bluuebunny/crossref_metadata_2025 huggingface dataset:
Figure S3 from Allysine-Targeted Molecular MRI Enables Early Prediction of Chemotherapy Response in Pancreatic Cancer
Authors: Ma Hua , Esfahani Shadi A. , Krishna Shriya , Ataeinia Bahar , Zhou Iris Y. , Rotile Nicholas J. , Weigand-Whittier Jonah , Boice Avery T. , Liss Andrew S. , Tanabe Kenneth K. , Caravan Peter
Published: August 2024

Figure S3. Dynamic tumor %nSI in PDAC6 tumor-bearing mice after injection of MnL3 or MnL4. MnL4 (0.1 mmol/kg, i.v.) only produced transient signal enhancement after injection, while MnL3 (0.1 mmol/kg, i.v.) produced persistent and higher signal enhancement in tumors. (n = 4 for each group).
Figure S10 from Allysine-Targeted Molecular MRI Enables Early Prediction of Chemotherapy Response in Pancreatic Cancer
Authors: Ma Hua , Esfahani Shadi A. , Krishna Shriya , Ataeinia Bahar , Zhou Iris Y. , Rotile Nicholas J. , Weigand-Whittier Jonah , Boice Avery T. , Liss Andrew S. , Tanabe Kenneth K. , Caravan Peter
Published: August 2024

Figure S10. Quantification of manganese content in the 1319 tumor 60 minutes post-injection of MnL3. After randomly assigning 1319 mice to the vehicle or FOLFIRINOX group, MnL3 showed equivalent tumor uptake (0.1 mmol/kg, i.v., 60 min p.i., n = 5 for each group, Student’s t-test. ns: not significant).
Data from HTS384 NCI60: The Next Phase of the NCI60 Screen
Authors: Kunkel Mark W. , Coussens Nathan P. , Morris Joel , Taylor Ronald C. , Dexheimer Thomas S. , Jones Eric M. , Doroshow James H. , Teicher Beverly A.
Published: August 2024

Abstract
The NCI60 human tumor cell line screen has been in operation as a service to the cancer research community for more than 30 years. The screen operated with 96-well plates, a 2-day exposure period to test agents, and following cell fixation, a visible absorbance endpoint by the protein-staining dye sulforhodamine B. In this study, we describe the next phase of this important cancer research tool, the HTS384 NCI60 screen. Although the cell lines remain the same, the updated screen is performed with 384-well plates, a 3-day exposure period to test agents, and a luminescent endpoint to measure cell viability based upon cellular ATP content. In this study, a library of 1,003 FDA-approved and investigational small-molecule anticancer agents was screened by the two NCI60 assays. The datasets were compared with a focus on targeted agents with at least six representatives in the library. For many agents, including inhibitors of EGFR, BRAF, MEK, ERK, and PI3K, the patterns of GI50 values were very similar between the screens with strong correlations between those patterns within the dataset from each screen. However, for some groups of targeted agents, including mTOR, BET bromodomain, and NAMPRTase inhibitors, there were limited or no correlations between the two datasets, although the patterns of GI50 values and correlations between those patterns within each dataset were apparent. Beginning in January 2024, the HTS384 NCI60 screen became the free screening service of the NCI to facilitate drug discovery by the cancer research community.

Significance: The new NCI60 cell line screen HTS384 shows robust patterns of response to oncology agents and substantial overlap with the classic screen, providing an updated tool for studying therapeutic agents.

See related commentary by Colombo and Corsello, p. 2397
Data from Extracellular Vesicles Containing circMYBL1 Induce CD44 in Adenoid Cystic Carcinoma Cells and Pulmonary Endothelial Cells to Promote Lung Metastasis
Authors: Fu Min , Gao Qian , Xiao Mian , Li Rui-Feng , Sun Xin-Yi , Li Sheng-Lin , Peng Xin , Ge Xi-Yuan
Published: August 2024

Adenoid cystic carcinoma (ACC) is a rare malignant epithelial neoplasm that arises in secretory glands and commonly metastasizes to the lungs. MYBL1 is frequently overexpressed in ACC and has been suggested to be a driver of the disease. In this study, we identified a circular RNA (circRNA) derived from MYBL1 pre-mRNA that was accompanied by the overexpression of MYBL1 in ACC. Overexpression of circMYBL1 was correlated with increased lung metastasis and poor overall survival in patients with ACC. Ectopic circMYBL1 overexpression promoted malignant phenotypes and lung metastasis of ACC cells. Mechanistically, circMYBL1 formed a circRNA–protein complex with CCAAT enhancer–binding protein β (CEBPB), which inhibited ubiquitin-mediated degradation and promoted nuclear translocation of CEBPB. In the nucleus, circMYBL1 increased the binding of CEBPB to the CD44 promoter region and enhanced its transcription. In addition, circMYBL1 was enriched in small extracellular vesicles (sEV) isolated from the plasma of patients with ACC. Treatment with sEVs containing circMYBL1 in sEVs enhanced prometastatic phenotypes of ACC cells, elevated the expression of CD44 in human pulmonary microvascular endothelial cells (HPMEC), and enhanced the adhesion between HPMECs and ACC cells. Moreover, circMYBL1 encapsulated in sEVs increased the arrest of circulating ACC cells in the lung and enhanced lung metastatic burden. These data suggest that circMYBL1 is a tumor-promoting circRNA that could serve as a potential biomarker and therapeutic target for ACC.

Significance: circMYBL1 stabilizes CEBPB and upregulates CD44 to promote adhesion between cancer cells and endothelial cells and enables lung metastasis of adenoid cystic carcinoma, suggesting that inhibition of this axis could improve patient outcomes.
Data from The DNA Methyltransferase Inhibitor 5-Aza-4′-thio-2′-Deoxycytidine Induces C>G Transversions and Acute Lymphoid Leukemia Development
Authors: Bertoli Ryan M. , Chung Yang Jo , Difilippantonio Michael J. , Wokasch Anthony , Marasco Madison R.B. , Klimaszewski Haley , Gammell Susannah , Zhu Yuelin J. , Walker Robert L. , Cao Dengchao , Khanna Ajay , Walter Matthew J. , Doroshow James H. , Meltzer Paul S. , Aplan Peter D.
Published: August 2024

Abstract
DNA methyltransferase inhibitors (DNMTi), most commonly cytidine analogs, are compounds that decrease 5′-cytosine methylation. DNMTi are used clinically based on the hypothesis that cytosine demethylation will lead to re-expression of tumor suppressor genes. 5-Aza-4′-thio-2′-deoxycytidine (Aza-TdCyd or ATC) is a recently described thiol-substituted DNMTi that has been shown to have anti-tumor activity in solid tumor models. In this study, we investigated the therapeutic potential of ATC in a murine transplantation model of myelodysplastic syndrome. ATC treatment led to the transformation of transplanted wild-type bone marrow nucleated cells into lymphoid leukemia, and healthy mice treated with ATC also developed lymphoid leukemia. Whole-exome sequencing revealed 1,000 acquired mutations, almost all of which were C>G transversions in a specific 5′-NCG-3′ context. These mutations involved dozens of genes involved in human lymphoid leukemia, such as Notch1, Pten, Pax5, Trp53, and Nf1. Human cells treated in vitro with ATC showed 1,000 acquired C>G transversions in a similar context. Deletion of Dck, the rate-limiting enzyme for the cytidine salvage pathway, eliminated C>G transversions. Taken together, these findings demonstrate a highly penetrant mutagenic and leukemogenic phenotype associated with ATC.

