bluuebunny/crossref_metadata_2025 huggingface dataset:
Table S6 from Diet Quality and All-Cause Mortality in Women with Breast Cancer from the Breast Cancer Family Registry
Authors: Haslam Danielle E. , John Esther M. , Knight Julia A. , Li Zhongyu , Buys Saundra S. , Andrulis Irene L. , Daly Mary B. , Genkinger Jeanine M. , Terry Mary Beth , Zhang Fang Fang
Published: May 2023

Table S6 Shows Hazard Ratios and 95% Confidence Intervals for All-Cause Mortality in Women Diagnosed with Breast Cancer According to AHEI and aMED, Stratified by Patient and Treatment Characteristics
Table S5 from Diet Quality and All-Cause Mortality in Women with Breast Cancer from the Breast Cancer Family Registry
Authors: Haslam Danielle E. , John Esther M. , Knight Julia A. , Li Zhongyu , Buys Saundra S. , Andrulis Irene L. , Daly Mary B. , Genkinger Jeanine M. , Terry Mary Beth , Zhang Fang Fang
Published: May 2023

Table S5 Shows Hazard Ratios and 95% Confidence Intervals for All-Cause Mortality in Women Diagnosed with Breast Cancer According to DASH and HEI-2015, Stratified by Patient and Treatment Characteristics
Table S1 from Diet Quality and All-Cause Mortality in Women with Breast Cancer from the Breast Cancer Family Registry
Authors: Haslam Danielle E. , John Esther M. , Knight Julia A. , Li Zhongyu , Buys Saundra S. , Andrulis Irene L. , Daly Mary B. , Genkinger Jeanine M. , Terry Mary Beth , Zhang Fang Fang
Published: May 2023

Table S1 Shows Criteria for Maximum Scoring for 4 Diet Quality Indices Using Standardized Cup and Ounce Equivalents from the MyPyramid Equivalents Database
Table S2 from Changes in Breast Cancer Risk and Risk Factor Profiles among U.S.-Born and Immigrant Asian American Women Residing in the San Francisco Bay Area
Authors: John Esther M. , Koo Jocelyn , Ingles Sue A. , Kurian Allison W. , Hines Lisa M.
Published: May 2023

Table S2 shows differences in the prevalence of risk factors comparing foreign-born Asian American, U.S.-born Asian American, and U.S.-born non-Hispanic White controls
Figure S1 from Changes in Breast Cancer Risk and Risk Factor Profiles among U.S.-Born and Immigrant Asian American Women Residing in the San Francisco Bay Area
Authors: John Esther M. , Koo Jocelyn , Ingles Sue A. , Kurian Allison W. , Hines Lisa M.
Published: May 2023

Figure S1 shows differences in the prevalence of risk factors between foreign-born and U.S.-born Asian American cases from the younger birth cohort
Supplementary Table 4 from Prevalence and Prognostic Significance of Malnutrition in Patients with Brain Metastasis
Authors: Liu Zheran , Zhang Yu , Pei Yiyan , He Yan , Yu Jiayi , Zhang Renjie , Wang Jingjing , Chong Weelic , Hai Yang , Peng Xingchen , Fang Fang
Published: May 2023

Supplementary Table 4 The results of multivariate Cox analyses of CONUT (A), PNI (B), and NRI (C) scores with overall survival in patients with brain metastases.
Supplementary Table 1 from Prevalence and Prognostic Significance of Malnutrition in Patients with Brain Metastasis
Authors: Liu Zheran , Zhang Yu , Pei Yiyan , He Yan , Yu Jiayi , Zhang Renjie , Wang Jingjing , Chong Weelic , Hai Yang , Peng Xingchen , Fang Fang
Published: May 2023

Supplementary Table 1. The comparison of baseline characteristics of included and excluded patients with brain metastases.
Table S4 from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Table 4 presents the outcomes of the meta-regression analysis conducted for CRP
Table S3C from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Table 3C present the risk of bias for the prospective cohort study, using the ROBINS-E
Table S3B from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Table 3B presents the risk of bias for non-randomised interventions, using the ROBINS-I
Table S2C from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Table 2C presents the study characteristics for non-randomised interventions
Table S2B from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Table 2B presents the study characteristics of randomised cross-over trials
Table S2A from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Table 2A presents the study characteristics for parallel group RCTs
Table S2A from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Table 2A presents the study characteristics of the Mendelian randomization studies
Figure S4 from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 4 presents funnel plots for physical activity to inflammatory biomarkers meta-analyses
Figure S3B from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 3B presents forest plots for physical activity and IL-6, by exercise type
Figure S5C from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 5C presents dose-response curves for adiponectin and breast cancer risk, by menopausal status
Figure S4D from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 4D presents the funnel plots from the meta-analysis of leptin and breast cancer risk
Figure S1A from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 1A presents forest plots for physical activity and CRP, by menopausal status
Figure S4B from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 4B presents forest plots for leptin and breast cancer risk, excluding studies where exogenous hormone use status was unknown
Figure 2 from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Forest plots for effects of physical activity compared with usual activity control in RCTs. A forest plot for (A) TNFα, (B) IL6, and (C) CRP.
Figure S2B from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 2B presents forest plots for TNF-alpha and breast cancer risk, excluding studies where exogenous hormone use status was unknown
Figure 2 from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Forest plots for effects of inflammatory biomarkers on breast cancer risk. Forest plots for (A) CRP, (B) TNFα, and (C) IL6.
Supplementary Materials and Methods from Risk and Protective Factors for Cancer Mortality among United States Service Members and Veterans (2001–2018)
Authors: Sharifian Neika , Carey Felicia R. , Seay Julia S. , Castañeda Sheila F. , Boyko Edward J. , Rull Rudolph P.
Published: May 2023

Supplementary Materials and Methods
Supplementary Figure 1 from Genetic Factors Associated with Absolute and Relative Plasma Concentrations of Calcitriol
Authors: Wilson Robin Taylor , Safford Susan E. , Ostrom Quinn T. , Wang Ming , McDonald Alicia C. , Salzberg Anna C. , Barnholtz-Sloan Jill S. , Richie John P.
Published: May 2023

