Real-world Outcomes of Immune Checkpoint Inhibitors (ICI) in Lung Cancer Patients Pubblico

Abstract

Background: Lung cancer is the most-common cancer attributing to incidence and mortality. There were 2.09 million new cases of lung cancer around the world in 2018. Different treatment methods including chemotherapy, surgery and radiation therapy have been applied to defeat the disease. Immunotherapy is one of those treatment and it can let patient’s immune system can detect and eliminate cancer tumors. Scientists have developed several immune checkpoint inhibitors (ICI) for immunotherapy to regulate our immune system’s response. In this study, we want to compare the overall survival (OS) and progression free survival (PFS) of lung cancer patients between different races, sex and social status. We also compare the rate of response among patients with different races.

Methods: Our data is collected from a retrospective review of clinical outcome of patients who received immunotherapy at Emory Winship Cancer Institute since 2013. There are 101 patients included in this study. We use Kaplan-Meier Method, Stratified Log-rank Test and Cox proportional hazards model to compare OS and PFS among patients who have NSCLC stratified by race, sex and PD-L1. Logistic regression model and Wald Chi-squared test are applied to check if there is any difference on partial response between different races.

Results: There is no significant difference on OS and PFS between SCLC or NSCLC patients with different race, sex and PD_L1 status. Even though histology, ECOG and TP53 show significant effect on OS and PFS in univariate model, all those effects are removed from multivariate model by backward selection. Furthermore, we find out that race also have no significant effect on response situation. The only significant factor that will influence response is smoking status.

Conclusion: Both stratified log-rank test and Cox proportional hazard model shows no significant impact of race/ethnicity, sex and PD-L1 status on efficacy of immunotherapy for lung cancer. The logistic regression model indicates that there is no difference on response between patients with different races. 

Table of Contents

1.     Introduction. 1

2.     Materials and Methods 3

2.1.       Patients Selection and Parameters Calculation. 3

2.2.       Statistical Analysis. 4

2.2.1.        Descriptive Analysis. 4

2.2.2.        Survival Analysis. 4

2.2.3.        Response Analysis. 6

3.     Results 7

3.1.       Result for Descriptive Analysis. 7

3.2.       Result for Survival Analysis. 8

3.2.1.        Result for Kaplan-Meier Method and Stratified Log-rank Test. 8

3.2.2.        Result for Cox Proportional Hazard Model 11

3.3.       Analysis for Response. 12

4.     Conclusion and Discussion. 12

5.     Reference. 14

6.     Tables 16

7.     Appendix. 35

About this Master's Thesis

Rights statement

• Permission granted by the author to include this thesis or dissertation in this repository. All rights reserved by the author. Please contact the author for information regarding the reproduction and use of this thesis or dissertation.

School	Rollins School of Public Health
Department	Biostatistics
Subfield / Discipline	Biostatistics - MPH & MSPH
Degree	M.P.H.
Submission	Master's Thesis
Language	English
Research Field	Health Sciences, Public Health Biology, Biostatistics
Parola chiave	Cox Proportional Hazards Model Log-rank Test Survival Analysis Lung Cancer
Committee Chair / Thesis Advisor	Chen, Zhengjia, Emory University
Committee Members	Huang, Eugene, Emory University

Ultima modifica

Primary PDF

Thumbnail	Title	Date Uploaded	Actions
	Real-world Outcomes of Immune Checkpoint Inhibitors (ICI) in Lung Cancer Patients ()	2019-04-08 19:36:35 -0400	Press to Select an action Download

Supplemental Files

Thumbnail	Title	Date Uploaded	Actions