Predictive Impact of Tumor Mutational Burden on Real-World Outcomes of First-Line Immune Checkpoint Inhibition in Metastatic Melanoma.

PURPOSE: The choice of threshold and reliability of high tumor mutational burden (TMB) to predict outcomes and guide treatment choice for patients with metastatic melanoma receiving first-line immune checkpoint inhibitor (ICI) therapy in the real world is not well known. METHODS: Using a deidentified nationwide (US-based) melanoma clinicogenomic database, we identified a real-world cohort of patients with metastatic melanoma (N = 497) who received first-line monotherapy anti-PD-1 (n = 240) or dual anti-PD-1 and anti-CTLA-4 ICI (n = 257) and had a tissue-based comprehensive genomic profiling test TMB score. RESULTS: TMB-high (TMB-H; ≥10 mutations per megabase [muts/Mb], n = 352, 71%) was independently predictive of superior real-world progression-free survival and overall survival versus TMB-low (<10 mut/Mb, n = 145, 29%) in both mono ICI (hazard ratio [HR], 0.45 [95% CI, 0.32 to 0.63]; P < .001; HR, 0.61 [95% CI, 0.41 to 0.90]; P = .01, respectively) and dual ICI (HR, 0.67 [95% CI, 0.49 to 0.90]; P = .009; HR, 0.61 [95% CI, 0.42 to 0.88]; P = .007, respectively) patients. Dual ICI offered no significant advantage in BRAFwt patients and unexpectedly demonstrated greatest benefit in the TMB 10-19 mut/Mb group, identifying a TMB-very high (≥20 mut/Mb, n = 247, 50%) BRAFmut patient subgroup for whom mono ICI may be preferable. CONCLUSION: TMB-H predicts superior outcomes on ICI while coassessment of BRAF status and TMB may inform first-line regimen choice.

Comparative Effectiveness of Immune Checkpoint Inhibitors vs Chemotherapy in Patients With Metastatic Colorectal Cancer With Measures of Microsatellite Instability, Mismatch Repair, or Tumor Mutational Burden.

Importance: The KEYNOTE-177 trial demonstrated that patients with metastatic colorectal cancer (MCRC) with high microsatellite instability (MSI-H) and/or mismatch repair deficiency (DMMR) have better outcomes when receiving first-line immune checkpoint inhibitors (ICIs) compared with chemotherapy. Data on performance of ICIs in patients with MCRC in standard practice settings remain limited, and direct MMR vs MSI outcome association comparisons are lacking. Objective: To validate MSI (determined by next-generation sequencing [NGS]) as a biomarker of ICI effectiveness among patients with MCRC in standard practice settings and examine the association of MSI assessed by NGS, DMMR by immunohistochemistry, and tumor mutational burden (cutoff, 10 mutations/megabase) with ICI outcomes. Design, Setting, and Participants: This comparative effectiveness research study of outcomes in prospectively defined biomarker subgroups used data from a deidentified clinicogenomic database and included patients who received Foundation Medicine testing (FoundationOne or FoundationOne CDx) during routine clinical care at approximately 280 US academic or community-based cancer clinics between March 2014 and December 2021. The population included 1 cohort of patients with MSI-H MCRC who received first-line ICIs or chemotherapy and a second cohort who received ICIs in any line of therapy (LOT) for biomarker examination. Exposures: ICI therapy or chemotherapy assigned at physician discretion without randomization. Main Outcomes and Measures: The main outcomes were time to next treatment (TTNT), progression-free survival (PFS), and overall survival (OS). Hazard ratios were adjusted for known prognostic imbalances. Comparisons of explanatory power used the likelihood ratio test. Results: A total of 138 patients (median age, 67.0 years [IQR, 56.2-74.0 years]; 73 [52.9%] female) with MSI-H MCRC received first-line ICIs or chemotherapy. A total of 182 patients (median age, 64.5 years [IQR, 55.2-72.0]; 98 [53.8%] female) received ICIs in any LOT. Patients receiving first-line ICIs vs chemotherapy had longer TTNT (median, not reached [NR] vs 7.23 months [IQR, 6.21-9.72 months]; adjusted hazard ratio [AHR], 0.17; 95% CI, 0.08-0.35; P < .001), PFS (median, 24.87 months [IQR, 19.10 months to NR] vs 5.65 months [IQR, 4.70-8.34 months]; AHR, 0.31; 95% CI, 0.18-0.52; P < .001), and OS (median, NR vs 24.1 months [IQR, 13.90 months to NR]; HR, 0.45; 95% CI, 0.23-0.88; P = .02). MSI added to DMMR better anticipated TTNT and PFS in patients receiving ICIs than DMMR alone. The same was not observed when DMMR evaluation was added to MSI. Conclusions and Relevance: In this comparative effectiveness research study, MSI assessed by NGS robustly identified patients with favorable outcomes on first-line ICIs vs chemotherapy and appeared to better anticipate ICI outcomes compared with DMMR.

