ABSTRACT
KRAS (Kirsten Rat Sarcoma) is the most common oncogenic mutation detected in patients with non-small cell lung cancer (NSCLC). However, the role of KRAS as either a prognostic factor or predictive factor (modifier of treatment effects) in NSCLC is not well established at this time. This systematic literature review (SLR) and meta-analysis synthesized the available evidence regarding the role of KRAS mutation as a predictive factor and/or prognostic factor of survival and response outcomes in patients with advanced/metastatic (stage IIIB-IV) NSCLC. Relevant clinical trials and observational studies were identified by searching MEDLINE, Embase and Cochrane Register of Controlled Trials. Meta-analyses were performed using data extracted from multivariable and univariable analyses from clinical studies to assess the empirical evidence of KRAS mutation status as a prognostic or/and predicitive factor. 43 selected studies were identified by the SLR and included in this meta-analysis. Pairwise meta-analyses of hazard ratios (HRs) reported in randomized controlled trials (RCTs) did not demonstrate a significant prognostic effect of mutant KRAS on overall survival (OS) (HR=1.10; 95% CI [0.88, 1.38]) or progression free survival (PFS) (HR=1.03; 95% CI [0.80, 1.33]). However, when conducting meta-analyses on HRs reported in observational studies, a statistically significant negative prognostic effect of mutant KRAS was observed (OS HR=1.71; 95% CI [1.07, 2.84]; PFS HR=1.18; 95% CI [1.02, 1.36]). Meta-analyses of objective response rate (ORR) in RCTs demonstrated a negative prognostic effect of mutant KRAS (RR=0.38; 95% CI [0.16, 0.63]). Limited data were available to evaluate the role of KRAS mutation as a predictive factor. In conclusion, this research offers evidence that KRAS mutation may be a negative prognostic factor for survival and response outcomes in patients with advanced/metastatic NSCLC, but further research is needed to address conflicting results on the importance of KRAS mutations as a predictive factor.
Subject(s)
Antineoplastic Combined Chemotherapy Protocols/therapeutic use , Biomarkers, Tumor/genetics , Carcinoma, Non-Small-Cell Lung/drug therapy , Lung Neoplasms/drug therapy , Proto-Oncogene Proteins p21(ras)/genetics , Antineoplastic Combined Chemotherapy Protocols/pharmacology , Biomarkers, Tumor/antagonists & inhibitors , Carcinoma, Non-Small-Cell Lung/genetics , Carcinoma, Non-Small-Cell Lung/mortality , Carcinoma, Non-Small-Cell Lung/secondary , DNA Mutational Analysis , ErbB Receptors/antagonists & inhibitors , ErbB Receptors/genetics , Humans , Lung Neoplasms/genetics , Lung Neoplasms/mortality , Lung Neoplasms/pathology , Mutation , Neoplasm Staging , Observational Studies as Topic , Prognosis , Progression-Free Survival , Protein Kinase Inhibitors/pharmacology , Protein Kinase Inhibitors/therapeutic use , Randomized Controlled Trials as TopicABSTRACT
Explicitly modeling underlying relationships between a survival endpoint and processes that generate longitudinal measured or reported outcomes potentially could improve the efficiency of clinical trials and provide greater insight into the various dimensions of the clinical effect of interventions included in the trials. Various strategies have been proposed for using longitudinal findings to elucidate intervention effects on clinical outcomes such as survival. The application of specifically Bayesian approaches for constructing models that address longitudinal and survival outcomes explicitly has been recently addressed in the literature. We review currently available methods for carrying out joint analyses, including issues of implementation and interpretation, identify software tools that can be used to carry out the necessary calculations, and review applications of the methodology.
Subject(s)
Clinical Trials as Topic/methods , Epidemiologic Research Design , Models, Statistical , Survival Analysis , Anti-HIV Agents/pharmacology , Bayes Theorem , Biomarkers, Pharmacological/blood , CD4 Lymphocyte Count , Clinical Trials as Topic/statistics & numerical data , Drug Design , Graft Rejection/immunology , Graft Rejection/prevention & control , HIV Infections/drug therapy , HIV Infections/immunology , HIV Infections/virology , Humans , Kidney Transplantation/adverse effects , Longitudinal Studies , Proportional Hazards Models , Quality of Life , Renal Insufficiency, Chronic/surgery , Software , Viral LoadABSTRACT
Joint modeling of longitudinal and survival data can provide more efficient and less biased estimates of treatment effects through accounting for the associations between these two data types. Sponsors of oncology clinical trials routinely and increasingly include patient-reported outcome (PRO) instruments to evaluate the effect of treatment on symptoms, functioning, and quality of life. Known publications of these trials typically do not include jointly modeled analyses and results. We formulated several joint models based on a latent growth model for longitudinal PRO data and a Cox proportional hazards model for survival data. The longitudinal and survival components were linked through either a latent growth trajectory or shared random effects. We applied these models to data from a randomized phase III oncology clinical trial in mesothelioma. We compared the results derived under different model specifications and showed that the use of joint modeling may result in improved estimates of the overall treatment effect.