Supplemental Figures 1a through 1d. Log linear plots of Plasma Calcitriol (1,25(OH)2D3) and plasma Calcitriol-24,25-Dihydroxyvitamin D3-Calcifediol Proportion ratio (C24CPR) in Relation to the Skin Melanin Index (SMI) and West African Genetic Ancestry Proportion (WGA) among healthy African American a European American participants.
Supplemental Table 4 from Genetic Factors Associated with Absolute and Relative Plasma Concentrations of Calcitriol
Authors: Wilson Robin Taylor , Safford Susan E. , Ostrom Quinn T. , Wang Ming , McDonald Alicia C. , Salzberg Anna C. , Barnholtz-Sloan Jill S. , Richie John P.
Published: May 2023

Supplemental Table 4. Calcitriol-24,25-dihydroxyvitamin D3-calcifediol proportion ratio (C24CPR) and calcitriol (1,25(OH)2D3) adjusted least squares mean by demographic and environmental factors.
Supplementary Table S9 from Estimating the Number of Men Living with Metastatic Prostate Cancer in the United States
Authors: Devasia Theresa P. , Mariotto Angela B. , Nyame Yaw A. , Etzioni Ruth
Published: May 2023

This supplemental table compares the prevalence by duration under the alternative scenarios considered in the sensitivity analysis.
Supplementary Table S6 from Estimating the Number of Men Living with Metastatic Prostate Cancer in the United States
Authors: Devasia Theresa P. , Mariotto Angela B. , Nyame Yaw A. , Etzioni Ruth
Published: May 2023

This supplemental table contains the 2018 estimates of metastatic prostate cancer prevalence when we assume better recurrent survival compared to de novo.
Supplementary Table S4 from Estimating the Number of Men Living with Metastatic Prostate Cancer in the United States
Authors: Devasia Theresa P. , Mariotto Angela B. , Nyame Yaw A. , Etzioni Ruth
Published: May 2023

This supplemental table contains the relative survival data for de novo metastatic men used to calculated prevalence projections under the assumptions of 1) worse recurrent survival and 2) better recurrent survival used in the sensitivity analysis.
Supplementary Figure S3 from Estimating the Number of Men Living with Metastatic Prostate Cancer in the United States
Authors: Devasia Theresa P. , Mariotto Angela B. , Nyame Yaw A. , Etzioni Ruth
Published: May 2023

Supplementary Figure S3: Population projections by race between 1990 and 2030 for all ages. Projections from2019 onward are taken from US Census population projections based on the 2010 census.
Table S2 from High-Throughput Peptide Arrays Identify Potential Diagnostic Autoantibody Signatures in Early-Stage Lung Adenocarcinoma
Authors: Luo Rongrong , Zhong Pei , Li Xiying , Cai Juan , Tao Yimin , Xiong Bangzhu , Zheng Hancheng , Zhang Zhishang , Tang Le , Yao Jiarui , Li Yingrui , Shi Yuankai , Han Xiaohong
Published: May 2023

Comparisons of positive rate between differential autoantibody signatures and tumor biomarkers in Early-LUAD.
Figure S5 from High-Throughput Peptide Arrays Identify Potential Diagnostic Autoantibody Signatures in Early-Stage Lung Adenocarcinoma
Authors: Luo Rongrong , Zhong Pei , Li Xiying , Cai Juan , Tao Yimin , Xiong Bangzhu , Zheng Hancheng , Zhang Zhishang , Tang Le , Yao Jiarui , Li Yingrui , Shi Yuankai , Han Xiaohong
Published: May 2023

Figure S5. Correlation between differential autoantibody signatures from Early-LUAD versus BLD and blood routine test. Some of the 143 differential autoantibody signatures positively correlated with RDW.CV.
Figure S2 from High-Throughput Peptide Arrays Identify Potential Diagnostic Autoantibody Signatures in Early-Stage Lung Adenocarcinoma
Authors: Luo Rongrong , Zhong Pei , Li Xiying , Cai Juan , Tao Yimin , Xiong Bangzhu , Zheng Hancheng , Zhang Zhishang , Tang Le , Yao Jiarui , Li Yingrui , Shi Yuankai , Han Xiaohong
Published: May 2023

Corresponding signal of previous reported autoantibodies for early lung cancer detection were evaluated in our study cohort with both HuProtTM protein microarray (detail data not shown) and peptide array. All peptides were aligned to 10 proteins using blastp method, 246 peptides were mapped under the set of e-value of 1 and identity of 90. Accumulated peptide signal was set as autoantibody signal of corresponding antigen protein. Nine autoantibodies had effective testing value in HuprotTM. a.t test P value of autoantibodies signals for Early-LUAD versus NHC from peptide array and HuProtTM detection respectively. Two autoantibodies from peptide array and 2 autoantibodies from HuProtTM showed higher signal in Early-LUAD group under P <0.05. Five autoantibodies from peptide array and 2 autoantibodies from HuProtTM showed higher signal in Early-LUAD group under P < 0.1.b.Volcano plot of peptides signals corresponding to 10 autoantigens. X-axis represents the log10 of the fold change, y-axis represents -log10 (P adjust) for Early-LUAD versus NHC. Significantly up-regulated and down-regulated peptides in Early-LUAD group are shown in light red and blue. Two hundred and forty-six peptides mapped to the 10 autoantibodies were shown in green, and 8 significantly upregulated peptides in Early-LUAD shown in green cross. No intersection between the green mapped signals and the peptides used for classifier model (dark red).
Figure S1 from High-Throughput Peptide Arrays Identify Potential Diagnostic Autoantibody Signatures in Early-Stage Lung Adenocarcinoma
Authors: Luo Rongrong , Zhong Pei , Li Xiying , Cai Juan , Tao Yimin , Xiong Bangzhu , Zheng Hancheng , Zhang Zhishang , Tang Le , Yao Jiarui , Li Yingrui , Shi Yuankai , Han Xiaohong
Published: May 2023