Multi-state survival models with treatment effects and biomarkers: Simulations for study design assessment.

BACKGROUND: Comparative effectiveness studies of cancer therapeutics in observational data face confounding by patterns of clinical treatment over time. The validity of survival analysis in longitudinal health records depends on study design choices including index date definition and model specification for covariate adjustment. METHODS: Overall survival in cancer is a multi-state transition process with mortality and treatment switching as competing risks. Parametric Weibull regression quantifies proportionality of hazards across lines of therapy in real-world cohorts of 12 solid tumor types. Study design assessments compare alternative analytic models in simulations with realistic disproportionality. The multi-state simulation framework is adaptable to alternative treatment effect profiles and exposure patterns. RESULTS: Event-specific hazards of treatment-switching and death are not proportional across lines of therapy in 12 solid tumor types. Study designs that include all eligible lines of therapy per subject showed lower bias and variance than designs that select one line per subject. Confounding by line number was effectively mitigated across a range of simulation scenarios by Cox proportional hazards models with stratified baseline hazards and inverse probability of treatment weighting. CONCLUSION: Quantitative study design assessment can inform the planning of observational research in clinical oncology by demonstrating the potential impact of model misspecification. Use of empirical parameter estimates in simulation designs adapts analytic recommendations to the clinical population of interest.

Direct Versus Calculated LDL Cholesterol and C-Reactive Protein in Cardiovascular Disease Risk Assessment in the Framingham Offspring Study.

BACKGROUND: Increases in circulating LDL cholesterol (LDL-C) and high-sensitivity C-reactive protein (hsCRP) concentrations are significant risk factors for cardiovascular disease (CVD). We assessed direct LDL-C and hsCRP concentrations compared to standard risk factors in the Framingham Offspring Study. METHODS: We used stored frozen plasma samples (-80 °C) obtained after an overnight fast from 3147 male and female participants (mean age, 58 years) free of CVD at cycle 6 of the Framingham Offspring Study. Overall, 677 participants (21.5%) had a CVD end point over a median of 16.0 years of follow-up. Total cholesterol (TC), triglyceride (TG), HDL cholesterol (HDL-C), direct LDL-C (Denka Seiken and Kyowa Medex methods), and hsCRP (Dade Behring method) concentrations were measured by automated analysis. LDL-C was also calculated by both the Friedewald and Martin methods. RESULTS: Considering all CVD outcomes on univariate analysis, significant factors included standard risk factors (age, hypertension, HDL-C, hypertension treatment, sex, diabetes, smoking, and TC concentration) and nonstandard risk factors (non-HDL-C, direct LDL-C and calculated LDL-C, TG, and hsCRP concentrations). On multivariate analysis, only the Denka Seiken direct LDL-C and the Dade Behring hsCRP were still significant on Cox regression analysis and improved the net risk reclassification index, but with modest effects. Discordance analysis confirmed the benefit of the Denka Seiken direct LDL-C method for prospective hard CVD endpoints (new-onset myocardial infarction, stroke, and/or CVD death). CONCLUSIONS: Our data indicate that the Denka Seiken direct LDL-C and Dade Behring hsCRP measurements add significant, but modest, information about CVD risk, compared to standard risk factors and/or calculated LDL-C.

