47 research outputs found

    Pembrolizumab monotherapy for previously treated metastatic triple-negative breast cancer: cohort A of the phase II KEYNOTE-086 study

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    ABSTRACT Background Treatment options for previously treated metastatic triple-negative breast cancer (mTNBC) are limited. In cohort A of the phase II KEYNOTE-086 study, we evaluated pembrolizumab as second or later line of treatment for patients with mTNBC. Patients and methods Eligible patients had centrally confirmed mTNBC, ≄1 systemic therapy for metastatic disease, prior treatment with anthracycline and taxane in any disease setting, and progression on or after the most recent therapy. Patients received pembrolizumab 200 mg intravenously every 3 weeks for up to 2 years. Primary end points were objective response rate in the total and PD-L1–positive populations, and safety. Secondary end points included duration of response, disease control rate (percentage of patients with complete or partial response or stable disease for ≄24 weeks), progression-free survival, and overall survival. Results All enrolled patients (N = 170) were women, 61.8% had PD-L1–positive tumors, and 43.5% had received ≄3 previous lines of therapy for metastatic disease. ORR (95% CI) was 5.3% (2.7–9.9) in the total and 5.7% (2.4–12.2) in the PD-L1–positive populations. Disease control rate (95% CI) was 7.6% (4.4–12.7) and 9.5% (5.1–16.8), respectively. Median duration of response was not reached in the total (range, 1.2+–21.5+) and in the PD-L1–positive (range, 6.3–21.5+) populations. Median PFS was 2.0 months (95% CI, 1.9–2.0), and the 6-month rate was 14.9%. Median OS was 9.0 months (95% CI, 7.6–11.2), and the 6-month rate was 69.1%. Treatment-related adverse events occurred in 103 (60.6%) patients, including 22 (12.9%) with grade 3 or 4 AEs. There were no deaths due to AEs. Conclusions Pembrolizumab monotherapy demonstrated durable antitumor activity in a subset of patients with previously treated mTNBC and had a manageable safety profile. Clinical trial registration ClinicalTrials.gov, NCT0244700

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    The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape : A Large-Scale Genome-Wide Interaction Study

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    Genome-wide association studies (GWAS) have identified more than 100 genetic variants contributing to BMI, a measure of body size, or waist-to-hip ratio (adjusted for BMI, WHRadjBMI), a measure of body shape. Body size and shape change as people grow older and these changes differ substantially between men and women. To systematically screen for age-and/or sex-specific effects of genetic variants on BMI and WHRadjBMI, we performed meta-analyses of 114 studies (up to 320,485 individuals of European descent) with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Each study tested the association of up to similar to 2.8M SNPs with BMI and WHRadjBMI in four strata (men 50y, women 50y) and summary statistics were combined in stratum-specific meta-analyses. We then screened for variants that showed age-specific effects (G x AGE), sex-specific effects (G x SEX) or age-specific effects that differed between men and women (G x AGE x SEX). For BMI, we identified 15 loci (11 previously established for main effects, four novel) that showed significant (FDR= 50y). No sex-dependent effects were identified for BMI. For WHRadjBMI, we identified 44 loci (27 previously established for main effects, 17 novel) with sex-specific effects, of which 28 showed larger effects in women than in men, five showed larger effects in men than in women, and 11 showed opposite effects between sexes. No age-dependent effects were identified for WHRadjBMI. This is the first genome-wide interaction meta-analysis to report convincing evidence of age-dependent genetic effects on BMI. In addition, we confirm the sex-specificity of genetic effects on WHRadjBMI. These results may providefurther insights into the biology that underlies weight change with age or the sexually dimorphism of body shape.Peer reviewe

    A Dynamic Job Scheduling Algorithm for Computational Grid

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