Reproducibility of peptides microarray. a, Heatmap of spearman correlation of 47 samples tested with 3 technical repeats within one batch. b. Heatmap of spearman correlation of 6 samples repeatedly tested across 3 batches.
Data from High-Throughput Peptide Arrays Identify Potential Diagnostic Autoantibody Signatures in Early-Stage Lung Adenocarcinoma
Authors: Luo Rongrong , Zhong Pei , Li Xiying , Cai Juan , Tao Yimin , Xiong Bangzhu , Zheng Hancheng , Zhang Zhishang , Tang Le , Yao Jiarui , Li Yingrui , Shi Yuankai , Han Xiaohong
Published: May 2023

AbstractBackground:
Early diagnosis is critical to lung adenocarcinoma patients’ survival but faces inadequacies in convenient early detection.
Methods:
We applied a comprehensive microarray of 130,000 peptides to detect “autoantibody signature” that is autoantibodies binding to mimotopes for early detection of stage 0–I LUAD. Plasma samples were collected from 147 early-stage lung adenocarcinoma (Early-LUAD), 108 benign lung disease (BLD), and 122 normal healthy controls (NHC). Clinical characteristics, low-dose CT (LDCT), and laboratory tests were incorporated into correlation analysis.
Results:
We identified 143 and 133 autoantibody signatures, distinguishing Early-LUAD from NHC/BLD in the discovery cohort. Autoantibody signatures significantly correlated with age, stage, tumor size, basophil count, and IgM level (P < 0.05). The random forest models based on differential autoantibody signatures displayed AUC of 0.92 and 0.87 to discern Early-LUAD from NHC/BLD in the validation cohort, respectively. Compared with LDCT, combining autoantibody signature and LDCT improved the positive predictive value from 50% to 78.33% (P = 0.049). In addition, autoantibody signatures displayed higher sensitivity of 72.4% to 81.0% compared with the combinational tumor markers (cyfra21.1, NSE, SCC, ProGRP) with a sensitivity of 22.4% (P = 0.000). Proteins matched by differential peptides were enriched in cancer-related PI3K/Akt, MAPK, and Wnt pathways. Overlaps between matched epitopes and autoantibody signatures illustrated the underlying engagement of autoantibodies in immune recognition.
Conclusions:
Collectively, autoantibody signatures identified by a high-throughput peptide microarray have the potential to detect Early-LUAD, which could assist LDCT to better diagnose Early-LUAD.
Impact:
Novel sensitive autoantibody signatures can adjuvant LDCT to better diagnose LUAD at very early stage.
Supplementary Tables S5-S11 from Integrating Tumor-Intrinsic and Immunologic Factors to Identify Immunogenic Breast Cancers from a Low-Risk Cohort: Results from the Randomized SweBCG91RT Trial
Authors: Stenmark Tullberg Axel , Sjöström Martin , Niméus Emma , Killander Fredrika , Chang S. Laura , Feng Felix Y. , Speers Corey W. , Pierce Lori J. , Kovács Anikó , Lundstedt Dan , Holmberg Erik , Karlsson Per
Published: May 2023

Supplementary MethodsTable S5. Clinical characteristics of the Sjöström and Servant cohortsTable S6. Comparison of included versus excluded patientsTable S7. 10-year follow-up for local recurrence (IBTR). Flexible parametric survival analysis with Royston-Parmar (RP) regression models used in Figures 2 and 3Table S8. 10-year follow-up for local recurrence (IBTR). Flexible parametric survival analysis with Royston-Parmar (RP) regression models with adjustment for other covariatesTable S9. Absolute number of events within different tertiles of Immunescore and Proliferative Index and depending on RT treatmentTable S10. Unadjusted and adjusted analysis of the interaction between the Integrated model and RT in the SweBCG91RT cohortTable S11. Distribution of subtypes in the tertiles of the Integrated score among high-risk patients
Supplementary Figure S5 from Integrating Tumor-Intrinsic and Immunologic Factors to Identify Immunogenic Breast Cancers from a Low-Risk Cohort: Results from the Randomized SweBCG91RT Trial
Authors: Stenmark Tullberg Axel , Sjöström Martin , Niméus Emma , Killander Fredrika , Chang S. Laura , Feng Felix Y. , Speers Corey W. , Pierce Lori J. , Kovács Anikó , Lundstedt Dan , Holmberg Erik , Karlsson Per
Published: May 2023

Ki67 was evaluated by a board-certified pathologist on TMAs.  
Supplementary Figure S4 from Integrating Tumor-Intrinsic and Immunologic Factors to Identify Immunogenic Breast Cancers from a Low-Risk Cohort: Results from the Randomized SweBCG91RT Trial
Authors: Stenmark Tullberg Axel , Sjöström Martin , Niméus Emma , Killander Fredrika , Chang S. Laura , Feng Felix Y. , Speers Corey W. , Pierce Lori J. , Kovács Anikó , Lundstedt Dan , Holmberg Erik , Karlsson Per
Published: May 2023

The interaction between the Integrated model and RT was significant in an unadjusted analysis (p=0.004) and an analysis adjusted for histological grade, age, ER status, and tumor size (p=0.008). The figure shows the predicted 10-year cumulative incidence of IBTR with 95% confidence intervals based on Integrated model score stratified by RT, based on Royston-Parmar models with estimates from Table S10. The stpm2 macro for Stata was used for the calculations. The dotted vertical lines represent percentiles of the Integrated score.
Supplementary Figure S2 from Integrating Tumor-Intrinsic and Immunologic Factors to Identify Immunogenic Breast Cancers from a Low-Risk Cohort: Results from the Randomized SweBCG91RT Trial
Authors: Stenmark Tullberg Axel , Sjöström Martin , Niméus Emma , Killander Fredrika , Chang S. Laura , Feng Felix Y. , Speers Corey W. , Pierce Lori J. , Kovács Anikó , Lundstedt Dan , Holmberg Erik , Karlsson Per
Published: May 2023

Immunescore (A), developed in this manuscript, showed a stronger correlation with tumor-infiltrating lymphocytes than other developed methods (B and C) to quantify the degree of immune infiltration.
Supplementary Figure S1 from Integrating Tumor-Intrinsic and Immunologic Factors to Identify Immunogenic Breast Cancers from a Low-Risk Cohort: Results from the Randomized SweBCG91RT Trial
Authors: Stenmark Tullberg Axel , Sjöström Martin , Niméus Emma , Killander Fredrika , Chang S. Laura , Feng Felix Y. , Speers Corey W. , Pierce Lori J. , Kovács Anikó , Lundstedt Dan , Holmberg Erik , Karlsson Per
Published: May 2023