Significance: Treatment with a DNA methyltransferase inhibitor generates a distinct mutation signature and triggers leukemic transformation, which has important implications for the research and clinical applications of these inhibitors.
Supplemental Table 1 from Age-Specific Cancer Mortality in the US During the COVID-19 Pandemic, March to December 2020
Authors: Shiels Meredith S. , Haque Anika T. , Freedman Neal D. , Kim Hae-Rin , Berrington de González Amy , Albert Paul S.
Published: August 2024

Supplemental Table 1. Adjusted rate ratios comparing cancer death rates during the COVID-19 period (March-December 2020) to the pre-COVID-19 period (January 2011-February 2020) age group.
Supplemental Figure 3 from Age-Specific Cancer Mortality in the US During the COVID-19 Pandemic, March to December 2020
Authors: Shiels Meredith S. , Haque Anika T. , Freedman Neal D. , Kim Hae-Rin , Berrington de González Amy , Albert Paul S.
Published: August 2024

Supplemental Figure 3. Adjusted rate ratios comparing lung cancer death rates during the COVID-19 period (March-December 2020) to the pre–COVID-19 period (January 2011-February 2020) by month by age group. Points represent adjusted rate ratios and lines represent 95% confidence intervals. Models adjusted for linear calendar time in months, and seasonal patterns with sine and cosine terms.
Supplemental Figure 2 from Age-Specific Cancer Mortality in the US During the COVID-19 Pandemic, March to December 2020
Authors: Shiels Meredith S. , Haque Anika T. , Freedman Neal D. , Kim Hae-Rin , Berrington de González Amy , Albert Paul S.
Published: August 2024

Supplemental Figure 2. Adjusted rate ratios comparing overall cancer death rates during the COVID-19 period (March-December 2020) to the pre–COVID-19 period (January 2011-February 2020) by month by age group. Points represent adjusted rate ratios and lines represent 95% confidence intervals. Models adjusted for linear calendar time in months, and seasonal patterns with sine and cosine terms.
Supplementary Table S2 from Adverse Childhood Experiences, Resilience, and Cardiovascular Disease in Adult Survivors of Childhood Cancer: A Report from the Childhood Cancer Survivor Study
Authors: Schwartz Lindsay F. , Stratton Kayla L. , Leisenring Wendy M. , Rodriguez Stefania M. , Alston Shani , McDonald Aaron , Vukadinovich Chris , Rinehardt Dayton , Oeffinger Kevin C. , Chow Eric J. , Krull Kevin R. , Brinkman Tara M. , Nathan Paul C. , Tan Marcia M. , McCrae Julie S. , Burkhardt Tiffany , Ness Kirsten K. , Armstrong Gregory T. , Henderson Tara O.
Published: August 2024

Matching frequencies for case to control participants
Supplementary Table S9 from Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms
Authors: Davidson Natalie R. , Barnard Mollie E. , Hippen Ariel A. , Campbell Amy , Johnson Courtney E. , Way Gregory P. , Dalley Brian K. , Berchuck Andrew , Salas Lucas A. , Peres Lauren C. , Marks Jeffrey R. , Schildkraut Joellen M. , Greene Casey S. , Doherty Jennifer A.
Published: August 2024

Survival analyses using consensusOV subtypes
Supplementary Table S7 from Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms
Authors: Davidson Natalie R. , Barnard Mollie E. , Hippen Ariel A. , Campbell Amy , Johnson Courtney E. , Way Gregory P. , Dalley Brian K. , Berchuck Andrew , Salas Lucas A. , Peres Lauren C. , Marks Jeffrey R. , Schildkraut Joellen M. , Greene Casey S. , Doherty Jennifer A.
Published: August 2024

Balanced accuracy comparing subtype labels from K-means and consensusOV for each subtype and dataset
Supplementary Table S4 from Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms
Authors: Davidson Natalie R. , Barnard Mollie E. , Hippen Ariel A. , Campbell Amy , Johnson Courtney E. , Way Gregory P. , Dalley Brian K. , Berchuck Andrew , Salas Lucas A. , Peres Lauren C. , Marks Jeffrey R. , Schildkraut Joellen M. , Greene Casey S. , Doherty Jennifer A.
Published: August 2024

SchildkrautW derived expression data
Supplementary Table S2 from Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms
Authors: Davidson Natalie R. , Barnard Mollie E. , Hippen Ariel A. , Campbell Amy , Johnson Courtney E. , Way Gregory P. , Dalley Brian K. , Berchuck Andrew , Salas Lucas A. , Peres Lauren C. , Marks Jeffrey R. , Schildkraut Joellen M. , Greene Casey S. , Doherty Jennifer A.
Published: August 2024

Common gene list
Supplementary Table S10 from Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms
Authors: Davidson Natalie R. , Barnard Mollie E. , Hippen Ariel A. , Campbell Amy , Johnson Courtney E. , Way Gregory P. , Dalley Brian K. , Berchuck Andrew , Salas Lucas A. , Peres Lauren C. , Marks Jeffrey R. , Schildkraut Joellen M. , Greene Casey S. , Doherty Jennifer A.
Published: August 2024

consensusOV subtype labels for SchildkrautB and SchildkrautW samples
Supplementary Table S1 from Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms
Authors: Davidson Natalie R. , Barnard Mollie E. , Hippen Ariel A. , Campbell Amy , Johnson Courtney E. , Way Gregory P. , Dalley Brian K. , Berchuck Andrew , Salas Lucas A. , Peres Lauren C. , Marks Jeffrey R. , Schildkraut Joellen M. , Greene Casey S. , Doherty Jennifer A.
Published: August 2024

