Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions.

To identify credible causal risk variants (CCVs) associated with different histotypes of epithelial ovarian cancer (EOC), we performed genome-wide association analysis for 470,825 genotyped and 10,163,797 imputed SNPs in 25,981 EOC cases and 105,724 controls of European origin. We identified five histotype-specific EOC risk regions (p value <5 × 10-8) and confirmed previously reported associations for 27 risk regions. Conditional analyses identified an additional 11 signals independent of the primary signal at six risk regions (p value <10-5). Fine mapping identified 4,008 CCVs in these regions, of which 1,452 CCVs were located in ovarian cancer-related chromatin marks with significant enrichment in active enhancers, active promoters, and active regions for CCVs from each EOC histotype. Transcriptome-wide association and colocalization analyses across histotypes using tissue-specific and cross-tissue datasets identified 86 candidate susceptibility genes in known EOC risk regions and 32 genes in 23 additional genomic regions that may represent novel EOC risk loci (false discovery rate <0.05). Finally, by integrating genome-wide HiChIP interactome analysis with transcriptome-wide association study (TWAS), variant effect predictor, transcription factor ChIP-seq, and motifbreakR data, we identified candidate gene-CCV interactions at each locus. This included risk loci where TWAS identified one or more candidate susceptibility genes (e.g., HOXD-AS2, HOXD8, and HOXD3 at 2q31) and other loci where no candidate gene was identified (e.g., MYC and PVT1 at 8q24) by TWAS. In summary, this study describes a functional framework and provides a greater understanding of the biological significance of risk alleles and candidate gene targets at EOC susceptibility loci identified by a genome-wide association study.

Racial and ethnic differences in epithelial ovarian cancer risk: an analysis from the Ovarian Cancer Association Consortium.

Limited estimates exist on risk factors for epithelial ovarian cancer (EOC) in Asian, Hispanic, and Native Hawaiian/Pacific Islander women. Participants in this study included 1734 Asian (n = 785 case and 949 control participants), 266 Native Hawaiian/Pacific Islander (n = 99 case and 167 control participants), 1149 Hispanic (n = 505 case and 644 control participants), and 24 189 White (n = 9981 case and 14 208 control participants) from 11 studies in the Ovarian Cancer Association Consortium. Logistic regression models estimated odds ratios (ORs) and 95% CIs for risk associations by race and ethnicity. Heterogeneity in EOC risk associations by race and ethnicity (P ≤ .02) was observed for oral contraceptive (OC) use, parity, tubal ligation, and smoking. We observed inverse associations with EOC risk for OC use and parity across all groups; associations were strongest in Native Hawaiian/Pacific Islander and Asian women. The inverse association for tubal ligation with risk was most pronounced for Native Hawaiian/Pacific Islander participants (odds ratio (OR) = 0.25; 95% CI, 0.13-0.48) compared with Asian and White participants (OR = 0.68 [95% CI, 0.51-0.90] and OR = 0.78 [95% CI, 0.73-0.85], respectively). Differences in EOC risk factor associations were observed across racial and ethnic groups, which could be due, in part, to varying prevalence of EOC histotypes. Inclusion of greater diversity in future studies is essential to inform prevention strategies. This article is part of a Special Collection on Gynecological Cancers.

Assessing sociodemographic and regional disparities in Oncotype DX Genomic Prostate Score uptake.

