bluuebunny/crossref_metadata_2025 huggingface dataset:
Supplementary Table from Mathematical Modeling and Mutational Analysis Reveal Optimal Therapy to Prevent Malignant Transformation in Grade II IDH-Mutant Gliomas
Authors: Aoki Kosuke , Suzuki Hiromichi , Yamamoto Takashi , Yamamoto Kimiyo N. , Maeda Sachi , Okuno Yusuke , Ranjit Melissa , Motomura Kazuya , Ohka Fumiharu , Tanahashi Kuniaki , Hirano Masaki , Nishikawa Tomohide , Shimizu Hiroyuki , Kitano Yotaro , Yamaguchi Junya , Yamazaki Shintaro , Nakamura Hideo , Takahashi Masamichi , Narita Yoshitaka , Nakada Mitsutoshi , Deguchi Shoichi , Mizoguchi Masahiro , Momii Yasutomo , Muragaki Yoshihiro , Abe Tatsuya , Akimoto Jiro , Wakabayashi Toshihiko , Saito Ryuta , Ogawa Seishi , Haeno Hiroshi , Natsume Atsushi
Published: March 2023

Supplementary Table S1-S4
Supplementary Material and Methods from Mathematical Modeling and Mutational Analysis Reveal Optimal Therapy to Prevent Malignant Transformation in Grade II IDH-Mutant Gliomas
Authors: Aoki Kosuke , Suzuki Hiromichi , Yamamoto Takashi , Yamamoto Kimiyo N. , Maeda Sachi , Okuno Yusuke , Ranjit Melissa , Motomura Kazuya , Ohka Fumiharu , Tanahashi Kuniaki , Hirano Masaki , Nishikawa Tomohide , Shimizu Hiroyuki , Kitano Yotaro , Yamaguchi Junya , Yamazaki Shintaro , Nakamura Hideo , Takahashi Masamichi , Narita Yoshitaka , Nakada Mitsutoshi , Deguchi Shoichi , Mizoguchi Masahiro , Momii Yasutomo , Muragaki Yoshihiro , Abe Tatsuya , Akimoto Jiro , Wakabayashi Toshihiko , Saito Ryuta , Ogawa Seishi , Haeno Hiroshi , Natsume Atsushi
Published: March 2023

Supplementary Material and Methods
Supplemental video 3 from NCI Imaging Data Commons
Authors: Fedorov Andrey , Longabaugh William J.R. , Pot David , Clunie David A. , Pieper Steve , Aerts Hugo J.W.L. , Homeyer André , Lewis Rob , Akbarzadeh Afshin , Bontempi Dennis , Clifford William , Herrmann Markus D. , Höfener Henning , Octaviano Igor , Osborne Chad , Paquette Suzanne , Petts James , Punzo Davide , Reyes Madelyn , Schacherer Daniela P. , Tian Mi , White George , Ziegler Erik , Shmulevich Ilya , Pihl Todd , Wagner Ulrike , Farahani Keyvan , Kikinis Ron
Published: March 2023

Supplemental video 3 from NCI Imaging Data Commons
Supplemental video 1 from NCI Imaging Data Commons
Authors: Fedorov Andrey , Longabaugh William J.R. , Pot David , Clunie David A. , Pieper Steve , Aerts Hugo J.W.L. , Homeyer André , Lewis Rob , Akbarzadeh Afshin , Bontempi Dennis , Clifford William , Herrmann Markus D. , Höfener Henning , Octaviano Igor , Osborne Chad , Paquette Suzanne , Petts James , Punzo Davide , Reyes Madelyn , Schacherer Daniela P. , Tian Mi , White George , Ziegler Erik , Shmulevich Ilya , Pihl Todd , Wagner Ulrike , Farahani Keyvan , Kikinis Ron
Published: March 2023

