Necitumumab 800mg/50ml solution for infusion vials

BackgroundThe objectives of this systematic review and meta-analysis were to compare the survival, toxicity, and quality of life of patients treated with necitumumab in combination with gemcitabine and cisplatin. These agents were investigated in published randomized controlled trials (RCTs) of patients with squamous non-small cell lung cancer (NSCLC) in the first-line setting.MethodsThe systematic review was executed on January 27, 2015, and updated on August 21, 2016, using a pre-specified search strategy. Searches were conducted using PubMed, Medline, and EMBASE, with supplemental searches using the Evidence Based Medicine Reviews and ClinicalTrials.gov to identify RCTs published in English from 1995-2016 and reporting at least one of the primary outcomes [overall survival (OS), progression-free survival (PFS), toxicity, or quality of life] in patients who received first-line treatment for advanced or metastatic squamous NSCLC. Study quality and risk of bias were assessed using the Physiotherapy Evidence Database (PEDro) scale and Cochrane risk of bias tool, respectively. A Baysian network meta-analysis was performed on the primary outcomes. Hazard ratios (HRs) were evaluated for the primary analysis; secondary analyses were conducted using median OS data. Planned sensitivity analyses were conducted including reanalysis using a Frequentist approach and limiting analyses to subsets based on clinical and demographic covariates.ResultsThe systematic literature review resulted in identification of 4,016 unique publications; 40 publications (35 unique trials) were eligible for inclusion. Eight studies connected to a common network for the OS analysis using HR data. The majority of studies were not limited to squamous NSCLC, thus analyzable data were limited to a subset of data within the published trials. Carboplatin + S-1 and necitumumab in combination with gemcitabine and cisplatin were associated with lower HRs for OS versus all other comparators. Nine studies connected to the network for the PFS analysis in which necitumumab in combination with gemcitabine and cisplatin was associated with the lowest HR. Data were not available to analyze toxicity or quality of life.ConclusionsAlthough the results suggest that carboplatin + S-1 and necitumumab in combination with gemcitabine and cisplatin may have value in terms of OS versus other comparators, the results should be interpreted with caution due to the limited number of studies (with few focused exclusively on squamous NSCLC) and wide credible intervals.

Abstract licence: CC BY-NC-ND

BackgroundMany circulating biomarkers are assessed at different time intervals during clinical studies. Despite of the success of standard joint models in predicting clinical outcomes using low-dimensional longitudinal data (1-2 biomarkers), significant computational challenges are encountered when applying these techniques to high-dimensional biomarker datasets. Modern machine- or deep-learning models show potential for multiple biomarker processes, but systematic evaluations and applications to high-dimensional data in the clinical settings have yet to be reported. We aimed to enhance the scalability of joint modeling and provide guidance on optimal approaches for high-dimensional biomarker data and outcomes.MethodsWe evaluated multiple deep-learning and machine-learning models using 24 clinical biomarkers and survival data from the SQUIRE trial, a phase 3 randomized clinical trial investigating necitumumab and standard gemcitabine/cisplatin treatment in patients with squamous non-small-cell lung cancer (NSCLC).ResultsOverall, we confirmed that longitudinal models enabled more accurate prediction of patients' survival compared to those solely based on baseline information. Coupling multivariate functional principal component analysis (MFPCA) with Cox regression (MFPCA-Cox) provided the highest predictive discrimination and accuracy for the NSCLC patients with AUC values of 0.7 - >0.8 at various landmark time points and prediction timeframes, outperforming recent advanced Transformer and convolutional neural network deep-learning algorithms (TransformerJM and Match-Net, respectively).ConclusionsIn conclusion, we identified that MFPCA-Cox represents a robust and versatile joint modeling algorithm for high-dimensional biomarker longitudinal data with irregular and missing data, capturing complex relationships within the data, yielding accurate predictions for both longitudinal biomarkers and survival outcomes, and gaining insights into the underlying dynamics.Trial registrationClinicalTrials.gov (NCT00981058; first posted on September 22, 2009).

Abstract licence: CC BY-NC-ND