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INTRODUCTION: Immunotherapy targeting the programmed cell death protein–1 (PD-1) axis elicits durable antitumor responses in multiple cancer types. However, clinical responses vary, and biomarkers predictive of response may help to identify patients who will derive the greatest therapeutic benefit. Clinically validated biomarkers predictive of response to the anti–PD-1 monoclonal antibody pembrolizumab include PD-1 ligand 1 (PD-L1) expression in specific cancers and high microsatellite instability (MSI-H) regardless of tumor type. Tumor mutational burden (TMB) and T cell–inflamed gene expression profile (GEP) are emerging predictive biomarkers for pembrolizumab. Both PD-L1 and GEP are inflammatory biomarkers indicative of a T cell–inflamed tumor microenvironment (TME), whereas TMB and MSI-H are indirect measures of tumor antigenicity generated by somatic tumor mutations. However, the relationship between these two categories of biomarkers is not well characterized. RATIONALE: This study assessed the potential for TMB and a T cell–inflamed GEP to jointly predict clinical response to pembrolizumab in >300 patient samples with advanced solid tumors and melanoma across 22 tumor types from four KEYNOTE clinical trials. To assess the individual and joint clinical utility of TMB and GEP, patients were stratified in four biomarker–defined clinical response groups [GEP low and TMB low (GEPlo TMBlo), GEP low and TMB high (GEPlo TMBhi), GEPhi TMBlo, and GEPhi TMBhi] based on predefined cutoffs for TMB and GEP. These patient–defined biomarker groups were further used to guide transcriptome and exome analyses of tumors in a large molecular database [The Cancer Genome Atlas (TCGA)] (n = 6384 tumors) to identify targetable patterns of biology that may modulate response and resistance. RESULTS: TMB and GEP exhibited only modest correlation and were independently predictive of response across the KEYNOTE clinical datasets. We found that objective response rates were strongest in patients with GEPhi TMBhi (37 to 57%), moderate in those with GEPhi TMBlo (12 to 35%) and GEPlo TMBhi (11 to 42%), and reduced or absent in those with GEPlo TMBlo (0 to 9%) (see the figure). Additionally, longer progression–free survival times were seen in patients with higher levels of both TMB and GEP. Findings were comparable when TMB and PD-L1 expression were jointly assessed. Within TCGA database, GEP and TMB again had a low correlation, demonstrating the potential to jointly stratify transcriptomic and genomic features across cancer types. Specific gene expression patterns reflective of TME biology showed significant associations with TMB, GEP, or both. In particular, gene set enrichment analysis identified proliferative and stromal, myeloid, and vascular biology corresponding to specific TMB-defined subgroups within GEPhi tumors. In TMBhi tumors, indication-dependent somatic DNA alterations in key cancer driver genes showed a strong negative association with GEP. CONCLUSION: This analysis shows that TMB and inflammatory biomarkers (T cell–in...