Supplemental video 1 from NCI Imaging Data Commons
Data from Apolipoprotein E Promotes Immune Suppression in Pancreatic Cancer through NF-κB–Mediated Production of CXCL1
Authors: Kemp Samantha B. , Carpenter Eileen S. , Steele Nina G. , Donahue Katelyn L. , Nwosu Zeribe C. , Pacheco Amanda , Velez-Delgado Ashley , Menjivar Rosa E. , Lima Fatima , The Stephanie , Espinoza Carlos E. , Brown Kristee , Long Daniel , Lyssiotis Costas A. , Rao Arvind , Zhang Yaqing , Pasca di Magliano Marina , Crawford Howard C.
Published: March 2023

Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with few effective therapeutic options. PDAC is characterized by an extensive fibroinflammatory stroma that includes abundant infiltrating immune cells. Tumor-associated macrophages (TAM) are prevalent within the stroma and are key drivers of immunosuppression. TAMs in human and murine PDAC are characterized by elevated expression of apolipoprotein E (ApoE), an apolipoprotein that mediates cholesterol metabolism and has known roles in cardiovascular and Alzheimer's disease but no known role in PDAC. We report here that ApoE is also elevated in peripheral blood monocytes in PDAC patients, and plasma ApoE protein levels stratify patient survival. Orthotopic implantation of mouse PDAC cells into syngeneic wild-type or in ApoE−/− mice showed reduced tumor growth in ApoE−/− mice. Histologic and mass cytometric (CyTOF) analysis of these tumors showed an increase in CD8+ T cells in tumors in ApoE−/− mice. Mechanistically, ApoE induced pancreatic tumor cell expression of Cxcl1 and Cxcl5, known immunosuppressive factors, through LDL receptor and NF-κB signaling. Taken together, this study reveals a novel immunosuppressive role of ApoE in the PDAC microenvironment.
Significance:
This study shows that elevated apolipoprotein E in PDAC mediates immune suppression and high serum apolipoprotein E levels correlate with poor patient survival.

See related commentary by Sherman, p. 4186
Data from Mathematical Modeling and Mutational Analysis Reveal Optimal Therapy to Prevent Malignant Transformation in Grade II IDH-Mutant Gliomas
Authors: Aoki Kosuke , Suzuki Hiromichi , Yamamoto Takashi , Yamamoto Kimiyo N. , Maeda Sachi , Okuno Yusuke , Ranjit Melissa , Motomura Kazuya , Ohka Fumiharu , Tanahashi Kuniaki , Hirano Masaki , Nishikawa Tomohide , Shimizu Hiroyuki , Kitano Yotaro , Yamaguchi Junya , Yamazaki Shintaro , Nakamura Hideo , Takahashi Masamichi , Narita Yoshitaka , Nakada Mitsutoshi , Deguchi Shoichi , Mizoguchi Masahiro , Momii Yasutomo , Muragaki Yoshihiro , Abe Tatsuya , Akimoto Jiro , Wakabayashi Toshihiko , Saito Ryuta , Ogawa Seishi , Haeno Hiroshi , Natsume Atsushi
Published: March 2023

Abstract
Isocitrate dehydrogenase-mutant low-grade gliomas (IDHmut-LGG) grow slowly but frequently undergo malignant transformation, which eventually leads to premature death. Chemotherapy and radiotherapy treatments prolong survival, but can also induce genetic (or epigenetic) alterations involved in transformation. Here, we developed a mathematical model of tumor progression based on serial tumor volume data and treatment history of 276 IDHmut-LGGs classified by chromosome 1p/19q codeletion (IDHmut/1p19qcodel and IDHmut/1p19qnoncodel) and performed genome-wide mutational analyses, including targeted sequencing and longitudinal whole-exome sequencing data. These analyses showed that tumor mutational burden correlated positively with malignant transformation rate, and chemotherapy and radiotherapy significantly suppressed tumor growth but increased malignant transformation rate per cell by 1.8 to 2.8 times compared with before treatment. This model revealed that prompt adjuvant chemoradiotherapy prolonged malignant transformation-free survival in small IDHmut-LGGs (≤ 50 cm3). Furthermore, optimal treatment differed according to genetic alterations for large IDHmut-LGGs (> 50 cm3); adjuvant therapies delayed malignant transformation in IDHmut/1p19qnoncodel but often accelerated it in IDHmut/1p19qcodel. Notably, PI3K mutation was not associated with malignant transformation but increased net postoperative proliferation rate and decreased malignant transformation-free survival, prompting the need for adjuvant therapy in IDHmut/1p19qcodel. Overall, this model uncovered therapeutic strategies that could prevent malignant transformation and, consequently, improve overall survival in patients with IDHmut-LGGs.
Significance:
A mathematical model successfully estimates malignant transformation-free survival and reveals a link between genetic alterations and progression, identifying precision medicine approaches for optimal treatment of IDH-mutant low-grade gliomas.
Data from NCI Imaging Data Commons
Authors: Fedorov Andrey , Longabaugh William J.R. , Pot David , Clunie David A. , Pieper Steve , Aerts Hugo J.W.L. , Homeyer André , Lewis Rob , Akbarzadeh Afshin , Bontempi Dennis , Clifford William , Herrmann Markus D. , Höfener Henning , Octaviano Igor , Osborne Chad , Paquette Suzanne , Petts James , Punzo Davide , Reyes Madelyn , Schacherer Daniela P. , Tian Mi , White George , Ziegler Erik , Shmulevich Ilya , Pihl Todd , Wagner Ulrike , Farahani Keyvan , Kikinis Ron
Published: March 2023

