bluuebunny/crossref_metadata_2025 huggingface dataset:
Supplementary Table S3 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

Conditional logistic regression models measuring odds of having a Grade 3 to -4 cardiovascular disease (CVD) outcome of interest based on exposure of interest with and without adjustments for each of the ten Adverse Childhood Experiences (ACEs)
Supplementary Table S5 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

SchildkrautB metadata
Supplementary Figure 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

SchildkrautB subtype labeling comparisons
Supplementary Figure S16 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 comparing consensusOV subtype specific survival by dataset
Supplementary Figure S12 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 k4
Supplemental Table 5 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 5: EGFR and PDL1 testing adjusting for rural or metropolitan hospitals.
Supplemental Table 2 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 2: Cohort Characteristics by EGFR Testing Status.
Supplementary Figure 2 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 2. Scatter plots of log-transformed BOPH ((A) sICAM (B) hs-CRP (C) IL-6 (D) Fibro (E) 8-Isoprostane) and log-transformed TNE-7 by Tobacco Use
Table S8 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 S8 shows hazard ratios for prostate cancer diagnosis, excluding men with HIV without Aid for AIDS registration.
Table S6 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 S6 shows rate ratios for prostate specific antigen (PSA) testing and prostate biopsies, excluding HIV positive men not in Aid for AIDS registration.
Figure 1 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

Selection of study population. The flow diagram shows the number of individuals who were excluded from the analysis and the reasons for exclusion.
Table 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

Table S1 shows the discrepancies in sample characteristics of the 18 studies used to compare the discrimination of textural features with PMD. The sample characteristics include the country where the study was initiated, sample size, validation methods, mean age at mammography and baseline, mean years that mammograms were taken before diagnosis, menopausal status, and variables match and adjustment.
Supplementary Figure 7 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 energy intake with all-cause mortality and cardiovascular disease, total cancer and physical activity, diet and adiposity related cancer incidence.Supplementary Figure 7(A-D) shows the joint association between physical activity and energy intake with all-cause mortality, cardiovascular disease, total cancer, and physical activity, diet, and adiposity-related cancer incidence (n=58,470).
Supplementary Figure 4 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 among never-smoker participants.Supplementary Figure 4(A-D) shows the joint association between physical activity and diet quality score with all-cause mortality, cardiovascular disease, total cancer, and physical activity, diet, and adiposity-related cancer incidence within the subgroup of never-smoker participants, consisting of 46,353 individuals.
Supplemental Figure S3 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 Figure S3: A: Rural Tobacco-Associated Cancer Incidence Rates, 2015 to 2019; B: Rural-Urban Tobacco-Associated Cancer Incidence Rate Differences, 2015 to 2019; C: Rural HPV-Associated Cancer Incidence Rates, 2015 to 2019; D: Rural-Urban HPV-Associated Cancer Incidence Rate Differences, 2015 to 2019; E: Rural Alcohol-Associated Cancer Incidence Rates, 2015 to 2019; F: Rural-Urban Alcohol-Associated Cancer Incidence Rate Differences, 2015 to 2019
Supplemental Figure S2 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 Figure S2: A: Rural Cervical Cancer Incidence Rates, 2015 to 2019 ; B: Rural-Urban Cervical Cancer Incidence Rate Differences, 2015 to 2019 ; C: Rural Female Breast Cancer Incidence Rates, 2015 to 2019; D: Rural-Urban Female Breast Cancer Incidence Rate Differences, 2015 to 2019; E: Rural Prostate Cancer Incidence Rates, 2015 to 2019 ;F: Rural-Urban Prostate Cancer Incidence Rate Differences, 2015 to 2019
Supplemental Figure S1 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 Figure S1: A: Rural All Cancer Incidence Rates, 2015 to 2019; B: Rural-Urban All Cancer Incidence Rate Differences, 2015 to 2019; C: Rural Lung Cancer Incidence Rates, 2015 to 2019; D: Rural-Urban Lung Cancer Incidence Rate Differences, 2015 to 2019; E: Rural Colorectal Cancer Incidence Rates, 2015 to 2019; F: Rural-Urban Colorectal Cancer Incidence Rate Differences, 2015 to 2019
Data from Racialized Economic Segregation and Treatment and Outcomes of Small Cell Lung Cancer
Authors: Bekele Bayu B. , Lian Min , Shrestha Pratibha , Nabi Oumarou , Kozower Benjamin , Baggstrom Maria Q. , Liu Ying
Published: August 2024

