Defining a Cancer Dependency Map.

Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.

Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells.

Cancer cells within individual tumors often exist in distinct phenotypic states that differ in functional attributes. While cancer cell populations typically display distinctive equilibria in the proportion of cells in various states, the mechanisms by which this occurs are poorly understood. Here, we study the dynamics of phenotypic proportions in human breast cancer cell lines. We show that subpopulations of cells purified for a given phenotypic state return towards equilibrium proportions over time. These observations can be explained by a Markov model in which cells transition stochastically between states. A prediction of this model is that, given certain conditions, any subpopulation of cells will return to equilibrium phenotypic proportions over time. A second prediction is that breast cancer stem-like cells arise de novo from non-stem-like cells. These findings contribute to our understanding of cancer heterogeneity and reveal how stochasticity in single-cell behaviors promotes phenotypic equilibrium in populations of cancer cells.

Causal association of blood lipids with all-cause and cause-specific mortality risk: a Mendelian randomization study.

Dyslipidemia has long been implicated in elevating mortality risk; yet, the precise associations between lipid traits and mortality remained undisclosed. Our study aimed to explore the causal effects of lipid traits on both all-cause and cause-specific mortality. One-sample Mendelian randomization (MR) with linear and nonlinear assumptions was conducted in a cohort of 407,951 European participants from the UK Biobank. Six lipid traits, consisting of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides, apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), and lipoprotein(a), were included to investigate the causal associations with mortality. Two-sample MR was performed to replicate the association between each lipid trait and all-cause mortality. Univariable MR results showed that genetically predicted higher ApoA1 was significantly associated with a decreased all-cause mortality risk (HR[95% CI]:0.93 [0.89-0.97], P value = 0.001), which was validated by the two-sample MR analysis. Higher lipoprotein(a) was associated with an increased risk of all-cause mortality (1.03 [1.01-1.04], P value = 0.002). Multivariable MR confirmed the direct causal effects of ApoA1 and lipoprotein(a) on all-cause mortality. Meanwhile, nonlinear MR found no evidence for nonlinearity between lipids and all-cause mortality. Our examination into cause-specific mortality revealed a suggestive inverse association between ApoA1 and cancer mortality, a significant positive association between lipoprotein(a) and cardiovascular disease mortality, and a suggestive positive association between lipoprotein(a) and digestive disease mortality. High LDL-C was associated with an increased risk of cardiovascular disease mortality but a decreased risk of neurodegenerative disease mortality. The findings suggest that implementing interventions to raise ApoA1 and decrease lipoprotein(a) levels may improve overall health outcomes and mitigate cancer and digestive disease mortality.

Genome-wide association and Mendelian randomisation analysis among 30,699 Chinese pregnant women identifies novel genetic and molecular risk factors for gestational diabetes and glycaemic traits.

AIMS/HYPOTHESIS: Gestational diabetes mellitus (GDM) is the most common disorder in pregnancy; however, its underlying causes remain obscure. This study aimed to investigate the genetic and molecular risk factors contributing to GDM and glycaemic traits. METHODS: We collected non-invasive prenatal test (NIPT) sequencing data along with four glycaemic and 55 biochemical measurements from 30,699 pregnant women during a 2 year period at Shenzhen Baoan Women's and Children's Hospital in China. Genome-wide association studies (GWAS) were conducted between genotypes derived from NIPTs and GDM diagnosis, baseline glycaemic levels and glycaemic levels after glucose challenges. In total, 3317 women were diagnosed with GDM, while 19,565 served as control participants. The results were replicated using two independent cohorts. Additionally, we performed one-sample Mendelian randomisation to explore potential causal associations between the 55 biochemical measurements and risk of GDM and glycaemic levels. RESULTS: We identified four genetic loci significantly associated with GDM susceptibility. Among these, MTNR1B exhibited the highest significance (rs10830963-G, OR [95% CI] 1.57 [1.45, 1.70], p=4.42×10-29), although its effect on type 2 diabetes was modest. Furthermore, we found 31 genetic loci, including 14 novel loci, that were significantly associated with the four glycaemic traits. The replication rates of these associations with GDM, fasting plasma glucose levels and 0 h, 1 h and 2 h OGTT glucose levels were 4 out of 4, 6 out of 9, 10 out of 11, 5 out of 7 and 4 out of 4, respectively. Mendelian randomisation analysis suggested that a genetically regulated higher lymphocytes percentage and lower white blood cell count, neutrophil percentage and absolute neutrophil count were associated with elevated glucose levels and an increased risk of GDM. CONCLUSIONS/INTERPRETATION: Our findings provide new insights into the genetic basis of GDM and glycaemic traits during pregnancy in an East Asian population and highlight the potential role of inflammatory pathways in the aetiology of GDM and variations in glycaemic levels. DATA AVAILABILITY: Summary statistics for GDM; fasting plasma glucose; 0 h, 1 h and 2h OGTT; and the 55 biomarkers are available in the GWAS Atlas (study accession no.: GVP000001, https://ngdc.cncb.ac.cn/gwas/browse/GVP000001) .