Abstract
The National Cancer Institute (NCI) Cancer Research Data Commons (CRDC) aims to establish a national cloud-based data science infrastructure. Imaging Data Commons (IDC) is a new component of CRDC supported by the Cancer Moonshot. The goal of IDC is to enable a broad spectrum of cancer researchers, with and without imaging expertise, to easily access and explore the value of deidentified imaging data and to support integrated analyses with nonimaging data. We achieve this goal by colocating versatile imaging collections with cloud-based computing resources and data exploration, visualization, and analysis tools. The IDC pilot was released in October 2020 and is being continuously populated with radiology and histopathology collections. IDC provides access to curated imaging collections, accompanied by documentation, a user forum, and a growing number of analysis use cases that aim to demonstrate the value of a data commons framework applied to cancer imaging research.
Significance:
This study introduces NCI Imaging Data Commons, a new repository of the NCI Cancer Research Data Commons, which will support cancer imaging research on the cloud.
Table S5 from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes
Authors: Griffin Rosalie , Hanson Heidi A. , Avery Brian J. , Madsen Michael J. , Sborov Douglas W. , Camp Nicola J.
Published: March 2023

Time to first-line treatment failure covariates and added predictive values.
Table S3 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: March 2023

Supplementary Table 3 presents the risk of bias assessment, using the ROBINS-E
Table S4 from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes
Authors: Griffin Rosalie , Hanson Heidi A. , Avery Brian J. , Madsen Michael J. , Sborov Douglas W. , Camp Nicola J.
Published: March 2023

Hazard ratios of high vs low risk early treatment failure.
Table 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: March 2023

Supplementary Table 2B presents the study characteristics of the prospective cohort studies
Table S3 from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes
Authors: Griffin Rosalie , Hanson Heidi A. , Avery Brian J. , Madsen Michael J. , Sborov Douglas W. , Camp Nicola J.
Published: March 2023

Progression free survival covariates and added predictive values.
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: March 2023

Supplementary Table 2A presents the study characteristics of the Mendelian randomization studies
Supplemental Methods from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes
Authors: Griffin Rosalie , Hanson Heidi A. , Avery Brian J. , Madsen Michael J. , Sborov Douglas W. , Camp Nicola J.
Published: March 2023

Expanded description of the SPECTRA approach.
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: March 2023

Supplementary Figure 5C presents dose-response curves for adiponectin and breast cancer risk, by menopausal status
Supplemental Figure S7 from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes
Authors: Griffin Rosalie , Hanson Heidi A. , Avery Brian J. , Madsen Michael J. , Sborov Douglas W. , Camp Nicola J.
Published: March 2023

CoMMpass spectra Hallmark pathway GSEA summary. Gene Set Enrichment Analysis (GSEA) results for all spectra using the Hallmark pathway set from MSigDB v7.4. Only pathways with a significant enrichment (p<0.000025) with at least one spectrum shown. The significance level (intensity) and direction of each enrichment are indicated: green negative normalized enrichment score (NES), purple positive NES. The Hallmark Process Category (Liberzon et al., 2015) for each gene set is shown far left.
Figure S5B 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: March 2023

