195P - Identification of biomarkers associated with checkpoint inhibitor pneumonitis based on serum proteomic approach and construction of an online interactive visual prediction model

Background

Checkpoint inhibitor pneumonitis(CIP) is one of the most common fatal adverse events in the new era of immune checkpoint inhibitor(ICI). Screening the high risk group of CIP is a major clinical challenge to improve the safety of ICI therapy. However, there are lack of effective biomarkers to predict CIP. The practical difficulties of early screening highlight the need to explore new biomarkers. The aim of this study is to determine predictive biomarkers of CIP through proteomics methods, and then build a prediction model, so as to improve the safety of ICI therapy.

Methods

This study prospectively recruited 98 tumor patients. Fasting peripheral blood was collected from patients before ICI therapy for the first time. Patients were dynamically followed up and assessed for CIP. Serum protein spectrum was analyzed and identified by MALDI-TOF mass spectrometry(MALDI-TOF-MS) and LC-MS. Confirmatory studies were conducted by ELISA to quantitatively evaluate the expression of serum protein. R language is used to construct online interactive prediction model. This study was approved by the Ethics Committee of the First Affiliated Hospital of Xi'an Jiaotong University (XJTU1AF2021LSK-001).

Results

31 patients with CIP constituted the case group. A total of 88 peptide peaks were detected by MALDI-TOF-MS. 15 protein polypeptides with the most significant differences were selected for further identification. After quantitative verification by ELISA analysis, it was found that plasma serine protease inhibitor (SERPINA5), A-kinase anchor protein 6 (AKAP6), tubulin alpha-4A chain (TUBA4A) were significantly highly expressed in CIP patients. Based on this, online interactive visual prediction model was constructed (https://mass-cip.shinyapps.io/DynNomapp/). Harrell's C-indices of the training set and validation set were 0.974 and 0.968, respectively. The calibration curve and clinical decision curve showed satisfactory predicted value.

Conclusions

SERPINA5, AKAP6 and TUBA4A are considered useful biomarkers for predicting CIP. The predictive model has the potential to be a convenient, intuitive, and personalized clinical tool for assessing the risk of CIP.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

This work was supported by Outstanding Youth Project of Shaanxi Province (2020JC-35), Research and development projects in Shaanxi Province (2022ZDLSF04-11), Natural Science Foundation of Shaanxi Province (2022JQ-796), Major project of innovation Fund of Chinese Society of Clinical Oncology-MSD (Y-MSD2020-024), Shaanxi Sanqin Scholars Innovation Team (2021-No. 32), Beijing Science Innovation Medical Development Fund (KC2021-JX-0186-5) and National Natural Science Foundation of China (No. 82272073).

Disclosure

All authors have declared no conflicts of interest.