AbstractBackground:
Little is known about the role of residential segregation in the treatment and outcomes of small cell lung cancer (SCLC), a highly recalcitrant disease, among non-Hispanic White (NHW) and non-Hispanic Black (NHB) patients.
Methods:
We used the Surveillance, Epidemiology, and End Results database to identify men and women diagnosed with SCLC from January 2007 to December 2015 (n = 38,393). An Index of Concentration at the Extremes was computed to measure county-level racialized economic segregation and categorized into Quartile 1 (most privileged: highest concentration of high-income NHW residents) through Quartile 4 (least privileged: highest concentration of low-income NHB residents). Multilevel logistic regression was used to estimate the ORs for extensive-stage diagnosis and nonadherence to guideline-recommended treatment. HRs for lung cancer–specific and overall mortalities were computed using multilevel Cox regression.
Results:
Patients in the least privileged counties had higher risks of nonadherence to guideline-recommended treatment [OR = 1.23; 95% confidence interval (CI): 1.08–1.40; Ptrend < 0.01], lung cancer–specific mortality (HR = 1.08; 95% CI: 1.04–1.12; Ptrend < 0.01), and all-cause mortality (HR = 1.13; 95% CI: 1.09–1.17; Ptrend < 0.0001) compared with patients in the most privileged counties. Adjustment for treatment did not significantly reduce the association with mortality. These associations were comparable between NHB and NHW patients. Segregation was not significantly associated with extensive-stage diagnosis.
Conclusions:
The results suggest that living in the neighborhoods with higher proportions of low-income households and Black residents had adverse impacts on stage-appropriate treatment of and survival from SCLC.
Impact:
This highlights the need for improving the access to quality lung cancer care in the less privileged neighborhoods.
Data 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

AbstractBackground:
We examined the joint associations of diet and device-measured intensity-specific physical activity (PA) with all-cause mortality (ACM), cardiovascular disease (CVD), and cancer incidence.
Methods:
We used data from 79,988 participants from the UK Biobank, a population-based prospective cohort study. Light PA (LPA), moderate-to-vigorous PA (MVPA), vigorous PA (VPA), and total PA (TPA) were measured using a wrist-worn accelerometer. Diet quality score (DQS) was based on 10 foods and ranged from 0 (unhealthiest) to 100 (healthiest) points. We derived joint PA and diet variables. Outcomes were ACM, CVD, and cancer incidence including PA, diet and adiposity-related (PDAR) cancer.
Results:
During a median follow-up of 8 years, 2,863 deaths occurred, 11,053 participants developed CVD, 7,005 developed cancer, and 3,400 developed PDAR cancer. Compared with the least favorable referent group (bottom PA tertile/low DQS), participants with middle and high (total and intensity specific) PA, except for LPA, had lower ACM risk and incident CVD risk, regardless of DQS. For example, among middle and high VPA and high DQS groups, CVD HR were 0.79 (95% CI, 0.74–0.86) and 0.75 (95% CI, 0.69–0.82), respectively. The pattern of cancer results was less pronounced but in agreement with the ACM and CVD incidence findings (e.g., HR, 0.90, 95% CI, 0.81–0.99; 0.88, 0.79–0.98; and 0.82, 0.74–0.92 among high VPA for low, moderate, and high DQS groups, respectively).
Conclusions:
Device-measured PA reveals novel joint associations with diet on health outcomes.
Impact:
Our results emphasize the crucial role of PA in addition to a healthy diet for reducing chronic diseases and mortality risk.
Data 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

