Da-Feng Lin, Hai-Lin Li, Ting Liu, Xiao-Fei Lv, Chuan-Miao Xie, Xiao-Min Ou, Jian Guan, Ye Zhang, Wen-Bin Yan, Mei-Lin He, Meng-Yuan Mao, Xun Zhao, Lian-Zhen Zhong, Wen-Hui Chen, Qiu-Yan Chen, Hai-Qiang Mai, Rou-Jun Peng, Jie Tian, Lin-Quan Tang, Di Dong
Background The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma (lrNPC) is limited due to their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in lrNPC. Methods This multicenter, retrospective study included 921 patients with lrNPC. A machine learning signature and nomogram based on pretreatment MRI features were developed for predicting overall survival (OS) in a training cohort and validated in two independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance index (C-index) and receiver operating characteristic curve analysis. Accordingly, patients were classified into risk groups. The biological characteristics and immune infiltration of the signature were explored by RNA sequencing (RNA-seq) analysis. Results The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving C-indexes of 0.729, 0.718, and 0.731 in the training, internal, and external validation cohorts, respectively. Integration of the signature and clinical variables significantly improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with significantly distinct OS rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-seq analysis revealed differences in interferon response and lymphocyte infiltration between risk groups. Conclusions An MRI-based radiomic signature predicted OS more accurately. The proposed signature associated with tumor immune heterogeneity may serve as a valuable tool to facilitate prognostic stratification and guide individualized management for lrNPC patients.
{"title":"Radiomic signatures associated with tumor immune heterogeneity predict survival in locally recurrent nasopharyngeal carcinoma","authors":"Da-Feng Lin, Hai-Lin Li, Ting Liu, Xiao-Fei Lv, Chuan-Miao Xie, Xiao-Min Ou, Jian Guan, Ye Zhang, Wen-Bin Yan, Mei-Lin He, Meng-Yuan Mao, Xun Zhao, Lian-Zhen Zhong, Wen-Hui Chen, Qiu-Yan Chen, Hai-Qiang Mai, Rou-Jun Peng, Jie Tian, Lin-Quan Tang, Di Dong","doi":"10.1093/jnci/djae081","DOIUrl":"https://doi.org/10.1093/jnci/djae081","url":null,"abstract":"Background The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma (lrNPC) is limited due to their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in lrNPC. Methods This multicenter, retrospective study included 921 patients with lrNPC. A machine learning signature and nomogram based on pretreatment MRI features were developed for predicting overall survival (OS) in a training cohort and validated in two independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance index (C-index) and receiver operating characteristic curve analysis. Accordingly, patients were classified into risk groups. The biological characteristics and immune infiltration of the signature were explored by RNA sequencing (RNA-seq) analysis. Results The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving C-indexes of 0.729, 0.718, and 0.731 in the training, internal, and external validation cohorts, respectively. Integration of the signature and clinical variables significantly improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with significantly distinct OS rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-seq analysis revealed differences in interferon response and lymphocyte infiltration between risk groups. Conclusions An MRI-based radiomic signature predicted OS more accurately. The proposed signature associated with tumor immune heterogeneity may serve as a valuable tool to facilitate prognostic stratification and guide individualized management for lrNPC patients.","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":"280 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140621637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jing Sun, Jianhui Zhao, Siyun Zhou, Xinxuan Li, Tengfei Li, Lijuan Wang, Shuai Yuan, Dong Chen, Philip J Law, Susanna C Larsson, Susan M Farrington, Richard S Houlston, Malcolm G Dunlop, Evropi Theodoratou, Xue Li