Circulating metabolomic markers linking diabetic kidney disease and incident cardiovascular disease in type 2 diabetes: analyses from the Hong Kong Diabetes Biobank.

AIMS/HYPOTHESIS: The aim of this study was to describe the metabolome in diabetic kidney disease (DKD) and its association with incident CVD in type 2 diabetes, and identify prognostic biomarkers. METHODS: From a prospective cohort of individuals with type 2 diabetes, baseline sera (N=1991) were quantified for 170 metabolites using NMR spectroscopy with median 5.2 years of follow-up. Associations of chronic kidney disease (CKD, eGFR<60 ml/min per 1.73 m2) or severely increased albuminuria with each metabolite were examined using linear regression, adjusted for confounders and multiplicity. Associations between DKD (CKD or severely increased albuminuria)-related metabolites and incident CVD were examined using Cox regressions. Metabolomic biomarkers were identified and assessed for CVD prediction and replicated in two independent cohorts. RESULTS: At false discovery rate (FDR)<0.05, 156 metabolites were associated with DKD (151 for CKD and 128 for severely increased albuminuria), including apolipoprotein B-containing lipoproteins, HDL, fatty acids, phenylalanine, tyrosine, albumin and glycoprotein acetyls. Over 5.2 years of follow-up, 75 metabolites were associated with incident CVD at FDR<0.05. A model comprising age, sex and three metabolites (albumin, triglycerides in large HDL and phospholipids in small LDL) performed comparably to conventional risk factors (C statistic 0.765 vs 0.762, p=0.893) and adding the three metabolites further improved CVD prediction (C statistic from 0.762 to 0.797, p=0.014) and improved discrimination and reclassification. The 3-metabolite score was validated in independent Chinese and Dutch cohorts. CONCLUSIONS/INTERPRETATION: Altered metabolomic signatures in DKD are associated with incident CVD and improve CVD risk stratification.

Heavy chain sequence-based classifier for the specificity of human antibodies.

Antibodies specifically bind to antigens and are an essential part of the immune system. Hence, antibodies are powerful tools in research and diagnostics. High-throughput sequencing technologies have promoted comprehensive profiling of the immune repertoire, which has resulted in large amounts of antibody sequences that remain to be further analyzed. In this study, antibodies were downloaded from IMGT/LIGM-DB and Sequence Read Archive databases. Contributing features from antibody heavy chains were formulated as numerical inputs and fed into an ensemble machine learning classifier to classify the antigen specificity of six classes of antibodies, namely anti-HIV-1, anti-influenza virus, anti-pneumococcal polysaccharide, anti-citrullinated protein, anti-tetanus toxoid and anti-hepatitis B virus. The classifier was validated using cross-validation and a testing dataset. The ensemble classifier achieved a macro-average area under the receiver operating characteristic curve (AUC) of 0.9246 from the 10-fold cross-validation, and 0.9264 for the testing dataset. Among the contributing features, the contribution of the complementarity-determining regions was 53.1% and that of framework regions was 46.9%, and the amino acid mutation rates occupied the first and second ranks among the top five contributing features. The classifier and insights provided in this study could promote the mechanistic study, isolation and utilization of potential therapeutic antibodies.