AbstractBackground:
Despite consistent improvements in cancer prevention and care, rural and urban disparities in cancer incidence persist in the United States. Our objective was to further examine rural–urban differences in cancer incidence and trends.
Methods:
We used the North American Association of Central Cancer Registries dataset to investigate rural–urban differences in 5-year age-adjusted cancer incidence (2015–2019) and trends (2000–2019), also examining differences by region, sex, race/ethnicity, and tumor site. Age-adjusted rates were calculated using SEER∗Stat 8.4.1, and trend analysis was done using Joinpoint, reporting annual percent changes (APC).
Results:
We observed higher all cancer combined 5-year incidence rates in rural areas (457.6 per 100,000) compared with urban areas (447.9), with the largest rural–urban difference in the South (464.4 vs. 449.3). Rural populations also exhibited higher rates of tobacco-associated, human papillomavirus–associated, and colorectal cancers, including early-onset cancers. Tobacco-associated cancer incidence trends widened between rural and urban from 2000 to 2019, with significant, but varying, decreases in urban areas throughout the study period, whereas significant rural decreases only occurred between 2016 and 2019 (APC = −0.96). Human papillomavirus–associated cancer rates increased in both populations until recently with urban rates plateauing whereas rural rates continued to increase (e.g., APC = 1.56, 2002–2019).
Conclusions:
Rural populations had higher overall cancer incidence rates and higher rates of cancers with preventive opportunities compared with urban populations. Improvements in these rates were typically slower in rural populations.
Impact:
Our findings underscore the complex nature of rural–urban disparities, emphasizing the need for targeted interventions and policies to reduce disparities and achieve equitable health outcomes.
Supplementary Table S3 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 Table S3. Results of RNA sequencing.
Supplementary Fig. S3 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. S3. 4-AOA plasma concentrations over time on days 1 and 14 of the first treatment cycle.
Supplementary Table S5 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

Association between plasma protein levels and risk of myeloid neoplasm (MN) with and without adjustment for clonal hematopoiesis (CH).
Supplementary Table S11 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 analyses of the causal effect of plasma protein levels on myeloid neoplasm (MN) risk in the UKBB.
Supplementary Table S1 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

List of genes with known relevance to hematologic cancer.
Supplementary Methods S1 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

Supplementary Methods
Supplementary Figure S6 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

Association between plasma protein levels and blood count parameters.
Supplementary Figure S4 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

Association between plasma proteins and common CH genes.
Supplementary Figure S2 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

Association between MN and plasma protein levels stratified by MN subtype (MPN vs. MDS/AML).
Supplementary Figure S1 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

Association between myeloid neoplasm and plasma protein levels stratified by disease subtype.
Supplementary Table S2 from Ultrasound-Guided Quantitative Fluorescence Molecular Endoscopy for Monitoring Response in Patients with Esophageal Cancer Following Neoadjuvant Chemoradiotherapy
Authors: Schmidt Iris , Zhao Xiaojuan , van der Waaij Anne M. , Meersma Gert Jan , Dijkstra Frederieke A. , Haveman Jan Willem , van Etten Boudewijn , Robinson Dominic J. , Kats-Ugurlu Gursah , Nagengast Wouter B.
Published: August 2024

Representativeness of Study Participants
Supplementary Table S1 from Ultrasound-Guided Quantitative Fluorescence Molecular Endoscopy for Monitoring Response in Patients with Esophageal Cancer Following Neoadjuvant Chemoradiotherapy
Authors: Schmidt Iris , Zhao Xiaojuan , van der Waaij Anne M. , Meersma Gert Jan , Dijkstra Frederieke A. , Haveman Jan Willem , van Etten Boudewijn , Robinson Dominic J. , Kats-Ugurlu Gursah , Nagengast Wouter B.
Published: August 2024

Overview of the absorption and scattering parameters used for tissue characterisation
Supplementary Table 3 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

MTL Cluster Marker Genes
Supplementary Figure 3 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

Cluster markers for each of the MTL clusters
Supplementary Figure S2 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

Sensitivity of EGFR non-amplified lines to MMAE and ADCs
Supplementary Figure 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 characterization of ADCs
Supplementary Figure S5 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

