BackgroundThe programmed death receptor 1 (PD-1) protein is a cell-surface receptor on certain lymphocytes that, with its ligand programmed death ligand 1 (PD-L1), helps to down-regulate immune responses. Many cancer types express PD-L1 and evade immune recognition via the PD-1/PD-L1 interaction. Precision therapies targeting the PD-1/PD-L1 pathway have the potential to improve response and thereby offer a novel treatment avenue to some patients with cancer. However, this new therapeutic approach requires reliable methods for identifying patients whose cancers are particularly likely to respond. Therefore, we conducted a systematic literature review assessing evidence on test validation and scoring algorithms for PD-L1 immunohistochemistry (IHC) tests that might be used to select potentially responsive patients with bladder/urothelial cell, lung, gastric, or ovarian cancers for immunotherapy treatment.Methods and resultsTo identify evidence on commercially available PD-L1 IHC assays, we systematically searched MEDLINE and Embase for relevant studies published between January 2010 and September 2016 and appraised abstracts from recent oncology conferences (January 2013 to November 2016). Publications that met the predefined inclusion criteria were extracted and key trends summarized.In total, 26 eligible primary studies were identified, all of which reported on the test validation metrics associated with PD-L1 IHC tests in lung cancer, most using immunohistochemistry testing. There was significant heterogeneity among the available tests for PD-L1. Specifically, no definitive cutoff for PD-L1 positivity was identifiable, with more than one threshold being reported for most antibodies. Studies also differed as to whether they evaluated tumor cells only or tumor cells and tumor-infiltrating immune cells. However, all of the tests developed and validated to support a therapeutic drug in the context of phase 2–3 clinical trials reported more than 90% inter-reader concordance. In contrast, other PD-L1 antibodies identified in the literature reported poorer concordance.ConclusionsPublished validation metric data for PD-L1 tests are mainly focused on immunohistochemistry tests from studies in lung cancer. The variability in test cutoffs and standards for PD-L1 testing suggests that there is presently no standardized approach. This current variability may have implications for the uptake of precision treatments.

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Objective: The main objective was to estimate a preference-based SF-6D index from the SF-36 for Japan and compare to the UK results.Study design and setting: The SF-6D was translated into Japanese. 249 health states defined by this version of the SF-6D were then valued by a representative sample of 600 members of the Japanese general population using standard gamble. These health state values were modelled using classical parametric random effects methods with individual level data and OLS on mean health state values, together with a new nonparametric approach using Bayesian methods of estimation.Results: All parametric models estimated on Japanese data were found to perform less well than their UK counterparts in terms of poorer goodness of fit, more inconsistencies, larger prediction errors and bias, and evidence of systematic bias in the predictions. Non-parametric models produce a substantial improvement in out of sample predictions. The physical, role and social dimensions have relatively larger decrements than pain and mental health compared to the UK.Conclusion: The differences between Japan and UK valuation of the SF-6D make it important to use the Japanese valuation data set estimated using the non-parametric Bayesian technique presented in this paper. The SF-36 is one of the most widely used generic health survey instruments.[2] There is substantial evidence of the ability of the eight dimension scores of the SF-36 to describe the health differences between patient groups and to track changes over time. However, the methods for scoring the SF-36 are not preference-based and do not provide a means to derive QALYs. The SF-6D was developed as a practical tool for obtaining a preference-based index from SF-36 data. [3] Given the high cost of undertaking valuation surveys, early work using preference-based measures has tended to use the valuation results from just one or two countries, which for the EQ-5D [4] and SF-6D has been the UK and for the HUI [5] has been Canada. However, significant differences have been found between countries, for example values obtained in the UK EQ-5D surveys and those from Japan [6] and USA [7] and between Canadian values for the HUI2 and those obtained in the UK [8]. This paper presents results from undertaking a valuation of the SF-6D in Japan and compares it to results for the UK.An important problem for preference-based measures has been their size and the consequent need to model health state values from valuation of a subset of possible states. Classical modelling with random effects estimated using generalised least squares (GLS) has met with some success, but has encountered major challenges due to the nature of the distribution of

Background: To achieve optimal outcomes, an individual approach is needed in the treatment and care of patients. The potential value of tumor mutational burden (TMB) status and/or programmed cell death ligand 1 (PD-L1) expression as biomarkers to predict which patients are most likely to respond to checkpoint inhibitors has been explored in many studies. The goal of this targeted literature review is to identify data available for TMB status and/or PD-L1 expression that predict response to checkpoint inhibitors and/or anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) antibodies. Methods: Targeted literature searches were performed using electronic medical databases (MEDLINE, Embase, and BIOSIS) and internet searches of specified sites. Bibliographies of key systematic literature reviews and meta-analyses also were reviewed for studies of interest.Results: The review identified 27 studies of non-small cell lung cancer (NSCLC), 40 studies of melanoma, 10 studies of urothelial cancer, and 5 studies of renal cell cancer indications. Studies also were identified in other cancer types, e.g., colorectal, breast, gastric, and Merkel cell cancer and squamous-cell carcinoma of the head and neck. Twelve trials, including six in NSCLC and four in melanoma, evaluated TMB as a predictor of outcomes. A TMB of ≥10 mutations per megabase was shown to be an effective biomarker in the CheckMate 227 study. PD-L1 expression was included in the majority of identified studies and was found to predict response in in melanoma and in all types of NSCLC. Prediction of response was not a prespecified analysis in some studies; others had small sample sizes and wide confidence intervals. A clear predictive trend for PD-L1 expression was not identified in renal, breast, gastric, or Merkel cell cancer.Conclusion: Based on data contained in this review, assessment of TMB status and PD-L1 expression may help enhance the prediction of response to checkpoint inhibition in some tumors, such as NSCLC and melanoma. In this rapidly growing area of research, further exploratory biomarkers are being investigated including tumor-infiltrating lymphocytes, immune profiling (e.g., effector T cells or regulatory T cells), epigenetic signatures, T-cell receptor repertoire, proteomics, microbiome, and metabolomics.