BACKGROUND: The Oncotype DX Genomic Prostate Score (ODX-GPS) is a gene expression assay that predicts disease aggressiveness. The objective of this study was to identify sociodemographic and regional factors associated with ODX-GPS uptake. METHODS: Data from Surveillance Epidemiology and End Results registries on men with localized prostate cancer with a Gleason score of 3 + 3 or 3 + 4, PSA ≤20 ng/mL, and stage T1c to T2c disease from 2013 through 2017 were linked with ODX-GPS data. Census-tract level neighborhood socioeconomic status (nSES) quintiles were constructed using a composite socioeconomic score. Multivariable logistic regression was used to estimate the associations of ODX-GPS uptake with age at diagnosis, race and ethnicity, nSES, geographic region, insurance type, and marital status, accounting for National Comprehensive Cancer Network risk group, year of diagnosis, and clustering by census tract. RESULTS: Among 111,434 eligible men, 5.5% had ODX-GPS test uptake. Of these, 78.3% were non-Hispanic White, 9.6% were Black, 6.7% were Hispanic, and 3.6% were Asian American. Black men had the lowest odds of ODX-GPS uptake (odds ratio, 0.70; 95% confidence interval [CI], 0.63-0.76). Those in the highest versus lowest quintile of nSES were 1.64 times more likely (95% CI, 1.38-2.94) to have ODX-GPS uptake. The odds of ODX-GPS uptake were statistically significantly higher among men residing in the Northeast, West, and Midwest compared to the South. CONCLUSIONS: Disparities in ODX-GPS uptake by race, ethnicity, nSES, and geographical region were identified. Concerted efforts should be made to ensure that this clinical test is equitably available.

Pre-diagnosis tea and coffee consumption and survival after a diagnosis of ovarian cancer: results from the Ovarian Cancer Association Consortium.

BACKGROUND: Tea and coffee are the most frequently consumed beverages in the world. Green tea in particular contains compounds with potential anti-cancer effects, but its association with survival after ovarian cancer is uncertain. METHODS: We investigated the associations between tea and coffee consumption before diagnosis and survival using data from 10 studies in the Ovarian Cancer Association Consortium. Data on tea (green, black, herbal), coffee and caffeine intake were available for up to 5724 women. We used Cox proportional hazards regression to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (CI). RESULTS: Compared with women who did not drink any green tea, consumption of one or more cups/day was associated with better overall survival (aHR = 0.84, 95% CI 0.71-1.00, p-trend = 0.04). A similar association was seen for ovarian cancer-specific survival in five studies with this information (aHR = 0.81, 0.66-0.99, p-trend = 0.045). There was no consistent variation between subgroups defined by clinical or lifestyle characteristics and adjustment for other aspects of lifestyle did not appreciably alter the estimates. We found no evidence of an association between coffee, black or herbal tea, or caffeine intake and survival. CONCLUSION: The observed association with green tea consumption before diagnosis raises the possibility that consumption after diagnosis might improve patient outcomes.

Early menopause and hormone therapy as determinants for lung health outcomes: a secondary analysis using the PLCO trial.

RATIONALE: Early natural menopause (early-M; <45 years of age) increases the risk of lung morbidities and mortalities in smokers. However, it is largely unknown whether early-M due to surgery demonstrates similar effects and whether menopausal hormone therapy (MHT) is protective against lung diseases. OBJECTIVES: To assess the associations of early-M and MHT with lung morbidities and mortalities using the prospective Prostate, Lung, Colorectal and Ovarian (PLCO) trial. METHODS: We estimated the risk among 69 706 postmenopausal women in the PLCO trial, stratified by menopausal types and smoking status. RESULTS: Early-M was associated with an increased risk of most lung disease and mortality outcomes in ever smokers with the highest risk seen for respiratory mortality (HR 1.98, 95% CI 1.34 to 2.92) in those with bilateral oophorectomy (BO). Early-M was positively associated with chronic bronchitis, and all-cause, non-cancer and respiratory mortality in never smokers with natural menopause or BO, with the highest risk seen for BO- respiratory mortality (HR 1.91, 95% CI 1.16 to 3.12). Ever MHT was associated with reduced all-cause, non-cancer and cardiovascular mortality across menopause types regardless of smoking status and was additionally associated with reduced risk of non-ovarian cancer, lung cancer (LC) and respiratory mortality in ever smokers. Among smokers, ever MHT use was associated with a reduction in HR for all-cause, non-cancer and cardiovascular mortality in a duration-dependent manner. CONCLUSIONS: Smokers with early-M should be targeted for smoking cessation and LC screening regardless of menopause types. MHT users had a lower likelihood of dying from LC and respiratory diseases in ever smokers.

Differential inhibition of intra- and inter-molecular protease cleavages by antiviral compounds.

