Ovarian cancer is the deadliest gynaecological malignancy, and most cases are diagnosed at an advanced stage. There is no population-wide screening; models for predicting risk of ovarian cancer could be useful in decreasing mortality. We aimed to provide the first comprehensive overview of models for predicting the risk of developing ovarian cancer and their performance, risk of bias, and applicability. We performed a systematic search through MEDLINE and Embase to identify articles in which models for predicting risk of developing ovarian cancer were developed and/or validated. Model characteristics and measures of predictive ability were extracted and summarised for each model. The Prediction model Risk of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability. We identified twenty-eight studies representing twenty-one ovarian cancer risk prediction models. Fifteen risk prediction models were developed or validated in the general population, while six were developed for high-risk populations, defined by family history, and one for patients with endometriosis. Twenty-three studies that reported discrimination and/or calibration had moderate-to-good performance. Risk prediction models with the best predictive ability reported an area under the receiver-operating characteristic curve (AUC) of 0.86 (95% confidence interval (CI) 0.84–0.87) for 2-year and an AUC of 0.77 (95% CI 0.76-0.78) for 10-year ovarian cancer risk in the general population. Several models for predicting short- and long-term risk of ovarian cancer have been published, of which a few have been validated in other populations. Some have limited predictive performance. Some models may be suitable for use in primary care or specialty clinics; however, assessment of their utility and validation in diverse cohorts is warranted. We aimed to identify in the published literature all models that have been developed to predict risk of developing ovarian cancer, and any studies that have been done to evaluate how well such models predict in another population. From the identified articles, we extracted information on the models developed or evaluated, as well as any information on their ability to accurately predict the risk of developing ovarian cancer in the future. We identified 28 articles and 21 models. Some were good at predicting the risk of ovarian cancer, but before they can be considered for use in practice, they would need to be evaluated in the population in which the prediction is to be made. If performing well, they may be useful for identifying individuals at a high risk of developing ovarian cancer who may benefit from early detection and prevention strategies or from enrolment into studies assessing the benefit of new such strategies. Ardasheva et al. provide an overview of models developed to predict the risk of developing ovarian cancer which may be used to identify individuals at a high risk.
Diagnosing tuberculosis (TB) in children living with HIV remains challenging due to difficulty in obtaining adequate samples, the paucibacillary nature of the disease, and symptom overlap with other infections. We previously identified hundreds of differentially expressed transcripts (DETs) in Ugandan and Southern African children living with HIV, yet their number limits clinical utility. Machine learning approaches can derive a compact, translatable diagnostic signature. We analyzed whole blood RNA-seq data from children living with HIV in Uganda (discovery, n = 59) and Southern Africa (Botswana, Eswatini; cross-regional test cohort, n = 38). An independent adult HIV-TB cohort from India (n = 25) provided an external test cohort. DETs were identified using DESeq2 and recursive feature elimination (RFE) with 10-fold cross-validation, and Random Forest was employed to derive a minimal signature. Performance was evaluated by AUC, sensitivity, and specificity. We derived a 13-transcript minimal signature from 566 DETs with a cross-validated AUC of 0.865 in the Ugandan cohort. In the Southern African cross-regional pediatric test cohort, AUC is 0.943 (95% CI 0.858–0.992), with all 16 NoTB cases correctly classified (specificity 1.000, Wilson 95% CI: 0.806–1.000). Performance in Indian adults is modest, with AUC of 0.773 (95% CI 0.564–0.929). Across cohorts, the signature outperforms neutrophil proportions alone. Nine transcripts converge on neutrophil degranulation. This 13-transcript signature, identified via machine learning, discriminates HIV-TB coinfection from HIV mono-infection in African children, with promising performance in a small Southern African cross-regional test cohort. The signature showed high specificity in African cohorts, though larger sample sizes are needed to establish its clinical utility as a rule-in test. Diagnosing tuberculosis (TB) in children with HIV is difficult because they often cannot provide the samples needed for standard tests, and their symptoms can look like other infections. In this study, we looked for an easier way to detect TB using a blood test. We had previously described how molecules called RNA transcripts differ between children with HIV and TB and those with HIV only. In this current study, we used advanced computer analysis to find the smallest number of RNA transcripts that can be used to distinguish children with HIV who have TB from those who do not. The signature correctly identified all children without TB in the Southern African test group, although this group was small. Nine of the 13 transcripts were linked to immune cells called neutrophils. These results suggest the signature may eventually help confirm TB in children with HIV, but validation in larger, diverse cohorts is essential before clinical use can be considered.