Gene sets from the molecular signatures database were used to create a model measuring immunological activity and immunomodulatory tumor-intrinsic factors. These were merged to create a model considering the interaction between the antitumoral immune response and tumor-intrinsic immunomodulatory factors.
Supplementary Figure S3 from Influence of Genomic Landscape on Cancer Immunotherapy for Newly Diagnosed Ovarian Cancer: Biomarker Analyses from the IMagyn050 Randomized Clinical Trial
Authors: Landen Charles N. , Molinero Luciana , Hamidi Habib , Sehouli Jalid , Miller Austin , Moore Kathleen N. , Taskiran Cagatay , Bookman Michael , Lindemann Kristina , Anderson Charles , Berger Regina , Myers Tashanna , Beiner Mario , Reid Thomas , Van Nieuwenhuysen Els , Green Andrew , Okamoto Aikou , Aghajanian Carol , Thaker Premal H. , Blank Stephanie V. , Khor Victor K. , Chang Ching-Wei , Lin Yvonne G. , Pignata Sandro
Published: May 2023

PFS according to homologous recombination and BRCA1/2 status in the subgroup of patients with IC ≥5%.
Supplementary Figure S2 from Influence of Genomic Landscape on Cancer Immunotherapy for Newly Diagnosed Ovarian Cancer: Biomarker Analyses from the IMagyn050 Randomized Clinical Trial
Authors: Landen Charles N. , Molinero Luciana , Hamidi Habib , Sehouli Jalid , Miller Austin , Moore Kathleen N. , Taskiran Cagatay , Bookman Michael , Lindemann Kristina , Anderson Charles , Berger Regina , Myers Tashanna , Beiner Mario , Reid Thomas , Van Nieuwenhuysen Els , Green Andrew , Okamoto Aikou , Aghajanian Carol , Thaker Premal H. , Blank Stephanie V. , Khor Victor K. , Chang Ching-Wei , Lin Yvonne G. , Pignata Sandro
Published: May 2023

Mutations in A, BRCA1 but not in B, BRCA2, are associated with PD-L1-positive tumors.
Supplementary Table S1 from Impact of Biomarker-Matched Therapies on Outcomes in Patients with Sarcoma Enrolled in Early-Phase Clinical Trials (SAMBA 101)
Authors: Carmagnani Pestana Roberto , Moyers Justin T. , Roszik Jason , Sen Shiraj , Hong David S. , Naing Aung , Herzog Cynthia E. , Fu Siqing , Piha-Paul Sarina A. , Rodon Jordi , Yap Timothy A. , Karp Daniel D. , Tsimberidou Apostolia M. , Pant Shubham , Zarzour Maria A. , Ratan Ravin , Ravi Vinod , Benjamin Robert S. , Lazar Alexander J. , Wang Wei-Lien , Daw Najat , Gill Jonathan B. , Harrison Douglas J. , Lewis Valerae O. , Roland Christina L. , Patel Shreyaskumar R. , Livingston J. Andrew , Somaiah Neeta , Ludwig Joseph A. , Conley Anthony P. , Hamerschlak Nelson , Gorlick Richard , Meric-Bernstam Funda , Subbiah Vivek
Published: May 2023

Summary of response outcomes across subgroups
Supplementary Figure S1 from Impact of Biomarker-Matched Therapies on Outcomes in Patients with Sarcoma Enrolled in Early-Phase Clinical Trials (SAMBA 101)
Authors: Carmagnani Pestana Roberto , Moyers Justin T. , Roszik Jason , Sen Shiraj , Hong David S. , Naing Aung , Herzog Cynthia E. , Fu Siqing , Piha-Paul Sarina A. , Rodon Jordi , Yap Timothy A. , Karp Daniel D. , Tsimberidou Apostolia M. , Pant Shubham , Zarzour Maria A. , Ratan Ravin , Ravi Vinod , Benjamin Robert S. , Lazar Alexander J. , Wang Wei-Lien , Daw Najat , Gill Jonathan B. , Harrison Douglas J. , Lewis Valerae O. , Roland Christina L. , Patel Shreyaskumar R. , Livingston J. Andrew , Somaiah Neeta , Ludwig Joseph A. , Conley Anthony P. , Hamerschlak Nelson , Gorlick Richard , Meric-Bernstam Funda , Subbiah Vivek
Published: May 2023

Treemap of most common drug mechanisms for patients treated in biomarker-matched studies.
Supplementary Figure S5 from BRD9 Degradation Disrupts Ribosome Biogenesis in Multiple Myeloma
Authors: Kurata Keiji , Samur Mehmet K. , Liow Priscilla , Wen Kenneth , Yamamoto Leona , Liu Jiye , Morelli Eugenio , Gulla Annamaria , Tai Yu-Tzu , Qi Jun , Hideshima Teru , Anderson Kenneth C.
Published: May 2023

BRD9 co-localized with BRD4 and MYC in ribosome biogenesis gene transcription start sites (TSS).
Supplementary Figure S2 from BRD9 Degradation Disrupts Ribosome Biogenesis in Multiple Myeloma
Authors: Kurata Keiji , Samur Mehmet K. , Liow Priscilla , Wen Kenneth , Yamamoto Leona , Liu Jiye , Morelli Eugenio , Gulla Annamaria , Tai Yu-Tzu , Qi Jun , Hideshima Teru , Anderson Kenneth C.
Published: May 2023

Ribosome biogenesis and translation signatures are upregulated in BRD9-depletion-sensitive MM cell lines.
Supplementary Figure S1 from BRD9 Degradation Disrupts Ribosome Biogenesis in Multiple Myeloma
Authors: Kurata Keiji , Samur Mehmet K. , Liow Priscilla , Wen Kenneth , Yamamoto Leona , Liu Jiye , Morelli Eugenio , Gulla Annamaria , Tai Yu-Tzu , Qi Jun , Hideshima Teru , Anderson Kenneth C.
Published: May 2023

