A Deep Learning Framework for Predicting Response to Therapy in Cancer

A major challenge in cancer treatment is predicting clinical response to anti-cancer drugs on a personalized basis. Using a pharmacogenomics database of 1,001 cancer cell lines, we trained deep neural networks for prediction of drug response and assessed their performance on multiple clinical cohorts. We demonstrate that deep neural networks outperform the current state in machine learning frameworks. We provide a proof of concept for the use of deep neural network-based frameworks to aid precision oncology strategies

Green Space and Internalizing or Externalizing Symptoms Among Children

Importance: Evidence suggests that living near green space supports mental health, but studies examining the association of green space with early mental health symptoms among children are rare. Objective: To evaluate the association between residential green space and early internalizing (eg, anxiety and depression) and externalizing (eg, aggression and rule-breaking) symptoms. Design, Setting, and Participants: Data for this cohort study were drawn from the Environmental Influences on Child Health Outcomes cohort; analysis was conducted from July to October 2023. Children born between 2007 and 2013 with outcome data in early (aged 2-5 years) and/or middle (aged 6-11 years) childhood who resided in 41 states across the US, drawing from clinic, hospital, and community-based cohorts, were included. Cohort sites were eligible if they recruited general population participants and if at least 30 children had outcome and residential address data to measure green space exposure. Nine cohorts with 13 sites met these criteria. Children diagnosed with autism or developmental delay were excluded, and 1 child per family was included. Exposures: Green space exposure was measured using a biannual (ie, summer and winter) Normalized Difference Vegetation Index, a satellite image-based indicator of vegetation density assigned to monthly residential history from birth to outcome assessment. Main Outcome and Measures: Child internalizing and externalizing symptoms were assessed using the Child Behavior Checklist for Ages 1½ to 5 or 6 to 18. The association between green space and internalizing and externalizing symptoms was modeled with multivariable linear regression using generalized estimating equations, adjusting for birthing parent educational level, age at delivery, child sex, prematurity, and neighborhood socioeconomic vulnerability. Models were estimated separately for early and middle childhood samples. Results: Among 2103 children included, 1061 (50.5%) were male; 606 (29.1%) identified as Black, 1094 (52.5%) as White, 248 (11.9%) as multiple races, and 137 (6.6%) as other races. Outcomes were assessed at mean (SD) ages of 4.2 (0.6) years in 1469 children aged 2 to 5 years and 7.8 (1.6) years in 1173 children aged 6 to 11 years. Greater green space exposure was associated with fewer early childhood internalizing symptoms in fully adjusted models (b = -1.29; 95% CI, -1.62 to -0.97). No associations were observed between residential green space and internalizing or externalizing symptoms in middle childhood. Conclusions and Relevance: In this study of residential green space and children's mental health, the association of green space with fewer internalizing symptoms was observed only in early childhood, suggesting a sensitive period for nature exposure. Policies protecting and promoting access to green space may help alleviate early mental health risk

Association of maternal education, neighborhood deprivation, and racial segregation with gestational age at birth by maternal race/ethnicity and United States Census region in the ECHO cohorts

Background: In the United States, disparities in gestational age at birth by maternal race, ethnicity, and geography are theorized to be related, in part, to differences in individual- and neighborhood-level socioeconomic status (SES). Yet, few studies have examined their combined effects or whether associations vary by maternal race and ethnicity and United States Census region. Methods: We assembled data from 34 cohorts in the Environmental influences on Child Health Outcomes (ECHO) program representing 10,304 participants who delivered a liveborn, singleton infant from 2000 through 2019. We investigated the combined associations of maternal education level, neighborhood deprivation index (NDI), and Index of Concentration at the Extremes for racial residential segregation (ICERace) on gestational weeks at birth using linear regression and on gestational age at birth categories (preterm, early term, post–late term relative to full term) using multinomial logistic regression. Results: After adjustment for NDI and ICERace, gestational weeks at birth was significantly lower among those with a high school diploma or less (−0.31 weeks, 95% CI: −0.44, −0.18), and some college (−0.30 weeks, 95% CI: −0.42, −0.18) relative to a master’s degree or higher. Those with a high school diploma or less also had an increased odds of preterm (aOR 1.59, 95% CI: 1.20, 2.10) and early term birth (aOR 1.26, 95% CI: 1.05, 1.51). In adjusted models, NDI quartile and ICERace quartile were not associated with gestational weeks at birth. However, higher NDI quartile (most deprived) associated with an increased odds of early term and late term birth, and lower ICERace quartile (least racially privileged) associated with a decreased odds of late or post-term birth. When stratifying by region, gestational weeks at birth was lower among those with a high school education or less and some college only among those living in the Northeast or Midwest. When stratifying by race and ethnicity, gestational weeks at birth was lower among those with a high school education or less only for the non-Hispanic White category. Conclusion: In this study, maternal education was consistently associated with shorter duration of pregnancy and increased odds of preterm birth, including in models adjusted for NDI and ICERace