Supplementary Figure 5B presents forest plots for adiponectin and breast cancer risk, excluding studies where exogenous hormone use status was unknown
Figure S5A 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: March 2023

Supplementary Figure 5A presents the forest plots for adiponectin and breast cancer risk, by menopausal status
Supplemental Figure S5 from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes
Authors: Griffin Rosalie , Hanson Heidi A. , Avery Brian J. , Madsen Michael J. , Sborov Douglas W. , Camp Nicola J.
Published: March 2023

Progression free survival spectra score. A) Quantile-quantile plot. B) Gaussian mixture modeling to identify patients at high and low risk of progression. C) PFS high-risk patients (n = 60, 50 events) had median survival of 9.7 months. PFS low-risk patients (n = 707, 342 events) had median survival of 35.7 months.
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: March 2023

Supplementary Figure 4D presents the funnel plots from the meta-analysis of leptin and breast cancer risk
Supplemental Figure S4 from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes
Authors: Griffin Rosalie , Hanson Heidi A. , Avery Brian J. , Madsen Michael J. , Sborov Douglas W. , Camp Nicola J.
Published: March 2023

Kaplan-Meier curves of overall survival by spectra and UAMS risk-groups in the CoMMpass data. CoMMpass patients were assigned to high and low risk separately using the OS Spectra score and the UAMS score (See Methods, Comparison to the UAMS risk score). Blue and orange lines: both methods agree on patient's risk category. Purple line: Spectra correctly identified high-risk patients (poorer survival) that are misclassified as low risk by UAMS. Green line: Spectra correctly identified low-risk patients (better survival) that are misclassified as high risk by UAMS.
Figure S4C 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: March 2023

Supplementary Figure 4C presents dose-response curves for leptin and breast cancer risk, by menopausal status
Supplemental Figure S3 from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes
Authors: Griffin Rosalie , Hanson Heidi A. , Avery Brian J. , Madsen Michael J. , Sborov Douglas W. , Camp Nicola J.
Published: March 2023

Scree plot of CoMMpass transcriptome dimensions. To limit overfitting in spectra derivation, we used dimension reduction to focus only on the first k spectra (largest k components of variation), selected using a scree test (Cattell, 1966).
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: March 2023

Supplementary Figure 4B presents forest plots for leptin and breast cancer risk, excluding studies where exogenous hormone use status was unknown
Figure S4A 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: March 2023

Supplementary Figure 4A presents forest plots for leptin and breast cancer risk, by menopausal status
Supplemental Figure S1 from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes
Authors: Griffin Rosalie , Hanson Heidi A. , Avery Brian J. , Madsen Michael J. , Sborov Douglas W. , Camp Nicola J.
Published: March 2023

A color analogy to illustrate the advantages of spectra variables for modeling. a) Individual observations of color. b) Dimension Reduction (additive color theory), all colors can be represented using 3 quantitative RGB variables. c) Standard-use, hierarchical clustering identifies 3 groups. Each sample is categorized into a group (A, B or C). Subsequent modeling uses one categorical variable. d) Multivariable modeling with quantitative spectra variables, multiple spectra integrated directly into a multivariable analysis. Each uncorrelated variable can be assessed separately for its predictive value for an outcome. This implementation retains higher resolution because the variables are quantitative and retain integrity to the initial data. Note, lower-resolution versions of xB and xG can be achieved using hierarchical groups, but the loss of quantification may lose power. xR cannot be captured by any group ordering and associations for this spectrum would be lost using hierarchical groups. Three dimensions shown for visualization purposes; transcriptome data is likely to harbor a higher number of dimensions.
Figure S3A 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: March 2023

Supplementary Figure 3A presents forest plots for IL-6 and breast cancer risk, by menopausal status
Table 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: March 2023

Corresponding abbreviated number for differential peptides probes in correlation plot.
Figure 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: March 2023

Supplementary Figure 2A presents forest plots for TNF-alpha and breast cancer risk, by menopausal status
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: March 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 S1E 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: March 2023

