bluuebunny/crossref_metadata_2025 huggingface dataset:
Supplementary Figure 4 from Incident Cardiovascular Disease Risk among Older Asian, Native Hawaiian and Pacific Islander Breast Cancer Survivors
Authors: Hashibe Mia , Wei Mei , Lee Catherine J. , Tao Randa , Koric Alzina , Wang Jing , Daud Anees , Tay Djin , Shen Jincheng , Lee Yuan-chin A. , Chang Chun-Pin E.
Published: January 2024

Supplementary Figure 4 shows the incidence rate per 1,000 person years for the composite CVD, acute myocardial infarction, heart failure, stroke/transient ischemic attack and ischemic heart disease for ANHPI breast cancer subgroups and NHW breast cancer patients in SEER Medicare.
Supplementary Table 3 from Male-Pattern Vertex Baldness Trajectories, Chest Hair Patterns, and Odds of Overall and Aggressive Prostate Cancer
Authors: Salmon Charlotte , Mesidor Miceline , Rousseau Marie-Claude , Richard Hugues , Weiss Deborah , Spence Andrea R. , Parent Marie-Elise
Published: January 2024

Supplementary Table 3. Association between ever baldness, onset and severity baldness, chest hair amount and odds of prostate cancer, overall and by cancer aggressiveness, after restricting to participants who had been screened for prostate cancer in the 2 years preceding the index date, Prostate Cancer & Environment Study, Montreal, Canada, 2005–2012
Supplementary Table 1 from Male-Pattern Vertex Baldness Trajectories, Chest Hair Patterns, and Odds of Overall and Aggressive Prostate Cancer
Authors: Salmon Charlotte , Mesidor Miceline , Rousseau Marie-Claude , Richard Hugues , Weiss Deborah , Spence Andrea R. , Parent Marie-Elise
Published: January 2024

Supplementary Table 1. Selection criteria for the number of trajectories
Table S7 from Investigating Causal Effects of Hematologic Traits on Lung Cancer: A Mendelian Randomization Study
Authors: Yang Zhanghuan , He Hao , He Guangxu , Zeng Chudai , Hu Qian
Published: January 2024

Heterogeneity Test and Pleiotropy Test for Identified Associations
Table S5 from Investigating Causal Effects of Hematologic Traits on Lung Cancer: A Mendelian Randomization Study
Authors: Yang Zhanghuan , He Hao , He Guangxu , Zeng Chudai , Hu Qian
Published: January 2024

Causal Effects Estimated via Seven Methods for Each Association
Supplementary Table 1 from Abbreviated Score to Assess Adherence to the 2018 WCRF/AICR Cancer Prevention Recommendations and Risk of Cancer in the UK Biobank
Authors: Malcomson Fiona C. , Parra-Soto Solange , Ho Frederick K. , Celis-Morales Carlos , Sharp Linda , Mathers John C.
Published: January 2024

Sociodemographic characteristics of UK Biobank participants with an abbreviated score, who are included in the present analysis, and those without an abbreviated score, excluded from this analysis
Supplementary Methods from Abbreviated Score to Assess Adherence to the 2018 WCRF/AICR Cancer Prevention Recommendations and Risk of Cancer in the UK Biobank
Authors: Malcomson Fiona C. , Parra-Soto Solange , Ho Frederick K. , Celis-Morales Carlos , Sharp Linda , Mathers John C.
Published: January 2024

Abbreviated score to assess adherence to the 2018 WCRF/AICR Cancer Prevention Recommendations
Supplementary Figure 2 from Abbreviated Score to Assess Adherence to the 2018 WCRF/AICR Cancer Prevention Recommendations and Risk of Cancer in the UK Biobank
Authors: Malcomson Fiona C. , Parra-Soto Solange , Ho Frederick K. , Celis-Morales Carlos , Sharp Linda , Mathers John C.
Published: January 2024

Distribution of total, abbreviated scores according to sex (n=288,702)
Supplementary Figure 1 from Abbreviated Score to Assess Adherence to the 2018 WCRF/AICR Cancer Prevention Recommendations and Risk of Cancer in the UK Biobank
Authors: Malcomson Fiona C. , Parra-Soto Solange , Ho Frederick K. , Celis-Morales Carlos , Sharp Linda , Mathers John C.
Published: January 2024

Flow chart of UK Biobank Study participants included in the present study
Table S1 from Investigating Causal Effects of Hematologic Traits on Lung Cancer: A Mendelian Randomization Study
Authors: Yang Zhanghuan , He Hao , He Guangxu , Zeng Chudai , Hu Qian
Published: January 2024

STROBE-MR Checklist
Figure S4 from Investigating Causal Effects of Hematologic Traits on Lung Cancer: A Mendelian Randomization Study
Authors: Yang Zhanghuan , He Hao , He Guangxu , Zeng Chudai , Hu Qian
Published: January 2024

Causal effects of identified traits considering smoking and bilirubin. (a) Causal effects of 3 identified hematological traits and lung cancer in ever/never smokers. RAPS: Robust Adjusted Profile Score. (b) Effects of 3 identified hematological traits after adjustment for smoking. (c) Effects of 3 identified hematological traits after adjustment for bilirubin level.
Supplemental Table 8 from Mobile Phone Use and Risks of Overall and 25 Site-Specific Cancers: A Prospective Study from the UK Biobank Study
Authors: Zhang Yanjun , Zhang Yuanyuan , Ye Ziliang , Yang Sisi , Liu Mengyi , Wu Qimeng , Zhou Chun , He Panpan , Gan Xiaoqin , Qin Xianhui
Published: January 2024

Supplemental Table 8. Hazard ratios of variables in the full model of multivariable Cox proportional hazards regression model on the risk of incident overall cancer, nonmelanoma skin cancer, and vulva cancer in women.
Supplemental Table 6 from Mobile Phone Use and Risks of Overall and 25 Site-Specific Cancers: A Prospective Study from the UK Biobank Study
Authors: Zhang Yanjun , Zhang Yuanyuan , Ye Ziliang , Yang Sisi , Liu Mengyi , Wu Qimeng , Zhou Chun , He Panpan , Gan Xiaoqin , Qin Xianhui
Published: January 2024