Background We aimed to identify plasma and urinary metabolites related to colorectal cancer (CRC) risk and elucidate their mediator role in the associations between modifiable risk factors and CRC. Methods Metabolite quantitative trait loci were derived from two published metabolomics genome-wide association studies (GWASs), and summary-level data were extracted for 651 plasma metabolites and 208 urinary metabolites. Genetic associations with CRC were obtained from a large-scale GWAS meta-analysis (100,204 cases; 154,587 controls) and the FinnGen cohort (4,957 cases; 304,197 controls). Mendelian randomization (MR) and colocalization analyses were performed to evaluate the causal roles of metabolites in CRC. Druggability evaluation was employed to prioritize potential therapeutic targets. Multivariable MR and mediation estimation were conducted to elucidate the mediating effects of metabolites on the associations between modifiable risk factors and CRC. Results The study identified 30 plasma metabolites and four urinary metabolites for CRC. Plasma sphingomyelin and urinary lactose, which were positively associated with CRC risk, could be modulated by drug interventions (ie, Olipudase alfa, Tilactase). Thirteen modifiable risk factors were associated with nine metabolites and eight of these modifiable risk factors were associated with CRC risk. These nine metabolites mediated the effect of modifiable risk factors (Actinobacteria, BMI, waist-hip ratio, fasting insulin, smoking initiation) on CRC. Conclusion This study identified key metabolite biomarkers associated with CRC and elucidated their mediator roles in the associations between modifiable risk factors and CRC. These findings provide new insights into the etiology and potential therapeutic targets for CRC and the etiological pathways of modifiable environmental factors with CRC.
{"title":"Systematic investigation of genetically determined plasma and urinary metabolites to discover potential interventional targets for colorectal cancer","authors":"Jing Sun, Jianhui Zhao, Siyun Zhou, Xinxuan Li, Tengfei Li, Lijuan Wang, Shuai Yuan, Dong Chen, Philip J Law, Susanna C Larsson, Susan M Farrington, Richard S Houlston, Malcolm G Dunlop, Evropi Theodoratou, Xue Li","doi":"10.1093/jnci/djae089","DOIUrl":"https://doi.org/10.1093/jnci/djae089","url":null,"abstract":"Background We aimed to identify plasma and urinary metabolites related to colorectal cancer (CRC) risk and elucidate their mediator role in the associations between modifiable risk factors and CRC. Methods Metabolite quantitative trait loci were derived from two published metabolomics genome-wide association studies (GWASs), and summary-level data were extracted for 651 plasma metabolites and 208 urinary metabolites. Genetic associations with CRC were obtained from a large-scale GWAS meta-analysis (100,204 cases; 154,587 controls) and the FinnGen cohort (4,957 cases; 304,197 controls). Mendelian randomization (MR) and colocalization analyses were performed to evaluate the causal roles of metabolites in CRC. Druggability evaluation was employed to prioritize potential therapeutic targets. Multivariable MR and mediation estimation were conducted to elucidate the mediating effects of metabolites on the associations between modifiable risk factors and CRC. Results The study identified 30 plasma metabolites and four urinary metabolites for CRC. Plasma sphingomyelin and urinary lactose, which were positively associated with CRC risk, could be modulated by drug interventions (ie, Olipudase alfa, Tilactase). Thirteen modifiable risk factors were associated with nine metabolites and eight of these modifiable risk factors were associated with CRC risk. These nine metabolites mediated the effect of modifiable risk factors (Actinobacteria, BMI, waist-hip ratio, fasting insulin, smoking initiation) on CRC. Conclusion This study identified key metabolite biomarkers associated with CRC and elucidated their mediator roles in the associations between modifiable risk factors and CRC. These findings provide new insights into the etiology and potential therapeutic targets for CRC and the etiological pathways of modifiable environmental factors with CRC.","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140637709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Measures of physical function clarify the prognostic blur of cancer survivorship.","authors":"Justin C Brown","doi":"10.1093/jnci/djae076","DOIUrl":"https://doi.org/10.1093/jnci/djae076","url":null,"abstract":"","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":"17 S3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140693001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julie A Wolfson, Elizabeth S Davis, Aniket Saha, Isaac Martinez, David McCall, Prachi Kothari, Julienne Brackett, David S Dickens, Alissa R. Kahn, Carla Schwalm, Archana Sharma, Joshua Richman, Branko Cuglievan, Smita Bhatia, C. Dai, Jennifer M Levine, Emily E. Johnston