Identification of selective inhibitors of cancer stem cells by high-throughput screening.

Screens for agents that specifically kill epithelial cancer stem cells (CSCs) have not been possible due to the rarity of these cells within tumor cell populations and their relative instability in culture. We describe here an approach to screening for agents with epithelial CSC-specific toxicity. We implemented this method in a chemical screen and discovered compounds showing selective toxicity for breast CSCs. One compound, salinomycin, reduces the proportion of CSCs by >100-fold relative to paclitaxel, a commonly used breast cancer chemotherapeutic drug. Treatment of mice with salinomycin inhibits mammary tumor growth in vivo and induces increased epithelial differentiation of tumor cells. In addition, global gene expression analyses show that salinomycin treatment results in the loss of expression of breast CSC genes previously identified by analyses of breast tissues isolated directly from patients. This study demonstrates the ability to identify agents with specific toxicity for epithelial CSCs.

Graphical education and appropriate time before elective colonoscopy make better bowel preparation.

Background: Inadequate bowel preparation leads to lower polyp detection rates, longer procedure times and lower cecal intubation rates. However, there is no consensus about high-quality bowel preparation, so our study evaluated graphical education and appropriate time before elective colonoscopy. Patients and Methods: We performed a secondary analysis of a national colorectal cancer screening programme of 738 patients. The patients were divided into a group given a graphical information manual (n = 242) or a word-only one (n = 496). They were also divided into groups according to the interval between bowel preparation and colonoscopy: 6-8 h (Group 1, n = 106), 9-12 h (Group 2, n = 228) and 13-17 h (Group 3, n = 402). All patients were scored according to the Boston Bowel Preparation Scale (BBPS) during the examination. Results: The bowel preparation of the graphical group was significantly better than the text group (P < 0.001). After adjustment, the bowel preparation score of Group 1 and Group 2 were both significantly higher than that of Group 3 (P = 0.012 and P = 0.032). Maximum BBPS was 6.31 when the interval time was 6.52 h (95% confidence interval: 5.95-6.66), and when the interval was <10 h, the BBPS was ≥6. Conclusion: High-quality bowel preparation was linked to graphical education and appropriate time before colonoscopy. We suggest that the interval between taking the first laxative and colonoscopy should be <10 h, preferably 6.5 h. Prospective multicentre research is needed to give more evidence of high-quality bowel preparation methods.

Relative leucocyte telomere length is associated with incident end-stage kidney disease and rapid decline of kidney function in type 2 diabetes: analysis from the Hong Kong Diabetes Register.

AIMS/HYPOTHESIS: Few large-scale prospective studies have investigated associations between relative leucocyte telomere length (rLTL) and kidney dysfunction in individuals with type 2 diabetes. We examined relationships between rLTL and incident end-stage kidney disease (ESKD) and the slope of eGFR decline in Chinese individuals with type 2 diabetes. METHODS: We studied 4085 Chinese individuals with type 2 diabetes observed between 1995 and 2007 in the Hong Kong Diabetes Register with stored baseline DNA and available follow-up data. rLTL was measured using quantitative PCR. ESKD was diagnosed based on the ICD-9 code and eGFR. RESULTS: In this cohort (mean ± SD age 54.3 ± 12.6 years) followed up for 14.1 ± 5.3 years, 564 individuals developed incident ESKD and had shorter rLTL at baseline (4.2 ± 1.2 vs 4.7 ± 1.2, p < 0.001) than the non-progressors (n = 3521). On Cox regression analysis, each ∆∆Ct decrease in rLTL was associated with an increased risk of incident ESKD (HR 1.21 [95% CI 1.13, 1.30], p < 0.001); the association remained significant after adjusting for baseline age, sex, HbA1c, lipids, renal function and other risk factors (HR 1.11 [95% CI 1.03, 1.19], p = 0.007). Shorter rLTL at baseline was associated with rapid decline in eGFR (>4% per year) during follow-up (unadjusted OR 1.22 [95% CI 1.15, 1.30], p < 0.001; adjusted OR 1.09 [95% CI 1.01, 1.17], p = 0.024). CONCLUSIONS/INTERPRETATION: rLTL is independently associated with incident ESKD and rapid eGFR loss in individuals with type 2 diabetes. Telomere length may be a useful biomarker for the progression of kidney function and ESKD in type 2 diabetes.

