RESUMEN
BACKGROUND: The Surveillance, Epidemiology, and End Results (SEER) Program with the National Cancer Institute tested whether population-based cancer registries can serve as honest brokers to acquire tissue and data in the SEER-Linked Virtual Tissue Repository (VTR) Pilot. METHODS: We collected formalin-fixed, paraffin-embedded tissue and clinical data from patients with pancreatic ductal adenocarcinoma (PDAC) and breast cancer (BC) for two studies comparing cancer cases with highly unusual survival (≥5 years for PDAC and ≤30 months for BC) to pair-matched controls with usual survival (≤2 years for PDAC and ≥5 years for BC). Success was defined as the ability for registries to acquire tissue and data on cancer cases with highly unusual outcomes. RESULTS: Of 98 PDAC and 103 BC matched cases eligible for tissue collection, sources of attrition for tissue collection were tissue being unavailable, control paired with failed case, second control that was not requested, tumor necrosis ≥20%, and low tumor cellularity. In total, tissue meeting the study criteria was obtained for 70 (71%) PDAC and 74 (72%) BC matched cases. For patients with tissue received, clinical data completeness ranged from 59% for CA-19-9 after treatment to >95% for margin status, whether radiation therapy and chemotherapy were administered, and comorbidities. CONCLUSIONS: The VTR Pilot demonstrated the feasibility of using SEER cancer registries as honest brokers to provide tissue and clinical data for secondary use in research. Studies using this program should oversample by 45% to 50% to obtain sufficient sample size and targeted population representation and involve subspecialty matter expert pathologists for tissue selection.
Asunto(s)
Neoplasias de la Mama , Carcinoma Ductal Pancreático , Neoplasias Pancreáticas , Programa de VERF , Humanos , Femenino , Proyectos Piloto , Carcinoma Ductal Pancreático/terapia , Carcinoma Ductal Pancreático/patología , Estados Unidos/epidemiología , Masculino , Neoplasias de la Mama/terapia , Neoplasias de la Mama/patología , Neoplasias de la Mama/epidemiología , Neoplasias Pancreáticas/terapia , Neoplasias Pancreáticas/patología , Neoplasias Pancreáticas/epidemiología , Persona de Mediana Edad , Anciano , National Cancer Institute (U.S.) , Bancos de Tejidos , Sistema de Registros , Adulto , Estudios de Casos y ControlesRESUMEN
BACKGROUND: Precision medicine has become a mainstay of cancer care in recent years. The National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) Program has been an authoritative source of cancer statistics and data since 1973. However, tumor genomic information has not been adequately captured in the cancer surveillance data, which impedes population-based research on molecular subtypes. To address this, the SEER Program has developed and implemented a centralized process to link SEER registries' tumor cases with genomic test results that are provided by molecular laboratories to the registries. METHODS: Data linkages were carried out following operating procedures for centralized linkages established by the SEER Program. The linkages used Match*Pro, a probabilistic linkage software, and were facilitated by the registries' trusted third party (an honest broker). The SEER registries provide to NCI limited datasets that undergo preliminary evaluation prior to their release to the research community. RESULTS: Recently conducted genomic linkages included OncotypeDX Breast Recurrence Score, OncotypeDX Breast Ductal Carcinoma in Situ, OncotypeDX Genomic Prostate Score, Decipher Prostate Genomic Classifier, DecisionDX Uveal Melanoma, DecisionDX Preferentially Expressed Antigen in Melanoma, DecisionDX Melanoma, and germline tests results in Georgia and California SEER registries. CONCLUSIONS: The linkages of cancer cases from SEER registries with genomic test results obtained from molecular laboratories offer an effective approach for data collection in cancer surveillance. By providing de-identified data to the research community, the NCI's SEER Program enables scientists to investigate numerous research inquiries.
Asunto(s)
Genómica , Neoplasias , Sistema de Registros , Programa de VERF , Humanos , Programa de VERF/estadística & datos numéricos , Estados Unidos/epidemiología , Neoplasias/genética , Neoplasias/epidemiología , Neoplasias/diagnóstico , Genómica/métodos , Sistema de Registros/estadística & datos numéricos , Femenino , Masculino , Pruebas Genéticas/métodos , Pruebas Genéticas/estadística & datos numéricos , Registro Médico Coordinado/métodos , National Cancer Institute (U.S.)RESUMEN
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.
RESUMEN
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.
RESUMEN
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 78â¯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 10â¯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.