Global MAD gene list
Supplementary Figure S8 from Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms
Authors: Davidson Natalie R. , Barnard Mollie E. , Hippen Ariel A. , Campbell Amy , Johnson Courtney E. , Way Gregory P. , Dalley Brian K. , Berchuck Andrew , Salas Lucas A. , Peres Lauren C. , Marks Jeffrey R. , Schildkraut Joellen M. , Greene Casey S. , Doherty Jennifer A.
Published: August 2024

Analysis of cluster stability
Supplementary Figure S7 from Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms
Authors: Davidson Natalie R. , Barnard Mollie E. , Hippen Ariel A. , Campbell Amy , Johnson Courtney E. , Way Gregory P. , Dalley Brian K. , Berchuck Andrew , Salas Lucas A. , Peres Lauren C. , Marks Jeffrey R. , Schildkraut Joellen M. , Greene Casey S. , Doherty Jennifer A.
Published: August 2024

SAM moderated t score correlation heatmaps for K-means and NMF
Supplementary Figure S4 from Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms
Authors: Davidson Natalie R. , Barnard Mollie E. , Hippen Ariel A. , Campbell Amy , Johnson Courtney E. , Way Gregory P. , Dalley Brian K. , Berchuck Andrew , Salas Lucas A. , Peres Lauren C. , Marks Jeffrey R. , Schildkraut Joellen M. , Greene Casey S. , Doherty Jennifer A.
Published: August 2024

Evaluation of sample quality in Schildkraut datasets
Supplementary Figure S2 from Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms
Authors: Davidson Natalie R. , Barnard Mollie E. , Hippen Ariel A. , Campbell Amy , Johnson Courtney E. , Way Gregory P. , Dalley Brian K. , Berchuck Andrew , Salas Lucas A. , Peres Lauren C. , Marks Jeffrey R. , Schildkraut Joellen M. , Greene Casey S. , Doherty Jennifer A.
Published: August 2024

Case selection for self-identified White study participants
Supplementary Figure S14 from Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms
Authors: Davidson Natalie R. , Barnard Mollie E. , Hippen Ariel A. , Campbell Amy , Johnson Courtney E. , Way Gregory P. , Dalley Brian K. , Berchuck Andrew , Salas Lucas A. , Peres Lauren C. , Marks Jeffrey R. , Schildkraut Joellen M. , Greene Casey S. , Doherty Jennifer A.
Published: August 2024

KM survival curves by study population
Supplementary Figure S11 from Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms
Authors: Davidson Natalie R. , Barnard Mollie E. , Hippen Ariel A. , Campbell Amy , Johnson Courtney E. , Way Gregory P. , Dalley Brian K. , Berchuck Andrew , Salas Lucas A. , Peres Lauren C. , Marks Jeffrey R. , Schildkraut Joellen M. , Greene Casey S. , Doherty Jennifer A.
Published: August 2024

SAM moderated t score correlation heatmaps by dataset for k3
Supplementary Figure S10 from Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms
Authors: Davidson Natalie R. , Barnard Mollie E. , Hippen Ariel A. , Campbell Amy , Johnson Courtney E. , Way Gregory P. , Dalley Brian K. , Berchuck Andrew , Salas Lucas A. , Peres Lauren C. , Marks Jeffrey R. , Schildkraut Joellen M. , Greene Casey S. , Doherty Jennifer A.
Published: August 2024

SchildkrautW subtype labeling comparisons
Supplementary table 2 from Understanding Benign Breast Disease and Subsequent Breast Cancer in Hispanic White Females: A Step Closer to Evidence-Based Management
Authors: Lohani Kush R. , Nibbe Andrea M. , Vierkant Robert A. , Pacheco-Spann Laura M. , Seymour Lisa R. , Vachon Celine M. , Sherman Mark E. , Winham Stacey J. , Degnim Amy C. , Hill Deirdre A.
Published: August 2024

SEER age-adjusted breast cancer incidence rates for six counties of New Mexico, by ethnicity and age.
Table S3 from Genetic Risk of Second Malignant Neoplasm after Childhood Cancer Treatment: A Systematic Review
Authors: Ducos Claire , Aba Naïla , Rosselli Filippo , Fresneau Brice , Al Ahmad Nachar Baraah , Zidane Monia , de Vathaire Florent , Benhamou Simone , Haddy Nadia
Published: August 2024

Table S3 displays all the keywords used to search articles in the WOS database.
Table S2 from Genetic Risk of Second Malignant Neoplasm after Childhood Cancer Treatment: A Systematic Review
Authors: Ducos Claire , Aba Naïla , Rosselli Filippo , Fresneau Brice , Al Ahmad Nachar Baraah , Zidane Monia , de Vathaire Florent , Benhamou Simone , Haddy Nadia
Published: August 2024

Table S2 displays all the keywords used to search articles in the Scopus database.
Supplementary Table 1 from Assessing the Relationship between Biomarkers of Exposure and Biomarkers of Potential Harm: PATH Study Wave 1 (2013 to 2014)
Authors: Chang Cindy M. , Thakur Sapna , Montes de Oca Ruben , Rostron Brian L. , Cheng Yu-Ching , Wright M. Jerry , van Bemmel Dana M. , Wang Lanqing , Hatsukami Dorothy K.
Published: August 2024

Supplementary Table 1. Regression models without covariates for the association between days since last smoked cigarettes and BOPH among people who used to smoke cigarettes exclusively and people who currently use e-cigarettes
Supplemental Table 4 from Prevalence of Epidermal Growth Factor Receptor and Programmed Death Ligand 1 Testing in a Population-Based Lung Cancer Surgical Resection Cohort from 2018 to 2022
Authors: Smeltzer Matthew P. , Akinbobola Olawale A. , Ray Meredith A. , Fehnel Carrie , Saulsberry Andrea , Dortch Kourtney R. , Pimenta Kelly , Matthews Anberitha T. , Osarogiagbon Raymond U.
Published: August 2024

Supplemental Table 4: Cohort Characteristics by PDL1 Testing Status.
Supplemental Table 3 from Prevalence of Epidermal Growth Factor Receptor and Programmed Death Ligand 1 Testing in a Population-Based Lung Cancer Surgical Resection Cohort from 2018 to 2022
Authors: Smeltzer Matthew P. , Akinbobola Olawale A. , Ray Meredith A. , Fehnel Carrie , Saulsberry Andrea , Dortch Kourtney R. , Pimenta Kelly , Matthews Anberitha T. , Osarogiagbon Raymond U.
Published: August 2024