Supplemental Table 6. Associations of mobile phone use status (users vs. non-users) with incident gliomas.
Figure S1 from Investigating Causal Effects of Hematologic Traits on Lung Cancer: A Mendelian Randomization Study
Authors: Yang Zhanghuan , He Hao , He Guangxu , Zeng Chudai , Hu Qian
Published: January 2024

Leave-one-out plot for hemoglobin concentration on lung carcinoma.
Supplemental Table 5 from Mobile Phone Use and Risks of Overall and 25 Site-Specific Cancers: A Prospective Study from the UK Biobank Study
Authors: Zhang Yanjun , Zhang Yuanyuan , Ye Ziliang , Yang Sisi , Liu Mengyi , Wu Qimeng , Zhou Chun , He Panpan , Gan Xiaoqin , Qin Xianhui
Published: January 2024

Supplemental Table 5. Hazard ratios of variables in the full model of multivariable Cox proportional hazards regression model on the risk of incident overall cancer, nonmelanoma skin cancer, prostate cancer and urinary tract cancer in men.
Supplementary Table 6 from Association between Dietary Anthocyanidins and Biliary Cancer Risk in 98,458 Participants: Results from a Prospective Study
Authors: Xiang Ling , Wu Dabin , Xu Zhiquan , Tang Yunhao , He Hongmei , Wang Yaxu , Gu Haitao , Peng Linglong
Published: January 2024

Supplementary Table 6. Sensitivity analyses on the association of total anthocyanidins with the risk of biliary cancer.
Supplemental Table 2 from Mobile Phone Use and Risks of Overall and 25 Site-Specific Cancers: A Prospective Study from the UK Biobank Study
Authors: Zhang Yanjun , Zhang Yuanyuan , Ye Ziliang , Yang Sisi , Liu Mengyi , Wu Qimeng , Zhou Chun , He Panpan , Gan Xiaoqin , Qin Xianhui
Published: January 2024

Supplemental Table 2. Characteristics of study participants by mobile phone use status in men and women.
Supplemental Table 12 from Mobile Phone Use and Risks of Overall and 25 Site-Specific Cancers: A Prospective Study from the UK Biobank Study
Authors: Zhang Yanjun , Zhang Yuanyuan , Ye Ziliang , Yang Sisi , Liu Mengyi , Wu Qimeng , Zhou Chun , He Panpan , Gan Xiaoqin , Qin Xianhui
Published: January 2024

Supplemental Table 12. Associations of mobile phone use behaviors with incident overall cancer, nonmelanoma skin cancer, and vulva cancer in women, among mobile phone users.
Supplemental Table S3 from Patient Willingness to Use a Pharmacy-Based Colorectal Cancer Screening Service: A National Survey of U.S. Adults
Authors: Shah Parth D. , Wangen Mary , Rohweder Catherine L. , Waters Austin R. , Odebunmi Olufeyisayo O. , Marciniak Macary W. , Ferrari Renée M. , Wheeler Stephanie B. , Brenner Alison T.
Published: January 2024

Conditions under which ambivalent or unwilling survey responders would be willing to participate in PharmFIT.
Supplemental Table S2 from Patient Willingness to Use a Pharmacy-Based Colorectal Cancer Screening Service: A National Survey of U.S. Adults
Authors: Shah Parth D. , Wangen Mary , Rohweder Catherine L. , Waters Austin R. , Odebunmi Olufeyisayo O. , Marciniak Macary W. , Ferrari Renée M. , Wheeler Stephanie B. , Brenner Alison T.
Published: January 2024

Latent variables and indicators used in measurement and structural equation models.
Supplemental Table S1 from Patient Willingness to Use a Pharmacy-Based Colorectal Cancer Screening Service: A National Survey of U.S. Adults
Authors: Shah Parth D. , Wangen Mary , Rohweder Catherine L. , Waters Austin R. , Odebunmi Olufeyisayo O. , Marciniak Macary W. , Ferrari Renée M. , Wheeler Stephanie B. , Brenner Alison T.
Published: January 2024

Survey response rate calculation.
Supplemental Figure 3 from Mobile Phone Use and Risks of Overall and 25 Site-Specific Cancers: A Prospective Study from the UK Biobank Study
Authors: Zhang Yanjun , Zhang Yuanyuan , Ye Ziliang , Yang Sisi , Liu Mengyi , Wu Qimeng , Zhou Chun , He Panpan , Gan Xiaoqin , Qin Xianhui
Published: January 2024

Supplemental Figure 3. Stratified analyses on the association of mobile phone use with the risk of incident overall cancer, nonmelanoma skin cancer, and vulva cancer in women, according to different age groups.
Supplementary Table 1 from The Breast Microbiome in Breast Cancer Risk and Progression: A Narrative Review
Authors: Peters Brandilyn A. , Kelly Libusha , Wang Tao , Loudig Olivier , Rohan Thomas E.
Published: January 2024

Summary of findings of the reviewed studies at the family taxonomic level.
Supplemental Table 3 from Adiposity, Weight Change, and Urinary Melatonin Levels among Men in the Multiethnic Cohort
Authors: Chowdhury-Paulino Ilkania M. , Vaselkiv Jane B. , Cheng Iona , Schernhammer Eva S. , Lin Zhike , Haiman Christopher A. , Le Marchand Loïc , Valdimarsdóttir Unnur , Wilkens Lynne R. , Markt Sarah C. , Mucci Lorelei A.
Published: January 2024

Supplemental Table 3: Associations between baseline body mass index and weight change with percent change (95% confidence intervals) in urinary melatonin levels among controls only in the Multiethnic cohort, overall and by racial/ethnic group
Supplementary Table S9 from Influence of Half-life and Smoking/Nonsmoking Ratio on Biomarker Consistency between Waves 1 and 2 of the Population Assessment of Tobacco and Health Study
Authors: Ashley David L. , Zhu Wanzhe , Bhandari Deepak , Wang Lanqing , Feng Jun , Wang Yuesong , Meng Lei , Xia Baoyun , Jarrett Jeffery M. , Chang Cindy M. , Kimmel Heather L. , Blount Benjamin C.
Published: January 2024