Adolescents and Young Adults (AYAs: 15-39 y) with cancer face unique vulnerabilities, yet remain under-represented on clinical trials, including adult registries of COVID-19 in cancer (AYAs: 8-12%). Thus, we leveraged the Pediatric Oncology COVID-19 Case Report (POCC) to examine the clinical course of COVID-19 among AYAs with cancer. POCC collects de-identified clinical and sociodemographic data regarding 0-39yo with cancer (AYAs = 37%) and COVID-19 from >100 institutions. Between 04/01/2020-11/28/2023, 191 older AYAs [22-39y] and 640 younger AYAs [15-21y] were captured. Older AYAs were less often hospitalized (p < .001), admitted to the intensive care unit (ICU, p = .02), and/or required respiratory support (p = .057). In multivariable analyses, older AYAs faced 80% lower odds of ICU admission but 2.3-times greater odds of changes to cancer-directed therapy. Unvaccinated patients had 5.4-times higher odds of ICU admission. Among AYAs with cancer, the COVID-19 course varies by age. These findings can inform pediatric/adult oncology teams surrounding COVID-19 management and prevention.
{"title":"Adolescents and young adults (AYA) with cancer: the clinical course of COVID-19 infections.","authors":"Julie A Wolfson, Elizabeth S Davis, Aniket Saha, Isaac Martinez, David McCall, Prachi Kothari, Julienne Brackett, David S Dickens, Alissa R. Kahn, Carla Schwalm, Archana Sharma, Joshua Richman, Branko Cuglievan, Smita Bhatia, C. Dai, Jennifer M Levine, Emily E. Johnston","doi":"10.1093/jnci/djae085","DOIUrl":"https://doi.org/10.1093/jnci/djae085","url":null,"abstract":"Adolescents and Young Adults (AYAs: 15-39 y) with cancer face unique vulnerabilities, yet remain under-represented on clinical trials, including adult registries of COVID-19 in cancer (AYAs: 8-12%). Thus, we leveraged the Pediatric Oncology COVID-19 Case Report (POCC) to examine the clinical course of COVID-19 among AYAs with cancer. POCC collects de-identified clinical and sociodemographic data regarding 0-39yo with cancer (AYAs = 37%) and COVID-19 from >100 institutions. Between 04/01/2020-11/28/2023, 191 older AYAs [22-39y] and 640 younger AYAs [15-21y] were captured. Older AYAs were less often hospitalized (p < .001), admitted to the intensive care unit (ICU, p = .02), and/or required respiratory support (p = .057). In multivariable analyses, older AYAs faced 80% lower odds of ICU admission but 2.3-times greater odds of changes to cancer-directed therapy. Unvaccinated patients had 5.4-times higher odds of ICU admission. Among AYAs with cancer, the COVID-19 course varies by age. These findings can inform pediatric/adult oncology teams surrounding COVID-19 management and prevention.","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":"18 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140697339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kyle A Mani, Xue Wu, Daniel E Spratt, Ming Wang, Nicholas G Zaorsky
Background In this study, we provide the largest analysis to date of a US-based cancer cohort to characterize death from COVID-19. Methods A total of 4,020,669 patients across 15 subtypes living with cancer in 2020 and included in the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database were abstracted. We investigated prognostic factors for death due to COVID-19 using a cox proportional hazards model and calculated hazard ratios (HRs). Standardized mortality ratios (SMRs) were calculated using observed mortality counts from SEER and expected mortality based on U.S. mortality rates. Results 291,323 patients died, with 14,821 (5.1%) deaths attributed to COVID-19 infection. The COVID-19 disease-specific mortality rate was 11.81/10,000-persons years, and SMR of COVID-19 was 2.30 (95% CI: 2.26-2.34, p < .0001). COVID-19 ranked as the second leading cause of death following ischemic heart disease (5.2%) among 26 non-cancer causes of death. Patients who are older (80+ vs < =49 years old: HR 21.47, 95% CI: 19.34-23.83), male (vs female: HR 1.46, 95% CI: 1.40-1.51), unmarried (vs married: HR 1.47, 95% CI: 1.42-1.53), and Hispanic or Non-Hispanic African American (vs Non-Hispanic White: HR 2.04, 95% CI: 1.94-2.14 and HR 2.03, 95% CI: 1.94-2.14, respectively) were at greatest risk of COVID-19 mortality. Conclusions and Relevance We observed that people living with cancer are at two times greater risk of dying from COVID-19 compared to the general US population. This work may be used by physicians and public health officials in the creation of survivorship programs that mitigate the risk of COVID-19 mortality.