IMPORTANCE: Most protease-targeted antiviral development evaluates the ability of small molecules to inhibit the cleavage of artificial substrates. However, before they can cleave any other substrates, viral proteases need to cleave themselves out of the viral polyprotein in which they have been translated. This can occur either intra- or inter-molecularly. Whether this process occurs intra- or inter-molecularly has implications for the potential for precursors to accumulate and for the effectiveness of antiviral drugs. We argue that evaluating candidate antivirals for their ability to block these cleavages is vital to drug development because the buildup of uncleaved precursors can be inhibitory to the virus and potentially suppress the selection of drug-resistant variants.

Accuracy of patient race and ethnicity data in a central cancer registry.

PURPOSE: Race and Hispanic ethnicity data can be challenging for central cancer registries to collect. We evaluated the accuracy of the race and Hispanic ethnicity variables collected by the Utah Cancer Registry compared to self-report. METHODS: Participants were 3,162 cancer survivors who completed questionnaires administered in 2015-2022 by the Utah Cancer Registry. Each survey included separate questions collecting race and Hispanic ethnicity, respectively. Registry-collected race and Hispanic ethnicity were compared to self-reported values for the same individuals. We calculated sensitivity and specificity for each race category and Hispanic ethnicity separately. RESULTS: Survey participants included 323 (10.2%) survivors identifying as Hispanic, a lower proportion Hispanic than the 12.1% in the registry Hispanic variable (sensitivity 88.2%, specificity 96.5%). For race, 43 participants (1.4%) self-identified as American Indian or Alaska Native (AIAN), 32 (1.0%) as Asian, 23 (0.7%) as Black or African American, 16 (0.5%) Pacific Islander (PI), and 2994 (94.7%) as White. The registry race variable classified a smaller proportion of survivors as members of each of these race groups except White. Sensitivity for classification of race as AIAN was 9.3%, Asian 40.6%, Black 60.9%, PI 25.0%, and specificity for each of these groups was > 99%. Sensitivity and specificity for White were 98.8% and 47.4%. CONCLUSION: Cancer registry race and Hispanic ethnicity data often did not match the individual's self-identification. Of particular concern is the high proportion of AIAN individuals whose race is misclassified. Continued attention should be directed to the accurate capture of race and ethnicity data by hospitals.

Deep learning uncertainty quantification for clinical text classification.

INTRODUCTION: Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network's confidence, in-depth analyses are needed to establish whether they are well calibrated. METHOD: In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) population-based cancer registries. In particular, we introduce multiple methods for selective classification to achieve a target level of accuracy on multiple classification tasks while minimizing the rejection amount-that is, the number of electronic pathology reports for which the model's predictions are unreliable. We evaluate the proposed methods by comparing our approach with the current in-house deep learning-based abstaining classifier. RESULTS: Overall, all the proposed selective classification methods effectively allow for achieving the targeted level of accuracy or higher in a trade-off analysis aimed to minimize the rejection rate. On in-distribution validation and holdout test data, with all the proposed methods, we achieve on all tasks the required target level of accuracy with a lower rejection rate than the deep abstaining classifier (DAC). Interpreting the results for the out-of-distribution test data is more complex; nevertheless, in this case as well, the rejection rate from the best among the proposed methods achieving 97% accuracy or higher is lower than the rejection rate based on the DAC. CONCLUSIONS: We show that although both approaches can flag those samples that should be manually reviewed and labeled by human annotators, the newly proposed methods retain a larger fraction and do so without retraining-thus offering a reduced computational cost compared with the in-house deep learning-based abstaining classifier.

Familial risk of epithelial ovarian cancer after accounting for gynaecological surgery: a population-based study.