Supplementary Table S9 shows the results of weighted multiple linear regression analysis of log-transformed urinary nornicotine with study covariates
Supplemental Table 1 from Adiposity, Weight Change, and Urinary Melatonin Levels among Men in the Multiethnic Cohort
Authors: Chowdhury-Paulino Ilkania M. , Vaselkiv Jane B. , Cheng Iona , Schernhammer Eva S. , Lin Zhike , Haiman Christopher A. , Le Marchand Loïc , Valdimarsdóttir Unnur , Wilkens Lynne R. , Markt Sarah C. , Mucci Lorelei A.
Published: January 2024

Supplemental Table 1: Baseline characteristics of men in the Multiethnic Cohort by categories of weight change since age 21
Supplementary Table S7 from Influence of Half-life and Smoking/Nonsmoking Ratio on Biomarker Consistency between Waves 1 and 2 of the Population Assessment of Tobacco and Health Study
Authors: Ashley David L. , Zhu Wanzhe , Bhandari Deepak , Wang Lanqing , Feng Jun , Wang Yuesong , Meng Lei , Xia Baoyun , Jarrett Jeffery M. , Chang Cindy M. , Kimmel Heather L. , Blount Benjamin C.
Published: January 2024

Supplementary Table S7 shows the results of weighted multiple linear regression analysis of log-transformed urinary cotinine N-oxide with study covariates
Supplementary Tables 1a-3d from A Population-Based Family Case–Control Study of Sun Exposure and Follicular Lymphoma Risk
Authors: Odutola Michael K. , van Leeuwen Marina T. , Bruinsma Fiona , Turner Jennifer , Hertzberg Mark , Seymour John F. , Prince H. Miles , Trotman Judith , Verner Emma , Roncolato Fernando , Opat Stephen , Lindeman Robert , Tiley Campbell , Milliken Samuel T. , Underhill Craig R. , Benke Geza , Giles Graham G. , Vajdic Claire M.
Published: January 2024

Supplementary Tables 1a-3d. Supplementary Table 1a: Bayesian information criterion for outdoor hours group-based trajectory model according to number of groups and trajectory outdoor hours. Supplementary Table 1b: Average posterior probability value and odds of correct classification for outdoor hours trajectory groups. Supplementary Table 1c: Estimated probability and the proportion of study members classified in each group according to the maximum posterior probability assignment rule. Supplementary Table 2a: Odds ratios and 95% confidence intervals for FL risk in relation to total lifetime outdoor hours during decade years among cases and related controls.Supplementary Table 2b: Odds ratios and 95% confidence intervals for FL risk in relation to outdoor hours on weekdays, weekends, and holidays at each decade year among cases and related controls. Supplementary Table 2c: Odds ratios and 95% confidence intervals for FL risk in relation to trajectories of outdoor hours across the decade years among cases and related controls. Supplementary Table 2d: Odds ratios and 95% confidence intervals for FL risk in relation to total lifetime outdoor hours during decade years among cases and unrelated controls. Supplementary Table 2e: Odds ratios and 95% confidence intervals for FL risk in relation to outdoor hours on weekdays, weekends, and holidays at each decade year among cases and unrelated controls. Supplementary Table 2f: Odds ratios and 95% confidence intervals for FL risk in relation to trajectories of outdoor hours across the decade years among cases and unrelated controls. Supplementary Table 3a: Bayesian information criterion for outdoor hours group-based trajectory model according to number of groups and trajectory outdoor hours – cases and related controls. Supplementary Table 3b: Bayesian information criterion for outdoor hours group-based trajectory model according to number of groups and trajectory outdoor hours – cases and unrelated controls. Supplementary Table 3c: Average posterior probability value and odds of correct classification for outdoor hours trajectory groups. Supplementary Table 3d: Estimated probability and the proportion of study members classified in each group according to the maximum posterior probability assignment rule.
Supplementary Figure 4 from A Population-Based Family Case–Control Study of Sun Exposure and Follicular Lymphoma Risk
Authors: Odutola Michael K. , van Leeuwen Marina T. , Bruinsma Fiona , Turner Jennifer , Hertzberg Mark , Seymour John F. , Prince H. Miles , Trotman Judith , Verner Emma , Roncolato Fernando , Opat Stephen , Lindeman Robert , Tiley Campbell , Milliken Samuel T. , Underhill Craig R. , Benke Geza , Giles Graham G. , Vajdic Claire M.
Published: January 2024

Supplementary Figure 4 shows the estimated trajectories of outdoor hours by control type for weekdays (A and B), weekends (C and D) and holidays (E and F)
Supplementary Table S41 from Influence of Half-life and Smoking/Nonsmoking Ratio on Biomarker Consistency between Waves 1 and 2 of the Population Assessment of Tobacco and Health Study
Authors: Ashley David L. , Zhu Wanzhe , Bhandari Deepak , Wang Lanqing , Feng Jun , Wang Yuesong , Meng Lei , Xia Baoyun , Jarrett Jeffery M. , Chang Cindy M. , Kimmel Heather L. , Blount Benjamin C.
Published: January 2024

Supplementary Table S41 shows scaled variance components and intra-class correlation coefficients resulting from mixed model analysis for all analytes
Supplementary Figure 2 from A Population-Based Family Case–Control Study of Sun Exposure and Follicular Lymphoma Risk
Authors: Odutola Michael K. , van Leeuwen Marina T. , Bruinsma Fiona , Turner Jennifer , Hertzberg Mark , Seymour John F. , Prince H. Miles , Trotman Judith , Verner Emma , Roncolato Fernando , Opat Stephen , Lindeman Robert , Tiley Campbell , Milliken Samuel T. , Underhill Craig R. , Benke Geza , Giles Graham G. , Vajdic Claire M.
Published: January 2024

Supplementary Figure 2 shows the factors considered in the directed acyclic graph for sun exposure and follicular lymphoma risk
Supplementary Table S4 from Influence of Half-life and Smoking/Nonsmoking Ratio on Biomarker Consistency between Waves 1 and 2 of the Population Assessment of Tobacco and Health Study
Authors: Ashley David L. , Zhu Wanzhe , Bhandari Deepak , Wang Lanqing , Feng Jun , Wang Yuesong , Meng Lei , Xia Baoyun , Jarrett Jeffery M. , Chang Cindy M. , Kimmel Heather L. , Blount Benjamin C.
Published: January 2024