{"title":"A population-based study of COVID-19 mortality risk in US cancer patients","authors":"Kyle A Mani, Xue Wu, Daniel E Spratt, Ming Wang, Nicholas G Zaorsky","doi":"10.1093/jnci/djae086","DOIUrl":"https://doi.org/10.1093/jnci/djae086","url":null,"abstract":"Background In this study, we provide the largest analysis to date of a US-based cancer cohort to characterize death from COVID-19. Methods A total of 4,020,669 patients across 15 subtypes living with cancer in 2020 and included in the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database were abstracted. We investigated prognostic factors for death due to COVID-19 using a cox proportional hazards model and calculated hazard ratios (HRs). Standardized mortality ratios (SMRs) were calculated using observed mortality counts from SEER and expected mortality based on U.S. mortality rates. Results 291,323 patients died, with 14,821 (5.1%) deaths attributed to COVID-19 infection. The COVID-19 disease-specific mortality rate was 11.81/10,000-persons years, and SMR of COVID-19 was 2.30 (95% CI: 2.26-2.34, p &lt; .0001). COVID-19 ranked as the second leading cause of death following ischemic heart disease (5.2%) among 26 non-cancer causes of death. Patients who are older (80+ vs &lt; =49 years old: HR 21.47, 95% CI: 19.34-23.83), male (vs female: HR 1.46, 95% CI: 1.40-1.51), unmarried (vs married: HR 1.47, 95% CI: 1.42-1.53), and Hispanic or Non-Hispanic African American (vs Non-Hispanic White: HR 2.04, 95% CI: 1.94-2.14 and HR 2.03, 95% CI: 1.94-2.14, respectively) were at greatest risk of COVID-19 mortality. Conclusions and Relevance We observed that people living with cancer are at two times greater risk of dying from COVID-19 compared to the general US population. This work may be used by physicians and public health officials in the creation of survivorship programs that mitigate the risk of COVID-19 mortality.","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":"114 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140556467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cathy J. Bradley, K. R. Yabroff, Ya‐Chen Tina Shih
{"title":"Clinic-based interventions for improving access to care: a good start.","authors":"Cathy J. Bradley, K. R. Yabroff, Ya‐Chen Tina Shih","doi":"10.1093/jnci/djae068","DOIUrl":"https://doi.org/10.1093/jnci/djae068","url":null,"abstract":"","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":"21 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140714494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hormuzd A Katki, Philip C Prorok, Philip E Castle, Lori M Minasian, Paul F Pinsky
Background Cancer screening trials have required large sample-sizes and long time-horizons to demonstrate cancer mortality reductions, the primary goal of cancer screening. We examine assumptions and potential power gains from exploiting information from testing control-arm specimens, which we call the “Intended Effect” (IE) analysis that we explain in detail herein. The IE analysis is particularly suited to tests that can be conducted on stored specimens in the control-arm, such as stored blood for multicancer detection (MCD) tests. Methods We simulated hypothetical MCD screening trials to compare power and sample-size for the standard vs IE analysis. Under two assumptions that we detail herein, we projected the IE analysis for 3 existing screening trials (National Lung Screening Trial (NLST), Minnesota Colon Cancer Control Study (MINN-FOBT-A), and Prostate, Lung, Colorectal, Ovarian Cancer Screening Trial—colorectal component (PLCO-CRC)). Results Compared to the standard analysis for the 3 existing trials, the IE design could have reduced cancer-specific mortality p-values 5-fold (NLST), 33-fold (MINN-FOBT-A), or 14,160-fold (PLCO-CRC), or alternately, reduced sample-size (90% power) by 26% (NLST), 48% (MINN-FOBT-A), or 59% (PLCO-CRC). For potential MCD trial designs requiring 100,000 subjects per-arm to achieve 90% power for multi-cancer mortality for the standard analysis, the IE analysis achieves 90% power for only 37,500-50,000 per arm, depending on assumptions concerning control-arm test-positives. Conclusions Testing stored specimens in the control arm of screening trials to conduct the IE analysis could substantially increase power to reduce sample-size or accelerate trials, and provide particularly strong power gains for MCD tests.