BACKGROUND: Uptake of risk-reducing surgery has increased among women at high risk of epithelial ovarian cancer. We sought to characterise familial risk of epithelial ovarian cancer histotypes in a population-based study after accounting for gynaecological surgeries, including bilateral oophorectomy. METHODS: We compared risk of epithelial ovarian cancer in relatives of 3536 epithelial ovarian cancer cases diagnosed in 1966-2016 and relatives of 35 326 matched controls. We used Cox competing risk models, incorporating bilateral oophorectomy as a competing risk, to estimate the relative risk of ovarian cancer in first-degree (FDR), second-degree (SDR) and third-degree (TDR) relatives from 1966 to 2016. We also estimated relative risks in time periods before (1966-1994, 1995-2004) and after (2005-2016) formal recommendations were made for prophylactic oophorectomy among women with pathogenic variants in BRCA1/2. RESULTS: The relative risks of epithelial ovarian cancer in FDRs, SDRs and TDRs of cases versus controls were 1.68 (95% CI 1.39 to 2.04), 1.51 (95% CI 1.30 to 1.75) and 1.34 (95% CI 1.20 to 1.48), respectively. Relative risks were greatest for high-grade serous, mucinous and 'other epithelial' histotypes. Relative risks were attenuated for case FDRs, but not for SDRs or TDRs, from 2005 onwards, consistent with the timing of recommendations for prophylactic surgery. CONCLUSION: Familial risk of epithelial ovarian cancer extends to TDRs, especially for high-grade serous and mucinous histotypes. Distant relatives share genes but minimal environment, highlighting the importance of germline inherited genetics in ovarian cancer aetiology. Increased ovarian cancer risk in distant relatives has implications for counselling and recommendations for prophylactic surgeries that, from our data, appear only to reach FDRs.

Development of message passing-based graph convolutional networks for classifying cancer pathology reports.

BACKGROUND: Applying graph convolutional networks (GCN) to the classification of free-form natural language texts leveraged by graph-of-words features (TextGCN) was studied and confirmed to be an effective means of describing complex natural language texts. However, the text classification models based on the TextGCN possess weaknesses in terms of memory consumption and model dissemination and distribution. In this paper, we present a fast message passing network (FastMPN), implementing a GCN with message passing architecture that provides versatility and flexibility by allowing trainable node embedding and edge weights, helping the GCN model find the better solution. We applied the FastMPN model to the task of clinical information extraction from cancer pathology reports, extracting the following six properties: main site, subsite, laterality, histology, behavior, and grade. RESULTS: We evaluated the clinical task performance of the FastMPN models in terms of micro- and macro-averaged F1 scores. A comparison was performed with the multi-task convolutional neural network (MT-CNN) model. Results show that the FastMPN model is equivalent to or better than the MT-CNN. CONCLUSIONS: Our implementation revealed that our FastMPN model, which is based on the PyTorch platform, can train a large corpus (667,290 training samples) with 202,373 unique words in less than 3 minutes per epoch using one NVIDIA V100 hardware accelerator. Our experiments demonstrated that using this implementation, the clinical task performance scores of information extraction related to tumors from cancer pathology reports were highly competitive.

Endometriosis Typology and Ovarian Cancer Risk.

Importance: Endometriosis has been associated with an increased risk of ovarian cancer; however, the associations between endometriosis subtypes and ovarian cancer histotypes have not been well-described. Objective: To evaluate the associations of endometriosis subtypes with incidence of ovarian cancer, both overall and by histotype. Design, Setting, and Participants: Population-based cohort study using data from the Utah Population Database. The cohort was assembled by matching 78 893 women with endometriosis in a 1:5 ratio to women without endometriosis. Exposures: Endometriosis cases were identified via electronic health records and categorized as superficial endometriosis, ovarian endometriomas, deep infiltrating endometriosis, or other. Main Outcomes and Measures: Estimated adjusted hazard ratios (aHRs), adjusted risk differences (aRDs) per 10 000 women, and 95% CIs for overall ovarian cancer, type I ovarian cancer, and type II ovarian cancer comparing women with each type of endometriosis with women without endometriosis. Models accounted for sociodemographic factors, reproductive history, and past gynecologic operations. Results: In this Utah-based cohort, the mean (SD) age at first endometriosis diagnosis was 36 (10) years. There were 597 women with ovarian cancer. Ovarian cancer risk was higher among women with endometriosis compared with women without endometriosis (aHR, 4.20 [95% CI, 3.59-4.91]; aRD, 9.90 [95% CI, 7.22-12.57]), and risk of type I ovarian cancer was especially high (aHR, 7.48 [95% CI, 5.80-9.65]; aRD, 7.53 [95% CI, 5.46-9.61]). Ovarian cancer risk was highest in women with deep infiltrating endometriosis and/or ovarian endometriomas for all ovarian cancers (aHR, 9.66 [95% CI, 7.77-12.00]; aRD, 26.71 [95% CI, 20.01-33.41]), type I ovarian cancer (aHR, 18.96 [95% CI, 13.78-26.08]; aRD, 19.57 [95% CI, 13.80-25.35]), and type II ovarian cancer (aHR, 3.72 [95% CI, 2.31-5.98]; aRD, 2.42 [95% CI, -0.01 to 4.85]). Conclusions and Relevance: Ovarian cancer risk was markedly increased among women with ovarian endometriomas and/or deep infiltrating endometriosis. This population may benefit from counseling regarding ovarian cancer risk and prevention and could be an important population for targeted screening and prevention studies.