Clinical impact and biological significance of BRD9 in multiple myeloma (MM) in vitro and in vivo.
Supplementary Data S3 from BRD9 Degradation Disrupts Ribosome Biogenesis in Multiple Myeloma
Authors: Kurata Keiji , Samur Mehmet K. , Liow Priscilla , Wen Kenneth , Yamamoto Leona , Liu Jiye , Morelli Eugenio , Gulla Annamaria , Tai Yu-Tzu , Qi Jun , Hideshima Teru , Anderson Kenneth C.
Published: May 2023

GSEA_RNAseq (GOBP)
Supplementary Data S1 from BRD9 Degradation Disrupts Ribosome Biogenesis in Multiple Myeloma
Authors: Kurata Keiji , Samur Mehmet K. , Liow Priscilla , Wen Kenneth , Yamamoto Leona , Liu Jiye , Morelli Eugenio , Gulla Annamaria , Tai Yu-Tzu , Qi Jun , Hideshima Teru , Anderson Kenneth C.
Published: May 2023

Correlations of dBRD9-A susceptibility
Figure S7 from Oral IRAK-4 Inhibitor CA-4948 Is Blood-Brain Barrier Penetrant and Has Single-Agent Activity against CNS Lymphoma and Melanoma Brain Metastases
Authors: Von Roemeling Christina A. , Doonan Bently P. , Klippel Kelena , Schultz Daniel , Hoang-Minh Lan , Trivedi Vrunda , Li Chenglong , Russell Rylynn A. , Kanumuri Raju S. , Sharma Abhisheak , Tun Han W. , Mitchell Duane A.
Published: May 2023

Supplemental Figure 7. Pattern of pNF-κB expression in human CNS tumors.
Figure S6 from Oral IRAK-4 Inhibitor CA-4948 Is Blood-Brain Barrier Penetrant and Has Single-Agent Activity against CNS Lymphoma and Melanoma Brain Metastases
Authors: Von Roemeling Christina A. , Doonan Bently P. , Klippel Kelena , Schultz Daniel , Hoang-Minh Lan , Trivedi Vrunda , Li Chenglong , Russell Rylynn A. , Kanumuri Raju S. , Sharma Abhisheak , Tun Han W. , Mitchell Duane A.
Published: May 2023

Supplemental Figure 6. CA-4948 inhibition of MAPK and NF-κB signaling in MBM.
Figure S5 from Oral IRAK-4 Inhibitor CA-4948 Is Blood-Brain Barrier Penetrant and Has Single-Agent Activity against CNS Lymphoma and Melanoma Brain Metastases
Authors: Von Roemeling Christina A. , Doonan Bently P. , Klippel Kelena , Schultz Daniel , Hoang-Minh Lan , Trivedi Vrunda , Li Chenglong , Russell Rylynn A. , Kanumuri Raju S. , Sharma Abhisheak , Tun Han W. , Mitchell Duane A.
Published: May 2023

Supplemental Figure 5: Chromatogram of CA-4948.
Figure S4 from Oral IRAK-4 Inhibitor CA-4948 Is Blood-Brain Barrier Penetrant and Has Single-Agent Activity against CNS Lymphoma and Melanoma Brain Metastases
Authors: Von Roemeling Christina A. , Doonan Bently P. , Klippel Kelena , Schultz Daniel , Hoang-Minh Lan , Trivedi Vrunda , Li Chenglong , Russell Rylynn A. , Kanumuri Raju S. , Sharma Abhisheak , Tun Han W. , Mitchell Duane A.
Published: May 2023

Supplemental Figure 4. CA-4948 viability assessment in A20 and B16F10 cancer cells.
Figure S3 from Oral IRAK-4 Inhibitor CA-4948 Is Blood-Brain Barrier Penetrant and Has Single-Agent Activity against CNS Lymphoma and Melanoma Brain Metastases
Authors: Von Roemeling Christina A. , Doonan Bently P. , Klippel Kelena , Schultz Daniel , Hoang-Minh Lan , Trivedi Vrunda , Li Chenglong , Russell Rylynn A. , Kanumuri Raju S. , Sharma Abhisheak , Tun Han W. , Mitchell Duane A.
Published: May 2023

Supplemental Figure 3. Active IRAK-4 expression in intracranial tumors.
Supplementary Table S2 from Zinc Finger MYND-Type Containing 8 (ZMYND8) Is Epigenetically Regulated in Mutant Isocitrate Dehydrogenase 1 (IDH1) Glioma to Promote Radioresistance
Authors: Carney Stephen V. , Banerjee Kaushik , Mujeeb Anzar , Zhu Brandon , Haase Santiago , Varela Maria L. , Kadiyala Padma , Tronrud Claire E. , Zhu Ziwen , Mukherji Devarshi , Gorla Preethi , Sun Yilun , Tagett Rebecca , Núñez Felipe J. , Luo Maowu , Luo Weibo , Ljungman Mats , Liu Yayuan , Xia Ziyun , Schwendeman Anna , Qin Tingting , Sartor Maureen A. , Costello Joseph F. , Cahill Daniel P. , Lowenstein Pedro R. , Castro Maria G.
Published: May 2023

Table S2. Liquid Chromatography and Mass Spectrometry Measurement of 2-hydroxyglutarate in the Conditioned Media of mIDH1 GCCs untreated, treated with vehicle (DMSO), or mIDH1 inhibitor (AGI-5198 or DS1001b)
Supplementary Table S1 from Zinc Finger MYND-Type Containing 8 (ZMYND8) Is Epigenetically Regulated in Mutant Isocitrate Dehydrogenase 1 (IDH1) Glioma to Promote Radioresistance
Authors: Carney Stephen V. , Banerjee Kaushik , Mujeeb Anzar , Zhu Brandon , Haase Santiago , Varela Maria L. , Kadiyala Padma , Tronrud Claire E. , Zhu Ziwen , Mukherji Devarshi , Gorla Preethi , Sun Yilun , Tagett Rebecca , Núñez Felipe J. , Luo Maowu , Luo Weibo , Ljungman Mats , Liu Yayuan , Xia Ziyun , Schwendeman Anna , Qin Tingting , Sartor Maureen A. , Costello Joseph F. , Cahill Daniel P. , Lowenstein Pedro R. , Castro Maria G.
Published: May 2023