{"title":"Increasing power in screening trials by testing control-arm specimens: Application to multicancer detection screening","authors":"Hormuzd A Katki, Philip C Prorok, Philip E Castle, Lori M Minasian, Paul F Pinsky","doi":"10.1093/jnci/djae083","DOIUrl":"https://doi.org/10.1093/jnci/djae083","url":null,"abstract":"Background Cancer screening trials have required large sample-sizes and long time-horizons to demonstrate cancer mortality reductions, the primary goal of cancer screening. We examine assumptions and potential power gains from exploiting information from testing control-arm specimens, which we call the “Intended Effect” (IE) analysis that we explain in detail herein. The IE analysis is particularly suited to tests that can be conducted on stored specimens in the control-arm, such as stored blood for multicancer detection (MCD) tests. Methods We simulated hypothetical MCD screening trials to compare power and sample-size for the standard vs IE analysis. Under two assumptions that we detail herein, we projected the IE analysis for 3 existing screening trials (National Lung Screening Trial (NLST), Minnesota Colon Cancer Control Study (MINN-FOBT-A), and Prostate, Lung, Colorectal, Ovarian Cancer Screening Trial—colorectal component (PLCO-CRC)). Results Compared to the standard analysis for the 3 existing trials, the IE design could have reduced cancer-specific mortality p-values 5-fold (NLST), 33-fold (MINN-FOBT-A), or 14,160-fold (PLCO-CRC), or alternately, reduced sample-size (90% power) by 26% (NLST), 48% (MINN-FOBT-A), or 59% (PLCO-CRC). For potential MCD trial designs requiring 100,000 subjects per-arm to achieve 90% power for multi-cancer mortality for the standard analysis, the IE analysis achieves 90% power for only 37,500-50,000 per arm, depending on assumptions concerning control-arm test-positives. Conclusions Testing stored specimens in the control arm of screening trials to conduct the IE analysis could substantially increase power to reduce sample-size or accelerate trials, and provide particularly strong power gains for MCD tests.","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140547562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Craig Evan Pollack, Veronica Garrison, Taylor Johnson, Amanda L Blackford, Robert Banks, William Howe, K Robin Yabroff, Lindsey Enewold
Background Lack of stable and affordable housing is an important social determinant of health. Federal housing assistance may buffer against housing vulnerabilities among low-income households, but research examining the association of housing assistance and cancer care has been limited. We introduce a new linkage of SEER-Medicare and Housing and Urban Development (HUD) administrative data. Methods Individuals enrolled in HUD public and assisted housing programs 2006-2021 were linked with cancer diagnoses 2006-2019 identified in the SEER-Medicare data from 16 states using Match*Pro probabilistic linkage software. HUD administrative data include timing and type of housing assistance and verified household income. Medicare administrative data are available through 2020. Results A total of 335,490 unique individuals who received housing assistance matched to SEER-Medicare data at any point in time, including 156,794 that recieved housing assistance around the time of their diagnosis (at least 6 months prior to diagnosis until 6 months after diagnosis or death). A total of 63,251 persons with housing assistance at the time of their diagnosis were aged 66 years and older and continuously enrolled in Medicare Parts A and B fee-for-service, 12,035 with lung, 8,866 with breast, 7,261 with colorectal, and 4,703 with prostate cancer. Conclusions This novel data linkage will be available through the National Cancer Institute and can be used to explore the ways in which housing assistance is associated with cancer diagnosis, care, and outcomes, including the role of housing assistance status in potentially reducing or contributing to inequities across racialized and ethnic groups.