Profiling the immune landscape in mucinous ovarian carcinoma.

OBJECTIVE: Mucinous ovarian carcinoma (MOC) is a rare histotype of ovarian cancer, with low response rates to standard chemotherapy, and very poor survival for patients diagnosed at advanced stage. There is a limited understanding of the MOC immune landscape, and consequently whether immune checkpoint inhibitors could be considered for a subset of patients. METHODS: We performed multicolor immunohistochemistry (IHC) and immunofluorescence (IF) on tissue microarrays in a cohort of 126 MOC patients. Cell densities were calculated in the epithelial and stromal components for tumor-associated macrophages (CD68+/PD-L1+, CD68+/PD-L1-), T cells (CD3+/CD8-, CD3+/CD8+), putative T-regulatory cells (Tregs, FOXP3+), B cells (CD20+/CD79A+), plasma cells (CD20-/CD79a+), and PD-L1+ and PD-1+ cells, and compared these values with clinical factors. Univariate and multivariable Cox Proportional Hazards assessed overall survival. Unsupervised k-means clustering identified patient subsets with common patterns of immune cell infiltration. RESULTS: Mean densities of PD1+ cells, PD-L1- macrophages, CD4+ and CD8+ T cells, and FOXP3+ Tregs were higher in the stroma compared to the epithelium. Tumors from advanced (Stage III/IV) MOC had greater epithelial infiltration of PD-L1- macrophages, and fewer PD-L1+ macrophages compared with Stage I/II cancers (p = 0.004 and p = 0.014 respectively). Patients with high epithelial density of FOXP3+ cells, CD8+/FOXP3+ cells, or PD-L1- macrophages, had poorer survival, and high epithelial CD79a + plasma cells conferred better survival, all upon univariate analysis only. Clustering showed that most MOC (86%) had an immune depleted (cold) phenotype, with only a small proportion (11/76,14%) considered immune inflamed (hot) based on T cell and PD-L1 infiltrates. CONCLUSION: In summary, MOCs are mostly immunogenically 'cold', suggesting they may have limited response to current immunotherapies.

Role of neighborhood context in ovarian cancer survival disparities: current research and future directions.

Ovarian cancer is the fifth leading cause of cancer-associated mortality among US women with survival disparities seen across race, ethnicity, and socioeconomic status, even after accounting for histology, stage, treatment, and other clinical factors. Neighborhood context can play an important role in ovarian cancer survival, and, to the extent to which minority racial and ethnic groups and populations of lower socioeconomic status are more likely to be segregated into neighborhoods with lower quality social, built, and physical environment, these contextual factors may be a critical component of ovarian cancer survival disparities. Understanding factors associated with ovarian cancer outcome disparities will allow clinicians to identify patients at risk for worse outcomes and point to measures, such as social support programs or transportation aid, that can help to ameliorate such disparities. However, research on the impact of neighborhood contextual factors in ovarian cancer survival and in disparities in ovarian cancer survival is limited. This commentary focuses on the following neighborhood contextual domains: structural and institutional context, social context, physical context represented by environmental exposures, built environment, rurality, and healthcare access. The research conducted to date is presented and clinical implications and recommendations for future interventions and studies to address disparities in ovarian cancer outcomes are proposed.