Supplemental Table 3: Cohort Demographics by Testing Status in Eligible Persons.
Supplemental Table 1 from Prevalence of Epidermal Growth Factor Receptor and Programmed Death Ligand 1 Testing in a Population-Based Lung Cancer Surgical Resection Cohort from 2018 to 2022
Authors: Smeltzer Matthew P. , Akinbobola Olawale A. , Ray Meredith A. , Fehnel Carrie , Saulsberry Andrea , Dortch Kourtney R. , Pimenta Kelly , Matthews Anberitha T. , Osarogiagbon Raymond U.
Published: August 2024

Supplemental Table 1: EGFR testing adjusting for rural or metropolitan hospitals and surgeon board certification as confounders in the parsimonious model.
Supplemental Methods 1 from Prevalence of Epidermal Growth Factor Receptor and Programmed Death Ligand 1 Testing in a Population-Based Lung Cancer Surgical Resection Cohort from 2018 to 2022
Authors: Smeltzer Matthew P. , Akinbobola Olawale A. , Ray Meredith A. , Fehnel Carrie , Saulsberry Andrea , Dortch Kourtney R. , Pimenta Kelly , Matthews Anberitha T. , Osarogiagbon Raymond U.
Published: August 2024

Supplemental Methods 1: Standard Operating Procedure. Molecular Data Entry.
Supplementary Figure 3 from Assessing the Relationship between Biomarkers of Exposure and Biomarkers of Potential Harm: PATH Study Wave 1 (2013 to 2014)
Authors: Chang Cindy M. , Thakur Sapna , Montes de Oca Ruben , Rostron Brian L. , Cheng Yu-Ching , Wright M. Jerry , van Bemmel Dana M. , Wang Lanqing , Hatsukami Dorothy K.
Published: August 2024

Supplementary Figure 3. Scatter plots of log-transformed BOPH ((A) sICAM (B) hs-CRP (C) IL-6 (D) Fibro (E) 8-Isoprostane) and log-transformed NNAL by Tobacco Use
Supplementary Table S2 from Disparities and Determinants of Testing for Early Detection of Cervical Cancer among Nepalese Women: Evidence from a Population-Based Survey
Authors: Rahman Md. Shafiur , Rahman Md. Mahfuzur , Acharya Kiran , Haruyama Rei , Shah Richa , Matsuda Tomohiro , Inoue Manami , Abe Sarah K.
Published: August 2024

Supplementary Table S2 presents the uptake of cervical cancer test among Nepalese women by their educational status and household socioeconomic status.
Supplemental Table 1 from Kidney Cancer Incidence among Non-Hispanic American Indian and Alaska Native Populations in the United States, 1999 to 2020
Authors: Melkonian Stephanie C. , Jim Melissa A. , Haverkamp Donald , Lee Madeleine , Janitz Amanda E. , Campbell Janis E.
Published: August 2024

Supplemental Table 1 shows Renal Cell Carcinoma Incidence by Geographic Region and Sex for non-Hispanic American Indian and Alaska Native and non-Hispanic White males and females for the years 2011-2020.
Supplementary Table S1 from Disparities and Determinants of Testing for Early Detection of Cervical Cancer among Nepalese Women: Evidence from a Population-Based Survey
Authors: Rahman Md. Shafiur , Rahman Md. Mahfuzur , Acharya Kiran , Haruyama Rei , Shah Richa , Matsuda Tomohiro , Inoue Manami , Abe Sarah K.
Published: August 2024

Supplementary Table S1 shows the details of the included covariates.
Supplemental Table 6 from Association of Urinary Biomarkers of Tobacco Exposure with Lung Cancer Risk in African American and White Cigarette Smokers in the Southern Community Cohort Study
Authors: Murphy Sharon E. , Guillermo Cherie , Thomson Nicole M. , Carmella Steven G. , Wittmann Milo , Aldrich Melinda C. , Cai Qiuyin , Sullivan Shannon M. , Stram Daniel O. , Le Marchand Loïc , Hecht Stephen S. , Blot William J. , Park S. Lani
Published: August 2024

Supplemental Table 6. Geometric mean of Biomarkers in cases and controls stratified by Race/Ethnicity, adjusted for creatinine
Supplemental Table 3 from Association of Urinary Biomarkers of Tobacco Exposure with Lung Cancer Risk in African American and White Cigarette Smokers in the Southern Community Cohort Study
Authors: Murphy Sharon E. , Guillermo Cherie , Thomson Nicole M. , Carmella Steven G. , Wittmann Milo , Aldrich Melinda C. , Cai Qiuyin , Sullivan Shannon M. , Stram Daniel O. , Le Marchand Loïc , Hecht Stephen S. , Blot William J. , Park S. Lani
Published: August 2024

Supplemental Table 3. Pearson Correlation Coefficients (R) for smoking-related urinary biomarkers
Table S4 from Prostate Cancer Diagnosis Rates among Insured Men with and without HIV in South Africa: A Cohort Study
Authors: Ruffieux Yann , Fernández Villalobos Nathalie V. , Didden Christiane , Haas Andreas D. , Chinogurei Chido , Cornell Morna , Egger Matthias , Maartens Gary , Folb Naomi , Rohner Eliane
Published: August 2024

Table S4 shows characteristics at diagnosis of prostate cancer, by HIV status and overall.
Table S2 from Prostate Cancer Diagnosis Rates among Insured Men with and without HIV in South Africa: A Cohort Study
Authors: Ruffieux Yann , Fernández Villalobos Nathalie V. , Didden Christiane , Haas Andreas D. , Chinogurei Chido , Cornell Morna , Egger Matthias , Maartens Gary , Folb Naomi , Rohner Eliane
Published: August 2024

Table S2 shows the cohort characteristics by population group
Table S12 from Prostate Cancer Diagnosis Rates among Insured Men with and without HIV in South Africa: A Cohort Study
Authors: Ruffieux Yann , Fernández Villalobos Nathalie V. , Didden Christiane , Haas Andreas D. , Chinogurei Chido , Cornell Morna , Egger Matthias , Maartens Gary , Folb Naomi , Rohner Eliane
Published: August 2024

Table S12 shows hazard ratios for incident prostate cancer diagnosis among men with HIV compared to men without HIV.
Table S11 from Prostate Cancer Diagnosis Rates among Insured Men with and without HIV in South Africa: A Cohort Study
Authors: Ruffieux Yann , Fernández Villalobos Nathalie V. , Didden Christiane , Haas Andreas D. , Chinogurei Chido , Cornell Morna , Egger Matthias , Maartens Gary , Folb Naomi , Rohner Eliane
Published: August 2024