Supplementary Table S4 shows the results of weighted multiple linear regression analysis of log-transformed urinary nicotine with study covariates
Table S2. from Adherence to 2018 WCRF/AICR Cancer Prevention Recommendations and Risk of Cancer: The Melbourne Collaborative Cohort Study
Authors: Peng Yang , Bassett Julie K. , Hodge Allison M. , Melaku Yohannes Adama , Afshar Nina , Hopper John L. , MacInnis Robert J. , Lynch Brigid M. , Smith-Warner Stephanie A. , Giles Graham G. , Milne Roger L. , Jayasekara Harindra
Published: January 2024

Hazard ratiosa for WCRF/AICR diet and lifestyle scores and total, alcohol-, obesity- and smoking-related, and digestive tract cancer risk excluding first two years of follow-up in the Melbourne Collaborative Cohort Study
Figure S6. from Adherence to 2018 WCRF/AICR Cancer Prevention Recommendations and Risk of Cancer: The Melbourne Collaborative Cohort Study
Authors: Peng Yang , Bassett Julie K. , Hodge Allison M. , Melaku Yohannes Adama , Afshar Nina , Hopper John L. , MacInnis Robert J. , Lynch Brigid M. , Smith-Warner Stephanie A. , Giles Graham G. , Milne Roger L. , Jayasekara Harindra
Published: January 2024

Hazard ratios per one-unit increment in WCRF/AICR standardized lifestyle score and cancer risk in the Melbourne Collaborative Cohort Study.
The plot shows hazard ratios for total, alcohol-, obesity- and smoking-related, and digestive tract cancer risk according to smoking status (A) and age at diagnosis (B). P for interaction is denoted by P.
Figure S5. from Adherence to 2018 WCRF/AICR Cancer Prevention Recommendations and Risk of Cancer: The Melbourne Collaborative Cohort Study
Authors: Peng Yang , Bassett Julie K. , Hodge Allison M. , Melaku Yohannes Adama , Afshar Nina , Hopper John L. , MacInnis Robert J. , Lynch Brigid M. , Smith-Warner Stephanie A. , Giles Graham G. , Milne Roger L. , Jayasekara Harindra
Published: January 2024

Hazard ratios per one-unit increment in WCRF/AICR diet and lifestyle scores and cancer risk in the Melbourne Collaborative Cohort Study.
The plot shows hazard ratios for total, alcohol-, obesity- and smoking-related, and digestive tract cancer risk for women (A) and men (B). P for interaction is denoted by P.
Figure S4. from Adherence to 2018 WCRF/AICR Cancer Prevention Recommendations and Risk of Cancer: The Melbourne Collaborative Cohort Study
Authors: Peng Yang , Bassett Julie K. , Hodge Allison M. , Melaku Yohannes Adama , Afshar Nina , Hopper John L. , MacInnis Robert J. , Lynch Brigid M. , Smith-Warner Stephanie A. , Giles Graham G. , Milne Roger L. , Jayasekara Harindra
Published: January 2024

Multivariable-adjusted hazard ratios for WCRF/AICR diet and lifestyle scores and total cancer risk in the Melbourne Collaborative Cohort Study.
The hazard ratios are shown by the solid black lines, the 95% confidence limits are shown by the solid grey lines and the reference lines are shown by the red dashed lines.
Figure S3. from Adherence to 2018 WCRF/AICR Cancer Prevention Recommendations and Risk of Cancer: The Melbourne Collaborative Cohort Study
Authors: Peng Yang , Bassett Julie K. , Hodge Allison M. , Melaku Yohannes Adama , Afshar Nina , Hopper John L. , MacInnis Robert J. , Lynch Brigid M. , Smith-Warner Stephanie A. , Giles Graham G. , Milne Roger L. , Jayasekara Harindra
Published: January 2024

Distribution of diet and lifestyle scores in the Melbourne Collaborative Cohort Study.
The plot shows descriptive statistics for the diet and lifestyle scores. The maximum possible scores: WCRF/AICR diet score=5, WCRF/AICR standardized lifestyle score=7, expanded lifestyle score=7, standardized lifestyle score + smoking=8. The horizontal lines within each box indicate the median and 25th and 75th percentiles, and circles indicate outliers.
Figure S2. from Adherence to 2018 WCRF/AICR Cancer Prevention Recommendations and Risk of Cancer: The Melbourne Collaborative Cohort Study
Authors: Peng Yang , Bassett Julie K. , Hodge Allison M. , Melaku Yohannes Adama , Afshar Nina , Hopper John L. , MacInnis Robert J. , Lynch Brigid M. , Smith-Warner Stephanie A. , Giles Graham G. , Milne Roger L. , Jayasekara Harindra
Published: January 2024

Flowchart showing selection of study participants.
The flowchart describes how the final study sample was determined. The boxes with dotted outline indicate exclusion criteria.
Supplementary Table S31 from Influence of Half-life and Smoking/Nonsmoking Ratio on Biomarker Consistency between Waves 1 and 2 of the Population Assessment of Tobacco and Health Study
Authors: Ashley David L. , Zhu Wanzhe , Bhandari Deepak , Wang Lanqing , Feng Jun , Wang Yuesong , Meng Lei , Xia Baoyun , Jarrett Jeffery M. , Chang Cindy M. , Kimmel Heather L. , Blount Benjamin C.
Published: January 2024

Supplementary Table S31 shows the results of weighted multiple linear regression analysis of log-transformed urinary N‐acetyl‐S‐(N‐methylcarbamoyl)‐L‐cysteine (MCaMA) with study covariates
Figure S1. from Adherence to 2018 WCRF/AICR Cancer Prevention Recommendations and Risk of Cancer: The Melbourne Collaborative Cohort Study
Authors: Peng Yang , Bassett Julie K. , Hodge Allison M. , Melaku Yohannes Adama , Afshar Nina , Hopper John L. , MacInnis Robert J. , Lynch Brigid M. , Smith-Warner Stephanie A. , Giles Graham G. , Milne Roger L. , Jayasekara Harindra
Published: January 2024

Recruitment and follow-up: the Melbourne Collaborative Cohort Study Diagram showing study recruitment, exposure ascertainment and follow-up for Melbourne Collaborative Cohort Study participants.
Supplementary Table S30 from Influence of Half-life and Smoking/Nonsmoking Ratio on Biomarker Consistency between Waves 1 and 2 of the Population Assessment of Tobacco and Health Study
Authors: Ashley David L. , Zhu Wanzhe , Bhandari Deepak , Wang Lanqing , Feng Jun , Wang Yuesong , Meng Lei , Xia Baoyun , Jarrett Jeffery M. , Chang Cindy M. , Kimmel Heather L. , Blount Benjamin C.
Published: January 2024