Asunto(s)
Endometriosis , Neoplasias Ováricas , Adulto , Anciano , Femenino , Humanos , Persona de Mediana Edad , Adulto Joven , Estudios de Cohortes , Endometriosis/clasificación , Endometriosis/epidemiología , Incidencia , Neoplasias Ováricas/diagnóstico , Neoplasias Ováricas/epidemiología , Neoplasias Ováricas/patología , Modelos de Riesgos Proporcionales , Factores de Riesgo , Utah/epidemiología , Estudios Retrospectivos , Ovario/patologíaRESUMEN
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.
RESUMEN
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.
Asunto(s)
Predisposición Genética a la Enfermedad , Estudio de Asociación del Genoma Completo , Neoplasias Ováricas , Polimorfismo de Nucleótido Simple , Humanos , Femenino , Neoplasias Ováricas/genética , Neoplasias Ováricas/patología , Carcinoma Epitelial de Ovario/genética , Transcriptoma , Factores de Riesgo , Genómica/métodos , Estudios de Casos y Controles , MultiómicaRESUMEN
BACKGROUND: High-grade serous carcinoma (HGSC) gene expression subtypes are associated with differential survival. We characterized HGSC gene expression in Black individuals and considered whether gene expression differences by self-identified race may contribute to poorer HGSC survival among Black versus White individuals. METHODS: We included newly generated RNA sequencing data from Black and White individuals and array-based genotyping data from four existing studies of White and Japanese individuals. We used K-means clustering, a method with no predefined number of clusters or dataset-specific features, to assign subtypes. Cluster- and dataset-specific gene expression patterns were summarized by moderated t-scores. We compared cluster-specific gene expression patterns across datasets by calculating the correlation between the summarized vectors of moderated t-scores. After mapping to The Cancer Genome Atlas-derived HGSC subtypes, we used Cox proportional hazards models to estimate subtype-specific survival by dataset. RESULTS: Cluster-specific gene expression was similar across gene expression platforms and racial groups. Comparing the Black population with the White and Japanese populations, the immunoreactive subtype was more common (39% vs. 23%-28%) and the differentiated subtype was less common (7% vs. 22%-31%). Patterns of subtype-specific survival were similar between the Black and White populations with RNA sequencing data; compared with mesenchymal cases, the risk of death was similar for proliferative and differentiated cases and suggestively lower for immunoreactive cases [Black population HR = 0.79 (0.55, 1.13); White population HR = 0.86 (0.62, 1.19)]. CONCLUSIONS: Although the prevalence of HGSC subtypes varied by race, subtype-specific survival was similar. IMPACT: HGSC subtypes can be consistently assigned across platforms and self-identified racial groups.
Asunto(s)
Cistadenocarcinoma Seroso , Neoplasias Ováricas , Humanos , Femenino , Neoplasias Ováricas/genética , Neoplasias Ováricas/etnología , Neoplasias Ováricas/patología , Neoplasias Ováricas/mortalidad , Cistadenocarcinoma Seroso/genética , Cistadenocarcinoma Seroso/patología , Cistadenocarcinoma Seroso/etnología , Cistadenocarcinoma Seroso/mortalidad , Persona de Mediana Edad , Población Blanca/genética , Población Blanca/estadística & datos numéricos , Clasificación del Tumor , Anciano , Negro o Afroamericano/genética , Negro o Afroamericano/estadística & datos numéricosRESUMEN
BACKGROUND: Limited estimates exist on risk factors for epithelial ovarian cancer (EOC) in Asian, Hispanic, and Native Hawaiian/Pacific Islander (NHPI) women. METHODS: Participants included 1734 Asian (785 cases, 949 controls), 266 NHPI (99 cases, 167 controls), 1149 Hispanic (505 cases, 644 controls), and 24,189 White (9,981 cases, 14,208 controls) women from 11 studies in the Ovarian Cancer Association Consortium. Logistic regression models estimated odds ratios (ORs) and 95% confidence intervals (CIs) for risk associations by race and ethnicity. RESULTS: Heterogeneity in EOC risk associations by race and ethnicity (p ≤ 0.02) 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 NHPI and Asian women. The inverse association for tubal ligation with risk was most pronounced for NHPI participants (OR=0.25, 95% CI 0.13-0.48), versus Asian and White participants, respectively (OR=0.68, 95% CI 0.51-0.90; OR=0.78, 95% CI 0.73-0.85). CONCLUSIONS: Differences in EOC risk factor associations were observed across racial and ethnic groups, which could in part be due to varying prevalence of EOC histotypes. Inclusion of greater diversity in future studies is essential to inform prevention strategies.