Performance of the 5hmC diagnostic model for CRC/AA by sex, tumor location, and mutation status.
Supplementary Figure S10 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

The interaction and/or co-modification modules identified from the candidate marker genes.
Supplementary Figure 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

Distribution of unique mapped reads in the 24-pooling libraries.
Figure 1 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

Basal-like/TNBC is associated with upregulated EGFR and G1/S-phase cell cycle genes. A, Gene expression analysis of EGFR, CCNA1, CCNE1, and CDK2 was stratified according to IHC-defined receptor status from five published databases, n = 6,173 primary tumors: Guy’s (TNBC vs. HER2+ vs. ER+, n = 131 vs. 32 vs. 14), Sweden Cancerome Analysis Network—Breast (SCAN-B) (n = 165 vs. 420 vs. 2,425), METABRIC (n = 101 vs. 117 vs. 347), The Cancer Genome Atlas (TCGA) (n = 112 vs. 158 vs. 426), International Cancer Genome Consortium (ICGC) (n = 73 vs. 4 vs. 182; refs. 26–30). CCNA1 was not included in the ICGC cohort, therefore marked as unavailable (n/a). The cohorts were analyzed by PAM50 classification [Basal-like (Basal), HER2, luminal A (LumA), luminal B (LumB), normal-like (Normal): Guy’s (n = 95, 28, 11, 10, 7), SCAN-B (n = 339, 327, 1657, 729, 221), METABRIC (n = 237, 181, 483, 383, 93), and TCGA (n = 232, 153, 345, 263, 91)]. All P values are compared against TNBC or Basal-like subtype. B, Expression of G1/S-phase genes were compared between low and high EGFR-expressing samples based on quartile ranges of gene expression values (EGFR-low, first quartile Q1; EGFR-high, fourth quartile Q4). Median-centered gene expression log2 values are shown. P values determined using Mann–Whitney U test. C, Gene expression was compared between matched pre-treatment and residual TNBC samples [KCL cohort: (n = 8); Royal Marsden Hospital cohort (n = 9); The Netherlands Cancer Institute cohort (n = 10)]. Cohort details: Supplementary Table S1. D, EGFR expression measured by flow cytometry in peripheral blood mononuclear cells (PBMC; n = 3) following Fc-receptor blocking solution.
Supplementary Table S3 from Chimeric Antigen Receptor T Cell with an Inducible Caspase-9 Suicide Gene Eradicates Uveal Melanoma Liver Metastases via B7-H3 Targeting
Authors: Ventin Marco , Cattaneo Giulia , Arya Shahrzad , Jia Jingyu , Gelmi Maria C. , Sun Yi , Maggs Luke , Ksander Bruce R. , Verdijk Robert M. , Boland Genevieve M. , Jenkins Russell W. , Haq Rizwan , Jager Martine J. , Wang Xinhui , Ryeom Sandra , Ferrone Cristina R.
Published: August 2024

Supplementary Table S3. Correlation between clinicopathological characteristics of patients with primary ocular UM tumors from the Leiden University Medical Center cohort and B7-H3 mRNA expression.
Figure S1 from Exposure–Response and Subgroup Analyses to Support Body Weight–Based Dosing of Brentuximab Vedotin in Children and Young Adults with Newly Diagnosed High-risk Classical Hodgkin Lymphoma
Authors: Zhang Zufei , Zhang Daping , Guo Wenchuan , Fenton Keenan , Narayanan Sujata , Jain Shweta , Jiang Joy , Castellino Sharon M. , Kelly Kara M. , Cole Peter D. , Keller Frank G. , Garg Amit , Chia Yen Lin
Published: August 2024