Table S1. RNA sequencing comparison data
Supplementary Methods S1 from Zinc Finger MYND-Type Containing 8 (ZMYND8) Is Epigenetically Regulated in Mutant Isocitrate Dehydrogenase 1 (IDH1) Glioma to Promote Radioresistance
Authors: Carney Stephen V. , Banerjee Kaushik , Mujeeb Anzar , Zhu Brandon , Haase Santiago , Varela Maria L. , Kadiyala Padma , Tronrud Claire E. , Zhu Ziwen , Mukherji Devarshi , Gorla Preethi , Sun Yilun , Tagett Rebecca , Núñez Felipe J. , Luo Maowu , Luo Weibo , Ljungman Mats , Liu Yayuan , Xia Ziyun , Schwendeman Anna , Qin Tingting , Sartor Maureen A. , Costello Joseph F. , Cahill Daniel P. , Lowenstein Pedro R. , Castro Maria G.
Published: May 2023

Supplementary Methods
Supplementary Table S5 from Randomized, Double-Blind, Placebo-Controlled Trial of MUC1 Peptide Vaccine for Prevention of Recurrent Colorectal Adenoma
Authors: Schoen Robert E. , Boardman Lisa A. , Cruz-Correa Marcia , Bansal Ajay , Kastenberg David , Hur Chin , Dzubinski Lynda , Kaufman Sharon F. , Rodriguez Luz M. , Richmond Ellen , Umar Asad , Szabo Eva , Salazar Andres , McKolanis John , Beatty Pamela , Pai Reetesh K. , Singhi Aatur D. , Jacqueline Camille M. , Bao Riyue , Diergaarde Brenda , McMurray Ryan P. , Strand Carrie , Foster Nathan R. , Zahrieh David M. , Limburg Paul J. , Finn Olivera J.
Published: May 2023

Adverse Events: Grade 3
Supplementary Table S1 from Randomized, Double-Blind, Placebo-Controlled Trial of MUC1 Peptide Vaccine for Prevention of Recurrent Colorectal Adenoma
Authors: Schoen Robert E. , Boardman Lisa A. , Cruz-Correa Marcia , Bansal Ajay , Kastenberg David , Hur Chin , Dzubinski Lynda , Kaufman Sharon F. , Rodriguez Luz M. , Richmond Ellen , Umar Asad , Szabo Eva , Salazar Andres , McKolanis John , Beatty Pamela , Pai Reetesh K. , Singhi Aatur D. , Jacqueline Camille M. , Bao Riyue , Diergaarde Brenda , McMurray Ryan P. , Strand Carrie , Foster Nathan R. , Zahrieh David M. , Limburg Paul J. , Finn Olivera J.
Published: May 2023

Baseline advanced adenoma characteristics
Supplementary Methods S1 from Randomized, Double-Blind, Placebo-Controlled Trial of MUC1 Peptide Vaccine for Prevention of Recurrent Colorectal Adenoma
Authors: Schoen Robert E. , Boardman Lisa A. , Cruz-Correa Marcia , Bansal Ajay , Kastenberg David , Hur Chin , Dzubinski Lynda , Kaufman Sharon F. , Rodriguez Luz M. , Richmond Ellen , Umar Asad , Szabo Eva , Salazar Andres , McKolanis John , Beatty Pamela , Pai Reetesh K. , Singhi Aatur D. , Jacqueline Camille M. , Bao Riyue , Diergaarde Brenda , McMurray Ryan P. , Strand Carrie , Foster Nathan R. , Zahrieh David M. , Limburg Paul J. , Finn Olivera J.
Published: May 2023

supplementary methods: trial administration
Supplementary Table 2 from Genomic Complexity Predicts Resistance to Endocrine Therapy and CDK4/6 Inhibition in Hormone Receptor–Positive (HR+)/HER2-Negative Metastatic Breast Cancer
Authors: Davis Andrew A. , Luo Jingqin , Zheng Tiantian , Dai Chao , Dong Xiaoxi , Tan Lu , Suresh Rama , Ademuyiwa Foluso O. , Rigden Caron , Rearden Timothy P. , Clifton Katherine , Weilbaecher Katherine , Frith Ashley , Tandra Pavan K. , Summa Tracy , Haas Brittney , Thomas Shana , Hernandez-Aya Leonel F. , Peterson Lindsay L. , Wang Xiaohong , Luo Shujun J. , Zhou Kemin , Du Pan , Jia Shidong , King Bonnie L. , Krishnamurthy Jairam , Ma Cynthia X.
Published: May 2023

Patient demographics.
Supplementary Figures S1-S15 from Genomic Complexity Predicts Resistance to Endocrine Therapy and CDK4/6 Inhibition in Hormone Receptor–Positive (HR+)/HER2-Negative Metastatic Breast Cancer
Authors: Davis Andrew A. , Luo Jingqin , Zheng Tiantian , Dai Chao , Dong Xiaoxi , Tan Lu , Suresh Rama , Ademuyiwa Foluso O. , Rigden Caron , Rearden Timothy P. , Clifton Katherine , Weilbaecher Katherine , Frith Ashley , Tandra Pavan K. , Summa Tracy , Haas Brittney , Thomas Shana , Hernandez-Aya Leonel F. , Peterson Lindsay L. , Wang Xiaohong , Luo Shujun J. , Zhou Kemin , Du Pan , Jia Shidong , King Bonnie L. , Krishnamurthy Jairam , Ma Cynthia X.
Published: May 2023

