bluuebunny/crossref_metadata_2025 huggingface dataset:
Supplementary Figure S1 from The Influence of DNA Repair Genes and Prenatal Tobacco Exposure on Risk of Childhood Acute Lymphoblastic Leukemia: A Gene–Environment Interaction Study
Authors: Wang Xinran , Zhong Charlie , Ma Xiaomei , Metayer Catherine , Mancuso Nicolas , Gauderman W. James , Wiemels Joseph L.
Published: January 2025

Supplementary Figure 1 shows bulk tissue expression for RECQL. This gene is expressed highly in pre-B lymphoblasts which is the target for transformation in childhood ALL.
Table S1 from Assessing 1-Year Comorbidity Prevalence and Its Survival Implications in Medicare Beneficiaries Diagnosed with Cancer: Insights from a New SEER–Medicare Resource
Authors: Noone Anne-Michelle , Mariotto Angela B. , Hong Yoon Duk , Enewold Lindsey
Published: January 2025

Supplementary Table 1 shows codes used to classify surgery types by cancer
Table S6 from Physical Activity during Adolescence and Early Adulthood and Breast Cancer Risk before Age 40 Years
Authors: Kehm Rebecca D. , Genkinger Jeanine M. , Knight Julia A. , MacInnis Robert J. , Liao Yuyan , Li Shuai , Weideman Prue C. , Chung Wendy K. , Kurian Allison W. , Colonna Sarah V. , Andrulis Irene L. , Buys Saundra S. , Daly Mary B. , John Esther M. , Hopper John L. , Terry Mary Beth
Published: January 2025

Hazard ratios and 95% confidence intervals for the joint association of recreational physical activity during adolescence and early adulthood with breast cancer risk before the age of 40 years in the full cohort, adjusted for breast cancer risk factors measured at study enrollment, n = 26,348
Table S4 from Physical Activity during Adolescence and Early Adulthood and Breast Cancer Risk before Age 40 Years
Authors: Kehm Rebecca D. , Genkinger Jeanine M. , Knight Julia A. , MacInnis Robert J. , Liao Yuyan , Li Shuai , Weideman Prue C. , Chung Wendy K. , Kurian Allison W. , Colonna Sarah V. , Andrulis Irene L. , Buys Saundra S. , Daly Mary B. , John Esther M. , Hopper John L. , Terry Mary Beth
Published: January 2025

Comparison of individuals included in versus excluded from the full cohort
Table S3 from Physical Activity during Adolescence and Early Adulthood and Breast Cancer Risk before Age 40 Years
Authors: Kehm Rebecca D. , Genkinger Jeanine M. , Knight Julia A. , MacInnis Robert J. , Liao Yuyan , Li Shuai , Weideman Prue C. , Chung Wendy K. , Kurian Allison W. , Colonna Sarah V. , Andrulis Irene L. , Buys Saundra S. , Daly Mary B. , John Esther M. , Hopper John L. , Terry Mary Beth
Published: January 2025

Average metabolic equivalents per week of recreational physical activity during adolescence and early adulthood by decade of birth year
Table S2 from Physical Activity during Adolescence and Early Adulthood and Breast Cancer Risk before Age 40 Years
Authors: Kehm Rebecca D. , Genkinger Jeanine M. , Knight Julia A. , MacInnis Robert J. , Liao Yuyan , Li Shuai , Weideman Prue C. , Chung Wendy K. , Kurian Allison W. , Colonna Sarah V. , Andrulis Irene L. , Buys Saundra S. , Daly Mary B. , John Esther M. , Hopper John L. , Terry Mary Beth
Published: January 2025

Cross-tabulation between recreational physical activity during adolescence and early adulthood in the full cohort (n = 26,348) and restricted cohort (n = 10,079)
Supplementary Table S2 from Diet Changes and Colorectal Cancer Risk in the UK Biobank
Authors: Hoang Tung , Cho Sooyoung , Shin Aesun
Published: January 2025

Assessment of Cox proportional hazard assumption in relation to dietary change and colorectal cancer risk
Supplementary Table S1 from Changes in Breast Cancer Screening Prevalence in the United States during the COVID-19 Pandemic, 2018 to 2022
Authors: Ashad-Bishop Kilan C. , Star Jessica , Giaquinto Angela N. , Smith Robert A. , Jemal Ahmedin , Bandi Priti
Published: January 2025

Supplementary Table S1: Enumeration of Included and Excluded Respondents
Supplementary Table S11 from Diet Changes and Colorectal Cancer Risk in the UK Biobank
Authors: Hoang Tung , Cho Sooyoung , Shin Aesun
Published: January 2025

Associations between dietary intake and colorectal cancer risk among 24,458 men and 26,182 women who had dietary data at both baseline and follow-up visit
Table S6 from Risk of Developing a Subsequent Primary Cancer among Adult Cancer Survivors
Authors: Warkentin Matthew T. , Cheung Winson Y. , Brenner Darren R. , O’Sullivan Dylan E.
Published: January 2025