{"title":"Housing Assistance Among Patients with Cancer: SEER-Medicare U.S. Department of Housing and Urban Development Data Linkage","authors":"Craig Evan Pollack, Veronica Garrison, Taylor Johnson, Amanda L Blackford, Robert Banks, William Howe, K Robin Yabroff, Lindsey Enewold","doi":"10.1093/jnci/djae082","DOIUrl":"https://doi.org/10.1093/jnci/djae082","url":null,"abstract":"Background Lack of stable and affordable housing is an important social determinant of health. Federal housing assistance may buffer against housing vulnerabilities among low-income households, but research examining the association of housing assistance and cancer care has been limited. We introduce a new linkage of SEER-Medicare and Housing and Urban Development (HUD) administrative data. Methods Individuals enrolled in HUD public and assisted housing programs 2006-2021 were linked with cancer diagnoses 2006-2019 identified in the SEER-Medicare data from 16 states using Match*Pro probabilistic linkage software. HUD administrative data include timing and type of housing assistance and verified household income. Medicare administrative data are available through 2020. Results A total of 335,490 unique individuals who received housing assistance matched to SEER-Medicare data at any point in time, including 156,794 that recieved housing assistance around the time of their diagnosis (at least 6 months prior to diagnosis until 6 months after diagnosis or death). A total of 63,251 persons with housing assistance at the time of their diagnosis were aged 66 years and older and continuously enrolled in Medicare Parts A and B fee-for-service, 12,035 with lung, 8,866 with breast, 7,261 with colorectal, and 4,703 with prostate cancer. Conclusions This novel data linkage will be available through the National Cancer Institute and can be used to explore the ways in which housing assistance is associated with cancer diagnosis, care, and outcomes, including the role of housing assistance status in potentially reducing or contributing to inequities across racialized and ethnic groups.","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140538678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emily Berry, Jeff Hostetter, Joseph Bachtold, Sarah Zamarripa, Keith E Argenbright
Background Colorectal Cancer (CRC) is the third most diagnosed cancer and the second leading cause of cancer death in the United States. Colonoscopy is an essential tool for screening, used both as a primary approach and follow-up to an abnormal stool-based CRC screening result. Colonoscopy quality is often measured with four key indicators: bowel preparation, cecal intubation, mean withdrawal time, and adenoma detection. Colonoscopies are most often performed by gastroenterologists (GI), however, in rural and medically underserved areas non-GI providers often perform colonoscopies. This study aims to evaluate the quality and safety of screening colonoscopies performed by non-GI providers, comparing their outcomes to those of GI providers. Methods Descriptive statistics were used to characterize the study population. Results for quality indicators were stratified by provider type and compared. Statistical significance was determined using p < 0.05 as the threshold for all comparisons; all p-values were two-sided. Results No statistical difference was found when comparing performance by provider type. Median performance for gastroenterologists, general surgeons, and family medicine providers ranged form 98—100% for cecal intubation; 97.4—100% for bowel preparation; 57.4—88.9% for male adenoma detection rate; 47.7—62.13% for female adenoma detection rate; and 0:12:10—0:20:16 for mean withdrawal time. All provider types met and exceeded the goal metric for each of the quality indicators (p < 0.001). In this analysis, non-GI providers can be expected to perform colonoscopies with similar quality to GI providers based on performance outcomes for the key quality metrics.
