody: 22C3) was performed and assessed by an expert uropathologist. All clinicopathological and molecular parameters were correlated with patients' overall survival (OS), progression-free survival (PFS), as well as objective response and disease control rates (ORR and DCR).
Results: Our clinical data analysis with 100 aUC patients found that the presence of Bellmunt risk factors was significantly associated with poor OS, PFS, ORR and DCR. PD-L1 IHC scores were not associated with OS or PFS. The TCGA-luminal infiltrated and the MDA-p53-like molecular subtypes showed longer OS times, while the TCGA-luminal subtype displayed the lowest response rate. Of the 8 assessed signatures, only high neuronal scores were associated with improved OS and PFS. Of the immune-related genes 94 correlated with OS of which 19 could be validated in an independent ICI transcriptome dataset (IMvigor210 study).
Conclusions: We identified clinicopathological and molecular factors that are associated with ICI treatment outcomes. PD-L1 IHC has a limited predictive value. Of the multigene molecular factors, the neuroendocrine signature, and the luminal infiltrated molecular subtype were associated with better ICI outcomes. In addition, 19 novel single ICI predictive genes could be identified and validated.