Understanding the molecular basis of drug treatments is important for drug screening and drug efficiency testing to identify suitable interventions. However, heterogeneity among individual patients contributes to variation in responses to different treatments, making it difficult to predict treatment outcomes based on the disease or drug type alone. Therefore, developing effective approaches for evaluating drug responses in individual patients is important. Functional analyses using patient-derived cell lines, patient-derived xenografts (PDXs), or organoids are conventionally used for such purposes; however, these models can be time-consuming and difficult to establish. We applied Drug Ex vivo Test (DExT) to tumor tissues from two clear cell renal cell carcinoma (ccRCC) PDX models. Tissues were exposed ex vivo to the dual mTOR complex inhibitor TAK-228 for up to 120 minutes, and treatment-associated phosphorylation changes were measured by quantitative mass spectrometry-based phosphoproteomic analysis relative to time-matched untreated controls. Here we show that DExT captures acute drug-induced phosphorylation changes in ccRCC tumor tissues. TAK-228 reduces phosphorylation of EIF4EBP1-S65, consistent with inhibition of mTOR signaling. These findings demonstrate the initial feasibility of DExT for measuring drug-induced molecular responses directly in tumor-derived tissue material and provide a foundation for further evaluation with additional therapeutic agents and clinical specimens. People with the same type of cancer can respond differently to the same treatment. Current drug-response tests often require growing tumor cells or creating tumor models, which take time and may not always succeed. We developed Drug Ex vivo Test (DExT), which applies a drug directly to tumor tissue for a short period and then measures molecular changes caused by the treatment. We tested DExT using tumor tissues from two clear cell kidney cancer models and a drug that blocks a cancer-related signaling pathway. We detected changes consistent with the expected drug effect. These findings support further development of DExT through larger studies using patient tumor samples and a wider range of drugs.
Pain is a universal problem, with millions of people around the world experiencing some kind of pain. Despite its importance, rigorous longitudinal evidence on the longer-term effects of pain across various countries remains scarce. This study takes one step toward addressing this gap by examining how pain predicts 56 diverse wellbeing and life-related outcomes using longitudinal data from 22 countries and one territory from Wave 1 and Wave 2 of the Global Flourishing Study (N = 207,919). Country-specific regression analyses were conducted, with estimates pooled using random-effects meta-analyses. Adjusting for demographic and childhood factors, we find that pain in Wave 1 negatively predicts flourishing, happiness, life satisfaction, inner peace, mental health, purpose, relationship satisfaction, belonging, orientation to promote good, delayed gratification, hope, gratitude, showing love, physical health, physical exercise, financial and material security, financial comfort, owning home, and being in the top income quintile in Wave 2. Conversely, pain in Wave 1 positively predicts higher traumatic distress, anxiety, depression, suffering, loneliness, perceived discrimination, health problems, and pain in Wave 2. Pain in Wave 1 has the largest positive effects on subsequent pain, health problems, and suffering, and the largest negative effects on subsequent secure flourishing, mental health, and financial security. This study offers some of the most comprehensive country-wide evidence to date on the potential enduring impact of pain, providing valuable insights for policymakers, healthcare providers, and global organisations focused on reducing health inequalities and enhancing human wellbeing. Understanding the long-term consequences of pain in different countries is highly important. Here, we used rigorous statistical techniques to explore the link between pain and subsequent wellbeing and life outcomes across 22 countries. We found that pain is negatively linked to subsequent flourishing, happiness, life satisfaction, inner peace, mental health, purpose, relationship satisfaction, belonging, orientation to promote good, delayed gratification, hope, gratitude, showing love, physical health, physical exercise, financial and material security, financial comfort, owning home, and being in the top income quintile. Conversely, pain is positively linked to subsequent traumatic distress, anxiety, depression, suffering, loneliness, perceived discrimination, health problems, and pain. These findings inform future research on potential mechanisms as well as health policy design. Macchia et al. use multi-country longitudinal data to explore the longer-term effects of pain across various countries. Authors find that pain is negatively linked to outcomes like flourishing, happiness, life satisfaction, and mental health among others, and positively linked to outcomes such as traumatic distress and anxiety.
Health outcomes, services and their drivers can now be mapped at fine spatial and temporal resolution, driven by growth in data, computing power and geospatial methods. Here we discuss what the field has achieved, where estimates can outpace their use, and what actions could close the gap.