Nonalbuminuric Diabetic Kidney Disease and Risk of All-Cause Mortality and Cardiovascular and Kidney Outcomes in Type 2 Diabetes: Findings From the Hong Kong Diabetes Biobank.

RATIONALE & OBJECTIVE: Nonalbuminuric diabetic kidney disease (DKD) has become the prevailing DKD phenotype. We compared the risks of adverse outcomes among patients with this phenotype compared with other DKD phenotypes. STUDY DESIGN: Multicenter prospective cohort study. SETTINGS & PARTICIPANTS: 19,025 Chinese adults with type 2 diabetes enrolled in the Hong Kong Diabetes Biobank. EXPOSURES: DKD phenotypes defined by baseline estimated glomerular filtration rate (eGFR) and albuminuria: no DKD (no decreased eGFR or albuminuria), albuminuria without decreased eGFR, decreased eGFR without albuminuria, and albuminuria with decreased eGFR. OUTCOMES: All-cause mortality, cardiovascular disease (CVD) events, hospitalization for heart failure (HF), and chronic kidney disease (CKD) progression (incident kidney failure or sustained eGFR reduction ≥40%). ANALYTICAL APPROACH: Multivariable Cox proportional or cause-specific hazards models to estimate the relative risks of death, CVD, hospitalization for HF, and CKD progression. Multiple imputation was used for missing covariates. RESULTS: Mean participant age was 61.1 years, 58.3% were male, and mean diabetes duration was 11.1 years. During 54,260 person-years of follow-up, 438 deaths, 1,076 CVD events, 298 hospitalizations for HF, and 1,161 episodes of CKD progression occurred. Compared with the no-DKD subgroup, the subgroup with decreased eGFR without albuminuria had higher risks of all-cause mortality (hazard ratio [HR], 1.59 [95% CI, 1.04-2.44]), hospitalization for HF (HR, 3.08 [95% CI, 1.82-5.21]), and CKD progression (HR, 2.37 [95% CI, 1.63-3.43]), but the risk of CVD was not significantly greater (HR, 1.14 [95% CI, 0.88-1.48]). The risks of death, CVD, hospitalization for HF, and CKD progression were higher in the setting of albuminuria with or without decreased eGFR. A sensitivity analysis that excluded participants with baseline eGFR <30 mL/min/1.73 m2 yielded similar findings. LIMITATIONS: Potential misclassification because of drug use. CONCLUSIONS: Nonalbuminuric DKD was associated with higher risks of hospitalization for HF and of CKD progression than no DKD, regardless of baseline eGFR.

High-density lipoprotein subclasses and cardiovascular disease and mortality in type 2 diabetes: analysis from the Hong Kong Diabetes Biobank.

OBJECTIVE: High-density lipoproteins (HDL) comprise particles of different size, density and composition and their vasoprotective functions may differ. Diabetes modifies the composition and function of HDL. We assessed associations of HDL size-based subclasses with incident cardiovascular disease (CVD) and mortality and their prognostic utility. RESEARCH DESIGN AND METHODS: HDL subclasses by nuclear magnetic resonance spectroscopy were determined in sera from 1991 fasted adults with type 2 diabetes (T2D) consecutively recruited from March 2014 to February 2015 in Hong Kong. HDL was divided into small, medium, large and very large subclasses. Associations (per SD increment) with outcomes were evaluated using multivariate Cox proportional hazards models. C-statistic, integrated discrimination index (IDI), and categorial and continuous net reclassification improvement (NRI) were used to assess predictive value. RESULTS: Over median (IQR) 5.2 (5.0-5.4) years, 125 participants developed incident CVD and 90 participants died. Small HDL particles (HDL-P) were inversely associated with incident CVD [hazard ratio (HR) 0.65 (95% CI 0.52, 0.81)] and all-cause mortality [0.47 (0.38, 0.59)] (false discovery rate < 0.05). Very large HDL-P were positively associated with all-cause mortality [1.75 (1.19, 2.58)]. Small HDL-P improved prediction of mortality [C-statistic 0.034 (0.013, 0.055), IDI 0.052 (0.014, 0.103), categorical NRI 0.156 (0.006, 0.252), and continuous NRI 0.571 (0.246, 0.851)] and CVD [IDI 0.017 (0.003, 0.038) and continuous NRI 0.282 (0.088, 0.486)] over the RECODe model. CONCLUSION: Small HDL-P were inversely associated with incident CVD and all-cause mortality and improved risk stratification for adverse outcomes in people with T2D. HDL-P may be used as markers for residual risk in people with T2D.