Body weight distribution of patients in the PK subpopulation and in those patients aged <13 years in the overall population who were not enrolled in the PK substudy.
Supplementary Table 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 Table S8: Summary of univariable (UVA) Cox regression analyses assessing the association of clinicopathological factors and dedifferentiated liposarcoma (DDLPS) subgroups with local recurrence-free survival (LRFS), overall survival (OS) and metastasis-free survival (MFS).
Supplementary Table S7 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 Table S7: Summary of statistical tests to assess the association between clinicopathological features and dedifferentiated liposarcoma (DDLPS) subgroups.
Supplementary Table 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 Table S5. Summary of multivariable (MVA) analyses assessing association of clinicopathological factors and LCP1 tertiles with local recurrence-free survival (LRFS) and overall survival (OS). Clinicopathological factors which were significantly associated with survival in univariable analyses were included in the MVA model.
Supplementary Table S10 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 Table S10. Clinicopathological characteristics of n=92 undifferentiated pleomorphic sarcoma (UPS) and dedifferentiated liposarcoma (DDLPS) cases. Summary features of the cohort. For continuous variables, the median, minimum (min) and maximum (max) values are indicated. For categorical variables, count and percentage are shown.
Supplementary Table S1 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

List of Trials Included in Analysis and Key Design Features
Supplementary Figure 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
Data 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

AbstractPurpose:
Detection of colorectal carcinomas at a time when there are more treatment options is associated with better outcomes. This prospective case–control study assessed the 5-hydroxymethylcytosine (5hmC) biomarkers in circulating cell-free DNA (cfDNA) for early detection of colorectal carcinoma and advanced adenomas (AA).
Experimental Design:
Plasma cfDNA samples from 2,576 study participants from the multicenter METHOD-2 study (NCT03676075) were collected, comprising patients with newly diagnosed colorectal carcinoma (n = 1,074), AA (n = 356), other solid tumors (n = 80), and non–colorectal carcinoma/AA controls (n = 1,066), followed by genome-wide 5hmC profiling using the 5hmC-Seal technique and the next-generation sequencing. A weighted diagnostic model for colorectal carcinoma (stage I–III) and AA was developed using the elastic net regularization in a discovery set and validated in independent samples.
Results:
Distribution of 5hmC in cfDNA reflected gene regulatory relevance and tissue of origin. Besides being confirmed in internal validation, a 96-gene model achieved an area under the curve (AUC) of 90.7% for distinguishing stage I–III colorectal carcinoma from controls in 321 samples from multiple centers for external validation, regardless of primary location or mutation status. This model also showed cancer-type specificity as well as high capacity for distinguishing AA from controls with an AUC of 78.6%. Functionally, differential 5hmC features associated with colorectal carcinoma and AA demonstrated relevance to colorectal carcinoma biology, including pathways such as calcium and MAPK signaling.
Conclusions:
Genome-wide mapping of 5hmC in cfDNA shows promise as a highly sensitive and specific noninvasive blood test to be integrated into screening programs for improving early detection of colorectal carcinoma and high-risk AA.
Data 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

AbstractPurpose:
A clinically meaningful attribute of some immune-oncology (IO) regimens is potential durable clinical benefit during a treatment-free interval. We characterize treatment-free survival (TFS) with and without ongoing toxicity in trials of frontline IO-VEGF tyrosine kinase inhibitor (TKI) combinations in patients with advanced renal cell carcinoma (aRCC).
Experimental Design:
Individual patient data were pooled by treatment arm from randomized trials submitted to the FDA evaluating IO-TKI combination in treatment-naïve aRCC with at least 30 months of median follow-up. OS, TFS, TFS with and without toxicity, and time to all protocol therapy cessation were assessed. TFS was estimated by 30-month restricted mean times, defined as area between Kaplan–Meier curves for two time-to-event endpoints originating at randomization: time to all protocol therapy cessation and time to subsequent systemic therapy initiation or death.
Results:
Three trials met criteria for analysis; 1,183 patients received IO-TKI versus 1,184 on control arms receiving TKI alone (sunitinib, SUN). IO-TKI and SUN groups spent 9% {2.7 months [95% confidence interval (CI), 1.8–3.5]} and 10% [2.9 months (95% CI, 2.1–3.8)] of the 30-month period alive and treatment-free, respectively. Mean TFS without grade ≥3 toxicity was 1.7 and 2.3 months in IO-TKI and SUN groups, respectively.
Conclusions:
In this post hoc partitioned survival analysis, TFS and TFS without toxicity appeared similar in the IO-TKI group compared with the SUN group. These findings may reflect contin-uation of TKI until progression per protocol design in all trials and discontinuation of IO after 2 years in two trials.