Supplementary Figure S1. High bTMB is associated with the presence of specific mutations.Supplementary Figure S2. ROC analysis to determine optimal bTMB cutoff.Supplementary Figure S3. High correlation between bTMB determined from WES and a 152-gene targeted sequencing panel.Supplementary Figure S4. Association between high cfDNA yield and shorter PFS.Supplementary Figure S5. High baseline tumor fraction is associated with shorter PFS.Supplementary Figure S6. bTMB scores are not associated with sites of metastatic spread.Supplementary Figure S7. Specific oncogenic signaling pathways are more frequently altered inpatients with high bTMB and bCNB scores.Supplementary Figure S8. Genomic landscape of all patients at baseline.Supplementary Figure S9. bTMB and bCNB at baseline vs. progression.Supplementary Figure S10. Correlation between baseline bCNB and bTMB.Supplementary Figure S11. Comparison of ctDNA dynamics as measured by ctDNA fraction andbCNB during treatment and progression.Supplementary Figure S12. Genome-wide plots of copy number changes across treatment timepoints.Supplementary Figure S13. Copy number changes detected for RB1 and BRCA2 genes acrosstreatment time points.Supplementary Figure S14. Relationship between ctDNA fraction and bTMB and bCNBSupplementary Figure S15. Relationship between tumor fraction and bCNB.
Supplementary Figure S6 from NPY Methylated ctDNA is a Promising Biomarker for Treatment Response Monitoring in Metastatic Colorectal Cancer
Authors: Janssens Katleen , Vanhoutte Greetje , Lybaert Willem , Demey Wim , Decaestecker Jochen , Hendrickx Koen , Rezaei Kalantari Hassan , Zwaenepoel Karen , Pauwels Patrick , Fransen Erik , Op de Beeck Ken , Van Camp Guy , Rolfo Christian , Peeters Marc
Published: May 2023

Follow-up of patients during treatment using methylated ctDNA.
Supplementary Figure S2 from NPY Methylated ctDNA is a Promising Biomarker for Treatment Response Monitoring in Metastatic Colorectal Cancer
Authors: Janssens Katleen , Vanhoutte Greetje , Lybaert Willem , Demey Wim , Decaestecker Jochen , Hendrickx Koen , Rezaei Kalantari Hassan , Zwaenepoel Karen , Pauwels Patrick , Fransen Erik , Op de Beeck Ken , Van Camp Guy , Rolfo Christian , Peeters Marc
Published: May 2023

Kaplan Meier curve for progression free survival.
Supplementary Figure S1 from NPY Methylated ctDNA is a Promising Biomarker for Treatment Response Monitoring in Metastatic Colorectal Cancer
Authors: Janssens Katleen , Vanhoutte Greetje , Lybaert Willem , Demey Wim , Decaestecker Jochen , Hendrickx Koen , Rezaei Kalantari Hassan , Zwaenepoel Karen , Pauwels Patrick , Fransen Erik , Op de Beeck Ken , Van Camp Guy , Rolfo Christian , Peeters Marc
Published: May 2023

Consort diagram.
Supplementary Table S3 from BAP1 Loss Is Associated with Higher ASS1 Expression in Epithelioid Mesothelioma: Implications for Therapeutic Stratification
Authors: Barnett Sarah E. , Kenyani Jenna , Tripari Martina , Butt Zohra , Grosman Rudi , Querques Francesca , Shaw Liam , Silva Luisa C. , Goate Zoe , Marciniak Stefan J. , Rassl Doris M. , Jackson Richard , Lian Lu-Yun , Szlosarek Peter W. , Sacco Joseph J. , Coulson Judy M.
Published: May 2023

Proteomics MeT5A isogenics and MPM cell lines
Supplementary Figures S1 to S16 from BAP1 Loss Is Associated with Higher ASS1 Expression in Epithelioid Mesothelioma: Implications for Therapeutic Stratification
Authors: Barnett Sarah E. , Kenyani Jenna , Tripari Martina , Butt Zohra , Grosman Rudi , Querques Francesca , Shaw Liam , Silva Luisa C. , Goate Zoe , Marciniak Stefan J. , Rassl Doris M. , Jackson Richard , Lian Lu-Yun , Szlosarek Peter W. , Sacco Joseph J. , Coulson Judy M.
Published: May 2023

Fig. S1. SNP6.0 analysis of parental MeT5A-BAP1+/+ cells.Fig. S2. Expression of catalytically active BAP1 in parental MeT5A.Fig. S3. Characterisation of BAP1 expression in gene-edited MeT5A.Fig. S4. Whole genome sequencing of gene-edited MeT5A.Fig. S5. Proliferative profiles for gene-edited MeT5A.Fig. S6. Enriched GO terms in gene-edited MeT5A SILAC-MS.Fig. S7. Enriched KEGG pathways in gene-edited MeT5A SILAC-MS include EMT.Fig. S8. Metabolite responses to BAP1 mutation in isogenic MeT5A.Fig. S9. Immunoblotting for differentially expressed metabolic enzymes identified by SILAC-MS in isogenic MeT5A cell lines.Fig. S10. Characterisation of BAP1-status for MPM cell panel.Fig. S11. Evaluating correlation between BAP1 and selected metabolic enzymes in a panel of MPM cell lines and the TCGA MESO pan-cancer dataset.Fig. S12. Validation of ASS1 response to BAP1 alteration.Fig. S13. Improved prognosis for epithelioid MPM patients with loss of nBAP1 and increased expression of ASS1.Fig. S14. Relationship between BAP1 and ASS1 transcripts in the TCGA Pan-Cancer datasets for other cancer types.Fig. S15. The influence of BAP1-status on response to inhibition of purine metabolism.Fig. S16. The influence of BAP1-status on response to ASS1 inhibition.
Supplementary Tables 1-4 from MYC/Glutamine Dependency Is a Therapeutic Vulnerability in Pancreatic Cancer with Deoxycytidine Kinase Inactivation-Induced Gemcitabine Resistance
Authors: Dash Suman , Ueda Takeshi , Komuro Akiyoshi , Amano Hisayuki , Honda Masahiko , Kawazu Masahito , Okada Hitoshi
Published: May 2023

Supplementary Table 1.sgRNA barcode PCR primers. PCR, polymerase chain reactionSupplementary Table 2.Pool A and Pool B log2 fold change of >10 upregulated sgRNA oligo sequencesSupplementary Table 3.Primer sequences used for RT-qPCR and CRISPR/Cas9 degradation-resistant HA-tagged DCK gene. RT-qPCR, real-time quantitative polymerase chain reactionSupplementary Table 4.Primer sequences used for sequencing DCK region
Supplementary Figures 1-4 from MYC/Glutamine Dependency Is a Therapeutic Vulnerability in Pancreatic Cancer with Deoxycytidine Kinase Inactivation-Induced Gemcitabine Resistance
Authors: Dash Suman , Ueda Takeshi , Komuro Akiyoshi , Amano Hisayuki , Honda Masahiko , Kawazu Masahito , Okada Hitoshi
Published: May 2023

