Communications medicine最新文献

Background: The transcription factor Hypoxia-Inducible Factor 2α (HIF2α) plays a crucial role in cancer cell adaptation to hypoxic conditions, particularly in clear cell renal cell carcinoma, promoting tumor growth and angiogenesis. Targeting HIF2α through pharmacologic inhibition offers a promising therapeutic strategy for HIF2α-driven cancers.

Methods: An in silico docking study using 10,000 drug-like compounds was performed using the previously solved X-ray crystal structure of HIF2α. Select compounds predicted to bind to the Per-Arnt-Sim-A (PAS-A) and PAS-B domains of HIF2α were further evaluated for biological activity in clear cell renal cell carcinoma and normal kidney cell lines. Biochemical and cell-based assays were performed to define the mechanism of action for a lead compound.

Results: Here, we identify Compound-c2 as a selective HIF2α inhibitor that binds to the PAS-B domain of HIF2α. Notably, Compound-c2 disrupts the interaction between HIF2α and the molecular chaperone Hsp70, leading to proteasomal degradation of HIF2α and the induction of apoptosis in ccRCC.

Conclusions: The distinctive inhibitory mechanism of the HIF2α inhibitor identified here, Compound-c2, sets it apart from previous HIF2α antagonists. This positions Compound-c2 as a promising alternative with potential applications in addressing drug resistance, providing a unique approach to inhibit HIF2α-related processes.

Background: The clinical symptoms of obstructive sleep apnea (OSA) are poorly correlated with disease severity based on the apnea-hypopnea index (AHI). The cumulative duration of respiratory effort assessed by mandibular jaw movement monitoring with automated analysis (REMOV) may better capture the clinical burden of OSA. This cross-sectional study assessed the association between REMOV and patient-reported outcomes (PROs), including sleepiness, fatigue, and depression.

Methods: One thousand adults referred for suspected OSA underwent polysomnography, REMOV analysis, and PRO assessment using validated questionnaires. Relationships between REMOV, AHI, and PROs were examined using principal component analysis and regression models.

Results: Median REMOV values align with OSA severity (6.5%, 23.4%, 28.8%, and 42.8% of total sleep time at AHI values of <5, 5-15, 15- < 30, and ≥30 events/h, respectively). REMOV is significantly associated with sleepiness, fatigue, and depression. These associations are most evident in patients with an AHI ≤ 15 events/h. AHI is not significantly associated with any PROs.

Conclusions: These data suggest that REMOV may serve as a complementary metric in OSA, especially in patients with mild disease. Incorporating REMOV into OSA severity grading may improve the alignment between PROs and therapeutic decisions.

Background: Homologous recombination deficiency (HRD) impacts cancer treatment strategies, particularly effective utilization of PARP inhibitors. However, the variability of different HRD assays has hampered the selection of oncology patients who may benefit from these therapies. Our study aims to use the whole genome landscape to better define HRD in a pan-cancer cohort.

Methods: We employed a whole genome sequencing HRD classifier that includes genome-wide signatures associated with HRD to analyze 580 tumor/normal paired samples. The HRD phenotype was correlated with genomic variants in BRCA1/2 and other homologous recombination repair genes.

Results: In this paper we show that the HRD phenotype is identified in various cancers including breast (21%), pancreaticobiliary (20%), gynecological (17%), prostate (9%), upper gastrointestinal (GI) (2%), and other cancers (1%). HRD cases are not confined to BRCA1/2 mutations; 24% of HRD cases are BRCA1/2 wild-type. A diverse range of gene alterations involved in HRD are elucidated, including biallelic mutations in FANCF, XRCC2, and FANCC, and deleterious structural variants. In a subset of cases, the whole genome sequencing-based classifier offers more insights and a better correlation to treatment response when compared to other assays.

Conclusions: Although HRD is a biomarker used to determine which cancer patients would benefit from PARP inhibitors, a lack of harmonization of tests to determine HRD status makes it challenging to interpret their results. Our study highlights the use of comprehensive whole genome sequencing analysis to better predict HRD and elucidates genomic mechanisms associated with this phenotype.

Background: The gut microbiota influences breast cancer development through the estrobolome, a collection of bacterial genes involved in estrogen metabolism. While estrogen and the gut microbiota mutually affect each other, the long-term effects of oral endocrine therapy (ET) on the gut microbiota remain unclear. Furthermore, the relationship between gut microbiota profiles and breast cancer recurrence is not well understood. This study aims to investigate the long-term impact of oral ET on gut microbiota composition in disease-free and recurrent breast cancer patients.

Methods: We enrolled 48 participants divided into four groups: tamoxifen only (Tam), letrozole only (Let), chemotherapy plus letrozole without recurrence (CLet), and chemotherapy plus letrozole with recurrence (Recu). Fecal samples were collected for 16S rRNA sequencing. Blood samples for cell-free DNA (cfDNA) analysis and tissue samples for EndoPredict (EPclin) scoring.