Do changes in DNA methylation mediate or interact with SNP variation? A pharmacoepigenetic analysis.

BACKGROUND: In studies with multi-omics data available, there is an opportunity to investigate interdependent mechanisms of biological causality. The GAW20 data set includes both DNA genotype and methylation measures before and after fenofibrate treatment. Using change in triglyceride (TG) levels pre- to posttreatment as outcome, we present a mediation analysis that incorporates methylation. This approach allows us to simultaneously consider a mediation hypothesis that genotype affects change in TG level by means of its effect on methylation, and an interaction hypothesis that the effect of change in methylation on change in TG levels differs by genotype. We select 322 single-nucleotide polymorphism-cytosine-phosphate-guanine (SNP-CpG) site pairs for mediation analysis on the basis of proximity and marginal genome-wide association study (GWAS) and epigenome-wide association study (EWAS) significance, and present results from the real-data sample of 407 individuals with complete genotype, methylation, TG levels, and covariate data. RESULTS: We identified 3 SNP-CpG site pairs with significant interaction effects at a Bonferroni-corrected significance threshold of 1.55E-4. None of the analyzed sites showed significant evidence of mediation. Power analysis by simulation showed that a sample size of at least 19,500 is needed to detect nominally significant indirect effects with true effect sizes equal to the point estimates at the locus with strongest evidence of mediation. CONCLUSIONS: These results suggest that there is stronger evidence for interaction between genotype and methylation on change in triglycerides than for methylation mediating the effect of genotype.

What life course theoretical models best explain the relationship between exposure to childhood adversity and psychopathology symptoms: recency, accumulation, or sensitive periods?

BACKGROUND: Although childhood adversity is a potent determinant of psychopathology, relatively little is known about how the characteristics of adversity exposure, including its developmental timing or duration, influence subsequent mental health outcomes. This study compared three models from life course theory (recency, accumulation, sensitive period) to determine which one(s) best explained this relationship. METHODS: Prospective data came from the Avon Longitudinal Study of Parents and Children (n = 7476). Four adversities commonly linked to psychopathology (caregiver physical/emotional abuse; sexual/physical abuse; financial stress; parent legal problems) were measured repeatedly from birth to age 8. Using a statistical modeling approach grounded in least angle regression, we determined the theoretical model(s) explaining the most variability (r2) in psychopathology symptoms measured at age 8 using the Strengths and Difficulties Questionnaire and evaluated the magnitude of each association. RESULTS: Recency was the best fitting theoretical model for the effect of physical/sexual abuse (girls r2 = 2.35%; boys r2 = 1.68%). Both recency (girls r2 = 1.55%) and accumulation (boys r2 = 1.71%) were the best fitting models for caregiver physical/emotional abuse. Sensitive period models were chosen alone (parent legal problems in boys r2 = 0.29%) and with accumulation (financial stress in girls r2 = 3.08%) more rarely. Substantial effect sizes were observed (standardized mean differences = 0.22-1.18). CONCLUSIONS: Child psychopathology symptoms are primarily explained by recency and accumulation models. Evidence for sensitive periods did not emerge strongly in these data. These findings underscore the need to measure the characteristics of adversity, which can aid in understanding disease mechanisms and determining how best to reduce the consequences of exposure to adversity.

Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits.

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution.

Multiethnic genome-wide meta-analysis of ectopic fat depots identifies loci associated with adipocyte development and differentiation.

