Abstract 409: A pre-treatment plasma extracellular vesicle-mRNA signature associates with checkpoint inhibitor pneumonitis in lung cancer patients
Authors: Bracht Jillian Wilhelmina , Viteri-Ramirez Santiago , Aguilar Andrés , Calabuig-Fariñas Silvia , García-Mosquera Juan José , Huang Chung-Ying , Duréndez-Sáez Elena , Potie Nicolas , Aldeguer Erika , Gimenez-Capitán Ana , Rodriguez Sonia , Roman Ruth , Aguado Cristina , Warren Sarah , Camps Carlos , Rosell Rafael , Jantus-Lewintre Eloisa , Molina-Vila Miguel-Angel , González-Cao Maria 
Published: July 2021

Introduction: Immune checkpoint inhibitors (ICIs) have demonstrated clinical efficacy in non-small cell lung cancer (NSCLC) patients (p). However, ICIs can also trigger a self-reactive response in the adjacent healthy lung tissue that can eventually lead to life-threatening immune-related adverse events (irAEs), like checkpoint inhibitor pneumonitis (CIP). We hypothesized that a pre-treatment state of chronic inflammation or immune system imbalance could predict which p are at higher risk of developing CIP.

Methodology: We retrospectively collected pre-ICI-treatment FFPE tumor tissue and matching plasma samples from 17 CIP- and 24 non-CIP lung cancer p. An additional 40 plasma samples, including 3 CIP and 37 non-CIP p were used as a validation cohort. The miRCURY exosome isolation kit (Qiagen) was used for extracellular vesicle (EV) enrichment from 500 μL of plasma and RNA was extracted using TRI-reagent. EV-mRNA was then pre-amplified (10 cycles) using the Low RNA Input Amplification kit (NanoString Technologies). FFPE mRNA was extracted using the High Pure FFPET RNA Isolation Kit (Roche). Gene expression analysis was performed on tissue and EV-derived mRNA using the NanoString nCounter platform with the Human PanCancer IO360 panel, which targets 770 genes related to tumor biology, immune response and microenvironment. Differential expression (DE) analysis was carried out based on the development of CIP. Finally, a classifier was created using a bioinformatic recursive feature elimination and a leave-one-out cross validation algorithm to predict which combination of genes is most effective to predict CIP development.

Results: DE analysis revealed 54 differentially expressed genes (DEGs) in pre-treatment tissue of CIP vs. non-CIP p. An 8-gene CIP mRNA signature was able to distinguish between the two cohorts with areas under the ROC curve (AUC) of 0.81-0.95. When analyzing plasma EV samples, we found 57 DEGs. The tissue CIP signature was not translatable to EVs, yielding AUCs of only 0.53-0.54. Therefore, we developed a new 4-gene EV-based mRNA signature that could differentiate CIP vs. non-CIP developing p with AUCs of 0.82-0.90 and an overall accuracy of 89.9%. The negative- and positive predictive values (NPV and PPV) were 92.7% and 78.6%, respectively with a Youden´s index of 0.67. The 4 genes included in the EV signature were upregulated in CIP p and were found to be involved in T-cell activation and immune cell localization to the tumor.

Conclusions: We have created a 4-gene EV-mRNA signature that associates with CIP development upon ICI treatment. Our results also indicate that plasma EV-mRNA was non-inferior to invasive tissue biopsy analysis in predicting CIP development. Validation studies in larger patient cohorts are ongoing.

Citation Format: Jillian Wilhelmina Bracht, Santiago Viteri-Ramirez, Andrés Aguilar, Silvia Calabuig-Fariñas, Juan José García-Mosquera, Chung-Ying Huang, Elena Duréndez-Sáez, Nicolas Potie, Erika Aldeguer, Ana Gimenez-Capitán, Sonia Rodriguez, Ruth Roman, Cristina Aguado, Sarah Warren, Carlos Camps, Rafael Rosell, Eloisa Jantus-Lewintre, Miguel-Angel Molina-Vila, Maria González-Cao. A pre-treatment plasma extracellular vesicle-mRNA signature associates with checkpoint inhibitor pneumonitis in lung cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 409.