Development and validation of novel immune-inflammation-based clinical predictive nomograms in HER2-negative advanced gastric cancer

Yan Yang; Yu Shao; Junjun Wang; Qianqian Cheng; Hanqi Yang; Yulong Li; Jing Liu; Yangyang Zhou; Zhengguang Zhou; Mingxi Wang; Baoan Ji; Jinghao Yao

doi:10.3389/fonc.2023.1185240

Development and validation of novel immune-inflammation-based clinical predictive nomograms in HER2-negative advanced gastric cancer

Front Oncol. 2023 Sep 6:13:1185240. doi: 10.3389/fonc.2023.1185240. eCollection 2023.

Authors

Yan Yang¹, Yu Shao¹, Junjun Wang², Qianqian Cheng¹, Hanqi Yang³, Yulong Li⁴, Jing Liu¹, Yangyang Zhou¹, Zhengguang Zhou¹, Mingxi Wang¹, Baoan Ji⁵, Jinghao Yao^{1

6}

Affiliations

¹ Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China.
² Department of Radiation Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China.
³ Department of Clinical Laboratory, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China.
⁴ Department of Surgical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China.
⁵ Department of Cancer Biology, Mayo Clinic, Jacksonville, FL, United States.
⁶ Department of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.

Abstract

Purpose: To explore the predictive value of multiple immune-inflammatory biomarkers including serum VEGFA and systemic immune-inflammation index (SII) in HER2-negative advanced gastric cancer (AGC) and establish nomograms for predicting the first-line chemotherapeutic efficacy, progression-free survival (PFS) and overall survival (OS) of patients with this fatal disease.

Methods: From November 2017 to April 2022, 102 and 34 patients with a diagnosis of HER2-negative AGC at the First Affiliated Hospital of Bengbu Medical College were enrolled as development and validation cohorts, respectively. Univariate and multivariate analyses were performed to evaluate the clinical value of the candidate indicators. The variables were screened using LASSO regression analysis. Predictive models were developed using significant predictors and are displayed as nomograms.

Results: Baseline VEGFA expression was significantly higher in HER2-negative AGC patients than in nonneoplastic patients and was associated with malignant serous effusion and therapeutic efficacy (all p<0.001). Multivariate analysis indicated that VEGFA was an independent predictor for first-line therapeutic efficacy and PFS (both p<0.01) and SII was an independent predictor for first-line PFS and OS (both p<0.05) in HER2-negative AGC patients. The therapeutic efficacy model had an R² of 0.37, a Brier score of 0.15, and a Harrell's C-index of 0.82 in the development cohort and 0.90 in the validation cohort. The decision curve analysis indicated that the model added more net benefits than VEGFA assessment alone. The PFS/OS models had Harrell's C-indexes of 0.71/0.69 in the development cohort and 0.71/0.62 in the validation cohort.

Conclusion: The established nomograms integrating serum VEGFA/SII and commonly available baseline characteristics provided satisfactory performance in predicting the therapeutic efficacy and prognosis of HER2-negative AGC patients.

Keywords: HER2-negative; VEGFA; advanced gastric cancer; nomogram; predictive model.

Grants and funding

This study was supported by the Natural Science Foundation of Anhui Province (No. 2008085MH238), the Excellent Young Talents Fund Program of Higher Education Institutions of Anhui Province (No. gxyq2022042), the 512 Talent Cultivation Plan of Bengbu Medical College (No. by51202208), and the Science Fund for Distinguished Young Scholars of the First Affiliated Hospital of Bengbu Medical College (No. 2019byyfyjq02).