背景 大肠癌（CRC）是美国第三大确诊癌症和第二大癌症死因。结肠镜检查是筛查的重要工具，既可作为初筛方法，也可作为粪便异常 CRC 筛查结果的后续检查。结肠镜检查的质量通常用四个关键指标来衡量：肠道准备、盲肠插管、平均退出时间和腺瘤检测。结肠镜检查通常由消化内科医生（GI）进行，但在农村和医疗服务不足的地区，非 GI 医疗服务提供者通常也会进行结肠镜检查。本研究旨在评估非 GI 医疗机构进行结肠镜筛查的质量和安全性，并将其结果与 GI 医疗机构的结果进行比较。方法 使用描述性统计来描述研究人群的特征。根据医疗机构类型对质量指标的结果进行分层并进行比较。所有比较均以 p < 0.05 为临界值，所有 p 值均为双侧。结果 在按医疗机构类型比较绩效时，未发现统计差异。消化内科医生、普外科医生和全科医生在盲肠插管方面的绩效中位数为 98%-100%；肠道准备方面为 97.4%-100%；男性腺瘤检出率为 57.4%-88.9%；女性腺瘤检出率为 47.7%-62.13%；平均退出时间为 0:12:10-0:20:16。所有类型的医疗服务提供者都达到或超过了每项质量指标的目标指标（p < 0.001）。在这项分析中，根据关键质量指标的绩效结果，非消化道医疗服务提供者的结肠镜检查质量与消化道医疗服务提供者类似。
{"title":"Evaluating Colonoscopy Quality by Performing Provider Type","authors":"Emily Berry, Jeff Hostetter, Joseph Bachtold, Sarah Zamarripa, Keith E Argenbright","doi":"10.1093/jnci/djae080","DOIUrl":"https://doi.org/10.1093/jnci/djae080","url":null,"abstract":"Background Colorectal Cancer (CRC) is the third most diagnosed cancer and the second leading cause of cancer death in the United States. Colonoscopy is an essential tool for screening, used both as a primary approach and follow-up to an abnormal stool-based CRC screening result. Colonoscopy quality is often measured with four key indicators: bowel preparation, cecal intubation, mean withdrawal time, and adenoma detection. Colonoscopies are most often performed by gastroenterologists (GI), however, in rural and medically underserved areas non-GI providers often perform colonoscopies. This study aims to evaluate the quality and safety of screening colonoscopies performed by non-GI providers, comparing their outcomes to those of GI providers. Methods Descriptive statistics were used to characterize the study population. Results for quality indicators were stratified by provider type and compared. Statistical significance was determined using p &lt; 0.05 as the threshold for all comparisons; all p-values were two-sided. Results No statistical difference was found when comparing performance by provider type. Median performance for gastroenterologists, general surgeons, and family medicine providers ranged form 98—100% for cecal intubation; 97.4—100% for bowel preparation; 57.4—88.9% for male adenoma detection rate; 47.7—62.13% for female adenoma detection rate; and 0:12:10—0:20:16 for mean withdrawal time. All provider types met and exceeded the goal metric for each of the quality indicators (p &lt; 0.001). In this analysis, non-GI providers can be expected to perform colonoscopies with similar quality to GI providers based on performance outcomes for the key quality metrics.","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140538616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiping Fang, Tomotaka Ugai, Carino Gurjao, Rong Zhong, Zhenhua Liu, Xinyuan Zhang, Peilu Wang, Jonathan Nowak, Molin Wang, Marios Giannakis, Shuji Ogino, Xuehong Zhang, Edward Giovannucci
Background We examined whether the association between alcohol consumption and CRC incidence was stronger for tumors with higher contributions of defective MMR (dMMR)-related tumor mutational signatures (TMSs). Methods We used data from 227,916 men and women who participated in the Nurses’ Health Study (1980-2016), the Nurses’ Health Study II (1991-2017), and the Health Professionals Follow-up Study (1986-2016). Dietary data was collected every 4 years through validated food frequency questionnaires. Relative contributions of two dMMR-related TMSs (c-dMMRa/SBS15 and c-dMMRb/SBS26) were quantified using whole-exome sequencing data in a subset of incident CRC cases. Duplication-method Cox proportional hazards regression models were used