Obesity, clinical, and genetic predictors for glycemic progression in Chinese patients with type 2 diabetes: A cohort study using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank.

BACKGROUND: Type 2 diabetes (T2D) is a progressive disease whereby there is often deterioration in glucose control despite escalation in treatment. There is significant heterogeneity to this progression of glycemia after onset of diabetes, yet the factors that influence glycemic progression are not well understood. Given the tremendous burden of diabetes in the Chinese population, and limited knowledge on factors that influence glycemia, we aim to identify the clinical and genetic predictors for glycemic progression in Chinese patients with T2D. METHODS AND FINDINGS: In 1995-2007, 7,091 insulin-naïve Chinese patients (mean age 56.8 ± 13.3 [SD] years; mean age of T2D onset 51.1 ± 12.7 years; 47% men; 28.4% current or ex-smokers; median duration of diabetes 4 [IQR: 1-9] years; mean HbA1c 7.4% ± 1.7%; mean body mass index [BMI] 25.3 ± 4.0 kg/m2) were followed prospectively in the Hong Kong Diabetes Register. We examined associations of BMI and other clinical and genetic factors with glycemic progression defined as requirement of continuous insulin treatment, or 2 consecutive HbA1c ≥8.5% while on ≥2 oral glucose-lowering drugs (OGLDs), with validation in another multicenter cohort of Hong Kong Diabetes Biobank. During a median follow-up period of 8.8 (IQR: 4.8-13.3) years, incidence of glycemic progression was 48.0 (95% confidence interval [CI] 46.3-49.8) per 1,000 person-years with 2,519 patients started on insulin. Among the latter, 33.2% had a lag period of 1.3 years before insulin was initiated. Risk of progression was associated with extremes of BMI and high HbA1c. On multivariate Cox analysis, early age at diagnosis, microvascular complications, high triglyceride levels, and tobacco use were additional independent predictors for glycemic progression. A polygenic risk score (PRS) including 123 known risk variants for T2D also predicted rapid progression to insulin therapy (hazard ratio [HR]: 1.07 [95% CI 1.03-1.12] per SD; P = 0.001), with validation in the replication cohort (HR: 1.24 [95% CI 1.06-1.46] per SD; P = 0.008). A PRS using 63 BMI-related variants predicted BMI (beta [SE] = 0.312 [0.057] per SD; P = 5.84 × 10-8) but not glycemic progression (HR: 1.01 [95% CI 0.96-1.05] per SD; P = 0.747). Limitations of this study include potential misdiagnosis of T2D and lack of detailed data of drug use during follow-up in the replication cohort. CONCLUSIONS: Our results show that approximately 5% of patients with T2D failed OGLDs annually in this clinic-based cohort. The independent associations of modifiable and genetic risk factors allow more precise identification of high-risk patients for early intensive control of multiple risk factors to prevent glycemic progression.

SNPs in PRKCA-HIF1A-GLUT1 are associated with diabetic kidney disease in a Chinese Han population with type 2 diabetes.

