RESEARCH ARTICLE


Prognostic Genomic Predictive Biomarkers for Early-Stage Lung Cancer Patients



Hojin Moon1, *, Alex Nguyen1, Evan Lee2
1 Department of Mathematics and Statistics, California State University, Long Beach, United States
2 Yale University, New Haven, CT 06520, United States


© 2021 Moon et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at Department of Mathematics and Statistics, California State University, Long Beach, CA 90840, United States; Tele: 9493819641; E-mail: hojin.moon@csulb.edu


Abstract

Aims:

Our goal is to find predictive genomic biomarkers in order to identify subgroups of early-stage lung cancer patients that are most likely to benefit from adjuvant chemotherapy with surgery (ACT).

Background:

Receiving ACT appears to have a better prognosis for more severe early-stage non-small cell lung cancer patients than surgical resection only. However, not all patients benefit from chemotherapy.

Objective:

Preliminary studies suggest that the application of ACT is associated with a better prognosis for more severe NSCLC patients compared to those who only underwent surgical resection. Given the immense personal and financial costs associated with ACT, finding the patients who are most likely to benefit from ACT is paramount. Thus, the purpose of this research is to utilize gene expression and clinical data from lung cancer patients to find treatment-associated genomic biomarkers.

Methods:

To investigate the treatment effect, a modified-covariate regularized Cox regression model with lasso penalty is implemented using National Cancer Institute gene expression data to find genomic biomarkers.

Results:

This research utilized an independent validation dataset involving 318 lung cancer patients to validate the models. In the validation set with 318 patients, the modified covariate Cox model with lasso penalty were able to show patients who followed their predicted recommendation (either ACT for low-risk group or OBS for the high-risk group, n = 171) have higher survival benefits than 147 patients who did not follow the recommendations (p < .0001).

Conclusion:

Based on validation data, patients who follow our predicted recommendation by genomic biomarkers selected from the proposed model will likely benefit from ACT.

Keywords: Microarray data, Personalized medicine, Subgroup analysis, Survival analysis, Regression, Genome.