Table S11 shows hazard ratios for prostate cancer diagnosis, restricting to men with an elevated prostate specific antigen test result (>4 ng/mL) followed by a prostate biopsy.
Table S10 from Prostate Cancer Diagnosis Rates among Insured Men with and without HIV in South Africa: A Cohort Study
Authors: Ruffieux Yann , Fernández Villalobos Nathalie V. , Didden Christiane , Haas Andreas D. , Chinogurei Chido , Cornell Morna , Egger Matthias , Maartens Gary , Folb Naomi , Rohner Eliane
Published: August 2024

Table S10 shows hazard ratios for prostate cancer diagnosis, restricting to men with at least one prostate specific antigen test.
Table 3 from Prostate Cancer Diagnosis Rates among Insured Men with and without HIV in South Africa: A Cohort Study
Authors: Ruffieux Yann , Fernández Villalobos Nathalie V. , Didden Christiane , Haas Andreas D. , Chinogurei Chido , Cornell Morna , Egger Matthias , Maartens Gary , Folb Naomi , Rohner Eliane
Published: August 2024

Hazard ratios for the association of different factors with an incident prostate cancer diagnosis.
Table 2 from Prostate Cancer Diagnosis Rates among Insured Men with and without HIV in South Africa: A Cohort Study
Authors: Ruffieux Yann , Fernández Villalobos Nathalie V. , Didden Christiane , Haas Andreas D. , Chinogurei Chido , Cornell Morna , Egger Matthias , Maartens Gary , Folb Naomi , Rohner Eliane
Published: August 2024

Rate or HR for PSA testing, prostate biopsies, prostate biopsies in men with elevated PSA, and prostatitis diagnosis.
Figure S1 from Prostate Cancer Diagnosis Rates among Insured Men with and without HIV in South Africa: A Cohort Study
Authors: Ruffieux Yann , Fernández Villalobos Nathalie V. , Didden Christiane , Haas Andreas D. , Chinogurei Chido , Cornell Morna , Egger Matthias , Maartens Gary , Folb Naomi , Rohner Eliane
Published: August 2024

Figure S1 shows hazard ratios for prostate cancer diagnosis over follow-up time.
Figure 3 from Prostate Cancer Diagnosis Rates among Insured Men with and without HIV in South Africa: A Cohort Study
Authors: Ruffieux Yann , Fernández Villalobos Nathalie V. , Didden Christiane , Haas Andreas D. , Chinogurei Chido , Cornell Morna , Egger Matthias , Maartens Gary , Folb Naomi , Rohner Eliane
Published: August 2024

Prostate cancer diagnosis rates by age and HIV status. The figure shows estimated prostate cancer diagnosis rates per 100,000 person-years by age (years) among men with and without HIV. The shaded areas represent 95% CIs.
Figure S1 from Mammographic Texture versus Conventional Cumulus Measure of Density in Breast Cancer Risk Prediction: A Literature Review
Authors: Ye Zhoufeng , Nguyen Tuong L. , Dite Gillian S. , MacInnis Robert J. , Hopper John L. , Li Shuai
Published: August 2024

Figure S1 shows that 18 publications were included in the analysis after applying our search strategy. We searched the PubMed database for original publications on mammographic texture analysis published up to November 2023. The searching strategy included (“texture” OR “texture analysis” OR “feature” OR “parenchymal pattern” OR “parenchymal analysis”) AND (“mammogram” OR “mammograms” OR “mammography”) AND (“risk” OR “risk assessment” OR “risk prediction”). The literature inclusion criteria were: 1) the publication is published in English, 2) full text is available, 3) the discriminatory performance of both texture-based features and Cumulus or mammographic density measures similar to Cumulus using the same dataset were reported.
Supplementary Table 5. from Joint Associations of Diet and Device-Measured Physical Activity with Mortality and Incident CVD and Cancer: A Prospective Analysis of the UK Biobank Study
Authors: Inan-Eroglu Elif , Ahmadi Matthew , Biswas Raaj Kishore , Ding Ding , Rezende Leandro F.M. , Lee I-Min , Giovannucci Edward L. , Stamatakis Emmanuel
Published: August 2024

Supplementary Table 5 shows the independent associations between dietary measures and all-cause mortality, cardiovascular disease, total cancer, as well as physical activity, diet, and adiposity-related cancer incidence without adjustment for total physical activity.
Supplemental Table S7 from Rural–Urban Cancer Incidence and Trends in the United States, 2000 to 2019
Authors: Semprini Jason , Gadag Khyathi , Williams Gawain , Muldrow Aniyah , Zahnd Whitney E.
Published: August 2024

Supplemental Table S7: Rate Ratios for Age-adjusted cancer incidence rates by rural-urban status and U.S. Census Region, 2015 to 2019
Supplementary Table 1. from Joint Associations of Diet and Device-Measured Physical Activity with Mortality and Incident CVD and Cancer: A Prospective Analysis of the UK Biobank Study
Authors: Inan-Eroglu Elif , Ahmadi Matthew , Biswas Raaj Kishore , Ding Ding , Rezende Leandro F.M. , Lee I-Min , Giovannucci Edward L. , Stamatakis Emmanuel
Published: August 2024

Supplementary Table 1 presents the baseline characteristics of the study sample, which includes 79,988 individuals, categorized by their respective diet quality index groups.
Supplemental Table S6 from Rural–Urban Cancer Incidence and Trends in the United States, 2000 to 2019
Authors: Semprini Jason , Gadag Khyathi , Williams Gawain , Muldrow Aniyah , Zahnd Whitney E.
Published: August 2024

Supplemental Table S6: Age-adjusted invasive cancer incidence rates and corresponding 95% confidence intervals per 100,000 by primary site and U.S. Census Region and rural status, 2015 to 2019 
Supplemental Table S5 from Rural–Urban Cancer Incidence and Trends in the United States, 2000 to 2019
Authors: Semprini Jason , Gadag Khyathi , Williams Gawain , Muldrow Aniyah , Zahnd Whitney E.
Published: August 2024

Supplemental Table S5: Rate Ratios for Age-adjusted cancer incidence rates by rural-urban status and race/ethnicity, 2015 to 2019
Supplementary Figure 5 from Joint Associations of Diet and Device-Measured Physical Activity with Mortality and Incident CVD and Cancer: A Prospective Analysis of the UK Biobank Study
Authors: Inan-Eroglu Elif , Ahmadi Matthew , Biswas Raaj Kishore , Ding Ding , Rezende Leandro F.M. , Lee I-Min , Giovannucci Edward L. , Stamatakis Emmanuel
Published: August 2024

