Abstract 195P
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.
Resources from the same session
155P - Exploring the utility of serum anti-tNASP antibodies as a screening biomarker in prostate, pancreatic, and ovarian cancer
Presenter: Oleg Alekseev
Session: Poster session 01
156P - The association between fibrotic endotypes, determined by pre-treatment serum levels of collagen metabolites, and survival outcomes in patients with pancreatic cancer
Presenter: Rasmus Pedersen
Session: Poster session 01
157P - CLDN18 fusions rather than expression is a biomarker related to the efficacy of paclitaxel in patients with ovarian metastasis of gastric cancer
Presenter: Pengfei Yu
Session: Poster session 01
158P - In silico analysis of HER2 enriched subtype and a HER2 index based on transcriptomic data of breast cancer compared to gastric and uterine serous carcinomas
Presenter: Arturo Gonzalez-Vilanova
Session: Poster session 01
159P - Better performance of pan-claudin18 antibodies on claudin18.2 detection in gastric adenocarcinoma than claudin18.2 specific antibody
Presenter: Shujuan NI
Session: Poster session 01
161P - Biomarkers of neoadjuvant combinational therapy for locally advanced gastric or gastroesophageal junction adenocarcinoma
Presenter: Yue Wang
Session: Poster session 01
162P - MR imaging biomarkers profiles in patients with prostate cancer treated with androgen deprivation therapy
Presenter: Angel Luis Sanchez Iglesias
Session: Poster session 01
163P - Genomic alterations in circulating tumor DNA (ctDNA) and response to ABBV-400 treatment in patients with advanced solid tumors
Presenter: Jair Bar
Session: Poster session 01
164P - Early evaluation of effectiveness and cost-effectiveness of ctDNA-guided selection for adjuvant chemotherapy in stage II colon cancer
Presenter: Astrid Kramer
Session: Poster session 01