Journal of Clinical Medicine (Mar 2022)

Comprehensive Characterization of Human Lung Large Cell Carcinoma Identifies Transcriptomic Signatures with Potential Implications in Response to Immunotherapy

  • Javier Ramos-Paradas,
  • David Gómez-Sánchez,
  • Aranzazu Rosado,
  • Alvaro C. Ucero,
  • Irene Ferrer,
  • Ricardo García-Luján,
  • Jon Zugazagoitia,
  • Nuria Carrizo,
  • Ana B. Enguita,
  • Esther Conde,
  • Eva M. Garrido-Martin,
  • Luis Paz-Ares

DOI
https://doi.org/10.3390/jcm11061500
Journal volume & issue
Vol. 11, no. 6
p. 1500

Abstract

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Lung cancer is the leading cause of cancer mortality worldwide, with non-small cell lung cancer (NSCLC) being the most prevalent histology. While immunotherapy with checkpoint inhibitors has shown outstanding results in NSCLC, the precise identification of responders remains a major challenge. Most studies attempting to overcome this handicap have focused on adenocarcinomas or squamous cell carcinomas. Among NSCLC subtypes, the molecular and immune characteristics of lung large cell carcinoma (LCC), which represents 10% of NSCLC cases, are not well defined. We hypothesized that specific molecular aberrations may impact the immune microenvironment in LCC and, consequently, the response to immunotherapy. To that end, it is particularly relevant to thoroughly describe the molecular genotype–immunophenotype association in LCC–to identify robust predictive biomarkers and improve potential benefits from immunotherapy. We established a cohort of 18 early-stage, clinically annotated, LCC cases. Their molecular and immune features were comprehensively characterized by genomic and immune-targeted sequencing panels along with immunohistochemistry of immune cell populations. Unbiased clustering defined two novel subgroups of LCC. Pro-immunogenic tumors accumulated certain molecular alterations, showed higher immune infiltration and upregulated genes involved in potentiating immune responses when compared to pro-tumorigenic samples, which favored tumoral progression. This classification identified a set of biomarkers that could potentially predict response to immunotherapy. These results could improve patient selection and expand potential benefits from immunotherapy.

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