S1. Supplementary results corresponding to Figures 1 and 2S2. Supplementary results corresponding to Figure 2Cas9-sgRNA-mediated genome editing in the DCK gene detected using direct sequenceS3. Supplementary results corresponding to Figure 2S4. Supplementary results corresponding to Figure 4
Supplementary Data from MYC/Glutamine Dependency Is a Therapeutic Vulnerability in Pancreatic Cancer with Deoxycytidine Kinase Inactivation-Induced Gemcitabine Resistance
Authors: Dash Suman , Ueda Takeshi , Komuro Akiyoshi , Amano Hisayuki , Honda Masahiko , Kawazu Masahito , Okada Hitoshi
Published: May 2023

References
Table S3 from Comparative Tumor Microenvironment Analysis of Primary and Recurrent Ovarian Granulosa Cell Tumors
Authors: Khlebus Eleonora , Vuttaradhi Veena K. , Welte Thomas , Khurana Namrata , Celestino Joseph , Beird Hannah C. , Gumbs Curtis , Little Latasha , Legarreta Alejandra Flores , Fellman Bryan M. , Nguyen Tri , Lawson Barrett , Ferri-Borgogno Sammy , Mok Samuel C. , Broaddus Russell R. , Gershenson David M. , Futreal P. Andrew , Hillman R. Tyler
Published: May 2023

Differentially Expressed Genes
Supplementary Figures 1-6 from Comparative Tumor Microenvironment Analysis of Primary and Recurrent Ovarian Granulosa Cell Tumors
Authors: Khlebus Eleonora , Vuttaradhi Veena K. , Welte Thomas , Khurana Namrata , Celestino Joseph , Beird Hannah C. , Gumbs Curtis , Little Latasha , Legarreta Alejandra Flores , Fellman Bryan M. , Nguyen Tri , Lawson Barrett , Ferri-Borgogno Sammy , Mok Samuel C. , Broaddus Russell R. , Gershenson David M. , Futreal P. Andrew , Hillman R. Tyler
Published: May 2023

S1. Heatmap of unsupervised hierarchical clustering of the top 1000 most variable genes.S2. Violin plots showing the expression levels of genes previously identified as differentially expressed genes between primary and recurrent tumors in Haltia et al., 2020.S3. Gene set enrichment analysis performed with GO terms and KEGG pathways showing that primarily immune-related and hormone-regulated gene sets expression are altered between primary and recurrent aGCTs.S4. Gene set enrichment analysis results showing that top enriched gene sets are significantly enriched in recurrent tumors in comparison with primary tumors.S5. Boxplot showing the fraction of each noncancerous cell type identified by CIBERSORTx (A) and xCell (B) in primary and recurrent tumor samples.S6. Heatmap showing the correlation between immune cells fractions.
Figure S8 from Bone Marrow Mesenchymal Stem Cells Induce Metabolic Plasticity in Estrogen Receptor–Positive Breast Cancer
Authors: Buschhaus Johanna M. , Rajendran Shrila , Chen Siyi , Wharram Bryan L. , Bevoor Avinash S. , Cutter Alyssa C. , Humphries Brock A. , Robison Tanner H. , Farfel Alex P. , Luker Gary D.
Published: May 2023

Figure S8. Single-cell metabolic responses over time. Cell type of interest is bolded in co-culture comparisons. Cumulative AUC changes in NADH lifetime over 30 minutes post-treatment in 10-minute intervals. For a single cell, each change is normalized to the initial NADH lifetime. Table shows changes in median values in picoseconds of NADH or CFP lifetime (ATP) after addition of each compound. Labels at the top of the table denote the relevant comparison in each column.
Figure S5 from Bone Marrow Mesenchymal Stem Cells Induce Metabolic Plasticity in Estrogen Receptor–Positive Breast Cancer
Authors: Buschhaus Johanna M. , Rajendran Shrila , Chen Siyi , Wharram Bryan L. , Bevoor Avinash S. , Cutter Alyssa C. , Humphries Brock A. , Robison Tanner H. , Farfel Alex P. , Luker Gary D.
Published: May 2023

Figure S5. Representative images of glucose and ATP FRET reporters in MSCs. Pseudocolor scale depicts high (red) and low (blue) CFP lifetime. Scale bar = 50 µm. Representative images of intracellular glucose (A) and ATP (B) in HS5 MSCs when in co-culture with ER+ breast cancer cells. Cancer cells are unlabeled in pseudocolor images.
Figure S2 from Bone Marrow Mesenchymal Stem Cells Induce Metabolic Plasticity in Estrogen Receptor–Positive Breast Cancer
Authors: Buschhaus Johanna M. , Rajendran Shrila , Chen Siyi , Wharram Bryan L. , Bevoor Avinash S. , Cutter Alyssa C. , Humphries Brock A. , Robison Tanner H. , Farfel Alex P. , Luker Gary D.
Published: May 2023

Figure S2. NADH lifetime and ROS quantification for ER+ breast cancer cells with CM and MSCs in monoculture and co-culture. Cell type of interest is bolded in co-culture comparisons. A-B) Histograms of NADH lifetime for HS5 (A) and HS27a (B) in monoculture or co-culture with MCF7, T47D, and HCC1428 (HCC) ER+ breast cancer cells. C-E) Histograms of NADH lifetime for MCF7 (C), T47D (D), and HCC1428 (HCC, E) cells in co-culture and monoculture with control or CM from HS5 or HS27a MSCs. Dashed lines represent cancer cells in CM and solid lines represent cancer cells in monoculture or co-culture with MSCs. N>100 cells.
Supplementary Figure Legends from The Elongin BC Complex Negatively Regulates AXL and Marks a Differentiated Phenotype in Melanoma
Authors: Schieven Sebastiaan M. , Traets Joleen J.H. , Vliet Alex v. , Baalen Martijn v. , Song Ji-Ying , Guimaraes Marcos Da Silva , Kuilman Thomas , Peeper Daniel S.
Published: May 2023

Supplementary Figure Legends 1-7