See related commentary by Stadler and Karrison, p. 3098
Data from Sequential Analysis of cfDNA Reveals Clonal Evolution in Patients with Neuroblastoma Receiving ALK-Targeted Therapy
Authors: Bobin Charles , Iddir Yasmine , Butterworth Charlotte , Masliah-Planchon Julien , Saint-Charles Alexandra , Bellini Angela , Bhalshankar Jaydutt , Pierron Gaelle , Combaret Valérie , Attignon Valéry , André Nicolas , Corradini Nadège , Dumont Benoit , Mansuy Ludovic , Khanfar Camille , Klein Sebastien , Briandet Claire , Plantaz Dominique , Millot Frederic , Thouvenin Sandrine , Aerts Isabelle , Ndounga-Diakou Lee Aymar , Laghouati Salim , Abbou Samuel , Jehanno Nina , Tissot Hubert , Renault Shufang , Baulande Sylvain , Raynal Virginie , Bozec Laurence , Bieche Ivan , Delattre Olivier , Berlanga Pablo , Schleiermacher Gudrun
Published: August 2024

AbstractPurpose:
The study of cell-free DNA (cfDNA) enables sequential analysis of tumor cell–specific genetic alterations in patients with neuroblastoma.
Experimental Design:
Eighteen patients with relapsing neuroblastoma having received lorlatinib, a third-generation ALK inhibitor, were identified (SACHA national registry and/or in the institution). cfDNA was analyzed at relapse for nine patients and sequentially for five patients (blood/bone marrow plasma) by performing whole-genome sequencing library construction followed by ALK-targeted ddPCR of the hotspot mutations [F1174L, R1275Q, and I1170N; variant allele fraction (VAF) detection limit 0.1%] and whole-exome sequencing (WES) to evaluate disease burden and clonal evolution, following comparison with tumor/germline WES.
Results:
Overall response rate to lorlatinib was 33% (CI, 13%–59%), with response observed in 6/10 cases without versus 0/8 cases with MYCN amplification (MNA). ALK VAFs correlated with the overall clinical disease status, with a VAF < 0.1% in clinical remission, versus higher VAFs (>30%) at progression. Importantly, sequential ALK ddPCR detected relapse earlier than clinical imaging. cfDNA WES revealed new SNVs, not seen in the primary tumor, in all instances of disease progression after lorlatinib treatment, indicating clonal evolution, including alterations in genes linked to tumor aggressivity (TP53) or novel targets (EGFR). Gene pathway analysis revealed an enrichment for genes targeting cell differentiation in emerging clones, and cell adhesion in persistent clones. Evidence of clonal hematopoiesis could be observed in follow-up samples.
Conclusions:
We demonstrate the clinical utility of combining ALK cfDNA ddPCR for disease monitoring and cfDNA WES for the study of clonal evolution and resistance mechanisms in patients with neuroblastoma receiving ALK-targeted therapy.
Table S4 from A Selective FGFR1/2 PROTAC Degrader with Antitumor Activity
Authors: Kong Ying , Zhao Xinyue , Wang Zhaofu , Yuan Siqi , Chen Sheng , Lou Shidi , Ma Shichao , Li Yunfeng , Wang Xinghao , Ge Yangfeng , Li Guobin , Yang Hongbing , Zhao Mengxi , Li Dandan , Zhang Hailong , Tan Wenfu , Wang Juan
Published: August 2024

Plasma protein binding data of BR-cpd7.
Supplementary Table 3 from INA03: A Potent Transferrin-Competitive Antibody–Drug Conjugate against CD71 for Safer Acute Leukemia Treatment
Authors: Bratti Manuela , Stubbs Elisa , Kolodych Sergii , Souchet Herve , Kelly Lois , Merlin Johanna , Marchal Michelle , Castellano Remy , Josselin Emmanuelle , Pasquer Hélène , Benajiba Lina , Puissant Alexandre , Koniev Oleksandr , Collette Yves , Belanger Coralie , Hermine Olivier , Monteiro Renato C. , Launay Pierre
Published: August 2024