Joint association between physical activity, and diet quality score with all-cause mortality and cardiovascular disease, total cancer and physical activity, diet and adiposity related cancer incidence with exclusion of events occurring in the first 2 years of follow-up.Supplementary Figure 5 (A-D) shows the joint association between physical activity and diet quality score with all-cause mortality (n=79,641) and cardiovascular disease (n=77,234), as well as total cancer (n=78,129) and physical activity, diet, and adiposity-related cancer (n=78,887) incidence, with an exclusion of events occurring within the first 2 years of follow-up.
Supplementary Figure 10 from Joint Associations of Diet and Device-Measured Physical Activity with Mortality and Incident CVD and Cancer: A Prospective Analysis of the UK Biobank Study
Authors: Inan-Eroglu Elif , Ahmadi Matthew , Biswas Raaj Kishore , Ding Ding , Rezende Leandro F.M. , Lee I-Min , Giovannucci Edward L. , Stamatakis Emmanuel
Published: August 2024

Joint dose-response association between physical activity volume (minutes/day), diet quality score with total cancer incidence.Supplementary Figure 10 shows the joint dose-response association between physical activity volume (minutes/day) and diet quality score with respect to total cancer incidence.
Data from Age-Specific Cancer Mortality in the US During the COVID-19 Pandemic, March to December 2020
Authors: Shiels Meredith S. , Haque Anika T. , Freedman Neal D. , Kim Hae-Rin , Berrington de González Amy , Albert Paul S.
Published: August 2024

AbstractBackground:
It is important to understand the impact of the COVID-19 pandemic on cancer death rates in 2020 in the US. We estimated whether there were larger-than-expected changes in cancer mortality rates from March to December 2020 after accounting for temporal and seasonal patterns using data from January 2011 to February 2020 by cancer type and age.
Methods:
We obtained death counts and underlying causes of death by cancer type, month/year (2011–2020), and age group from the National Center for Health Statistics and population estimates from the US Census Bureau. Poisson regression was used to test for significant changes in cancer death rates from March to December 2020 compared with prior years.
Results:
After accounting for temporal trends and seasonal patterns, total cancer death rates were significantly lower than expected during March to December 2020 among 55- to 64-year-olds and ≥75-year-olds, but not in other age groups. Cancer death rates were 2% lower than expected from March to June among 55- to 64-year-olds and 2% to 3% lower from March to July and December among ≥75-year-olds. Among ≥75-year-olds, colorectal cancer death rates were lower from March to June [rate ratios (RR) = 0.94–0.96; P < 0.05]; however, lung cancer death rates were 5% lower across each month (all RRs = 0.95; P < 0.05).
Conclusions:
In the US, cancer death rates based on the underlying cause of death were broadly similar to expected rates from March to December 2020. However, cancer death rates were lower than expected among 55- to 64-year-olds and ≥75-year-olds, likely due to COVID-19 as a competing cause of death.
Impact:
Cancer mortality rates from 2020 should be interpreted with caution.
Data from Assessing the Relationship between Biomarkers of Exposure and Biomarkers of Potential Harm: PATH Study Wave 1 (2013 to 2014)
Authors: Chang Cindy M. , Thakur Sapna , Montes de Oca Ruben , Rostron Brian L. , Cheng Yu-Ching , Wright M. Jerry , van Bemmel Dana M. , Wang Lanqing , Hatsukami Dorothy K.
Published: August 2024

AbstractBackground:
The adequacy of biomarkers of potential harm (BOPH) for assessing tobacco products was explored based on their ability to distinguish tobacco use from non-use, change with cessation, and to show biological gradient.
Methods:
The sample included individuals with biomarker data in wave 1 of the Population Assessment of Tobacco Health study who never used tobacco, currently smoke cigarettes exclusively, used to smoke cigarettes exclusively (quit in past 12 months), currently use smokeless tobacco exclusively, and currently use e-cigarettes exclusively. We compared BOPH levels between groups and assessed the relationships between log-transformed biomarkers of exposure [BOE; total nicotine equivalents including seven nicotine metabolites (TNE-7), 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanonol (NNAL), N-acetyl-S-(2-cyanoethyl)-L-cysteine, 1-hydroxypyrene, cadmium, and serum cotinine (SCOT)], and BOPH [high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), fibrinogen, soluble intercellular adhesion molecule-1 (sICAM-1), and 8-isoprostane].
Results:
Among people who smoke, both sICAM-1 and 8-isoprostane distinguished smoking from non-use and were associated with all six BOE. Among people who use smokeless tobacco, 8-isoprostane was associated with TNE-7 and NNAL whereas hs-CRP was associated with SCOT. Among people who use e-cigarettes, no associations between BOPH and BOE were observed.
Conclusions:
Both sICAM-1 and 8-isoprostane may be useful for assessing the use or changes in use of some tobacco products. Studies examining their predictive validity could further strengthen our understanding of these two biomarkers.
Impact:
We found that two biomarkers of potential harm, soluble intercellular adhesion molecule-1 and 8-isoprostane, may have utility in studies assessing the potential harm of tobacco use in absence of long-term epidemiological studies.
Data from Prevalence of Epidermal Growth Factor Receptor and Programmed Death Ligand 1 Testing in a Population-Based Lung Cancer Surgical Resection Cohort from 2018 to 2022
Authors: Smeltzer Matthew P. , Akinbobola Olawale A. , Ray Meredith A. , Fehnel Carrie , Saulsberry Andrea , Dortch Kourtney R. , Pimenta Kelly , Matthews Anberitha T. , Osarogiagbon Raymond U.
Published: August 2024

