Integrative immune transcriptomic classification improves patient selection for precision immunotherapy in advanced gastro-oesophageal adenocarcinoma

Autor: Manuel Cabeza-Segura, Valentina Gambardella, Francisco Gimeno-Valiente, Juan Antonio Carbonell-Asins, Lorena Alarcón-Molero, Arturo González-Vilanova, Sheila Zuñiga-Trejos, Pilar Rentero-Garrido, Rosana Villagrasa, Mireia Gil, Ana Durá, Paula Richart, Noelia Alonso, Marisol Huerta, Susana Roselló, Desamparados Roda, Noelia Tarazona, Carolina Martínez-Ciarpaglini, Josefa Castillo, Andrés Cervantes, Tania Fleitas
Rok vydání: 2022
Předmět:
Zdroj: BRITISH JOURNAL OF CANCER
r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVA
instname
ISSN: 1532-1827
0007-0920
DOI: 10.1038/s41416-022-02005-z
Popis: Background Advanced gastro-oesophageal cancer (GEA) treatment has been improved by the introduction of immune checkpoint inhibitors (CPIs), yet identifying predictive biomarkers remains a priority, particularly in patients with a combined positive score (CPS) Design Through integrative transcriptomic and clinicopathological analyses, we studied in both a retrospective and a prospective cohort, the immune tumour microenvironment features. We analysed the cell types composing the immune infiltrate highlighting their functional activity. Results This integrative study allowed the identification of four different groups across our patients. Among them, we identified a cluster whose tumours expressed the most gene signatures related to immunomodulatory pathways and immunotherapy response. These tumours presented an enriched immune infiltrate showing high immune function activity that could potentially achieve the best benefit from CPIs. Finally, our findings were proven in an external CPI-exposed population, where the use of our transcriptomic results combined with CPS helped better identify those patients who could benefit from immunotherapy than using CPS alone (p = 0.043). Conclusions This transcriptomic classification could improve precision immunotherapy for GEA.
Databáze: OpenAIRE
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