Neoantigen presentation arises as a result of tumor-specifi c mutations and is a critical component of immune surveillance that can be abrogated by somatic LOH of the human leukocyte antigen class I (HLA-I) locus. To understand the role of HLA-I LOH in oncogenesis and treatment, we utilized a pan-cancer genomic dataset of 83,644 patient samples, a small subset of which had treatment outcomes with immune checkpoint inhibitors (ICI). HLA-I LOH was common (17%) and unexpectedly had a nonlinear relationship with tumor mutational burden (TMB). HLA-I LOH was frequent at intermediate TMB, yet prevalence decreased above 30 mutations/megabase, suggesting highly mutated tumors require alternate immune evasion mechanisms. In ICI-treated patients with nonsquamous non-small cell lung cancer, HLA-I LOH was a signifi cant negative predictor of overall survival. Survival prediction improved when combined with TMB, suggesting TMB with HLA-I LOH may better identify patients likely to benefi t from ICIs. SIGnIFICAnCE:This work shows the pan-cancer landscape of HLA-I LOH, revealing an unexpected "Goldilocks" relationship between HLA-I LOH and TMB, and demonstrates HLA-I LOH as a signifi cant negative predictor of outcomes after ICI treatment. These data informed a combined predictor of outcomes after ICI and have implications for tumor vaccine development.

Background Patients on chronic dialysis have among the highest mortality and hospitalization rates. In the non-renal literature, functional dependence is recognized as a contributor to subsequent disability, recurrent hospitalization, and increased mortality. A higher burden of functional dependence with progressive worsening of renal function has been observed in several studies, suggesting functional dependence may contribute to both morbidity and mortality in dialysis patients. Study Design Prospective cohort study Setting & Participants 7,226 hemodialysis patients from 12 countries in the Dialysis Outcomes and Practice Patterns Study (DOPPS) phase 4 (2009–2011) with self-reported data on functional status (FS). Predictor Patients’ ability to perform 13 basic and instrumental Activities of Daily Living (ADL) was summarized to create an overall FS score ranging from 1.25 (most dependent) to 13 (functionally independent). Outcome Cox regression was used to estimate the association between FS and all-cause mortality, adjusting for several demographic and clinical risk factors for mortality. Median follow-up was 17.2 months. Results The proportion of patients who could perform each ADL task without assistance ranged from 97% (eating) to 47% (doing housework). 36% of patients could perform all 13 tasks without assistance (FS=13), and 14% of patients had high functional dependence (FS < 8). Functionally independent patients were younger and had many indicators of better health status including higher quality of life. Compared with functionally independent patients, the adjusted hazard ratio for mortality was 2.37 (95% confidence interval =1.92–2.94) for patients with FS < 8. Limitations Possible non-response bias and residual confounding Conclusions We found a high burden of functional dependence across all age groups and across all DOPPS countries. When adjusting for several known mortality risk factors, including age, access type, cachexia and multi-morbidity, functional dependence was a strong, consistent predictor of mortality.

Purpose External control data from completed clinical trials and electronic health records can be valuable for the design and analysis of future clinical trials. We discuss the use of external control data for early stopping decisions in randomized clinical trials (RCTs). Methods We specify interim analyses (IAs) approaches for RCTs, which allow investigators to integrate external data into early futility stopping decisions. IAs utilize predictions based on early data from the RCT, possibly combined with external data. These predictions at IAs express the probability that the trial will generate significant evidence of positive treatment effects. The trial is discontinued if this predictive probability becomes smaller than a pre-specified threshold. We quantify efficiency gains and risks associated with the integration of external data into interim decisions. We then analyze a collection of glioblastoma (GBM) datasets, to investigate if the balance of efficiency gains and risks justify the integration of external data into the IAs of future GBM RCTs. Results Our analyses illustrate the importance of accounting for potential differences between the distributions of prognostic variables in the RCT and in the external data to effectively leverage external data for interim decisions. Using GBM datasets, we estimate that the integration of external data increases the probability of early stopping of ineffective experimental treatments by up to 25% compared to IAs that don’t leverage external data. Additionally, we observe a reduction of the probability of early discontinuation for effective experimental treatments, which improves the RCT power. Conclusion Leveraging external data for IAs in RCTs can support early stopping decisions and reduce the number of enrolled patients when the experimental treatment is ineffective.

Psychiatric researchers tend to select the discordant co-twin design when they seek to hold constant genetic influence while estimating exposure-associated disease risk. The epidemiologic case-crossover research design developed for the past two decades represents a viable alternative, not often seen in psychiatric studies. Here, we turn to the epidemiologic case-crossover approach to examine the idea that cannabis onset is a proximal trigger for cocaine use, with the power of 'subject-as-own-control' research used to hold constant antecedent characteristics of the individual drug user, including genetic influence and other traits experienced up to the time of the observed hazard and control intervals. Data are from newly incident cocaine users identified in the 2002-2006 U.S. National Surveys on Drug Use and Health. Among these cocaine users, 48 had both cannabis onset and cocaine onset in the same month-long hazard interval; the expected value is 30 users, based on the control interval we had pre-specified for case-crossover estimation (estimated relative risk, RR = 1.6; exact mid-p = 0.042). Within the framework of a subject-as-own-control design, the evidence is consistent with the hypothesis that cannabis onset is a proximal trigger for cocaine use, with genetic influences (and many environmental conditions and processes) held constant. Limitations are noted and implications discussed.