AbstractBackground:
Biomarker-directed therapy requires biomarker testing. We assessed the patterns of epidermal growth factor receptor (EGFR) and programmed death ligand 1 (PDL1) testing in a non–small cell lung cancer (NSCLC) resection cohort. We hypothesized that testing would increase but be unevenly distributed across patient-, provider- and institution-level demographics.
Methods:
We examined the population-based Mid-South Quality of Surgical Resection (MS-QSR) cohort of NSCLC resections. We evaluated the proportions receiving EGFR and PDL1 testing before and after approval of biomarker-directed adjuvant therapy (2018–2020 vs. 2021–2022). We used association tests and logistic regression to compare factors.
Results:
From 2018 to 2022, 1,687 patients had NSCLC resection across 12 MS-QSR institutions: 1,045 (62%) from 2018 to 2020 and 642 (38%) from 2021 to 2022. From 2018 to 2020, 11% had EGFR testing versus 38% in 2021 to 2022 (56% in those meeting ADAURA trial inclusion criteria, P < 0.0001). From 2018 to 2020, 8% had PDL1 testing versus 20% in 2021 to 2022 (P < 0.0001). EGFR testing did not significantly differ by age (P = 0.07), sex (P = 0.99), race (P = 0.33), or smoking history (P = 0.28); PDL1 testing did not differ significantly by age (P = 0.47), sex (P = 0.41), race (P = 0.51), or health insurance (P = 0.07). Testing was significantly less likely in nonteaching and non–Commission on Cancer–accredited hospitals and after resection by cardiothoracic or general surgeons (vs. general thoracic surgeons; all P < 0.05).
Conclusions:
EGFR and PDL1 testing increased after approval of biomarker-directed adjuvant therapies. However, testing rates were still suboptimal and differed by institutional- and provider-level factors.
Impact:
The association of institutional, pathologist, and surgeon characteristics with differences in testing demonstrate the need for more standardization in testing processes.
Supplementary Fig. S2 from A Proof-of-Concept Study of Sequential Treatment with the HDAC Inhibitor Vorinostat following BRAF and MEK Inhibitors in BRAFV600-Mutated Melanoma
Authors: Embaby Alaa , Huijberts Sanne C.F.A. , Wang Liqin , Leite de Oliveira Rodrigo , Rosing Hilde , Nuijen Bastiaan , Sanders Joyce , Hofland Ingrid , van Steenis Charlaine , Kluin Roelof J.C. , Lieftink Cor , Smith Christopher G. , Blank Christian U. , van Thienen Johannes V. , Haanen John B.A.G. , Steeghs Neeltje , Opdam Frans L. , Beijnen Jos H. , Huitema Alwin D.R. , Bernards Rene , Schellens Jan H.M. , Wilgenhof Sofie
Published: August 2024

Supplementary Fig. S2. Vorinostat plasma concentrations over time on days 1 and 14 of the first treatment cycle.
Supplementary Figure S10 from Plasma Proteomic Signature Predicts Myeloid Neoplasm Risk
Authors: Tran Duc , Beeler J. Scott , Liu Jie , Wiley Brian , Chan Irenaeus C.C. , Xin Zilan , Kramer Michael H. , Batchi-Bouyou Armel L. , Zong Xiaoyu , Walter Matthew J. , Petrone Giulia E.M. , Chlamydas Sarantis , Ferraro Francesca , Oh Stephen T. , Link Daniel C. , Busby Ben , Cao Yin , Bolton Kelly L.
Published: August 2024

Two-sample Mendelian randomization (MR) analyses of the causal effect of plasma protein levels on myeloid neoplasm (MN) risk in the UKBB (N=381,485).
Supplementary Table 2 from Mapping the Single-Cell Differentiation Landscape of Osteosarcoma
Authors: Truong Danh D. , Weistuch Corey , Murgas Kevin A. , Admane Prasad , King Bridgette L. , Chauviere Lee Jes , Lamhamedi-Cherradi Salah-E. , Swaminathan Jyothishmathi , Daw Najat C. , Gordon Nancy , Gopalakrishnan Vidya , Gorlick Richard G. , Somaiah Neeta , Deasy Joseph O. , Mikos Antonios G. , Tannenbaum Allen , Ludwig Joseph
Published: August 2024

OS Cluster Marker Genes
Supplementary Table S1 from Bystander Effects, Pharmacokinetics, and Linker-Payload Stability of EGFR-Targeting Antibody-Drug Conjugates Losatuxizumab Vedotin and Depatux-M in Glioblastoma Models
Authors: Jain Sonia , Griffith Jessica I. , Porath Kendra A. , Rathi Sneha , Le Jiayan , Pasa Tugce I. , Decker Paul A. , Gupta Shiv K. , Hu Zeng , Carlson Brett L. , Bakken Katrina , Burgenske Danielle M. , Feldsien Thomas M. , Lefebvre Didier R. , Vaubel Rachael A. , Eckel-Passow Jeanette E. , Reilly Edward B. , Elmquist William F. , Sarkaria Jann N.
Published: August 2024

In vitro treatment doses
Supplementary Figure S11 from A 5-Hydroxymethylcytosine-Based Noninvasive Model for Early Detection of Colorectal Carcinomas and Advanced Adenomas: The METHOD-2 Study
Authors: Chang Wenju , Zhang Zhou , Jia Baoqing , Ding Kefeng , Pan Zhizhong , Su Guoqiang , Zhang Wei , Liu Tianyu , Zhong Yunshi , He Guodong , Ren Li , Wei Ye , Li Dongdong , Cui Xiaolong , Yang Jun , Shi Yixiang , Bissonnette Marc , He Chuan , Zhang Wei , Fan Jia , Xu Jianmin
Published: August 2024

Exploration of differential gene expression using TCGA.
Supplementary Appendix S1 from A 5-Hydroxymethylcytosine-Based Noninvasive Model for Early Detection of Colorectal Carcinomas and Advanced Adenomas: The METHOD-2 Study
Authors: Chang Wenju , Zhang Zhou , Jia Baoqing , Ding Kefeng , Pan Zhizhong , Su Guoqiang , Zhang Wei , Liu Tianyu , Zhong Yunshi , He Guodong , Ren Li , Wei Ye , Li Dongdong , Cui Xiaolong , Yang Jun , Shi Yixiang , Bissonnette Marc , He Chuan , Zhang Wei , Fan Jia , Xu Jianmin
Published: August 2024

Study protocol
Figure 3 from Anti-EGFR Antibody–Drug Conjugate Carrying an Inhibitor Targeting CDK Restricts Triple-Negative Breast Cancer Growth
Authors: Cheung Anthony , Chenoweth Alicia M. , Johansson Annelie , Laddach Roman , Guppy Naomi , Trendell Jennifer , Esapa Benjamina , Mavousian Antranik , Navarro-Llinas Blanca , Haider Syed , Romero-Clavijo Pablo , Hoffmann Ricarda M. , Andriollo Paolo , Rahman Khondaker M. , Jackson Paul , Tsoka Sophia , Irshad Sheeba , Roxanis Ioannis , Grigoriadis Anita , Thurston David E. , Lord Christopher J. , Tutt Andrew N. J. , Karagiannis Sophia N.
Published: August 2024