Supplementary Figure 1E presents the funnel plot from the meta-analysis of CRP and breast cancer risk
Figure S4 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: March 2023

Figure S4. Autoantibodies differentially expressed in plasma samples from patients with Early-LUAD versus BLD. Each boxplot represents an individual peptide signature in the two sample sets.
Figure S1D 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: March 2023

Supplementary Figure 1D presents dose-response curves for CRP and breast cancer risk, by menopausal status
Figure S1C 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: March 2023

Supplementary Figure 1C present forest plots for CRP and breast cancer risk, excluding studies that had a moderate risk of bias
Figure S3 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: March 2023

Figure S3. Autoantibodies differentially expressed in plasma samples from patients with Early-LUAD versus NHC. Each boxplot represents an individual peptide signature in the two sample sets.
Figure S1B 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: March 2023

Supplementary Figure 1B presents forest plots for CRP and breast cancer risk, excluding studies where exogenous hormone use status was unknown
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: March 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.
Supplementary Table S3 from Neighborhood Socioeconomic Status and Racial and Ethnic Survival Disparities in Oral Cavity and Laryngeal Cancer
Authors: Liu Yupeng , Zhong Lydia , Puram Sidharth V. , Mazul Angela L.
Published: March 2023

Hazard ratios, 95% confidence intervals, and p-values of oral cavity and larynx cancer patients in a multivariable analysis.
Supplementary Table S2 from Neighborhood Socioeconomic Status and Racial and Ethnic Survival Disparities in Oral Cavity and Laryngeal Cancer
Authors: Liu Yupeng , Zhong Lydia , Puram Sidharth V. , Mazul Angela L.
Published: March 2023

Table summarizing 5-year overall survival rate of laryngeal cancer patients.
Supplementary Table S1 from Neighborhood Socioeconomic Status and Racial and Ethnic Survival Disparities in Oral Cavity and Laryngeal Cancer
Authors: Liu Yupeng , Zhong Lydia , Puram Sidharth V. , Mazul Angela L.
Published: March 2023

Table summarizing 5-year overall survival rate of oral cavity cancer patients.
Supplementary Figure S4 from Neighborhood Socioeconomic Status and Racial and Ethnic Survival Disparities in Oral Cavity and Laryngeal Cancer
Authors: Liu Yupeng , Zhong Lydia , Puram Sidharth V. , Mazul Angela L.
Published: March 2023

Four KM curves depicting the 5-year overall survival of larynx cancer patients in Asian subgroups.
Table S7 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: March 2023

Table S7 Shows Hazard Ratios and 95% Confidence Intervals for Total Mortality in Women Diagnosed with Breast Cancer According to Diet Quality Index Stratified by Race and Ethnicity
Table S6C 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: March 2023

Supplementary Table 6C presents findings of individual non-randomised interventions
Table S6B 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: March 2023

Supplementary Table 6B presents findings of individual randomised cross-over studies
Table S5 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: March 2023

Supplementary Table 5 presents the outcomes of the meta-regression analysis conducted for IL-6
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: March 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: March 2023

Supplementary Table 3B presents the risk of bias for non-randomised interventions, using the ROBINS-I
Table S2D 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: March 2023

Supplementary Table 2D presents the study characteristics of the prospective cohort study
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: March 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: March 2023

Supplementary Table 2A presents the study characteristics for parallel group RCTs
Table S1 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: March 2023

Supplementary Table 1 presents the search terminology used in the systematic review
Figure S3A 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: March 2023

Supplementary Figure 3A presents forest plots for physical activity and IL-6, by menopausal status
Figure 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: March 2023

Supplementary Figure 2B presents forest plots for physical activity and TNF-alpha, by exercise type
Figure 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: March 2023

Supplementary Figure 2A present forest plots for physical activity and TNF-alpha, by menopausal status
Figure S1B 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: March 2023

Supplementary Figure 1B presents forest plots for physical activity and CRP, by exercise type
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: March 2023