RESUMEN
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.
Asunto(s)
Etnicidad , Neoplasias , Humanos , Estados Unidos , Hispánicos o Latinos , Negro o Afroamericano , Sistema de Registros , Blanco , Neoplasias/epidemiologíaRESUMEN
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.
Asunto(s)
Aprendizaje Profundo , Humanos , Incertidumbre , Redes Neurales de la Computación , Algoritmos , Aprendizaje AutomáticoRESUMEN
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.
Asunto(s)
Antivirales , Enterovirus , Inhibidores de Proteasa Viral , Proteasas Virales , Antivirales/farmacología , Antivirales/química , Proteolisis , Proteasas Virales/metabolismo , Inhibidores de Proteasa Viral/farmacología , Enterovirus/efectos de los fármacos , Enterovirus/fisiología , Poliproteínas/metabolismoRESUMEN
Understanding the social and environmental causes of cancer in the United States, particularly in marginalized communities, is a major research priority. Population-based cancer registries are essential for advancing this research, given their nearly complete capture of incident cases within their catchment areas. Most registries limit the release of address-level geocodes linked to cancer outcomes to comply with state health departmental regulations. These policies ensure patient privacy, uphold data confidentiality, and enhance trust in research. However, these restrictions also limit the conduct of high-quality epidemiologic studies on social and environmental factors that may contribute to cancer burden. Geomasking refers to computational algorithms that distort locational data to attain a balance between effectively "masking" the original address location while faithfully maintaining the spatial structure in the data. We propose that the systematic deployment of scalable geomasking algorithms could accelerate research on social and environmental contributions across the cancer continuum by reducing measurement error bias while also protecting privacy. We encourage multidisciplinary teams of registry officials, geospatial analysts, cancer researchers, and others engaged in this form of research to evaluate and apply geomasking procedures based on feasibility of implementation, accuracy, and privacy protection to accelerate population-based research on social and environmental causes of cancer.
Asunto(s)
Neoplasias , Privacidad , Humanos , Estados Unidos , Confidencialidad , Sistema de Registros , Confianza , Neoplasias/epidemiologíaRESUMEN
Introduction: High-grade serous carcinoma (HGSC) gene expression subtypes are associated with differential survival. We characterized HGSC gene expression in Black individuals and considered whether gene expression differences by race may contribute to poorer HGSC survival among Black versus non-Hispanic White individuals. Methods: We included newly generated RNA-Seq data from Black and White individuals, and array-based genotyping data from four existing studies of White and Japanese individuals. We assigned subtypes using K-means clustering. Cluster- and dataset-specific gene expression patterns were summarized by moderated t-scores. We compared cluster-specific gene expression patterns across datasets by calculating the correlation between the summarized vectors of moderated t-scores. Following mapping to The Cancer Genome Atlas (TCGA)-derived HGSC subtypes, we used Cox proportional hazards models to estimate subtype-specific survival by dataset. Results: Cluster-specific gene expression was similar across gene expression platforms. Comparing the Black study population to the White and Japanese study populations, the immunoreactive subtype was more common (39% versus 23%-28%) and the differentiated subtype less common (7% versus 22%-31%). Patterns of subtype-specific survival were similar between the Black and White populations with RNA-Seq data; compared to mesenchymal cases, the risk of death was similar for proliferative and differentiated cases and suggestively lower for immunoreactive cases (Black population HR=0.79 [0.55, 1.13], White population HR=0.86 [0.62, 1.19]). Conclusions: A single, platform-agnostic pipeline can be used to assign HGSC gene expression subtypes. While the observed prevalence of HGSC subtypes varied by race, subtype-specific survival was similar.