Daytime light exposure is a strong predictor of seasonal variation in sleep and circadian timing of university students.

In the absence of electric light, sleep for humans typically starts soon after dusk and at higher latitudes daily sleep timing changes seasonally as photoperiod changes. However, access to electric light shields humans from natural photoperiod changes, and whether seasonal changes in sleep occur despite this isolation from the natural light-dark cycle remains a matter of controversy. We measured sleep timing in over 500 university students living in the city of Seattle, WA (47.6°N) throughout the four seasons; we show that even when students are following a school schedule, sleep timing is delayed during the fall and winter. For instance, during the winter school days, students fell asleep 35 min later and woke up 27 min later (under daylight-savings time) than students during the summer school days, a change that is an hour larger relative to solar midnight. Furthermore, chronotype defined by mid-sleep on free days corrected for oversleep (MSFc), an indirect estimate of circadian phase, was more than 30 min later in the winter compared with the summer. Analysis of the effect of light exposure showed that the number of hours of light exposure to at least 50 lux during the daytime was a stronger predictor of MSFc than the exposure time to this illuminance after dusk. Specifically, MSFc was advanced by 30 min for each additional hour of light exposure during daytime and delayed by only 15 min for each additional hour of postdusk exposure to light. Additionally, the time of the day of exposure to high light intensities was more predictive of MSFc when daytime exposure was considered than when exposure for the full 24-h day was considered. Our results show that although sleep time is highly synchronized to social time, a delayed timing of sleep is evident during the winter months. They also suggest that daily exposure to daylight is key to prevent this delayed phase of the circadian clock and thus circadian disruption that is typically exacerbated in high-latitude winters.

Undergraduate students' neurophysiological reasoning: what we learn from the attractive distractors students select.

The basis for mastering neurophysiology is understanding ion movement across cell membranes. The Electrochemical Gradients Assessment Device (EGAD) is a 17-item test assessing students' understanding of fundamental concepts of neurophysiology, e.g., electrochemical gradients and resistance, synaptic transmission, and stimulus strength. We collected responses to the EGAD from 534 students from seven institutions nationwide, before and after instruction. We determined the relative difficulty of neurophysiology topics and noted that students did better on "what" questions compared to "how" questions, particularly those integrating concentration gradient and electric forces to predict ion movement. We also found that, even after instruction, students selected one incorrect answer, at a rate greater than random chance for nine questions. We termed these incorrect answers attractive distractors. Most attractive distractors contained terms associated with concentration gradients, equilibrium, or anthropomorphic and teleological reasoning, and incorrect answers containing multiple terms were more attractive. We used χ2 analysis and alluvial diagrams to investigate how individual students moved or did not move between answer choices on the pre- and posttest. Interestingly, students selecting the attractive distractor on the pretest were just as likely as other incorrect students to move to the correct answer on the posttest. In contrast, of students incorrect on both the pre- and posttest, students who selected the attractive distractor on the pretest were more likely to stick with this answer on the posttest than students choosing other incorrect answers. Combining the EGAD results with alluvial diagrams can inform neurophysiology instruction to address points of student confusion.NEW & NOTEWORTHY Investigating students' alternative reasoning in neurophysiology, this research is the first to investigate how analyzing the most common incorrect answer can shed light on the concepts students struggle with when reasoning about neurophysiological problems, especially those dealing with both chemical and electrical driving forces to predict ion movement across cell membranes.

Covariational reasoning and item context affect language in undergraduate mass balance written explanations.