OBJECTIVE: To explore the relationship between SNPs in PRKCA-HIF1A-GLUT1 and diabetic kidney disease in Chinese Han people. MATERIALS AND METHODS: A total of 2552 participants from Shanghai Diabetes Institute Inpatient Database of Shanghai Jiao Tong University Affiliated Sixth People's Hospital were involved in the stage 1 cross-sectional population. A total of 6015 subjects from the Hong Kong Diabetes Register were included for validation. Genotyping of participants was conducted by the MassARRAY Compact Analyzer (Agena Bioscience). The data were analysed by plink, SAS, Haploview. RESULTS: We identified variants associated with diabetic kidney disease in stage 1. Rs1681851 (P = .0105, OR = 1.331) in GLUT1 as well as rs2301108 (P = .0085, OR = 1.289) and rs79865957 (P = .0204, OR = 1.263) in HIF1A were significantly associated with diabetic kidney disease. Regarding DKD-related traits, rs1681851 was associated with plasma creatinine levels (P = .0169, ß = 4.822) and eGFR (P = .0457, ß = -6.956). Moreover, the results showed the interactions between PRKCA-GLUT1 in the occurrence of DKD. We further sought validation of the 17 SNPs in a prospective cohort and found that rs900836 and rs844501 were associated with the percentage change in eGFR slope. We performed a meta-analysis of case-control analysis data from the Hong Kong samples together with the stage 1 data from Shanghai. Rs9894851 showed significant correlation with the serum creatinine level as well as eGFR and no SNP showed association with DKD after meta-analysis. CONCLUSIONS: Our results suggest potential association between SNPs in PRKCA-HIF1A-GLUT1 and diabetic kidney disease in Chinese Han people.

Progression of glucose intolerance and cardiometabolic risk factors over a decade in Chinese women with polycystic ovary syndrome: A case-control study.

BACKGROUND: Polycystic ovary syndrome (PCOS) is associated with increased metabolic risk, though data on long-term follow-up of cardiometabolic traits are limited. We postulated that Chinese women with PCOS would have higher risk of incident diabetes and cardiometabolic abnormalities than those without PCOS during long-term follow-up. METHODS AND FINDINGS: One hundred ninety-nine Chinese women with PCOS diagnosed by the Rotterdam criteria and with a mean age of 41.2 years (SD = 6.4) completed a follow-up evaluation after an average of 10.6 ± 1.3 years. Two hundred twenty-five women without PCOS (mean age: 54.1 ± 6.7 years) who underwent baseline and follow-up evaluation over the same period were used for comparison. Progression of glycaemic status of women both with and without PCOS was assessed by using 75-g oral glucose tolerance test (OGTT) screening with the adoption of 2009 American Diabetes Association diagnostic criteria. The frequency of impaired glucose regulation, hypertension, and hyperlipidaemia of women with PCOS at follow-up has increased from 31.7% (95% CI 25.2%-38.1%) to 47.2% (95% CI 40.3%-54.2%), 16.1% (95% CI 11.0%-21.2%) to 34.7% (95% CI 28.1%-41.3%), and 52.3% (95% CI 45.3%-59.2%) to 64.3% (95% CI 57.7%-71.0%), respectively. The cumulative incidence of diabetes mellitus (DM) in follow-up women with PCOS is 26.1% (95% CI 20.0%-32.2%), almost double that in the cohort of women without PCOS (p < 0.001). Age-standardised incidence of diabetes among women with PCOS was 22.12 per 1,000 person-years (95% CI 10.86-33.37) compared with the local female population incidence rate of 8.76 per 1,000 person-years (95% CI 8.72-8.80) and 10.09 per 1,000 person-years (95% CI 4.92-15.26, p < 0.001) for women without PCOS in our study. Incidence rate for women with PCOS aged 30-39 years was 20.56 per 1,000 person-years (95% CI 12.57-31.87), which is approximately 10-fold higher than that of the age-matched general female population in Hong Kong (1.88 per 1,000 person-years, [95% CI 1.85-1.92]). The incidence rate of type 2 DM (T2DM) of both normal-weight and overweight women with PCOS was around double that of corresponding control groups (normal weight: 8.96 [95% CI 3.92-17.72] versus 4.86 per 1,000 person-years [95% CI 2.13-9.62], p > 0.05; overweight/obese: 28.64 [95% CI 19.55-40.60] versus 14.1 per 1,000 person-years [95% CI 8.20-22.76], p < 0.05). Logistic regression analysis identified that baseline waist-to-hip ratio (odds ratio [OR] = 1.71 [95% CI 1.08-2.69], p < 0.05) and elevated triglyceride (OR = 6.63 [95% CI 1.23-35.69], p < 0.05) are associated with the progression to T2DM in PCOS. Limitations of this study include moderate sample size with limited number of incident diabetes during follow-up period and potential selection bias. CONCLUSIONS: High risk of diabetes and increased cardiovascular disease risk factors among Chinese women with PCOS are highlighted in this long-term follow-up study. Diabetes onset was, on average, 10 years earlier among women with PCOS than in women without PCOS.

