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Background: This study examined patients with advanced non-small-cell lung cancer who received long-term avelumab (anti-PD-L1) in a large phase Ib trial (JAVELIN Solid Tumor). Methods: Patients receiving >2 years of avelumab were reviewed and exploratory descriptive analyses were conducted. Results: Individuals with varying baseline characteristics who had received up to 6 years of avelumab were reviewed. Overall, 37/340 (10.9%) had received ≥2 years of treatment; in this subgroup, best response was complete response in 5.4%, partial response in 59.5% and stable disease in 29.7%; 51.4% had continued treatment beyond disease progression. Conclusions: In this study, 11% of patients with advanced non-small-cell lung cancer received ≥2 years of avelumab treatment and experienced prolonged response or continued clinical benefit. Clinical Trial Registration: NCT02395172 (ClinicalTrials.gov).

Background: Avelumab first‐line (1 L) maintenance is a standard of care for advanced urothelial carcinoma (aUC) based on the JAVELIN Bladder 100 phase 3 trial, which showed that avelumab 1 L maintenance + best supportive care (BSC) significantly prolonged overall survival (OS) and progression‐free survival (PFS) vs BSC alone in patients who were progression free after receiving 1 L platinum‐containing chemotherapy. Here, we comprehensively screened JAVELIN Bladder 100 trial datasets to identify prognostic factors that define subpopulations of patients with longer or shorter OS irrespective of treatment, and predictive factors that select patients who could obtain a greater OS benefit from avelumab 1 L maintenance treatment. Methods: We performed machine learning analyses to screen a large set of baseline covariates, including patient demographics, disease characteristics, laboratory values, molecular biomarkers, and patient‐reported outcomes. Covariates were identified from previously reported analyses and established prognostic and predictive markers. Variables selected from random survival forest models were processed further in univariate Cox models with treatment interaction and visually inspected using correlation analysis and Kaplan–Meier curves. Results were summarized in a multivariable Cox model. Results: Prognostic baseline covariates associated with OS included in the final model were assignment to avelumab 1 L maintenance treatment, Eastern Cooperative Oncology Group performance status, site of metastasis, sum of longest target lesion diameters, levels of C‐reactive protein and alkaline phosphatase in blood, lymphocyte proportion in intratumoral stroma, tumor mutational burden, and tumor CD8+ T‐cell infiltration. Potential predictive factors included site of metastasis, tumor mutation burden, and tumor CD8+ T‐cell infiltration. An analysis in patients with PD‐L1+ tumors had similar findings to those in the overall population. Conclusions: Machine learning analyses of data from the JAVELIN Bladder 100 trial identified potential prognostic and predictive factors for avelumab 1 L maintenance treatment in patients with aUC, which warrant further evaluation in other clinical datasets. [ABSTRACT FROM AUTHOR]

Background: This study examined patients with advanced non-small-cell lung cancer who received long-term avelumab (anti-PD-L1) in a large phase Ib trial (JAVELIN Solid Tumor). Methods: Patients receiving >2 years of avelumab were reviewed and exploratory descriptive analyses were conducted. Results: Individuals with varying baseline characteristics who had received up to 6 years of avelumab were reviewed. Overall, 37/340 (10.9%) had received ≥2 years of treatment; in this subgroup, best response was complete response in 5.4%, partial response in 59.5% and stable disease in 29.7%; 51.4% had continued treatment beyond disease progression. Conclusions: In this study, 11% of patients with advanced non-small-cell lung cancer received ≥2 years of avelumab treatment and experienced prolonged response or continued clinical benefit. Clinical Trial Registration: NCT02395172 (ClinicalTrials.gov). [ABSTRACT FROM AUTHOR]

An addendum of the ICH E9 guideline on Statistical Principles for Clinical Trials was released in November 2019 introducing the estimand framework. This new framework aims to align trial objectives and statistical analyses by requiring a precise definition of the inferential quantity of interest, that is, the estimand. This definition explicitly accounts for intercurrent events, such as switching to new anticancer therapies for the analysis of overall survival (OS), the gold standard in oncology. Traditionally, OS in confirmatory studies is analyzed using the intention‐to‐treat (ITT) approach comparing treatment groups as they were initially randomized regardless of whether treatment switching occurred and regardless of any subsequent therapy (treatment‐policy strategy). Regulatory authorities and other stakeholders often consider ITT results as most relevant. However, the respective estimand only yields a clinically meaningful comparison of two treatment arms if subsequent therapies are already approved and reflect clinical practice. We illustrate different scenarios where subsequent therapies are not yet approved drugs and thus do not reflect clinical practice. In such situations the hypothetical strategy could be more meaningful from patient's and prescriber's perspective. The cross‐industry Oncology Estimand Working Group (www.oncoestimand.org) was initiated to foster a common understanding and consistent implementation of the estimand framework in oncology clinical trials. This paper summarizes the group's recommendations for appropriate estimands in the presence of treatment switching, one of the key intercurrent events in oncology clinical trials. We also discuss how different choices of estimands may impact study design, data collection, trial conduct, analysis, and interpretation. [ABSTRACT FROM AUTHOR]

Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). In this study, the kernel converts the genomic information of 2 individuals into a quantitative value reflecting their genetic similarity. With the selection of the kernel, one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for the topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are crucial in the context of GWAS, because genes associated with a disease often interact. Thus, we propose a novel kernel that incorporates the topology of pathways and information on interactions. Using simulation studies, we demonstrate that the proposed method maintains the type I error correctly and can be more effective in the identification of pathways associated with a disease than non-network-based methods. We apply our approach to genome-wide association case-control data on lung cancer and rheumatoid arthritis. We identify some promising new pathways associated with these diseases, which may improve our current understanding of the genetic mechanisms. © 2014 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]