4124 - The prognostic value of selected immunological panel in predicting the prognosis of early-stage resectable non-small cell lung cancer

Background

Lung cancer is one of the most fatal cancer types globally. Currently, the immune status in the tumor microenvironment (TME) is highly valued. The immune status includes the expression of checkpoint molecules, co-stimulatory molecules, tumor-infiltrated lymphocyte status and TCR signal status, etc., which harbor different mechanisms in regulating the immune cells in the TME. However, the expression status of some biomarkers may have inner statistical characteristics, which may leave clues to the immune status and related clinical outcomes. In this study, a panel of 9 immune biomarkers was selected for the possible prediction of clinical outcomes in early stage resectable NSCLC.

Methods

Immunohistochemistry (IHC) was performed on surgical samples from 139 patients with NSCLC. Cox regression analysis and subgroup analysis of OS and RFS were conducted for the screening of biomarkers. The Principle Component Analysis (PCA) was conducted on screened biomarkers, including factorial analysis, principle component regression and principle component ranking. A database search was performed in order to verify the clinical characteristics. An Artificial Neuron Network analysis was established for the prediction of clinical outcomes.

Results

6 out of 9 biomarkers are considered significant and were selected for the data analysis. The KMO-Bartlett’s Sphericity test is valid (0.658>0.5, p = 0.0001). The principle component regression results indicate the OS is correlated with the principle component Z4(Y = 0.316×Z4 + 2.298, R = 0.189, p = 0.026). Also, the PFS also correlated with the principle component Z4 and Z2 (Y = 0.314×Z4 + 0.255×Z2 + 2.061, R = 0.249, p = 0.013). Principle components ranking (PCrank) is calculated and after the determination of cutoff 0.2, the intergroup comparison of subgroup_high and subgroup_low is significant in OS (p = 0.025). Database searching validates our results. The ANN model successfully predicts the clinical outcome, with the testing accuracy of 94.1% and 96.2% in models 1 and 2, respectively.

Conclusions

The selected immunological panel has promising potential in predicting the prognosis of resectable NSCLC.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

This study was supported in part by a grant from National Natural Science Foundation of China (81802255), Shanghai Pujiang Program (17PJD036) and a grant from Shanghai Municipal Commission of Health and Family Planning Program (20174Y0131). National key research & development project (2016YFC0902300). Major disease clinical skills enhancement program of three year action plan for promoting clinical skills and clinical innovation in municipal hospitals, Shanghai Shen Kang Hospital Development Center Clinical Research Plan of SHDC (16CR1001A). The fundamental research funds for the central universities.

Disclosure

All authors have declared no conflicts of interest.