Background: MUC16 is a mucin marker that is frequently mutated in melanoma, but whether MUC16 mutations could be useful as a surrogate biomarker for tumor mutation burden (TMB) remains unclear.Methods: This study rigorously evaluates the MUC16 mutation as a clinical biomarker in cutaneous melanoma by utilizing genomic and clinical data from patient samples from The Cancer Genome Atlas (TCGA) and two independent validation cohorts. We further extended the analysis to studies with patients treated with immunotherapies.Results: Analysis results showed that samples with MUC16 mutations had a higher TMB than the samples of wild-type, with strong statistical significance (P < 0.001) in all melanoma cohorts tested. Associations between MUC16 mutations and TMB remained statistically significant after adjusting for potential confounding factors in the TCGA cohort [OR, 9.28 (95% confidence interval (CI), 5.18-17.39); P < 0.001], Moffitt cohort [OR, 31.95 (95% CI, 8.71-163.90); P < 0.001], and Yale cohort [OR, 8.09 (95% CI, 3.12-23.79); P < 0.01]. MUC16 mutations were also found to be associated with overall survival in the TCGA [HR, 0.62; (95% CI, 0.45-0.85); P < 0.01] and Moffitt cohorts [HR, 0.49 (95% CI, 0.28-0.87); P ¼ 0.014]. Strikingly, MUC16 is the only top frequently mutated gene for which prognostic significance was observed. MUC16 mutations were also found valuable in predicting anti-CTLA-4 and anti-PD-1 therapy responses.Conclusions: MUC16 mutation appears to be a useful predictive marker of global TMB and patient survival in melanoma.Impact: This is, to the best of our knowledge, the first systematic evaluation of MUC16 mutation as a clinical biomarker and a predictive biomarker for immunotherapy in melanoma.
The application of finer scale geographically resolved AP exposures made it possible to study acute effects of PM on CVD mortality in a large metropolitan area. Our study results demonstrated the continued presence of a dose response relationship of increased risk of CVD mortality within this lower range of PM exposure.
Background
The extent to which cardiovascular disease (
CVD
) risk factors across the menopause explain racial/ethnic differences in subclinical vascular disease in late midlife women is not well documented and was explored in a multi‐ethnic cohort.
Methods and Results
Participants (n=1357; mean age 60 years) free of clinical
CVD
from the Study of Women's Health Across the Nation had common carotid artery intima‐media thickness, interadventitial diameter, and carotid plaque presence assessed by ultrasonography on average 13.7 years after baseline visit. Early to late midlife time‐averaged cumulative burden of traditional
CVD
risk factors calculated using serial measures from baseline to the ultrasound visit were generally less favorable in black and Hispanic women compared with white and Chinese women, including education and smoking status and time‐averaged cumulative blood pressure, high‐density lipoprotein cholesterol, and fasting insulin. Independent of these risk factors,
BMI
, and medications, common carotid artery intima‐media thickness was thicker in black women, interadventitial diameter was wider in Chinese women, yet plaque presence was lower in black and Hispanic women compared with white women.
CVD
risk factor associations with subclinical vascular measures did not vary by race/ethnicity except for high‐density lipoprotein cholesterol on common carotid artery intima‐media thickness; an inverse association between high‐density lipoprotein cholesterol and common carotid artery intima‐media thickness was observed in Chinese and Hispanic but not in white or black women.
Conclusions
Race/ethnicity did not particularly moderate the association between traditional
CVD
risk factors measured across the menopause transition and late midlife subclinical vascular disease. Unmeasured socioeconomic, cultural, and nontraditional biological risk factors likely play a role in racial/ethnic differences in vascular health and merit further exploration.
Assessment of programmed death ligand 1 (PD-L1) expression by immunohistochemistry (IHC) has emerged as an important predictive biomarker across multiple tumor types. However, manual quantitation of PD-L1 positivity can be difficult and leads to substantial inter-observer variability. Although the development of artificial intelligence (AI) algorithms may mitigate some of the challenges associated with manual assessment and improve the accuracy of PD-L1 expression scoring, use of AI-based approaches to oncology biomarker scoring and drug development has been sparse, primarily due to the lack of large-scale clinical validation studies across multiple cohorts and tumor types. We developed AI-powered algorithms to evaluate PD-L1 expression on tumor cells by IHC and compared it with manual IHC scoring in urothelial carcinoma, non-small cell lung cancer, melanoma, and squamous cell carcinoma of the head and neck (prospectively determined during the phase II and III CheckMate clinical trials). 1,746 slides were retrospectively analyzed, the largest investigation of digital pathology algorithms on clinical trial datasets performed to date. AI-powered quantification of PD-L1 expression on tumor cells identified more PD-L1–positive samples compared with manual scoring at cutoffs of ≥1% and ≥5% in most tumor types. Additionally, similar improvements in response and survival were observed in patients identified as PD-L1–positive compared with PD-L1–negative using both AI-powered and manual methods, while improved associations with survival were observed in patients with certain tumor types identified as PD-L1–positive using AI-powered scoring only. Our study demonstrates the potential for implementation of digital pathology-based methods in future clinical practice to identify more patients who would benefit from treatment with immuno-oncology therapy compared with current guidelines using manual assessment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.