Supplementary Table S30 shows the results of weighted multiple linear regression analysis of log-transformed urinary phenylglyoxylic acid (PhGA) with study covariates
Supplementary Table S6 from Effects of KRAS Genetic Interactions on Outcomes in Cancers of the Lung, Pancreas, and Colorectum
Authors: Grabski Isabella N. , Heymach John V. , Kehl Kenneth L. , Kopetz Scott , Lau Ken S. , Riely Gregory J. , Schrag Deborah , Yaeger Rona , Irizarry Rafael A. , Haigis Kevin M.
Published: January 2024

Supplementary Table S6: Associations between clusters and covariates of interest for NSCLC, CRC, and PDAC, reported as odds ratios except for age, which is reported as the untransformed coefficient. The 95% Bonferroni confidence intervals are included in parentheses, corrected over the number of clusters for each tested covariate.
Supplementary Table S3 from Effects of KRAS Genetic Interactions on Outcomes in Cancers of the Lung, Pancreas, and Colorectum
Authors: Grabski Isabella N. , Heymach John V. , Kehl Kenneth L. , Kopetz Scott , Lau Ken S. , Riely Gregory J. , Schrag Deborah , Yaeger Rona , Irizarry Rafael A. , Haigis Kevin M.
Published: January 2024

Supplementary Table S3: Demographic characteristics of the patients included and not included in the B-PCA analyses for NSCLC, CRC, and PDAC.
Supplementary Table S27 from Influence of Half-life and Smoking/Nonsmoking Ratio on Biomarker Consistency between Waves 1 and 2 of the Population Assessment of Tobacco and Health Study
Authors: Ashley David L. , Zhu Wanzhe , Bhandari Deepak , Wang Lanqing , Feng Jun , Wang Yuesong , Meng Lei , Xia Baoyun , Jarrett Jeffery M. , Chang Cindy M. , Kimmel Heather L. , Blount Benjamin C.
Published: January 2024

Supplementary Table S27 shows the results of weighted multiple linear regression analysis of log-transformed urinary N-acetyl‐S‐(3‐hydroxypropyl)‐L‐cysteine (3HPMA) with study covariates
Supplementary Table S2 from Effects of KRAS Genetic Interactions on Outcomes in Cancers of the Lung, Pancreas, and Colorectum
Authors: Grabski Isabella N. , Heymach John V. , Kehl Kenneth L. , Kopetz Scott , Lau Ken S. , Riely Gregory J. , Schrag Deborah , Yaeger Rona , Irizarry Rafael A. , Haigis Kevin M.
Published: January 2024

Supplementary Table S2: Results for additional regression analyses.
Supplementary Table S26 from Influence of Half-life and Smoking/Nonsmoking Ratio on Biomarker Consistency between Waves 1 and 2 of the Population Assessment of Tobacco and Health Study
Authors: Ashley David L. , Zhu Wanzhe , Bhandari Deepak , Wang Lanqing , Feng Jun , Wang Yuesong , Meng Lei , Xia Baoyun , Jarrett Jeffery M. , Chang Cindy M. , Kimmel Heather L. , Blount Benjamin C.
Published: January 2024

Supplementary Table S26 shows the results of weighted multiple linear regression analysis of log-transformed urinary N‐acetyl‐S‐(3,4‐dihydroxybutyl)‐L‐cysteine (34HBMA) with study covariates
Supplementary Figure S2 from Effects of KRAS Genetic Interactions on Outcomes in Cancers of the Lung, Pancreas, and Colorectum
Authors: Grabski Isabella N. , Heymach John V. , Kehl Kenneth L. , Kopetz Scott , Lau Ken S. , Riely Gregory J. , Schrag Deborah , Yaeger Rona , Irizarry Rafael A. , Haigis Kevin M.
Published: January 2024

Supplementary Figure S2: CONSORT diagram depicting the number of included and excluded samples, and the reasons for exclusion, at each step of analysis for the CRC cohort.
Supplementary Table S24 from Influence of Half-life and Smoking/Nonsmoking Ratio on Biomarker Consistency between Waves 1 and 2 of the Population Assessment of Tobacco and Health Study
Authors: Ashley David L. , Zhu Wanzhe , Bhandari Deepak , Wang Lanqing , Feng Jun , Wang Yuesong , Meng Lei , Xia Baoyun , Jarrett Jeffery M. , Chang Cindy M. , Kimmel Heather L. , Blount Benjamin C.
Published: January 2024

Supplementary Table S24 shows the results of weighted multiple linear regression analysis of log-transformed urinary 3,4-methylhippuric acid (34MHA) with study covariates
Supplementary Figure S1 from Effects of KRAS Genetic Interactions on Outcomes in Cancers of the Lung, Pancreas, and Colorectum
Authors: Grabski Isabella N. , Heymach John V. , Kehl Kenneth L. , Kopetz Scott , Lau Ken S. , Riely Gregory J. , Schrag Deborah , Yaeger Rona , Irizarry Rafael A. , Haigis Kevin M.
Published: January 2024

Supplementary Figure S1: CONSORT diagram depicting the number of included and excluded samples, and the reasons for exclusion, at each step of analysis for the NSCLC cohort.
Supplementary Table S23 from Influence of Half-life and Smoking/Nonsmoking Ratio on Biomarker Consistency between Waves 1 and 2 of the Population Assessment of Tobacco and Health Study
Authors: Ashley David L. , Zhu Wanzhe , Bhandari Deepak , Wang Lanqing , Feng Jun , Wang Yuesong , Meng Lei , Xia Baoyun , Jarrett Jeffery M. , Chang Cindy M. , Kimmel Heather L. , Blount Benjamin C.
Published: January 2024

