Purpose: Protein tyrosine phosphatase receptor type T (PTPRT), which is a well‐known phosphatase and mutates frequently in melanoma and non‐small cell lung cancer (NSCLC). Our research aims to elucidate its mutation association with immune checkpoint inhibitors (ICI) efficacy.Methods: We integrated whole‐exome sequencing (WES)‐based somatic mutation profiles and clinical characteristics of 631 melanoma samples received ICI agents from eight studies and 109 NSCLC samples from two studies. For validation, 321 melanoma and 350 NSCLC immunotherapy samples with targeted next‐generation sequencing (NGS) were employed. Besides, an independent NSCLC cohort contained 240 samples was also collected for further corroboration. Distinct immune infiltration was evaluated according to the PTPRT mutational status.Results: In the WES melanoma cohort, patients with PTPRT mutations harbored a significantly elevated ICI response rate (40.5% vs. 28.6%, p = 0.036) and a prolonged survival outcome (35.3 vs. 24.9 months, p = 0.006). In the WES NSCLC cohort, the favorable response and immunotherapy survival were also observed in PTPRT‐mutated patients (p = 0.036 and 0.019, respectively). For the validation cohorts, the associations of PTRPT mutations with better prognoses were identified in melanoma, NSCLC, and pan‐cancer patients with targeted‐NGS (all p < 0.05). Moreover, immunology analyses showed the higher mutation burden, increased lymphocyte infiltration, decreased‐ activated‐stroma, and immune response pathways were detected in patients with PTPRT mutations.Conclusion: Our investigation indicates that PTPRT mutations may be considered as a potential indicator for assessing ICI efficacy in melanoma and NSCLC, even across multiple cancers. Further prospective validation cohorts are warranted.

Background: The COVID-19 has caused a sizeable global outbreak and has been declared as a public health emergency of international concern. Sufficient evidence shows that temperature has an essential link with respiratory infectious diseases. The objectives of this study were to describe the exposure-response relationship between ambient temperature, including extreme temperatures, and mortality of COVID-19. Methods: The Poisson distributed lag non-linear model (DLNM) was constructed to evaluate the non-linear delayed effects of ambient temperature on death, by using the daily new death of COVID-19 and ambient temperature data from January 10 to March 31, 2020, in Wuhan, China. Results: During the period mentioned above, the average daily number of COVID-19 deaths was approximately 45.2. Poisson distributed lag non-linear model showed that there was a non-linear relationship (U-shape) between the effect of ambient temperature and mortality. With confounding factors controlled, the daily cumulative relative death risk decreased by 12.3% (95% CI [3.4, 20.4%]) for every 1.0 °C increase in temperature. Moreover, the delayed effects of the low temperature are acute and short-term, with the most considerable risk occurring in 5–7 days of exposure. The delayed effects of the high temperature appeared quickly, then decrease rapidly, and increased sharply 15 days of exposure, mainly manifested as acute and long-term effects. Sensitivity analysis results demonstrated that the results were robust. Conclusions: The relationship between ambient temperature and COVID-19 mortality was non-linear. There was a negative correlation between the cumulative relative risk of death and temperature. Additionally, exposure to high and low temperatures had divergent impacts on mortality.

Immune checkpoint inhibitors (ICIs) are most commonly used for melanoma and non-small cell lung cancer (NSCLC) patients. FAT atypical cadherin 1 (FAT1), which frequently mutates in melanoma and NSCLC. In this study, we aim to investigate the association ofFAT1mutations with ICI response and outcome. We collected somatic mutation profiles and clinical information from ICI-treated 631 melanoma and 109 NSCLC samples, respectively. For validation, a pan-cancer cohort with 1661 patients in an immunotherapy setting was also used. Melanoma and NSCLC samples from the Cancer Genome Atlas were used to evaluate the potential immunologic mechanisms ofFAT1mutations. In melanoma, patients withFAT1mutations had a significantly improved survival outcome than those wild-type patients (HR: 0.67, 95% CI: 0.46–0.97,P = 0.033). An elevated ICI response rate also appeared inFAT1-mutated patients (43.2% vs. 29.2%,P = 0.032). Associations ofFAT1mutations with improved prognosis and ICI response were confirmed in NSCLC patients. In the pan-cancer cohort, the association betweenFAT1mutations and favorable ICI outcome was further validated (HR: 0.74, 95% CI: 0.58–0.96,P = 0.022). Genomic and immunologic analysis showed that a high mutational burden, increased infiltration of immune-response cells, decreased infiltration of immune-suppressive cells, interferon and cell cycle-related pathways were enriched in patients withFAT1mutations. Our study revealed thatFAT1mutations were associated with better immunogenicity and ICI efficacy, which may be considered as a biomarker for selecting patients to receive immunotherapy.

Background: Despite the acknowledged sex-related differences in immune response and immune checkpoint inhibitor (ICI) efficacy, little is known about the sex disparities in melanoma of novel genomic determinants for ICI therapies.Methods: Pretreatment genomic profiles and clinical characteristics of 631 melanoma patients treated with ICIs (i.e., inhibitors of CTLA-4, PD-1/PD-L1, or both) were comprehensively curated. Genomic factors, i.e., significantly mutated genes (SMGs), mutational signatures, and molecular subtypes were identified, and their associations with ICI treatment efficacy in male and female patients were evaluated.Results: Of the 15 SMGs identified in this study, three genes (i.e., CFH, DGKG, and PPP6C) were found to exhibit sex differences with respect to ICI efficacy. Among these, CFH mutations exhibited both response rate and survival benefits in male, but not in female patients. A total of four mutational signatures (i.e., signatures 1, 4, 7, and 11) were extracted. Male patients with signature 4 (also known as smoking-related signature) had an inferior ICI response rate and overall survival. However, this association was not significant in females. An immune subtype based on mutational activities was found to be significantly associated with poor ICI survival in female patients.Conclusion: We uncovered several sex-dependent genomic correlates of response to ICI treatment, such as male-biased CFH mutations and signature 4 and the female-biased immune resistance subtype. The findings derived from this research provide clues for exploring different immunotherapeutic approaches in male and female patients with melanoma.

Background: The objective of this study was to investigate the use of ensemble methods to improve the prediction of fetal macrosomia and large for gestational age from prenatal ultrasound imaging measurements. Methods: We evaluated and compared the prediction accuracies of nonlinear and quadratic mixed-effects models coupled with 26 different empirical formulas for estimating fetal weights in predicting large fetuses at birth. The data for the investigation were taken from the Successive Small-for-Gestational-Age-Births study. Ensemble methods, a class of machine learning techniques, were used to improve the prediction accuracies by combining the individual models and empirical formulas. Results: The prediction accuracy of individual statistical models and empirical formulas varied considerably in predicting macrosomia but varied less in predicting large for gestational age. Two ensemble methods, voting and stacking, with model selection, can combine the strengths of individual models and formulas and can improve the prediction accuracy. Conclusions: Ensemble learning can improve the prediction of fetal macrosomia and large for gestational age and have the potential to assist obstetricians in clinical decisions.