Supplementary Figure 1A presents forest plots for physical activity and CRP, by menopausal status
Data from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes
Authors: Griffin Rosalie , Hanson Heidi A. , Avery Brian J. , Madsen Michael J. , Sborov Douglas W. , Camp Nicola J.
Published: March 2023

AbstractBackground:
Transcriptome studies are gaining momentum in genomic epidemiology, and the need to incorporate these data in multivariable models alongside other risk factors brings demands for new approaches.
Methods:
Here we describe SPECTRA, an approach to derive quantitative variables that capture the intrinsic variation in gene expression of a tissue type. We applied the SPECTRA approach to bulk RNA sequencing from malignant cells (CD138+) in patients from the Multiple Myeloma Research Foundation CoMMpass study.
Results:
A set of 39 spectra variables were derived to represent multiple myeloma cells. We used these variables in predictive modeling to determine spectra-based risk scores for overall survival, progression-free survival, and time to treatment failure. Risk scores added predictive value beyond known clinical and expression risk factors and replicated in an external dataset. Spectrum variable S5, a significant predictor for all three outcomes, showed pre-ranked gene set enrichment for the unfolded protein response, a mechanism targeted by proteasome inhibitors which are a common first line agent in multiple myeloma treatment. We further used the 39 spectra variables in descriptive modeling, with significant associations found with tumor cytogenetics, race, gender, and age at diagnosis; factors known to influence multiple myeloma incidence or progression.
Conclusions:
Quantitative variables from the SPECTRA approach can predict clinical outcomes in multiple myeloma and provide a new avenue for insight into tumor differences by demographic groups.
Impact:
The SPECTRA approach provides a set of quantitative phenotypes that deeply profile a tissue and allows for more comprehensive modeling of gene expression with other risk factors.
Data 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: March 2023

Abstract
This review synthesized and appraised the evidence for an effect of inflammation on breast cancer risk. Systematic searches identified prospective cohort and Mendelian randomization studies relevant to this review. Meta-analysis of 13 biomarkers of inflammation were conducted to appraise the evidence for an effect breast cancer risk; we examined the dose–response of these associations. Risk of bias was evaluated using the ROBINS-E tool and the quality of evidence was appraised with Grading of Recommendations Assessment, Development, and Evaluation. Thirty-four observational studies and three Mendelian randomization studies were included. Meta-analysis suggested that women with the highest levels of C-reactive protein (CRP) had a higher risk of developing breast cancer [risk ratio (RR) = 1.13; 95% confidence interval (CI), 1.01–1.26] compared with women with the lowest levels. Women with highest levels of adipokines, particularly adiponectin (RR = 0.76; 95% CI, 0.61–0.91) had a reduced breast cancer risk, although this finding was not supported by Mendelian randomization analysis. There was little evidence of an effect of cytokines, including TNFα and IL6, on breast cancer risk. The quality of evidence for each biomarker ranged from very low to moderate. Beyond CRP, the published data do not clearly support the role of inflammation in the development of breast cancer.
Data 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: March 2023

Abstract
This review synthesized and appraised the evidence for an effect of inflammation on breast cancer risk. Systematic searches identified prospective cohort and Mendelian randomization studies relevant to this review. Meta-analysis of 13 biomarkers of inflammation were conducted to appraise the evidence for an effect breast cancer risk; we examined the dose–response of these associations. Risk of bias was evaluated using the ROBINS-E tool and the quality of evidence was appraised with Grading of Recommendations Assessment, Development, and Evaluation. Thirty-four observational studies and three Mendelian randomization studies were included. Meta-analysis suggested that women with the highest levels of C-reactive protein (CRP) had a higher risk of developing breast cancer [risk ratio (RR) = 1.13; 95% confidence interval (CI), 1.01–1.26] compared with women with the lowest levels. Women with highest levels of adipokines, particularly adiponectin (RR = 0.76; 95% CI, 0.61–0.91) had a reduced breast cancer risk, although this finding was not supported by Mendelian randomization analysis. There was little evidence of an effect of cytokines, including TNFα and IL6, on breast cancer risk. The quality of evidence for each biomarker ranged from very low to moderate. Beyond CRP, the published data do not clearly support the role of inflammation in the development of breast cancer.