Supplementary Table S23 shows the results of weighted multiple linear regression analysis of log-transformed urinary 2‐methylhippuric acid (2-MHA) with study covariates
Supplementary Table S22 from Influence of Half-life and Smoking/Nonsmoking Ratio on Biomarker Consistency between Waves 1 and 2 of the Population Assessment of Tobacco and Health Study
Authors: Ashley David L. , Zhu Wanzhe , Bhandari Deepak , Wang Lanqing , Feng Jun , Wang Yuesong , Meng Lei , Xia Baoyun , Jarrett Jeffery M. , Chang Cindy M. , Kimmel Heather L. , Blount Benjamin C.
Published: January 2024

Supplementary Table S22 shows the results of weighted multiple linear regression analysis of log-transformed urinary N‐acetyl‐S‐(1‐cyano‐2‐hydroxyethyl)‐L‐cysteine (1CyHEMA) with study covariates
Supplemental Table 1 from Pre- and Postnatal Exposures to Tobacco Smoking and Survival of Childhood Acute Lymphoblastic and Myeloid Leukemias in California, United States
Authors: Metayer Catherine , Morimoto Libby M. , Kang Alice Y. , Sanchez Alvarez Jacklyn , Winestone Lena E.
Published: January 2024

Supplemental Table 1 describes the relationship between parental tobacco use and socio-demographic characteristics.
Supplementary Table S21 from Influence of Half-life and Smoking/Nonsmoking Ratio on Biomarker Consistency between Waves 1 and 2 of the Population Assessment of Tobacco and Health Study
Authors: Ashley David L. , Zhu Wanzhe , Bhandari Deepak , Wang Lanqing , Feng Jun , Wang Yuesong , Meng Lei , Xia Baoyun , Jarrett Jeffery M. , Chang Cindy M. , Kimmel Heather L. , Blount Benjamin C.
Published: January 2024

Supplementary Table S21 shows the results of weighted multiple linear regression analysis of log-transformed urinary N‐acetyl‐S‐(2‐cyanoethyl)‐L‐cysteine (2CyEMA) with study covariates
Supplemental Figure 1 from Pre- and Postnatal Exposures to Tobacco Smoking and Survival of Childhood Acute Lymphoblastic and Myeloid Leukemias in California, United States
Authors: Metayer Catherine , Morimoto Libby M. , Kang Alice Y. , Sanchez Alvarez Jacklyn , Winestone Lena E.
Published: January 2024

Supplemental Figure 1 shows the post-hoc DAG with causal and biasing paths for the relationship between child’s exposure to tobacco smoking (Exposure) and survival of childhood leukemia (Outcome). The minimal sufficient adjustment set related to sociodemographic factors included parental income, parental education, and child’s race/ethnicity. The year at diagnosis did not impact the risk estimates by more than 10% and was not retained in the final model.
Supplementary Table S20 from Influence of Half-life and Smoking/Nonsmoking Ratio on Biomarker Consistency between Waves 1 and 2 of the Population Assessment of Tobacco and Health Study
Authors: Ashley David L. , Zhu Wanzhe , Bhandari Deepak , Wang Lanqing , Feng Jun , Wang Yuesong , Meng Lei , Xia Baoyun , Jarrett Jeffery M. , Chang Cindy M. , Kimmel Heather L. , Blount Benjamin C.
Published: January 2024

Supplementary Table S20 shows the results of weighted multiple linear regression analysis of log-transformed urinary N‐acetyl‐S‐(2‐carbamoyl‐2‐hydroxyethyl)‐L‐cysteine (2CaHEMA) with study covariates
Supplementary Table S2 from Influence of Half-life and Smoking/Nonsmoking Ratio on Biomarker Consistency between Waves 1 and 2 of the Population Assessment of Tobacco and Health Study
Authors: Ashley David L. , Zhu Wanzhe , Bhandari Deepak , Wang Lanqing , Feng Jun , Wang Yuesong , Meng Lei , Xia Baoyun , Jarrett Jeffery M. , Chang Cindy M. , Kimmel Heather L. , Blount Benjamin C.
Published: January 2024

Supplementary Table S2 shows the reference biomarker half-lives used in the analysis
Supplementary Table S3 from Racial and Ethnic Disparities in the Use of Robot-Assisted Surgery and Minimally Invasive Surgery in Pelvic Cancer Treatment: A Systematic Review
Authors: Mao Jialin , Genkinger Jeanine M. , Rundle Andrew G. , Wright Jason D. , Aryal Suvekshya , Liebeskind Alexander Y. , Tehranifar Parisa
Published: January 2024

Quality assessment of studies included in the systematic review
Supplementary Table S19 from Influence of Half-life and Smoking/Nonsmoking Ratio on Biomarker Consistency between Waves 1 and 2 of the Population Assessment of Tobacco and Health Study
Authors: Ashley David L. , Zhu Wanzhe , Bhandari Deepak , Wang Lanqing , Feng Jun , Wang Yuesong , Meng Lei , Xia Baoyun , Jarrett Jeffery M. , Chang Cindy M. , Kimmel Heather L. , Blount Benjamin C.
Published: January 2024

Supplementary Table S19 shows the results of weighted multiple linear regression analysis of log-transformed urinary N‐acetyl‐S‐(2‐carbamoylethyl)‐L‐cysteine (2CaEMA) with study covariates
Supplementary Table S17 from Influence of Half-life and Smoking/Nonsmoking Ratio on Biomarker Consistency between Waves 1 and 2 of the Population Assessment of Tobacco and Health Study
Authors: Ashley David L. , Zhu Wanzhe , Bhandari Deepak , Wang Lanqing , Feng Jun , Wang Yuesong , Meng Lei , Xia Baoyun , Jarrett Jeffery M. , Chang Cindy M. , Kimmel Heather L. , Blount Benjamin C.
Published: January 2024

Supplementary Table S17 shows the results of weighted multiple linear regression analysis of log-transformed urinary 1-naphthol with study covariates
Supplementary Figure 1 from Racial and Ethnic Disparities in the Use of Robot-Assisted Surgery and Minimally Invasive Surgery in Pelvic Cancer Treatment: A Systematic Review
Authors: Mao Jialin , Genkinger Jeanine M. , Rundle Andrew G. , Wright Jason D. , Aryal Suvekshya , Liebeskind Alexander Y. , Tehranifar Parisa
Published: January 2024

Supplementary Figure 1. Studies included in the systematic review by cancer site and publication year.
Data from Incident Cardiovascular Disease Risk among Older Asian, Native Hawaiian and Pacific Islander Breast Cancer Survivors
Authors: Hashibe Mia , Wei Mei , Lee Catherine J. , Tao Randa , Koric Alzina , Wang Jing , Daud Anees , Tay Djin , Shen Jincheng , Lee Yuan-chin A. , Chang Chun-Pin E.
Published: January 2024