Associations between area-level arsenic exposure and adverse birth outcomes: An Echo-wide cohort analysis

BackgroundDrinking water is a common source of exposure to inorganic arsenic. In the US, the Safe Drinking Water Act (SDWA) was enacted to protect consumers from exposure to contaminants, including arsenic, in public water systems (PWS). The reproductive effects of preconception and prenatal arsenic exposure in regions with low to moderate arsenic concentrations are not well understood.ObjectivesThis study examined associations between preconception and prenatal exposure to arsenic violations in water, measured via residence in a county with an arsenic violation in a regulated PWS during pregnancy, and five birth outcomes: birth weight, gestational age at birth, preterm birth, small for gestational age (SGA), and large for gestational age (LGA).MethodsData for arsenic violations in PWS, defined as concentrations exceeding 10 parts per billion, were obtained from the Safe Drinking Water Information System. Participants of the Environmental influences on Child Health Outcomes Cohort Study were matched to arsenic violations by time and location based on residential history data. Multivariable, mixed effects regression models were used to assess the relationship between preconception and prenatal exposure to arsenic violations in drinking water and birth outcomes.ResultsCompared to unexposed infants, continuous exposure to arsenic from three months prior to conception through birth was associated with 88.8 g higher mean birth weight (95% CI: 8.2, 169.5), after adjusting for individual-level confounders. No statistically significant associations were observed between any preconception or prenatal violations exposure and gestational age at birth, preterm birth, SGA, or LGA.ConclusionsOur study did not identify associations between preconception and prenatal arsenic exposure, defined by drinking water exceedances, and adverse birth outcomes. Exposure to arsenic violations in drinking water was associated with higher birth weight. Future studies would benefit from more precise geodata of water system service areas, direct household drinking water measurements, and exposure biomarkers

Machine learning and data mining frameworks for predicting drug response in cancer:An overview and a novel <i>in silico</i> screening process based on association rule mining

A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs on a personalized basis. The success of such a task largely depends on the ability to develop computational resources that integrate big "omic" data into effective drug-response models. Machine learning is both an expanding and an evolving computational field that holds promise to cover such needs. Here we provide a focused overview of: 1) the various supervised and unsupervised algorithms used specifically in drug response prediction applications, 2) the strategies employed to develop these algorithms into applicable models, 3) data resources that are fed into these frameworks and 4) pitfalls and challenges to maximize model performance. In this context we also describe a novel in silico screening process, based on Association Rule Mining, for identifying genes as candidate drivers of drug response and compare it with relevant data mining frameworks, for which we generated a web application freely available at: https://compbio.nyumc.org/drugs/. This pipeline explores with high efficiency large sample-spaces, while is able to detect low frequency events and evaluate statistical significance even in the multidimensional space, presenting the results in the form of easily interpretable rules. We conclude with future prospects and challenges of applying machine learning based drug response prediction in precision medicine.</p

Birth outcomes in relation to neighborhood food access and individual food insecurity during pregnancy in the Environmental Influences on Child Health Outcomes (ECHO)-wide cohort study

BackgroundLimited access to healthy foods, resulting from residence in neighborhoods with low-food access or from household food insecurity, is a public health concern. Contributions of these measures during pregnancy to birth outcomes remain understudied.ObjectivesWe examined associations between neighborhood food access and individual food insecurity during pregnancy with birth outcomes.MethodsWe used data from 53 cohorts participating in the nationwide Environmental Influences on Child Health Outcomes-Wide Cohort Study. Participant inclusion required a geocoded residential address or response to a food insecurity question during pregnancy and information on birth outcomes. Exposures include low-income-low-food-access (LILA, where the nearest supermarket is &gt;0.5 miles for urban or &gt;10 miles for rural areas) or low-income-low-vehicle-access (LILV, where few households have a vehicle and &gt;0.5 miles from the nearest supermarket) neighborhoods and&nbsp;individual food insecurity. Mixed-effects models estimated associations with birth outcomes, adjusting for socioeconomic and pregnancy characteristics.ResultsAmong 22,206 pregnant participants (mean age 30.4 y) with neighborhood food access data, 24.1% resided in LILA neighborhoods and 13.6% in LILV neighborhoods. Of 1630 pregnant participants with individual-level food insecurity data (mean age 29.7 y), 8.0% experienced food insecurity. Residence in LILA (compared with non-LILA) neighborhoods was associated with lower birth weight [β -44.3 g; 95% confidence interval (CI): -62.9, -25.6], lower birth weight-for-gestational-age z-score (-0.09 SD units; -0.12, -0.05), higher odds of small-for-gestational-age [odds ratio (OR) 1.15; 95% CI: 1.00, 1.33], and lower odds of large-for-gestational-age (0.85; 95% CI: 0.77, 0.94). Similar findings were observed for residence in LILV neighborhoods. No associations of individual food insecurity with birth outcomes were observed.ConclusionsResidence in LILA or LILV neighborhoods during pregnancy is associated with adverse birth outcomes. These findings highlight the need for future studies examining whether investing in neighborhood resources to improve food access during pregnancy would promote equitable birth outcomes

