The Impact of Afatinib on Survival in Advanced Non-Small Cell Lung Cancer: A Meta-Analysis of Randomized Controlled

Chi Wang1, Yun Li1, Li Ke2

  • 1Central Laboratory of Medical Research Centre, Anhui Provincial Hospital, Anhui Medical University, Hefei, 230001, China.

Journal of Cancer
|March 12, 2019
PubMed

Insights

Afatinib significantly improves survival and response rates for advanced non-small cell lung cancer (NSCLC) patients with EGFR mutations. This meta-analysis confirms its efficacy without increasing fatal adverse events, highlighting its clinical potential.

Area of Science:

  • Oncology
  • Pharmacology
  • Clinical Trials

Background:

  • Afatinib, a second-generation epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI), is FDA-approved for advanced non-small cell lung cancer (NSCLC) with EGFR mutations.
  • The study aims to evaluate the efficacy and safety of afatinib in advanced NSCLC through a comprehensive meta-analysis.

Purpose of the Study:

  • To assess the efficacy of afatinib in advanced NSCLC, focusing on progression-free survival (PFS), overall survival (OS), and overall response rate (ORR).
  • To evaluate the safety profile of afatinib, including the incidence of grade 3/4 adverse events and fatal adverse events (FAEs).
  • To conduct subgroup analyses based on control type for all endpoints to provide nuanced insights.

Main Methods:

  • Systematic literature search conducted across PubMed, PMC database, EMBASE, Cochrane Library, and Web of Science.
  • Inclusion of seven randomized controlled trials (RCTs) involving a total of 3093 patients.
  • Statistical analysis using meta-analysis to compare afatinib with control groups for efficacy and safety endpoints.

Main Results:

  • Afatinib significantly prolonged PFS (HR = 0.57, P = 0.00) and increased OS (HR = 0.91, P = 0.04) and ORR (RR = 1.82, P = 0.01) compared to control groups.
  • Significant increases in the incidence of diarrhea (RR = 8.9, P = 0.00), rash (RR = 7.31, P = 0.01), and stomatitis (RR = 6.45, P = 0.03) were observed with afatinib.
  • No significant difference in fatal adverse events (FAEs) was found between afatinib and control groups (RR = 0.75, P = 0.41).

Conclusions:

  • Afatinib demonstrates significant efficacy in extending survival and improving response rates in advanced NSCLC patients.
  • The safety profile indicates an increased risk of specific adverse events like diarrhea, rash, and stomatitis, but not fatal adverse events.
  • Afatinib is a promising EGFR-TKI with substantial clinical potential for treating advanced NSCLC.

Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
665
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
765
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
583
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
611
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
416
Drug Control Governance: Regulatory Bodies and Their Impact01:03

Drug Control Governance: Regulatory Bodies and Their Impact

Drug control governance involves the oversight and regulation of pharmaceuticals to ensure their safety and efficacy while preventing illegal drug use and trafficking. Regulatory bodies, including the US Food and Drug Administration (FDA) and the European Union's European Medicines Agency (EMA), play a central role in this process. These agencies evaluate the safety and efficacy of drugs before they can be marketed. They fund clinical trials and assess the benefits and risks associated with...
534