Supplementary Table 3
Supplementary Table 2 from INA03: A Potent Transferrin-Competitive Antibody–Drug Conjugate against CD71 for Safer Acute Leukemia Treatment
Authors: Bratti Manuela , Stubbs Elisa , Kolodych Sergii , Souchet Herve , Kelly Lois , Merlin Johanna , Marchal Michelle , Castellano Remy , Josselin Emmanuelle , Pasquer Hélène , Benajiba Lina , Puissant Alexandre , Koniev Oleksandr , Collette Yves , Belanger Coralie , Hermine Olivier , Monteiro Renato C. , Launay Pierre
Published: August 2024

Supplementary Table 2
Supplementary Table 1 from INA03: A Potent Transferrin-Competitive Antibody–Drug Conjugate against CD71 for Safer Acute Leukemia Treatment
Authors: Bratti Manuela , Stubbs Elisa , Kolodych Sergii , Souchet Herve , Kelly Lois , Merlin Johanna , Marchal Michelle , Castellano Remy , Josselin Emmanuelle , Pasquer Hélène , Benajiba Lina , Puissant Alexandre , Koniev Oleksandr , Collette Yves , Belanger Coralie , Hermine Olivier , Monteiro Renato C. , Launay Pierre
Published: August 2024

Supplementary Table 1
Supplemental Figure S9 from Enhancing Standard of Care Chemotherapy Efficacy Using DNA-Dependent Protein Kinase (DNA-PK) Inhibition in Preclinical Models of Ewing Sarcoma
Authors: Collins Victor J. , Ludwig Katelyn R. , Nelson Ariana E. , Sundara Rajan Soumya , Yeung Choh , Vulikh Ksenia , Isanogle Kristine A. , Mendoza Arnulfo , Difilippantonio Simone , Karim Baktiar O. , Caplen Natasha J. , Heske Christine M.
Published: August 2024

Evaluation of the DNA-PK inhibitors plus TOP2 poisons in preclinical models of EWS
Supplemental Figure S8 from Enhancing Standard of Care Chemotherapy Efficacy Using DNA-Dependent Protein Kinase (DNA-PK) Inhibition in Preclinical Models of Ewing Sarcoma
Authors: Collins Victor J. , Ludwig Katelyn R. , Nelson Ariana E. , Sundara Rajan Soumya , Yeung Choh , Vulikh Ksenia , Isanogle Kristine A. , Mendoza Arnulfo , Difilippantonio Simone , Karim Baktiar O. , Caplen Natasha J. , Heske Christine M.
Published: August 2024

Defining parameters for the evaluation of effects of DNA-PK inhibitors plus TOP2 poisons in preclinical models of EWS
Figure S2 from Antitumor Activity of a Novel LAIR1 Antagonist in Combination with Anti-PD1 to Treat Collagen-Rich Solid Tumors
Authors: Rodriguez Bertha L. , Huang Jiawei , Gibson Laura , Fradette Jared J. , Chen Hung-I.H. , Koyano Kikuye , Cortez Czrina , Li Betty , Ho Carmence , Ashique Amir M. , Lin Vicky Y. , Crawley Suzanne , Roda Julie M. , Chen Peirong , Fan Bin , Kim Jeong , Sissons James , Sitrin Jonathan , Kaplan Daniel D. , Gibbons Don L. , Rivera Lee B.
Published: August 2024

TME-related gene expression in TME masks.
Supplemental Table 1 from Blockade of Discoidin Domain Receptor Signaling with Sitravatinib Reveals DDR2 as a Mediator of Neuroblastoma Pathogenesis and Metastasis
Authors: Rozen Esteban J. , Frantz William , Wigglesworth Kim , Vessella Theadora , Zhou Hong S. , Shohet Jason M.
Published: August 2024