See related article by XXXX et al., p. 000
Data 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: March 2023

Abstract
The protective effect of physical activity on breast cancer incidence may partially be mediated by inflammation. Systematic searches of Medline, EMBASE, and SPORTDiscus were performed to identify intervention studies, Mendelian randomization studies, and prospective cohort studies that examined the effects of physical activity on circulating inflammatory biomarkers in adult women. Meta-analyses were performed to generate effect estimates. Risk of bias was assessed, and the Grading of Recommendations Assessment, Development, and Evaluation system was used to determine the overall quality of the evidence. Thirty-five intervention studies and one observational study met the criteria for inclusion. Meta-analyses of randomized controlled trials (RCT) indicated that, compared with control groups, exercise interventions reduced levels of C-reactive protein (CRP) [standardized mean difference (SMD) = −0.27, 95% confidence interval (CI) = −0.62 to 0.08), tumor necrosis factor alpha (TNFα, SMD = −0.63, 95% CI = −1.04 to −0.22), interleukin-6 (IL6, SMD = −0.55, 95% CI = −0.97 to −0.13) and leptin (SMD = −0.50, 95% CI = −1.10 to 0.09). Owing to heterogeneity in effect estimates and imprecision, evidence strength was graded as low (CRP, leptin) or moderate (TNFα and IL6). High-quality evidence indicated that exercise did not change adiponectin levels (SMD = 0.01, 95% CI = −0.14 to 0.17). These findings provide support for the biological plausibility of the first part of the physical activity—inflammation—breast cancer pathway.
Table S8 from Immunologic Predictors for Clinical Responses during Immune Checkpoint Blockade in Patients with Myelodysplastic Syndromes
Authors: Lee Sung-Eun , Wang Feng , Grefe Maison , Trujillo-Ocampo Abel , Ruiz-Vasquez Wilfredo , Takahashi Koichi , Abbas Hussein A. , Borges Pamella , Antunes Dinler Amaral , Al-Atrash Gheath , Daver Naval , Molldrem Jeffrey J. , Futreal Andrew , Garcia-Manero Guillermo , Im Jin S.
Published: March 2023

Table S8. Genes used for gene set analysis
Table S1-7 from Immunologic Predictors for Clinical Responses during Immune Checkpoint Blockade in Patients with Myelodysplastic Syndromes
Authors: Lee Sung-Eun , Wang Feng , Grefe Maison , Trujillo-Ocampo Abel , Ruiz-Vasquez Wilfredo , Takahashi Koichi , Abbas Hussein A. , Borges Pamella , Antunes Dinler Amaral , Al-Atrash Gheath , Daver Naval , Molldrem Jeffrey J. , Futreal Andrew , Garcia-Manero Guillermo , Im Jin S.
Published: March 2023

Table S1. Baseline characteristics of patients Table S2-S7. The 40 most statistically significant differential expression results
Figure S4 from Immunologic Predictors for Clinical Responses during Immune Checkpoint Blockade in Patients with Myelodysplastic Syndromes
Authors: Lee Sung-Eun , Wang Feng , Grefe Maison , Trujillo-Ocampo Abel , Ruiz-Vasquez Wilfredo , Takahashi Koichi , Abbas Hussein A. , Borges Pamella , Antunes Dinler Amaral , Al-Atrash Gheath , Daver Naval , Molldrem Jeffrey J. , Futreal Andrew , Garcia-Manero Guillermo , Im Jin S.
Published: March 2023

Figure S4. Changes in T cell subsets at the time of the best responses
Figure S3 from Immunologic Predictors for Clinical Responses during Immune Checkpoint Blockade in Patients with Myelodysplastic Syndromes
Authors: Lee Sung-Eun , Wang Feng , Grefe Maison , Trujillo-Ocampo Abel , Ruiz-Vasquez Wilfredo , Takahashi Koichi , Abbas Hussein A. , Borges Pamella , Antunes Dinler Amaral , Al-Atrash Gheath , Daver Naval , Molldrem Jeffrey J. , Futreal Andrew , Garcia-Manero Guillermo , Im Jin S.
Published: March 2023