Mass balance (MB) reasoning offers a rich topic for examination of students' scientific thinking and skills, as it requires students to account for multiple inputs and outputs within a system and apply covariational reasoning. Using previously validated constructed response prompts for MB, we examined 1,920 student-constructed responses (CRs) aligned to an emerging learning progression to determine how student language changes from low (1) to high (4) covariational reasoning levels. As students' abilities and thinking change with Context, we used the same general prompt in six physiological contexts. We asked how Level and Context affect student language and what language is conserved across Contexts at higher reasoning Levels. Using diversity methods, we found student language becomes more similar as covariational reasoning level increases. Using text analysis, we found context-dependent words at each Level; however, the type of context words changed. Specifically, at Level 1, students used context words that are tangential to MB reasoning, while Level 4 responses used words that specify inputs and outputs for the given Item Context. Further, at Level 4, students shared 30% of language across the six contexts and leveraged context-independent words including rate, equal, and some form of slower/lower/smaller. Together, these data demonstrate that Context affects undergraduate MB language at all covariational reasoning levels, but that the language becomes more specific and similar as Level increases. These findings encourage instructors to foster context-independent, comparative, and summative language during instruction to functionally build MB and covariational reasoning skills across contexts.NEW & NOTEWORTHY This article builds on the work of Scott et al. (Scott EE, Cerchiara J, McFarland JL, Wenderoth MP, Doherty JH. J Res Sci Teach 1: 37, 2023) and Shiroda et al. (Shiroda M, Fleming MP, Haudek KC. Front Educ 8: 989836, 2023) to quantitatively examine student language in written explanations of mass balance across six contexts using constructed response assessments. These results present an evaluation of student mass balance language and provide researchers and practitioners with tools to assist students in constructing scientific mass balance reasoning explanations.

Oaks to arteries: the Physiology Core Concept of flow down gradients supports transfer of student reasoning.

The Physiology Core Concept of flow down gradients is a major concept in physiology, as pressure gradients are the key driving force for the bulk flow of fluids in biology. However, students struggle to understand that this principle is foundational to the mechanisms governing bulk flow across diverse physiological systems (e.g., blood flow, phloem sap flow). Our objective was to investigate whether bulk flow items that differ in scenario context (i.e., taxa, amount of scientific terminology, living or nonliving system) or in which aspect of the pressure gradient is kept constant (i.e., starting pressure or pressure gradient) influence undergraduate students' reasoning. Item scenario context did not impact the type of reasoning students used. However, students were more likely to use the Physiology Core Concept of "flow down [pressure] gradients" when the pressure gradient was kept constant and less likely to use this concept when the starting pressure was kept constant. We also investigated whether item scenario context or which aspect of the pressure gradient is kept constant impacted how consistent students were in the type of reasoning they used across two bulk flow items on the same homework. Most students were consistent across item scenario contexts (76%) and aspects of the pressure gradient kept constant (70%). Students who reasoned using "flow down gradients" on the first item were the most consistent (86, 89%), whereas students using "pressures indicate (but don't cause) flow" were the least consistent (43, 34%). Students who are less consistent know that pressure is somehow involved or indicates fluid flow but do not have a firm grasp of the concept of a pressure gradient as the driving force for fluid flow. These findings are the first empirical evidence to support the claim that using Physiology Core Concept reasoning supports transfer of knowledge across different physiological systems.NEW & NOTEWORTHY These findings are the first empirical evidence to support the claim that using Physiology Core Concept reasoning supports transfer of knowledge across different physiological systems.

Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer.

Clinical trial results have recently demonstrated that inhibiting inflammation by targeting the interleukin-1ß pathway can offer a significant reduction in lung cancer incidence and mortality, highlighting a pressing and unmet need to understand the benefits of inflammation-focused lung cancer therapies at the genetic level. While numerous genome-wide association studies (GWAS) have explored the genetic etiology of lung cancer, there remains a large gap between the type of information that may be gleaned from an association study and the depth of understanding necessary to explain and drive translational findings. Thus, in this study we jointly model and integrate extensive multiomics data sources, utilizing a total of 40 genome-wide functional annotations that augment previously published results from the International Lung Cancer Consortium (ILCCO) GWAS, to prioritize and characterize single nucleotide polymorphisms (SNPs) that increase risk of squamous cell lung cancer through the inflammatory and immune responses. Our work bridges the gap between correlative analysis and translational follow-up research, refining GWAS association measures in an interpretable and systematic manner. In particular, reanalysis of the ILCCO data highlights the impact of highly associated SNPs from nuclear factor-κB signaling pathway genes as well as major histocompatibility complex mediated variation in immune responses. One consequence of prioritizing likely functional SNPs is the pruning of variants that might be selected for follow-up work by over an order of magnitude, from potentially tens of thousands to hundreds. The strategies we introduce provide informative and interpretable approaches for incorporating extensive genome-wide annotation data in analysis of genetic association studies.