Oligonucleotide primer sequences and shRNA clones used in this work.
Supplementary Table S1 from Targeting CD33+ Acute Myeloid Leukemia with GLK-33, a Lintuzumab–Auristatin Conjugate with a Wide Therapeutic Window
Authors: Satomaa Tero , Pynnönen Henna , Aitio Olli , Hiltunen Jukka O. , Pitkänen Virve , Lähteenmäki Tuula , Kotiranta Titta , Heiskanen Annamari , Hänninen Anna-Liisa , Niemelä Ritva , Helin Jari , Kuusanmäki Heikki , Vänttinen Ida , Rathod Ramji , Nieminen Anni I. , Yatkin Emrah , Heckman Caroline A. , Kontro Mika , Saarinen Juhani
Published: August 2024

Properties and IC50 values of leukemia cell lines and patient samples.
Supplemental Figure 8 from Blockade of Discoidin Domain Receptor Signaling with Sitravatinib Reveals DDR2 as a Mediator of Neuroblastoma Pathogenesis and Metastasis
Authors: Rozen Esteban J. , Frantz William , Wigglesworth Kim , Vessella Theadora , Zhou Hong S. , Shohet Jason M.
Published: August 2024

DDR1 and DDR2 in cancer cell lines.
Supplemental Figure 4 from Blockade of Discoidin Domain Receptor Signaling with Sitravatinib Reveals DDR2 as a Mediator of Neuroblastoma Pathogenesis and Metastasis
Authors: Rozen Esteban J. , Frantz William , Wigglesworth Kim , Vessella Theadora , Zhou Hong S. , Shohet Jason M.
Published: August 2024

Relative weight (%) along time (age) of mice from Kelly orthotopic tumor model from Figure 2D-F).
Table S4 from TNIK Inhibition Sensitizes TNIK-Overexpressing Lung Squamous Cell Carcinoma to Radiotherapy
Authors: Nguyen Triet , Carrieri Francesca A. , Connis Nick , Lafargue Audrey , Chang Jinhee , Chan Aaron , Shetty Amol C. , Song Yang , Hoang Tung , Jagtap Shreya , Chowdhury Dipanwita D. , Khan Muhammad A. , Gabrielson Kathleen L. , Rezaee Mohammad , Torres-Ayuso Pedro , Brognard John , Hann Christine L. , Tran Phuoc T.
Published: August 2024

Synergy scores obtained from LSCC cell lines treated with NCB-0846 in combination with cisplatin/etoposide with NCB-0846 as the pretreatment. Cell viability was evaluated 48h after the addition of cisplatin/etoposide.
Table S1 from TNIK Inhibition Sensitizes TNIK-Overexpressing Lung Squamous Cell Carcinoma to Radiotherapy
Authors: Nguyen Triet , Carrieri Francesca A. , Connis Nick , Lafargue Audrey , Chang Jinhee , Chan Aaron , Shetty Amol C. , Song Yang , Hoang Tung , Jagtap Shreya , Chowdhury Dipanwita D. , Khan Muhammad A. , Gabrielson Kathleen L. , Rezaee Mohammad , Torres-Ayuso Pedro , Brognard John , Hann Christine L. , Tran Phuoc T.
Published: August 2024

Culture media for cell lines used in the study.
Figure S13 from TNIK Inhibition Sensitizes TNIK-Overexpressing Lung Squamous Cell Carcinoma to Radiotherapy
Authors: Nguyen Triet , Carrieri Francesca A. , Connis Nick , Lafargue Audrey , Chang Jinhee , Chan Aaron , Shetty Amol C. , Song Yang , Hoang Tung , Jagtap Shreya , Chowdhury Dipanwita D. , Khan Muhammad A. , Gabrielson Kathleen L. , Rezaee Mohammad , Torres-Ayuso Pedro , Brognard John , Hann Christine L. , Tran Phuoc T.
Published: August 2024

Mechanisms of radiosensitization via TNIK inhibition.
Supplemental Table 1 from Colon Age: A Metric for Whether and How to Screen Male Veterans for Early-Onset Colorectal Cancer
Authors: Imperiale Thomas F. , Myers Laura J. , Barker Barry C. , Stump Timothy E. , Daggy Joanne K.
Published: August 2024

Supplemental Table 1: Final Models