RESUMEN
BACKGROUND: Although early detection of lung cancer through screening is associated with better prognosis, most lung cancers are diagnosed among unscreened individuals. We therefore sought to characterize pathways to lung cancer diagnosis among unscreened individuals. METHODS: Participants were individuals with lung cancer who did not undergo asymptomatic lung cancer screening (n = 13) and healthcare providers who may be involved in the pathway to lung cancer diagnosis (n = 13). We conducted semi-structured interviews to identify themes in lung cancer patients' narratives of their cancer diagnoses and providers' personal and/or professional experiences of various pathways to lung cancer diagnoses, to identify delays in diagnosis. We audio-recorded, transcribed, and coded interviews in two stages. First, we conducted deductive coding using three time-period intervals from the Models of Pathways to Treatment framework: appraisal, help-seeking, and diagnostic (i.e., excluding pre-treatment). Second, we conducted inductive coding to identify themes within each time-period interval, and classified these themes as either barriers or facilitators to diagnosis. Coding and thematic summarization were completed independently by two separate analysts who discussed for consensus. RESULTS: Eight of the patient participants had formerly smoked, and five had never smoked. We identified eight barrier/facilitator themes within the three time-period intervals. Within the appraisal interval, the barrier theme was (1) minimization or misattribution of symptoms, and the facilitator theme was (2) acknowledgment of symptoms. Within the help-seeking interval, the barrier theme was (3) hesitancy to seek care, and the facilitator theme was (4) routine care. Within the diagnosis interval, barrier themes were (5) health system challenges, and (6) social determinants of health; and facilitator themes were (7) severe symptoms and known risk factors, and (8) self-advocacy. Many themes were interrelated, including minimization or misattribution of symptoms and hesitancy to seek care, which may collectively contribute to care and imaging delays. CONCLUSIONS: Interventions to reduce hesitancy to seek care may facilitate timely lung cancer diagnoses. More prompt referral to imaging-especially computed tomography (CT)-among symptomatic patients, along with patient self-advocacy for imaging, may reduce delays in diagnosis.
Asunto(s)
Neoplasias Pulmonares , Humanos , Neoplasias Pulmonares/diagnóstico , Detección Precoz del Cáncer , Investigación Cualitativa , Personal de SaludRESUMEN
BACKGROUND: Single-cell gene expression profiling provides unique opportunities to understand tumor heterogeneity and the tumor microenvironment. Because of cost and feasibility, profiling bulk tumors remains the primary population-scale analytical strategy. Many algorithms can deconvolve these tumors using single-cell profiles to infer their composition. While experimental choices do not change the true underlying composition of the tumor, they can affect the measurements produced by the assay. RESULTS: We generated a dataset of high-grade serous ovarian tumors with paired expression profiles from using multiple strategies to examine the extent to which experimental factors impact the results of downstream tumor deconvolution methods. We find that pooling samples for single-cell sequencing and subsequent demultiplexing has a minimal effect. We identify dissociation-induced differences that affect cell composition, leading to changes that may compromise the assumptions underlying some deconvolution algorithms. We also observe differences across mRNA enrichment methods that introduce additional discrepancies between the two data types. We also find that experimental factors change cell composition estimates and that the impact differs by method. CONCLUSIONS: Previous benchmarks of deconvolution methods have largely ignored experimental factors. We find that methods vary in their robustness to experimental factors. We provide recommendations for methods developers seeking to produce the next generation of deconvolution approaches and for scientists designing experiments using deconvolution to study tumor heterogeneity.
Asunto(s)
Perfilación de la Expresión Génica , Neoplasias Ováricas , Humanos , Femenino , Perfilación de la Expresión Génica/métodos , Algoritmos , Análisis de Secuencia de ARN/métodos , Neoplasias Ováricas/genética , Transcriptoma , Microambiente TumoralRESUMEN
Background: Type 1 diabetes mellitus (T1DM) is a rare, but serious immune-related adverse event (irAE) of immune checkpoint inhibitors (ICIs). Our goal was to characterize treatment outcomes associated with ICI-induced T1DM through analysis of clinical, immunological and proteomic data. Methods: This was a single-center case series of patients with solid tumors who received ICIs and subsequently had a new diagnosis of T1DM. ICD codes and C-peptide levels were used to identify patients for chart review to confirm ICI-induced T1DM. Baseline blood specimens were studied for proteomic and immunophenotypic changes. Results: Between 2011 and 2023, 18 of 3744 patients treated at Huntsman Cancer Institute with ICIs were confirmed to have ICI-induced T1DM (0.48%). Eleven of the 18 patients received anti-PD1 monotherapy, 4 received anti-PD1 plus chemotherapy or targeted therapy, and 3 received ipilimumab plus nivolumab. The mean time to onset was 218 days (range 22-418 days). Patients had sudden elevated serum glucose within 2-3 weeks prior to diagnosis. Sixteen (89%) presented with diabetic ketoacidosis. Three of 12 patients had positive T1DM-associated autoantibodies. All patients with T1DM became insulin-dependent through follow-up. At median follow-up of 21.9 months (range 8.4-82.4), no patients in the melanoma group had progressed or died from disease. In the melanoma group, best responses were 2 complete response and 2 partial response while on active treatment; none in the adjuvant group had disease recurrence. Proteomic analysis of baseline blood suggested low inflammatory (IL-6, OSMR) markers and high metabolic (GLO1, DXCR) markers in ICI-induced T1DM cohort. Conclusions: Our case series demonstrates rapid onset and irreversibility of ICI-induced T1DM. Melanoma patients with ICI-induced T1DM display excellent clinical response and survival. Limited proteomic data also suggested a unique proteomic profile. Our study helps clinicians to understand the unique clinical presentation and long-term outcomes of this rare irAE for best clinical management.