Lung cancer and tobacco smoking in Crete, Greece: reflections from a population-based cancer registry from 1992 to 2013

Introduction The Cancer Registry of Crete is a regional population database that collects cancer morbidity/mortality data along with several risk factors. The current study assessed the geographical variation of lung cancer among ever and never smokers in Crete during the last 20 years. Material and Methods Lung cancer patient records (1992–2013) including information on medical history and smoking habits were obtained from the Cancer Registry of Crete. Age-Adjusted Incidence Rates (AAIR), prevalence of smoking among lung cancer patients and the Population-Attributable Fraction (PAF%) of tobacco smoking were estimated. Kaplan-Meier curves, grouped per smoking status were constructed, and spatio-temporal analyses were carried out to assess the geographical variations of lung cancer and smoking (a = 0.05). Results New lung cancer cases in Crete accounted for 9% of all cancers (AAIRboth genders = 40.2/100,000/year, AAIRmales = 73.1/100,000/year, AAIRfemales = 11.8/100,000/year). Ever smokers presented significantly higher incidence compared to ex-smokers (p = 0.02) and never smokers (p < 0.001). The highest increase was observed in ever smokers (AAIR1992 = 19.2/100,000/year, AAIR2013 = 25.4/100,000/year, p = 0.03), while never smokers presented the lowest increase from 1992 to 2013 (AAIR1992 = 5.3/100,000/year, AAIR2013 = 6.8/100,000/year, p = 0.2). The PAF% of lung cancer mortality is 86% for both genders (males: 89%, females: 78%). AAIRs ranged from 25 to 50/100,000/year, while significant geographical differences were observed among the municipalities of Crete (p = 0.02). Smokers living in the south-east urban regions presented higher risk of dying from lung cancer (RR = 2.2; 95%CI = 1.3–3.5). Conclusions The constant increase of lung cancer rates among both genders, especially in females, outlines the need for targeted, geographically-oriented, life-style preventive measures. Design of population-based screening programs, tobacco awareness campaigns and smoking cessation programs in lung cancer hot spots could be guide by these findings

Parent-reported child appetite moderates relationships between child genetic obesity risk and parental feeding practices

BackgroundFood parenting practices are associated with child weight. Such associations may reflect the effects of parents' practices on children's food intake and weight. However, longitudinal, qualitative, and behavioral genetic evidence suggests these associations could, in some cases, reflect parents' response to children's genetic risk for obesity, an instance of gene–environment correlation. We tested for gene–environment correlations across multiple domains of food parenting practices and explored the role of parent-reported child appetite in these relationships.Materials and methodsData on relevant variables were available for N = 197 parent–child dyads (7.54 ± 2.67 years; 44.4% girls) participating in RESONANCE, an ongoing pediatric cohort study. Children's body mass index (BMI) polygenic risk score (PRS) were derived based on adult GWAS data. Parents reported on their feeding practices (Comprehensive Feeding Practices Questionnaire) and their child's eating behavior (Child Eating Behavior Questionnaire). Moderation effects of child eating behaviors on associations between child BMI PRS and parental feeding practices were examined, adjusting for relevant covariates.ResultsOf the 12 parental feeding practices, 2 were associated with child BMI PRS, namely, restriction for weight control (β = 0.182, p = 0.011) and teaching about nutrition (β = −0.217, p = 0.003). Moderation analyses demonstrated that when children had high genetic obesity risk and showed moderate/high (vs. low) food responsiveness, parents were more likely to restrict food intake to control weight.ConclusionOur results indicate that parents may adjust their feeding practices in response to a child's genetic propensity toward higher or lower bodyweight, and the adoption of food restriction to control weight may depend on parental perceptions of the child's appetite. Research using prospective data on child weight and appetite and food parenting from infancy is needed to further investigate how gene–environment relationships evolve through development