Progression of diabetic kidney disease and trajectory of kidney function decline in Chinese patients with Type 2 diabetes.

Diabetes is a major cause of end stage renal disease (ESRD), yet the natural history of diabetic kidney disease is not well understood. We aimed to identify patterns of estimated GFR (eGFR) trajectory and to determine the clinical and genetic factors and their associations of these different patterns with all-cause mortality in patients with type 2 diabetes. Among 6330 patients with baseline eGFR >60 ml/min per 1.73 m2 in the Hong Kong Diabetes Register, a total of 456 patients (7.2%) developed Stage 5 chronic kidney disease or ESRD over a median follow-up of 13 years (incidence rate 5.6 per 1000 person-years). Joint latent class modeling was used to identify different patterns of eGFR trajectory. Four distinct and non-linear trajectories of eGFR were identified: slow decline (84.3% of patients), curvilinear decline (6.5%), progressive decline (6.1%) and accelerated decline (3.1%). Microalbuminuria and retinopathy were associated with accelerated eGFR decline, which was itself associated with all-cause mortality (odds ratio [OR] 6.9; 95% confidence interval [CI]: 5.6-8.4 for comparison with slow eGFR decline). Of 68 candidate genetic loci evaluated, the inclusion of five loci (rs11803049, rs911119, rs1933182, rs11123170, and rs889472) improved the prediction of eGFR trajectories (net reclassification improvement 0.232; 95% CI: 0.057--0.406). Our study highlights substantial heterogeneity in the patterns of eGFR decline among patients with diabetic kidney disease, and identifies associated clinical and genetic factors that may help to identify those who are more likely to experience an accelerated decline in kidney function.

Genetic and clinical variables identify predictors for chronic kidney disease in type 2 diabetes.

Type 2 diabetes and chronic kidney disease (CKD) may share common risk factors. Here we used a 3-stage procedure to discover novel predictors of CKD by repeatedly applying a stepwise selection based on the Akaike information criterion to subsamples of a prospective complete-case cohort of 2755 patients. This cohort encompassed 25 clinical variables and 36 genetic variants associated with type 2 diabetes, obesity, or fasting plasma glucose. We compared the performance of the clinical, genetic, and clinico-genomic models and used net reclassification improvement to evaluate the impact of top selected genetic variants to the clinico-genomic model. Associations of selected genetic variants with CKD were validated in 2 independent cohorts followed by meta-analyses. Among the top 6 single-nucleotide polymorphisms selected from clinico-genomic data, three (rs478333 of G6PC2, rs7754840 and rs7756992 of CDKAL1) contributed toward the improvement of prediction performance. The variant rs478333 was associated with rapid decline (over 4% per year) in estimated glomerular filtration rate. In a meta-analysis of 2 replication cohorts, the variants rs478333 and rs7754840 showed significant associations with CKD after adjustment for conventional risk factors. Thus, this novel 3-stage approach to a clinico-genomic data set identified 3 novel genetic predictors of CKD in type 2 diabetes. This method can be applied to similar data sets containing clinical and genetic variables to select predictors for clinical outcomes.

Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer.