AbstractBackground:
Cardiotoxicity among breast cancer survivors is associated with chemotherapy and radiation therapy. The risk of cardiovascular disease (CVD) among Asian, Native Hawaiian and Pacific Islander (ANHPI) breast cancer survivors in the United States is unknown.
Methods:
We used the SEER-Medicare linked database to estimate the risk of CVD among older breast cancer survivors. International Classification of Disease diagnosis codes were used to identify incident CVD outcomes. Cox proportional hazards models were used to estimate HRs and 95% confidence intervals (CI) comparing ANHPI with Non-Hispanic White (NHW) patients with breast cancer for CVD, and among ANHPI race and ethnicity groups.
Results:
A total of 7,122 ANHPI breast cancer survivors and 21,365 NHW breast cancer survivors were identified. The risks of incident heart failure and ischemic heart disease were lower among ANHPI compared with NHW breast cancer survivors (HRheart failure, 0.72; 95% CI, 0.61–0.84; HRheart disease, 0.74; 95% CI, 0.63–0.88). Compared with Japanese patients with breast cancer, Filipino, Asian Indian and Pakistani, and Native Hawaiian breast cancer survivors had higher risks of heart failure. ischemic heart disease and death. Among ANHPI breast cancer survivors, risk factors for heart failure included older age, higher comorbidity score, distant cancer stage and chemotherapy.
Conclusions:
Our results support heterogeneity in CVD outcomes among breast cancer survivors among ANHPI race and ethnicity groups. Further research is needed to elucidate the disparities experienced among ANHPI breast cancer survivors.
Impact:
Filipino, Asian Indian and Pakistani, and Native Hawaiian patients with breast cancer had higher risks of heart failure, ischemic heart disease and death among ANHPI patients with breast cancer.
Data from Male-Pattern Vertex Baldness Trajectories, Chest Hair Patterns, and Odds of Overall and Aggressive Prostate Cancer
Authors: Salmon Charlotte , Mesidor Miceline , Rousseau Marie-Claude , Richard Hugues , Weiss Deborah , Spence Andrea R. , Parent Marie-Elise
Published: January 2024

AbstractBackground:
The link between hormones and hair growth is well established. Inconsistent associations have been found between hair patterns and cancer of the prostate, a hormone-dependent organ. We assessed vertex baldness trajectories, chest hair amount, and their relationships with the odds of developing prostate cancer in a large case–control study in Montreal, Canada.
Methods:
In-person interviews were conducted with 1,931 incident prostate cancer cases and 1,994 population-based age-matched (±5 years) controls. Participants reported their hair patterns using the validated Hamilton–Norwood scale of baldness for 10-year increments starting at age 30, and their current amount of chest hair. Group-based trajectories were used to identify men sharing similar patterns of vertex baldness severity over adulthood. Multivariable logistic regression assessed associations between indicators of baldness (frontal, vertex, age at onset, severity, and trajectories), chest hair, and odds of prostate cancer.
Results:
Vertex balding onset at age 30 was associated with increased odds of overall prostate cancer [Odds ratio (OR), 1.30; 95% confidence interval (CI), 1.03–1.64]. Men in the trajectory characterized by early moderate vertex baldness and developing severe baldness had increased odds of overall (OR, 1.42; 95% CI, 1.03–1.96) and especially aggressive prostate cancer (OR, 1.98; 95% CI, 1.21–3.22) compared with men without baldness. Men with little chest hair had higher odds of aggressive tumors than those with a moderate amount/a lot of chest hair.
Conclusions:
Early-onset moderate vertex baldness that progresses and having little chest hair may be useful biomarkers of aggressive prostate cancer.
Impact:
Integration of early-onset vertex balding patterns into risk prediction models of aggressive prostate cancer should be envisaged.
Data from Mobile Phone Use and Risks of Overall and 25 Site-Specific Cancers: A Prospective Study from the UK Biobank Study
Authors: Zhang Yanjun , Zhang Yuanyuan , Ye Ziliang , Yang Sisi , Liu Mengyi , Wu Qimeng , Zhou Chun , He Panpan , Gan Xiaoqin , Qin Xianhui
Published: January 2024

AbstractBackground:
The association between mobile phone use and incident cancers remains uncertain. We aimed to investigate the relationships of mobile phone use with incident overall and 25 site-specific cancers in men and women.
Methods:
A total of 431,861 participants ages 38 to 73 years without prior cancers were included from the UK Biobank. Of these, 46.7% were male. Participants who used a mobile phone at least once per week to make or receive calls were defined as mobile phone users. The study outcomes were incident overall and 25 site-specific cancers.
Results:
During a median follow-up of 10.7 years, 35,401 (17.5%) men and 30,865 (13.4%) women developed overall cancer. Mobile phone use was significantly associated with higher risks of incident overall cancer [HR, 1.09; 95% confidence interval (CI): 1.06–1.12], nonmelanoma skin cancer (NMSC; HR, 1.08; 95% CI: 1.03–1.14), urinary tract cancer (HR, 1.18; 95% CI:1.05–1.32), and prostate cancer (HR, 1.19; 95% CI: 1.13–1.25) in men, and incident overall cancer (HR, 1.03; 95% CI: 1.00–1.06), NMSC (HR, 1.07; 95% CI: 1.01–1.13), and vulva cancer (HR, 1.74; 95% CI: 1.00–3.02) in women, but not with other cancers. Among mobile phone users, there was a dose–response relationship of length of mobile phone use with incident NMSC in men and women, and prostate cancer in men (all Ptrend < 0.05).
Conclusions:
There was a dose–response relationship of length of mobile phone use with incident NMSC in men and women, and prostate cancer in men.
Impact:
Our findings underscore the importance of limiting mobile phone use or keeping a distance from mobile phone for primary prevention of NMSC and prostate cancer.
Data from Effects of KRAS Genetic Interactions on Outcomes in Cancers of the Lung, Pancreas, and Colorectum
Authors: Grabski Isabella N. , Heymach John V. , Kehl Kenneth L. , Kopetz Scott , Lau Ken S. , Riely Gregory J. , Schrag Deborah , Yaeger Rona , Irizarry Rafael A. , Haigis Kevin M.
Published: January 2024