Ethnicity, socioeconomic status, income inequality, and colorectal cancer outcomes: evidence from the 4C2 collaboration.

PURPOSE: National Cancer Institute (NCI)-Designated Cancer Centers are required to assess and address the needs of their catchments. In rural regions, catchment areas are vast, populations small, and infrastructure for data capture limited, making analyses of cancer patterns challenging. METHODS: The four NCI-Designated Comprehensive Cancer Centers in the southern Rocky Mountain region formed the Four Corners Collaboration (4C2) to address these challenges. Colorectal cancer (CRC) was identified as a disease site where disparities exist. The 4C2 leaders examined how geographic and sociodemographic characteristics were correlated to stage at diagnosis and survival in the region and compared those relationships to a sample from the surveillance, epidemiology, and end results (SEER) program. RESULTS: In 4C2, Hispanics were more likely to live in socioeconomically disadvantaged areas relative to their counterparts in the SEER program. These residency patterns were positively correlated with later stage diagnosis and higher mortality. Living in an area with high-income inequality was positively associated with mortality for Non-Hispanic whites in 4C2. In SEER, Hispanics had a slightly higher likelihood of distant stage disease, and disadvantaged socioeconomic status was associated with poor survival. CONCLUSION: CRC interventions in 4C2 will target socioeconomically disadvantaged areas, especially those with higher income inequality, to improve outcomes among Hispanics and Non-Hispanic whites. The collaboration demonstrates how bringing NCI-Designated Cancer Centers together to identify and address common population catchment issues provides opportunity for pooled analyses of small, but important populations, and thus, capitalize on synergies among researchers to reduce cancer disparities.

Endometriosis and menopausal hormone therapy impact the hysterectomy-ovarian cancer association.

OBJECTIVE: To evaluate the association between hysterectomy and ovarian cancer, and to understand how hormone therapy (HT) use and endometriosis affect this association. METHODS: We conducted a pooled analysis of self-reported data from 11 case-control studies in the Ovarian Cancer Association Consortium (OCAC). Women with (n = 5350) and without ovarian cancer (n = 7544) who never used HT or exclusively used either estrogen-only therapy (ET) or estrogen+progestin therapy (EPT) were included. Risk of invasive epithelial ovarian cancer adjusted for duration of ET and EPT use and stratified on history of endometriosis was determined using odds ratios (ORs) with 95% confidence intervals (CIs). RESULTS: Overall and among women without endometriosis, there was a positive association between ovarian cancer risk and hysterectomy (OR = 1.19, 95% CI 1.09-1.31 and OR = 1.20, 95% CI 1.09-1.32, respectively), but no association upon adjusting for duration of ET and EPT use (OR = 1.04, 95% CI 0.94-1.16 and OR = 1.06, 95% CI 0.95-1.18, respectively). Among women with a history of endometriosis, there was a slight inverse association between hysterectomy and ovarian cancer risk (OR = 0.93, 95% CI 0.69-1.26), but this association became stronger and statistically significant after adjusting for duration of ET and EPT use (OR = 0.69, 95% CI 0.48-0.99). CONCLUSIONS: The hysterectomy-ovarian cancer association is complex and cannot be understood without considering duration of ET and EPT use and history of endometriosis. Failure to take these exposures into account in prior studies casts doubt on their conclusions. Overall, hysterectomy is not risk-reducing for ovarian cancer, however the inverse association among women with endometriosis warrants further investigation.