Results: Here we show that long-term ET administration does not significantly alter overall gut microbial composition. However, patients with recurrence display lower α-diversity and higher abundances of Sutterella and Ruminococcus compared with non-recurrent patients. cfDNA profiles do not differ significantly between groups. Notably, high EPclin scores predict chemotherapy benefit, but recurrence still occurs in some cases. In such patients, gut microbial markers effectively distinguish recurrence and are associated with poorer progression-free survival, particularly in those with larger tumors.

Conclusions: This study provides the first human evidence with long-term ET administration to reveal that, besides genetic profiles, the gut microbiota is another critical factor that we should consider in the influence and prediction of breast cancer recurrence in the future.

Background: Exposure to violence and psychological distress are positively correlated across populations. Microbiota-gut-brain crosstalk research supports that the microbiota is affected by environmental stressors and may influence mental state. Accordingly, we explored how the microbiota relates to exposure to violence and distress in midlife, a pivotal yet underexplored period. This life stage is marked by emerging vulnerability to chronic stress and mental health decline yet offers opportunities for early identification and intervention.

Methods: We characterized the fecal microbiota of a previosly snowball-recruited Israeli-Muslim cohort (n = 305, 40-65 yrs) exposed to ongoing and increasing violence (during adulthood) and examined correlations with subjective reports of exposure to violence and psychological distress. We then used machine learning to leverage microbiota profiles and exposure to violence, classifying individuals into high- and low-distress categories.

Results: We identify unique microbial signatures associated with increasing exposure to violence and distress. Some associated bacteria were previously identified in the literature, while others were not yet described in the context of the gut-brain axis. Microbial profiles associated with violence and distress are largely non-overlapping, yet we are able to classify participants into high- and low distress categories using a combination of microbiota and violence variables. This combined model outperforms those using only microbiota or demographics, but its classification accuracy remains modest (with a median area-under-the-curve of 0.595 (IQR 0.045).

Conclusions: This research sheds light on the microbiota-gut-brain axis, highlighting that psychological distress and exposure to violence are differentially associated with microbiota composition in midlife. These cross-sectional findings, together with moderate classification into distress classes based on the microbiome, suggest that holistic, context-aware approaches would benefit proactive mental health interventions.

Background: Ovarian cancer is a major female reproductive health issue with heterogeneous biological features on its subtypes, which may require different therapeutic strategies. Glucagon-like peptide-1 receptor (GLP-1R) agonists were reported to be beneficial for ovarian cancer, but the causal effects and mechanisms on its heterogeneous subtypes remain unclear.

Methods: We used genetic variants robustly associated with gene expression, protein level, splicing event, and DNA methylation of GLP-1R in six endocrine-related tissues (N ≤ 35,431) as genetic instruments to proxy the effect of GLP-1R agonism. To increase power, we conducted a meta-analysis of genome-wide association studies of ovarian cancer (29,066 cases, 461,542 controls), and identified 12 genome-wide associated variants, including two previously unreported variants: rs77247401 (MIR1208) and rs56159231 (PLEKHM1).

Results: Here we show that gene expression of GLP-1R in pancreas is associated with a reduced risk of overall ovarian cancer risk odds ratio ([OR] = 0.94, 95% confidence interval [CI] 0.89-1.00) and endometrioid ovarian cancer (ENOC; OR = 0.83, 95% CI = 0.72-0.95), which the finding is validated using splicing event of GLP-1R in pancreas (OR = 0.13, 95% CI = 0.02-0.86). However, null association is found for GLP-1R expression in pancreas with other ovarian cancer subtypes. The phenome-wide MR followed by mediation MR identifies six body composition and metabolic factors as mediators, including 18:2 linoleic acid.

Conclusions: The protective effect of GLP-1R agonists on ovarian cancer, especially ENOC, needs further validation in large-scale and well-conducted clinical trials.

Background: The long-term health consequences of childhood body size and whether it can be mitigated by a healthy adult lifestyle remains unclear. This study aims to explore the associations between childhood body size and the risk of mortality and major non-communicable diseases (NCDs), and the role of a lifestyle in adulthood in these associations.

Methods: This study included 358,990 UK Biobank participants (mean age 56.3 years, 53.2% female). Childhood body size at age 10 was self-reported as thinner, average, or plumper. Adult lifestyle factors included physical activity, diet, sleep duration, smoking, and alcohol consumption. Outcomes included risk of mortality and 47 NCDs. Cox regression models were used to estimate associations between childhood body size and outcomes. Mediation and interaction analyses assessed the role of adult lifestyle in these associations.