The effects of many protease inhibitor (PI)-selected mutations on the susceptibility to individual PIs are unknown. We analyzed in vitro susceptibility test results on 2,725 HIV-1 protease isolates. More than 2,400 isolates had been tested for susceptibility to fosamprenavir, indinavir, nelfinavir, and saquinavir; 2,130 isolates had been tested for susceptibility to lopinavir; 1,644 isolates had been tested for susceptibility to atazanavir; 1,265 isolates had been tested for susceptibility to tipranavir; and 642 isolates had been tested for susceptibility to darunavir. We applied least-angle regression (LARS) to the 200 most common mutations in the data set and identified a set of 46 mutations associated with decreased PI susceptibility of which 40 were not polymorphic in the eight most common HIV-1 group M subtypes. We then used least-squares regression to ascertain the relative contribution of each of these 46 mutations. The median number of mutations associated with decreased susceptibility to each PI was 28 (range, 19 to 32), and the median number of mutations associated with increased susceptibility to each PI was 2.5 (range, 1 to 8). Of the mutations with the greatest effect on PI susceptibility, I84AV was associated with decreased susceptibility to eight PIs; V32I, G48V, I54ALMSTV, V82F, and L90M were associated with decreased susceptibility to six to seven PIs; I47A, G48M, I50V, L76V, V82ST, and N88S were associated with decreased susceptibility to four to five PIs; and D30N, I50L, and V82AL were associated with decreased susceptibility to fewer than four PIs. This study underscores the greater impact of nonpolymorphic mutations compared with polymorphic mutations on decreased PI susceptibility and provides a comprehensive quantitative assessment of the effects of individual mutations on susceptibility to the eight clinically available PIs.HIV-1 protease inhibitors (PIs) are the mainstays of salvage therapy. As the number of licensed PIs has increased, it has become important to identify whether and how each PI-selected mutation affects cross-resistance to each of the other PIs. In a previous study (41), we previously applied several data mining approaches to assess associations between HIV-1 protease genotype and phenotype test results for the first-generation PIs: amprenavir (APV), the active component of the prodrug fosamprenavir (FPV), atazanavir (ATV), indinavir (IDV), lopinavir (LPV), nelfinavir (NFV), and saquinavir (SQV). Specifically, we used a data set containing about 300 susceptibility results for ATV, 500 for LPV, and 800 for FPV, IDV, NFV, and SQV (41). We used a predefined list of PIselected mutations in this previous study to reduce the number of independent variables influencing PI susceptibility.Here we analyze a data set that contains between 1,600 and 2,600 isolates tested for susceptibility to the first-generation PIs and about 600 and 1,200 isolates tested for susceptibility to darunavir (DRV) and tipranavir (TPV), respectively. We use two regression methods in tandem: one to identi...

The limitations of the classical or traditional paradigm of decision research are increasingly apparent, even though there has been a substantial body of empirical research on medical decision-making over the past 40 years. As decision-support technology continues to proliferate in medical settings, it is imperative that "basic science" decision research develop a broader-based and more valid foundation for the study of medical decision-making as it occurs in the natural setting. This paper critically reviews both traditional and recent approaches to medical decision making, considering the integration of problem-solving and decision-making research paradigms, the role of conceptual knowledge in decision-making, and the emerging paradigm of naturalistic decision-making. We also provide an examination of technology-mediated decision-making. Expanding the scope of decision research will better enable us to understand optimal decision processes, suitable coping mechanisms under suboptimal conditions, the development of expertise in decision-making, and ways in which decision-support technology can successfully mediate decision processes.

The purpose of this review is to organize various published conceptions of health numeracy and to discuss how health numeracy contributes to the productive use of quantitative information for health. We define health numeracy as the individual-level skills needed to understand and use quantitative health information, including basic computation skills, ability to use information in documents and non-text formats such as graphs, and ability to communicate orally. We also identify two other factors affecting whether a consumer can use quantitative health information: design of documents and other information artifacts, and health-care providers' communication skills. We draw upon the distributed cognition perspective to argue that essential ingredients for the productive use of quantitative health information include not only health numeracy but also good provider communication skills, as well as documents and devices that are designed to enhance comprehension and cognition.

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A multifaceted usability approach provided important insight regarding use of technology by an elderly chronic-care patient population and more generally, for understanding how home health initiatives can more effectively use such technology.

A critical goal of vaccine development for a wide variety of pathogens is the induction of potent and durable mucosal immunity. However, it has been assumed that this goal would be difficult to achieve by systemic vaccination due to the anatomic and functional distinctness of the systemic and mucosal immune systems and the resultant compartmentalization of immune responses. In this study, we show that Ag-specific CD8+ T lymphocytes traffic efficiently to mucosal surfaces following systemic vaccination. Intramuscular immunization with recombinant adenovirus (rAd) vector-based vaccines expressing SIV Gag resulted in potent, durable, and functional CD8+ T lymphocyte responses at multiple mucosal effector sites in both mice and rhesus monkeys. In adoptive transfer studies in mice, vaccine-elicited systemic CD8+ T lymphocytes exhibited phenotypic plasticity, up-regulated mucosal homing integrins and chemokine receptors, and trafficked rapidly to mucosal surfaces. Moreover, the migration of systemic CD8+ T lymphocytes to mucosal compartments accounted for the vast majority of Ag-specific mucosal CD8+ T lymphocytes induced by systemic vaccination. Thus, i.m. vaccination can overcome immune compartmentalization and generate robust mucosal CD8+ T lymphocyte memory. These data demonstrate that the systemic and mucosal immune systems are highly coordinated following vaccination.