Variation in body fat distribution contributes to the metabolic sequelae of obesity. The genetic determinants of body fat distribution are poorly understood. The goal of this study was to gain new insights into the underlying genetics of body fat distribution by conducting sample-size-weighted fixed-effects genome-wide association meta-analyses in up to 9,594 women and 8,738 men of European, African, Hispanic and Chinese ancestry, with and without sex stratification, for six traits associated with ectopic fat (hereinafter referred to as ectopic-fat traits). In total, we identified seven new loci associated with ectopic-fat traits (ATXN1, UBE2E2, EBF1, RREB1, GSDMB, GRAMD3 and ENSA; P < 5 × 10-8; false discovery rate < 1%). Functional analysis of these genes showed that loss of function of either Atxn1 or Ube2e2 in primary mouse adipose progenitor cells impaired adipocyte differentiation, suggesting physiological roles for ATXN1 and UBE2E2 in adipogenesis. Future studies are necessary to further explore the mechanisms by which these genes affect adipocyte biology and how their perturbations contribute to systemic metabolic disease.

Comparison of multiple single-nucleotide variant association tests in a meta-analysis of Genetic Analysis Workshop 19 family and unrelated data.

BACKGROUND: Meta-analysis has been widely used in genetic association studies to increase sample size and to improve power, both in the context of single-variant analysis, as well as for gene-based tests. Meta-analysis approaches for haplotype analysis have not been extensively developed and used, and have not been compared with other ways of jointly analysing multiple genetic variants. METHODS: We propose a novel meta-analysis approach for a gene-based haplotype association test, and compare it with an existing meta-analysis approach of the sequence kernel association test (SKAT), using the unrelated samples and family samples of the Genetic Analysis Workshop 19 data sets. We performed association tests with diastolic blood pressure and restricted our analyses to all variants in exonic regions on all odd chromosomes. RESULTS: Meta-analysis of haplotype results and SKAT identified different genes. The most significantly associated gene identified by SKAT was the ALCAM gene on chromosome 3 with a p value of 7.0 × 10- 5. Two of the most associated genes identified by the haplotype method were FPGT (p = 6.7 × 10- 8) on chromosome 1 and SPARC (p = 3.3 × 10- 7) on chromosome 5. Both genes were previously implicated in blood pressure regulation and hypertension. CONCLUSION: We compared two meta-analysis approaches to jointly analyze multiple variants: SKAT and haplotype tests. The difference in observed results may be because the haplotype method considered all observed haplotypes, whereas SKAT weighted variants inversely to their minor allele frequency, masking the effects of common variants. The two approaches identified different top genes, and appear to be complementary.

An Empirical Comparison of Joint and Stratified Frameworks for Studying G × E Interactions: Systolic Blood Pressure and Smoking in the CHARGE Gene-Lifestyle Interactions Working Group.

Studying gene-environment (G × E) interactions is important, as they extend our knowledge of the genetic architecture of complex traits and may help to identify novel variants not detected via analysis of main effects alone. The main statistical framework for studying G × E interactions uses a single regression model that includes both the genetic main and G × E interaction effects (the "joint" framework). The alternative "stratified" framework combines results from genetic main-effect analyses carried out separately within the exposed and unexposed groups. Although there have been several investigations using theory and simulation, an empirical comparison of the two frameworks is lacking. Here, we compare the two frameworks using results from genome-wide association studies of systolic blood pressure for 3.2 million low frequency and 6.5 million common variants across 20 cohorts of European ancestry, comprising 79,731 individuals. Our cohorts have sample sizes ranging from 456 to 22,983 and include both family-based and population-based samples. In cohort-specific analyses, the two frameworks provided similar inference for population-based cohorts. The agreement was reduced for family-based cohorts. In meta-analyses, agreement between the two frameworks was less than that observed in cohort-specific analyses, despite the increased sample size. In meta-analyses, agreement depended on (1) the minor allele frequency, (2) inclusion of family-based cohorts in meta-analysis, and (3) filtering scheme. The stratified framework appears to approximate the joint framework well only for common variants in population-based cohorts. We conclude that the joint framework is the preferred approach and should be used to control false positives when dealing with low-frequency variants and/or family-based cohorts.