Medical Treatment of Lung Cancer: Can Immune Cells Predict the Response? A Systematic Review

Philippe Rochigneux (1) , Alejandro Garcia (2) , Brice Chanez (1) , Anne Madroszyk (3) , Daniel Olive (1) , Edward Garon (2)

1 CRCM - Centre de Recherche en Cancérologie de Marseille
2 UC - University of California
3 IPC - Institut Paoli-Calmettes

The landscape for medical treatment of lung cancer has irreversibly changed since the development of immuno-oncology (IO). Yet, while immune checkpoint blockade (ICB) revealed that T lymphocytes play a major role in lung cancer, the precise dynamic of innate and adaptive immune cells induced by anticancer treatments including chemotherapy, targeted therapy, and/or ICB is poorly understood. In lung cancer, studies evaluating specific immune cell populations as predictors of response to medical treatment are scarce, and knowledge is fragmented. Here, we review the different techniques allowing the detection of immune cells in the tumor and blood (multiplex immunohistochemistry and immunofluorescence, RNA-seq, DNA methylation pattern, mass cytometry, functional tests). In addition, we present data that consider different baseline immune cell populations as predictors of response to medical treatments of lung cancer. We also review the potential for assessing dynamic changes in cell populations during treatment as a biomarker. As powerful tools for immune cell detection and data analysis are available, clinicians and researchers could increase understanding of mechanisms of efficacy and resistance in addition to identifying new targets for IO by developing translational studies that decipher the role of different immune cell populations during lung cancer treatments.

Mots clés: lung cancer; immune biomarker; immune monitoring; chemotherapy; immunotherapy

Domaines: Immunologie

Philippe Rochigneux, Alejandro Garcia, Brice Chanez, Anne Madroszyk, Daniel Olive, et al.. Medical Treatment of Lung Cancer: Can Immune Cells Predict the Response? A Systematic Review. Frontiers in Immunology, 2020, 11, ⟨10.3389/fimmu.2020.01036⟩. ⟨hal-03152719⟩