AbstractBackground:
KRAS is among the most commonly mutated oncogenes in cancer, and previous studies have shown associations with survival in many cancer contexts. Evidence from both clinical observations and mouse experiments further suggests that these associations are allele- and tissue-specific. These findings motivate using clinical data to understand gene interactions and clinical covariates within different alleles and tissues.
Methods:
We analyze genomic and clinical data from the AACR Project GENIE Biopharma Collaborative for samples from lung, colorectal, and pancreatic cancers. For each of these cancer types, we report epidemiological associations for different KRAS alleles, apply principal component analysis (PCA) to discover groups of genes co-mutated with KRAS, and identify distinct clusters of patient profiles with implications for survival.
Results:
KRAS mutations were associated with inferior survival in lung, colon, and pancreas, although the specific mutations implicated varied by disease. Tissue- and allele-specific associations with smoking, sex, age, and race were found. Tissue-specific genetic interactions with KRAS were identified by PCA, which were clustered to produce five, four, and two patient profiles in lung, colon, and pancreas. Membership in these profiles was associated with survival in all three cancer types.
Conclusions:
KRAS mutations have tissue- and allele-specific associations with inferior survival, clinical covariates, and genetic interactions.
Impact:
Our results provide greater insight into the tissue- and allele-specific associations with KRAS mutations and identify clusters of patients that are associated with survival and clinical attributes from combinations of genetic interactions with KRAS mutations.
Data from Pre- and Postnatal Exposures to Tobacco Smoking and Survival of Childhood Acute Lymphoblastic and Myeloid Leukemias in California, United States
Authors: Metayer Catherine , Morimoto Libby M. , Kang Alice Y. , Sanchez Alvarez Jacklyn , Winestone Lena E.
Published: January 2024

AbstractBackground:
Tobacco smoke adversely affects the prognosis of adult cancers including myeloid leukemia, but less is known in children.
Methods:
We evaluated whether pre- and postnatal exposures to tobacco smoke decrease 5-year survival of 1,235 childhood acute lymphoblastic leukemia (ALL) and 188 childhood acute myeloid leukemia (AML) cases derived from a population-based case–control study in California. Cases were diagnosed between 1995 and 2015 (median follow-up time of 13.2 years overall). We obtained data on tobacco smoking (before conception, during pregnancy, after birth), parental education and income, clinical features, and vital status through 2020. Cox proportional hazards regression was used to calculate hazard ratios (HR) and 95% confidence intervals (CI) for mortality associated with smoking, adjusting for sociodemographic characteristics and risk group (ALL only).
Results:
About 23% of mothers and 39% of fathers reported smoking and 130 children with ALL and 52 with AML died within 5 years. For AML, increased risks of death were observed among children whose fathers smoked before conception compared with nonsmoking fathers [HR = 1.41; 95% confidence interval (CI), 0.95–3.44 and 3.47; 95% CI, 1.37–8.81, respectively for <20 vs. ≥20 cigarettes per day; Ptrend = 0.01]. HR for child's passive smoking was 1.74, 95% CI, 0.81–3.73. Paternal preconception smoking may also reduce 5-year survival among ALL with favorable prognostic molecular subtypes (high hyperdiploidy and absence of IKZF1 gene deletion), although the associations did not reach statistical significance (Pheterogeneity = 0.07).
Conclusions:
Paternal preconception smoking decreased 5-year survival of childhood AML.
Impact:
Knowledge of exposure to tobacco smoking should be integrated in the treatment plan of childhood leukemias.
Abstract A003: HPV infection causes dependence on alternative DNA damage response pathways providing cancer specific targets for radiosensitization
Authors: Goldstein Michael , Kotwal Akhil , Gabriel Nishanth , Buchser William , Sentmanat Monica , Markovina Stephanie , Schwarz Julie , Cui Xia
Published: January 2024

Targeted radiosensitization is an attractive approach to improving the efficacy of radiotherapy in cancer treatment. However, drugs targeting ubiquitous DNA damage response (DDR) pathways are not cancer specific and can increase radiation related side effects. Thus, there is an unmet clinical need for tumor specific molecular targets. To address this need we sought to identify DNA damage signaling pathways that are unique to cancer cells. We hypothesized that by expressing its genome in host cells high-risk Human Papilloma Virus (HPV) can rewire DNA damage signaling making HPV-positive tumor cells dependent on alternative pathways to survive radiation, which can be exploited for targeted radiosensitization of HPV-induced cancers. Using a CRISPR/Cas9 screen in cells that selectively express HPV16 proteins E6 or E7 we uncovered genes that cells rely on to survive radiation-induced DNA damage in the context of E6 or E7 expression. We demonstrate that targeting these newly identified genes sensitizes cancer cells to radiation specifically in the presence of E6 or E7. We provide mechanistic insights showing that E6 and E7 alter the cellular epigenome by inhibiting histone lysine acetylation and tri-methylation, respectively. In this setting, a member of a centromeric protein complex identified in the screen is critical for mitotic progression and survival in E6-expressing cells following radiation exposure. Deletion of this factor resulted in delayed mitotic progression, chromosomal missegregation and rapid post-mitotic death in irradiated HPV-positive cells, a phenotype that was mitigated by ablation of E6 expression. Furthermore, we identified an E3 ubiquitin ligase that promotes survival in E7-expressing cells following radiation exposure by alleviating perturbations during DNA replication. Together, we have uncovered DDR pathways that are critical for cell survival after radiation exposure specifically in HPV-positive cells. Our findings suggest that expression of viral proteins E6 and E7 rewires DNA damage signaling networks causing dependence on alternative response pathways. We propose that these pathways can be targeted for tumor-specific radiosensitization of HPV-induced cancers.

Citation Format: Michael Goldstein, Akhil Kotwal, Nishanth Gabriel, William Buchser, Monica Sentmanat, Stephanie Markovina, Julie Schwarz, Xia Cui. HPV infection causes dependence on alternative DNA damage response pathways providing cancer specific targets for radiosensitization [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: DNA Damage Repair: From Basic Science to Future Clinical Application; 2024 Jan 9-11; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2024;84(1 Suppl):Abstract nr A003.