Results: Here we show that, individuals with plumper body size have a higher risk of mortality and 26 NCDs, compared to those with average childhood body size, where 1.07% to 28.54% of these risks are mediated by adult lifestyle. Thinner body size is associated with increased risk of 24 NCDs, with 2.12% to 32.59% of the risks mediated by adult lifestyle. Significant interactions are observed between plumper childhood body size and adult lifestyle for all-cause mortality and 6 NCDs, including hypertension, alcohol problems, constipation, diverticular disease, chronic obstructive pulmonary disease, and chronic kidney disease.

Conclusions: Both plumper and thinner body sizes during childhood are associated with an increased risk of developing NCDs later in life. However, adherence to a healthier lifestyle in adulthood may partially mitigate these long-term health risks, especially for individuals with larger childhood body size.

Background: Chronological age does not capture individual health or resilience. Advances in metabolomics have enabled development of molecular aging biomarkers that capture deviations between biological and chronological age, highlighting how genetics, environment, and lifestyle shape biological aging. Despite their promise, metabolomic biomarkers face challenges such as interpretability, non-linearity, and reproducibility.

Methods: We have developed a metabolomic predictor of biological age based on untargeted metabolomic profiling of individuals aged 45-85 years from the Canadian Longitudinal Study on Aging. To enhance interpretability, we first identified metabolites related to health based on variance heterogeneity. For metabolites with identifiable optimal levels, or "sweet spots", we modeled non-linearity using deviations from these values. A penalized regression model was trained on the Frailty Index using sweet spot deviations as predictors.

Results: Here we show that the Sweet Spot Clock built on 178 health-related metabolites is strongly associated with all-cause mortality (HR = 1.08, p = 5.8×10^-12, C-index=0.841) and age-related diseases. The biomarker outperforms models trained on chronological age and those using raw metabolite levels, underscoring the importance of modeling non-linearity. It remains predictive after adjusting for age, sex, lifestyle and socioeconomic factors, though its added value over standard health and demographic measures is modest. The model generalizes to an independent cohort of individuals aged 85 years and older.

Conclusions: The Sweet Spot Clock provides a reproducible and interpretable measure of biological age. By modeling deviations from optimal metabolite levels and training on health status rather than age, it offers a tool for understanding aging heterogeneity and identifying individuals at risk of health decline.

Background: Systematic profiling of plasma proteins in population studies offers a complementary approach to discovery of novel risk factors and may provide new insights into the causes of coronary heart disease.

Methods: To explore relationships between the circulating proteome and coronary heart disease (CHD), we evaluated associations of 4780 plasma proteins with incident CHD in the Cardiovascular Health Study (CHS, N=2856, 575 CHD events) and replicated significant associations in the Atherosclerosis Risk in Communities Study (ARIC, N = 10456; 1375 events).

Results: We find that 11 proteins significantly associate with incident CHD after adjusting for risk factors; and eight significantly replicated in ARIC. Several proteins correlate with carotid intimal medial thickness and CHD associations are attenuated in participants without subclinical atherosclerosis. Macrophage metalloelastase (MMP12) is the strongest observed association (Hazard Ratio, 1.31; 95% Confidence Interval, 1.19-1.44). Mendelian randomization (MR) identifies a causal relationship between higher MMP12 and lower CHD (Odds Ratio, OR 0.94) and ischemic stroke (OR 0.90) risk, while reverse MR found that genetic propensity to CHD increased MMP12. Taken together, multivariable MR confirms a direct protective effect of higher plasma MMP12 on CHD risk and a genetic effect of atherosclerosis and CHD on elevating MMP12.

Conclusions: Proteomic analyses reveal associations with incident CHD and genomic evidence suggests that therapeutic MMP12 inhibition may confer adverse cardiovascular effects.

Background: Despite rapid advances in treatment, breast cancer remains the leading cause of cancer mortality in women, with triple negative breast cancers having a particularly poor prognosis. Some tumors have (epi)genetic alterations causing homologous recombination deficiency, providing opportunities for targeted therapeutics including poly (ADP-ribose) polymerase inhibitors. However, the effects of targeted treatments are variable; therefore, functional assays are needed to predict the best personalized treatment options.

Methods: We developed a high-throughput spheroid-based assay using patient-derived breast cancer xenograft models sensitive and resistant to cisplatin. Methods were developed for automatic spheroid segmentation using deep learning to measure response of spheroids to treatment with cisplatin, olaparib and radiotherapy. We developed a method to distinguish between sensitive and resistant tumors based on predicting the percentage of responding and non-responding spheroids.

Results: Here we show that differences in treatment response between cisplatin-sensitive and resistant tumors faithfully correspond with the expected in vivo responses. The assay is able to discriminate between olaparib-sensitive and resistant tumors based on predicting the percentage of responding and non-responding spheroids.

Conclusions: We demonstrate that this assay, guided by automatic spheroid segmentation using deep learning, may report on the tumor's sensitivity to therapies with the potential to be applied to functional precision oncology for breast cancer.