Stochastic conjugation of cetuximab to CDK inhibitor and ADC internalization in live breast cancer cells. A, Flow cytometric evaluation of surface EGFR expression (TNBC: MDA-MB-468, HCC1143, HCC1806, MDA-MB-231, HCC1937, SUM149, and CAL51; HER2+: SKBR3; ER+: MCF7, T47D; nontumorigenic epithelial cell model: MCF10A; immune cell model: human B lymphocytes RPMI8866, RPMI8226, and monocytic cell line U937; human primary melanocyte: melanocyte). B, EGFR mRNA expression from the Cancer Cell Line Encyclopedia database showed a positive correlation with surface EGFR measured by flow cytometry in A (Spearman’s rank coefficient, r = 0.723). A high level of correlation was found between EGFR and cyclin E (r = 0.738), but not with cyclin A or CDK2. Nonsignificant P values are marked as NS. C, Top, Schematic diagram of stochastic ADC conjugation by antibody reduction with TCEP and then conjugation to SNS-032 via MC-Val–Ala-PAB. Middle, HIC analysis confirmed an average DAR of 4.4. Bottom, SEC trace indicates negligible ADC aggregation and minimal free linker–payload (less than 0.8%). D, Surface plasmon resonance analysis demonstrated similar binding affinity (KD) for cetuximab (0.73 nmol/L) and ADC (1.28 nmol/L). Isotype IgG1 and isotype ADC showed no measurable binding. E, Monitoring internalization of Fabfluor-pH-labeled cetuximab, ADC, or isotype control (10 nmol/L) by Incucyte live-cell imaging. Phase and red fluorescence time-course images were captured for 24 hours. Images of internalized antibody display in cytosolic, low pH lysosomal vesicle-associated red fluorescence in cells. Scale bar, 0.2 mm. F, Cells were seeded in Matrigel for 5 days, allowing the formation of spheroids. Fabfluor-pH-labeled antibodies or ADC (10 nmol/L) were introduced in the Matrigel and showed rapid internalization in EGFR-high MDA-MB-468 and MDA-MB-231, whereas EGFR-low CAL51 displayed little red fluorescence signals. A low level of internalization was observed for isotype or isotype-ADC controls. Scale bar, 0.5 mm. P values determined by two-tailed unpaired t test of three independent experiments compared with isotype control.
Supplementary Figure S8 from Clinical Implications and Molecular Features of Extracellular Matrix Networks in Soft Tissue Sarcomas
Authors: Pankova Valeriya , Krasny Lukas , Kerrison William , Tam Yuen B. , Chadha Madhumeeta , Burns Jessica , Wilding Christopher P. , Chen Liang , Chowdhury Avirup , Perkins Emma , Lee Alexander T.J. , Howell Louise , Guljar Nafia , Sisley Karen , Fisher Cyril , Chudasama Priya , Thway Khin , Jones Robin L. , Huang Paul H.
Published: August 2024

Supplementary Figure S8. Association of the proteoglycan gene expression score with survival outcomes in The Cancer Genome Atlas sarcoma (TCGA- SARC) cohort.
Supplementary Figure S6 from Clinical Implications and Molecular Features of Extracellular Matrix Networks in Soft Tissue Sarcomas
Authors: Pankova Valeriya , Krasny Lukas , Kerrison William , Tam Yuen B. , Chadha Madhumeeta , Burns Jessica , Wilding Christopher P. , Chen Liang , Chowdhury Avirup , Perkins Emma , Lee Alexander T.J. , Howell Louise , Guljar Nafia , Sisley Karen , Fisher Cyril , Chudasama Priya , Thway Khin , Jones Robin L. , Huang Paul H.
Published: August 2024

Supplementary Figure S6. Identification of LCP1 as a candidate prognostic factor in LMS.
Supplementary Figure S5 from Clinical Implications and Molecular Features of Extracellular Matrix Networks in Soft Tissue Sarcomas
Authors: Pankova Valeriya , Krasny Lukas , Kerrison William , Tam Yuen B. , Chadha Madhumeeta , Burns Jessica , Wilding Christopher P. , Chen Liang , Chowdhury Avirup , Perkins Emma , Lee Alexander T.J. , Howell Louise , Guljar Nafia , Sisley Karen , Fisher Cyril , Chudasama Priya , Thway Khin , Jones Robin L. , Huang Paul H.
Published: August 2024

Supplementary Figure S5. Assessment of leiomyosarcoma (LMS) extracellular matrix (ECM) solution and its effects of cell migration.
Supplementary Figure S4 from Clinical Implications and Molecular Features of Extracellular Matrix Networks in Soft Tissue Sarcomas
Authors: Pankova Valeriya , Krasny Lukas , Kerrison William , Tam Yuen B. , Chadha Madhumeeta , Burns Jessica , Wilding Christopher P. , Chen Liang , Chowdhury Avirup , Perkins Emma , Lee Alexander T.J. , Howell Louise , Guljar Nafia , Sisley Karen , Fisher Cyril , Chudasama Priya , Thway Khin , Jones Robin L. , Huang Paul H.
Published: August 2024

Supplementary Figure S4. Differentially expressed matrisome proteins in synovial sarcoma (SS) cases (n=42) that either received preoperative treatment or were untreated.
Supplementary Figure S3 from Clinical Implications and Molecular Features of Extracellular Matrix Networks in Soft Tissue Sarcomas
Authors: Pankova Valeriya , Krasny Lukas , Kerrison William , Tam Yuen B. , Chadha Madhumeeta , Burns Jessica , Wilding Christopher P. , Chen Liang , Chowdhury Avirup , Perkins Emma , Lee Alexander T.J. , Howell Louise , Guljar Nafia , Sisley Karen , Fisher Cyril , Chudasama Priya , Thway Khin , Jones Robin L. , Huang Paul H.
Published: August 2024

Supplementary Figure S3. Expression of matrisome proteins in a desmoid tumour cohort (n=37) split into 3 groups based on the % tumour content.
Supplementary Table S2 from A Pooled Analysis of Treatment-Free Survival in Advanced Renal Cell Carcinoma
Authors: Chang Elaine , Zhou Jiaxi , Song Chi , Gittleman Haley , Fernandes Laura , Weinstock Chana , Atkins Michael B. , Agrawal Sundeep , Sridhara Rajeshwari , Gormley Nicole , Tang Shenghui , Suzman Daniel L. , Amiri-Kordestani Laleh , Kluetz Paul G. , Pazdur Richard , Rini Brian I. , McDermott David F. , Regan Meredith M.
Published: August 2024

Treatment-Free Survival and Time on Protocol Therapy With and Without Toxicity in IO-VEGF TKI and Sunitinib Groups
Supplementary Figure S3 from A Pooled Analysis of Treatment-Free Survival in Advanced Renal Cell Carcinoma
Authors: Chang Elaine , Zhou Jiaxi , Song Chi , Gittleman Haley , Fernandes Laura , Weinstock Chana , Atkins Michael B. , Agrawal Sundeep , Sridhara Rajeshwari , Gormley Nicole , Tang Shenghui , Suzman Daniel L. , Amiri-Kordestani Laleh , Kluetz Paul G. , Pazdur Richard , Rini Brian I. , McDermott David F. , Regan Meredith M.
Published: August 2024

Time on Each Drug as Monotherapy in IO-TKI Combination