Figure S3. Flow-cytometry gating strategy. CD, cluster of differentiation, Treg, regulatory T cell
Data from Immunologic Predictors for Clinical Responses during Immune Checkpoint Blockade in Patients with Myelodysplastic Syndromes
Authors: Lee Sung-Eun , Wang Feng , Grefe Maison , Trujillo-Ocampo Abel , Ruiz-Vasquez Wilfredo , Takahashi Koichi , Abbas Hussein A. , Borges Pamella , Antunes Dinler Amaral , Al-Atrash Gheath , Daver Naval , Molldrem Jeffrey J. , Futreal Andrew , Garcia-Manero Guillermo , Im Jin S.
Published: March 2023

AbstractPurpose:
The aim of this study is to determine immune-related biomarkers to predict effective antitumor immunity in myelodysplastic syndrome (MDS) during immunotherapy (IMT, αCTLA-4, and/or αPD-1 antibodies) and/or hypomethylating agent (HMA).
Experimental Design:
Peripheral blood samples from 55 patients with MDS were assessed for immune subsets, T-cell receptor (TCR) repertoire, mutations in 295 acute myeloid leukemia (AML)/MDS-related genes, and immune-related gene expression profiling before and after the first treatment.
Results:
Clinical responders treated with IMT ± HMA but not HMA alone showed a significant expansion of central memory (CM) CD8+ T cells, diverse TCRβ repertoire pretreatment with increased clonality and emergence of novel clones after the initial treatment, and a higher mutation burden pretreatment with subsequent reduction posttreatment. Autophagy, TGFβ, and Th1 differentiation pathways were the most downregulated in nonresponders after treatment, while upregulated in responders. Finally, CTLA-4 but not PD-1 blockade attributed to favorable changes in immune landscape.
Conclusions:
Analysis of tumor–immune landscape in MDS during immunotherapy provides clinical response biomarkers.
Figure S3 from JMJD6 Shapes a Pro-tumor Microenvironment via ANXA1-Dependent Macrophage Polarization in Breast Cancer
Authors: Cioni Bianca , Ratti Silvia , Piva Annamaria , Tripodi Irene , Milani Matteo , Menichetti Francesca , Langella Tiziana , Botti Laura , De Cecco Loris , Chiodoni Claudia , Lecis Daniele , Colombo Mario P.
Published: March 2023

Effect of Jmjd6 silencing in PyMT41C-cells. A) Real-Time PCR showing the levels of Jmjd6 in PyMT-41C cells transfected with non-targeting siRNA (siCtr) or specific for Jmjd6. Graph is the average of two independent experiments. Two-tailed unpaired t student test was applied to calculate statistical significance. B) Western blot performed on conditioned medium collected from PyMT-41C transfected as in A). Red Ponceau is shown as loading control. C) IF to detect ANXA1 levels and D) lipid droplet formation in cell transfected as in A).
Supplementary Figures 1-8 from Targeting Unc51-like Autophagy Activating Kinase 1 (ULK1) Overcomes Adaptive Drug Resistance in Acute Myelogenous Leukemia
Authors: Bhattacharya Seemana , Piya Sujan , Ma Huaxian , Sharma Priyanka , Zhang Qi , Baran Natalia , Ruvolo Vivian R. , McQueen Teresa , Davis R. Eric , Pourebrahim Rasoul , Konopleva Marina , Kantarjian Hagop , Cosford Nicholas D. P. , Andreeff Michael , Borthakur Gautam
Published: March 2023

S1. Effect of genetic abrogation of ULK1 in vitro and in vivo.
S2. ULK1 inhibitor SBI-0206965 has anti-leukemic activity and can overcome autophagic adaptation in drug-resistant cells.
S3. ULK1 inhibition reverses drug-resistance and synergizes with AML therapies.
S4. ULK1 inhibition changes signaling in AML cells.
S5. ULK1 inhibition hampers ROS.
S6. ULK1 inhibition accumulates dysfunctional mitochondria.
S7. ULK1 inhibition impairs colony forming ability and delays leukemia progression.
S8. In vitro and in vivo synergy of ULK1 inhibition with AML therapies.