Asunto(s)
Diabetes Mellitus Tipo 1 , Melanoma , Humanos , Inhibidores de Puntos de Control Inmunológico , Glucemia , Proteómica , Recurrencia Local de NeoplasiaRESUMEN
BACKGROUND: Management of localized or recurrent prostate cancer since the 1990s has been based on risk stratification using clinicopathological variables, including Gleason score, T stage (based on digital rectal exam), and prostate-specific antigen (PSA). In this study a novel prognostic test, the Decipher Prostate Genomic Classifier (GC), was used to stratify risk of prostate cancer progression in a US national database of men with prostate cancer. METHODS: Records of prostate cancer cases from participating SEER (Surveillance, Epidemiology, and End Results) program registries, diagnosed during the period from 2010 through 2018, were linked to records of testing with the GC prognostic test. Multivariable analysis was used to quantify the association between GC scores or risk groups and use of definitive local therapy after diagnosis in the GC biopsy-tested cohort and postoperative radiotherapy in the GC-tested cohort as well as adverse pathological findings after prostatectomy. RESULTS: A total of 572â545 patients were included in the analysis, of whom 8927 patients underwent GC testing. GC biopsy-tested patients were more likely to undergo active active surveillance or watchful waiting than untested patients (odds ratio [OR] =2.21, 95% confidence interval [CI] = 2.04 to 2.38, P < .001). The highest use of active surveillance or watchful waiting was for patients with a low-risk GC classification (41%) compared with those with an intermediate- (27%) or high-risk (11%) GC classification (P < .001). Among National Comprehensive Cancer Network patients with low and favorable-intermediate risk, higher GC risk class was associated with greater use of local therapy (OR = 4.79, 95% CI = 3.51 to 6.55, P < .001). Within this subset of patients who were subsequently treated with prostatectomy, high GC risk was associated with harboring adverse pathological findings (OR = 2.94, 95% CI = 1.38 to 6.27, P = .005). Use of radiation after prostatectomy was statistically significantly associated with higher GC risk groups (OR = 2.69, 95% CI = 1.89 to 3.84). CONCLUSIONS: There is a strong association between use of the biopsy GC test and likelihood of conservative management. Higher genomic classifier scores are associated with higher rates of adverse pathology at time of surgery and greater use of postoperative radiotherapy.In this study the Decipher Prostate Genomic Classifier (GC) was used to analyze a US national database of men with prostate cancer. Use of the GC was associated with conservative management (ie, active surveillance). Among men who had high-risk GC scores and then had surgery, there was a 3-fold higher chance of having worrisome findings in surgical specimens.
Asunto(s)
Neoplasias de la Próstata , Masculino , Humanos , Estados Unidos/epidemiología , Medición de Riesgo/métodos , Neoplasias de la Próstata/epidemiología , Neoplasias de la Próstata/genética , Neoplasias de la Próstata/terapia , Antígeno Prostático Específico , Próstata/cirugía , Próstata/patología , GenómicaRESUMEN
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.
Asunto(s)
Solución de Problemas , Pensamiento , Humanos , Estudiantes , Aprendizaje , EscrituraRESUMEN
Generally, risk stratification models for cancer use effect estimates from risk/protective factor analyses that have not assessed potential interactions between these exposures. We have developed a 4-criterion framework for assessing interactions that includes statistical, qualitative, biological, and practical approaches. We present the application of this framework in an ovarian cancer setting because this is an important step in developing more accurate risk stratification models. Using data from 9 case-control studies in the Ovarian Cancer Association Consortium, we conducted a comprehensive analysis of interactions among 15 unequivocal risk and protective factors for ovarian cancer (including 14 non-genetic factors and a 36-variant polygenic score) with age and menopausal status. Pairwise interactions between the risk/protective factors were also assessed. We found that menopausal status modifies the association among endometriosis, first-degree family history of ovarian cancer, breastfeeding, and depot-medroxyprogesterone acetate use and disease risk, highlighting the importance of understanding multiplicative interactions when developing risk prediction models.