to assess the association between alcohol consumption and the risk of CRC subtypes according to different contributions of the TMSs. All statistical tests were 2-sided. Results We documented 825 incident CRC cases with available TMS data over 26-36 years of follow-up. The association between alcohol consumption and CRC incidence was stronger for tumors with higher contributions of c-dMMRb/SBS26 (P-heterogeneitytrend = 0.02) compared to tumors with lower contributions of this TMS. Compared with nondrinkers, drinkers with ≥15 g/d of alcohol had a high risk of c-dMMRb/SBS26-high CRC [multivariable-adjusted HR: 2.43 (95% CI: 1.55-3.82)], but not c-dMMRb/SBS26-low CRC [0.86 (95% CI: 0.57-1.28)] or c-dMMRb/SBS26-moderate CRC [1.14 (95% CI: 0.76-1.71)]. No significant differential associations were observed for c-dMMRa/SBS15 (P-heterogeneitytrend = 0.41). Conclusions High alcohol consumption was associated with an increased incidence of CRC containing higher contributions of c-dMMRb/SBS26, suggesting that alcohol consumption may be involved in colorectal carcinogenesis through the DNA mismatch repair pathway.
{"title":"Alcohol and colorectal cancer risk subclassified by mutational signatures of DNA mismatch repair deficiency","authors":"Aiping Fang, Tomotaka Ugai, Carino Gurjao, Rong Zhong, Zhenhua Liu, Xinyuan Zhang, Peilu Wang, Jonathan Nowak, Molin Wang, Marios Giannakis, Shuji Ogino, Xuehong Zhang, Edward Giovannucci","doi":"10.1093/jnci/djae078","DOIUrl":"https://doi.org/10.1093/jnci/djae078","url":null,"abstract":"Background We examined whether the association between alcohol consumption and CRC incidence was stronger for tumors with higher contributions of defective MMR (dMMR)-related tumor mutational signatures (TMSs). Methods We used data from 227,916 men and women who participated in the Nurses’ Health Study (1980-2016), the Nurses’ Health Study II (1991-2017), and the Health Professionals Follow-up Study (1986-2016). Dietary data was collected every 4 years through validated food frequency questionnaires. Relative contributions of two dMMR-related TMSs (c-dMMRa/SBS15 and c-dMMRb/SBS26) were quantified using whole-exome sequencing data in a subset of incident CRC cases. Duplication-method Cox proportional hazards regression models were used to assess the association between alcohol consumption and the risk of CRC subtypes according to different contributions of the TMSs. All statistical tests were 2-sided. Results We documented 825 incident CRC cases with available TMS data over 26-36 years of follow-up. The association between alcohol consumption and CRC incidence was stronger for tumors with higher contributions of c-dMMRb/SBS26 (P-heterogeneitytrend = 0.02) compared to tumors with lower contributions of this TMS. Compared with nondrinkers, drinkers with ≥15 g/d of alcohol had a high risk of c-dMMRb/SBS26-high CRC [multivariable-adjusted HR: 2.43 (95% CI: 1.55-3.82)], but not c-dMMRb/SBS26-low CRC [0.86 (95% CI: 0.57-1.28)] or c-dMMRb/SBS26-moderate CRC [1.14 (95% CI: 0.76-1.71)]. No significant differential associations were observed for c-dMMRa/SBS15 (P-heterogeneitytrend = 0.41). Conclusions High alcohol consumption was associated with an increased incidence of CRC containing higher contributions of c-dMMRb/SBS26, suggesting that alcohol consumption may be involved in colorectal carcinogenesis through the DNA mismatch repair pathway.","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140348980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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