A comprehensive understanding of the molecular vulnerabilities of every type of cancer will provide a powerful roadmap to guide therapeutic approaches. Efforts such as The Cancer Genome Atlas Project will identify genes with aberrant copy number, sequence, or expression in various cancer types, providing a survey of the genes that may have a causal role in cancer. A complementary approach is to perform systematic loss-of-function studies to identify essential genes in particular cancer cell types. We have begun a systematic effort, termed Project Achilles, aimed at identifying genetic vulnerabilities across large numbers of cancer cell lines. Here, we report the assessment of the essentiality of 11,194 genes in 102 human cancer cell lines. We show that the integration of these functional data with information derived from surveying cancer genomes pinpoints known and previously undescribed lineage-specific dependencies across a wide spectrum of cancers. In particular, we found 54 genes that are specifically essential for the proliferation and viability of ovarian cancer cells and also amplified in primary tumors or differentially overexpressed in ovarian cancer cell lines. One such gene, PAX8, is focally amplified in 16% of high-grade serous ovarian cancers and expressed at higher levels in ovarian tumors. Suppression of PAX8 selectively induces apoptotic cell death of ovarian cancer cells. These results identify PAX8 as an ovarian lineage-specific dependency. More generally, these observations demonstrate that the integration of genome-scale functional and structural studies provides an efficient path to identify dependencies of specific cancer types on particular genes and pathways.

Network-based approach for drug repurposing against mpox.

The current outbreak of mpox presents a significant threat to the global community. However, the lack of mpox-specific drugs necessitates the identification of additional candidates for clinical trials. In this study, a network medicine framework was used to investigate poxviruses-human interactions to identify potential drugs effective against the mpox virus (MPXV). The results indicated that poxviruses preferentially target hubs on the human interactome, and that these virally-targeted proteins (VTPs) tend to aggregate together within specific modules. Comorbidity analysis revealed that mpox is closely related to immune system diseases. Based on predicted drug-target interactions, 268 drugs were identified using the network proximity approach, among which 23 drugs displaying the least side-effects and significant proximity to MPXV were selected as the final candidates. Lastly, specific drugs were explored based on VTPs, differentially expressed proteins, and intermediate nodes, corresponding to different categories. These findings provide novel insights that can contribute to a deeper understanding of the pathogenesis of MPXV and development of ready-to-use treatment strategies based on drug repurposing.

SARS-CoV-2 Omicron infection augments the magnitude and durability of systemic and mucosal immunity in triple-dose CoronaVac recipients.

The inactivated whole-virion vaccine, CoronaVac, is one of the most widely used coronavirus disease 2019 (COVID-19) vaccines worldwide. There is a paucity of data indicating the durability of the immune response and the impact of immune imprinting induced by CoronaVac upon Omicron infection. In this prospective cohort study, 41 recipients of triple-dose CoronaVac and 14 unvaccinated individuals were recruited. We comprehensively profiled adaptive immune parameters in both groups, including spike-specific immunoglobulin (Ig) G and IgA titers, neutralizing activity, B cells, circulating follicular helper T (cTfh) cells, CD4+ and CD8+ T cells, and their memory subpopulations at 12 months after the third booster dose and at 4 and 20 weeks after Omicron BA.5 infection. Twelve months after the third CoronaVac vaccination, spike-specific antibodies and cellular responses were detectable in most vaccinated individuals. BA.5 infection significantly augmented the magnitude, cross-reactivity, and durability of serum neutralization activities, Fc-mediated phagocytosis, nasal spike-specific IgA responses, memory B cells, activated cTfh cells, memory CD4+ T cells, and memory CD8+ T cells for both the ancestral strain and Omicron subvariants, compared to unvaccinated individuals. Notably, the increase in BA.5-specific immunity after breakthrough infection was consistently comparable to or higher than that of the ancestral strain, suggesting no evidence of immune imprinting. Immune landscape analyses showed that vaccinated individuals have better synchronization of multiple immune components than unvaccinated individuals upon heterologous infection. Our data provide detailed insight into the protective role of the inactivated COVID-19 vaccine in shaping humoral and cellular immunity to Omicron infection. IMPORTANCE: There is a paucity of data indicating the durability of the immune response and the impact of immune imprinting induced by CoronaVac upon Omicron breakthrough infection. In this prospective cohort study, the anti-severe acute respiratory syndrome coronavirus 2 adaptive responses were analyzed before and after the Omicron BA.5 infection. Our data provide detailed insight into the protective role of the inactivated COVID-19 vaccine in shaping humoral and cellular immune responses to heterologous Omicron infection. CLINICAL TRIAL: This study is registered with ClinicalTrials.gov as NCT05680896.