Supplemental Table 6. Risk of developing a subsequent primary cancer by follow-up period and year of first primary cancer diagnosis. Follow-up was broken into four contiguous periods from the start of the survival, defined as six months after diagnosis of the first primary cancer, until the end of follow-up. Observed and expected number of subsequent primary cancers and the standardized incidence ratios with confidence intervals are reported according to first primary cancer site and sex.
Table S5 from Risk of Developing a Subsequent Primary Cancer among Adult Cancer Survivors
Authors: Warkentin Matthew T. , Cheung Winson Y. , Brenner Darren R. , O’Sullivan Dylan E.
Published: January 2025

Supplemental Table 5. Standardized incidence ratios (SIR) and 95% confidence intervals for the risk of a different-site subsequent primary cancer by year of first primary cancer diagnosis and sex.
Table S1 from Risk of Developing a Subsequent Primary Cancer among Adult Cancer Survivors
Authors: Warkentin Matthew T. , Cheung Winson Y. , Brenner Darren R. , O’Sullivan Dylan E.
Published: January 2025

Supplemental Table 1. Cancer site definitions based on those used in the Canadian Cancer Statistics (CCS) reports.
Figure S3 from Risk of Developing a Subsequent Primary Cancer among Adult Cancer Survivors
Authors: Warkentin Matthew T. , Cheung Winson Y. , Brenner Darren R. , O’Sullivan Dylan E.
Published: January 2025

Supplemental Figure 3. Cumulative incidence functions for the risk of a site-specific subsequent primary cancer. Cumulative incidences are presented for the secondary primary cancers (SPC) sites with the five highest estimated risks at 10-years since the time of surviving the first primary cancer (FPC). We present the 12 most common FPC sites. Panel titles include the total number of FPC for the given cancer site.
Figure S2 from Risk of Developing a Subsequent Primary Cancer among Adult Cancer Survivors
Authors: Warkentin Matthew T. , Cheung Winson Y. , Brenner Darren R. , O’Sullivan Dylan E.
Published: January 2025

Supplemental Figure 2. Number of cancer survivors over time by cancer site. The number of cancer survivors for each cancer site based on the year of the first primary cancer (FPC) diagnosis in Alberta (2004 to 2020). Cancer sites are ordered left-to-right based on the total number of cancer survivors over the entire period. For each cancer site, bars are ordered left-to-right from 2004 (lightest) to 2020 (darkest). A minimum of 6 months of survival is required for our definition of cancer survivors, so only cancers diagnosed between January and June of 2020 were potentially eligible for inclusion.
Supplementary Table 2 from Alcohol and Cannabis Use Associated with Cardiometabolic Biomarkers among “All of Us” Cancer Survivors
Authors: Arizpe Angel , Chapman Tiffany M. , Rodriguez Claudia , Carvajal Alberto , Queen Katelyn J. , Navarro Stephanie , Ochoa-Dominguez Carol Y. , Kim Sue E. , Toledo-Corral Claudia M. , Farias Albert J.
Published: January 2025

Supplementary Table 2: Exploration of missing outcome data by exposure variables
Supplementary Table S1 from Biomarkers of Nicotine and Toxicant Exposure by E-Liquid Nicotine Concentration Level among US Adult Exclusive E-Cigarette Users
Authors: Dai Hongying Daisy , Reyes Sara , Buckley James , Maloney Patrick
Published: January 2025

Supplementary Table S1 describes sample characteristics by “I Don’t Know”, nicotine, and no-nicotine e-cigarette use groups.
Supplementary Table S5 from Characterization of the Biological Variability of the Angiome Biomarkers over Time in Healthy Participants
Authors: Liu Yingmiao , Li Jiatong , Lyu Jing , Howard Lauren E. , Sibley Alexander B. , Starr Mark D. , Brady John C. , Arrowood Christy , Kohn Elise C. , Ivy S. Percy , Hurwitz Herbert I. , Abbruzzese James L. , Owzar Kouros , Nixon Andrew B.
Published: January 2025

Supplementary Table S5 shows three potential measures of variance.
Supplementary Table S2 from Characterization of the Biological Variability of the Angiome Biomarkers over Time in Healthy Participants
Authors: Liu Yingmiao , Li Jiatong , Lyu Jing , Howard Lauren E. , Sibley Alexander B. , Starr Mark D. , Brady John C. , Arrowood Christy , Kohn Elise C. , Ivy S. Percy , Hurwitz Herbert I. , Abbruzzese James L. , Owzar Kouros , Nixon Andrew B.
Published: January 2025

Supplementary Table S2 shows the association of sex with biomarker levels.
Supplementary Table S1 from Characterization of the Biological Variability of the Angiome Biomarkers over Time in Healthy Participants
Authors: Liu Yingmiao , Li Jiatong , Lyu Jing , Howard Lauren E. , Sibley Alexander B. , Starr Mark D. , Brady John C. , Arrowood Christy , Kohn Elise C. , Ivy S. Percy , Hurwitz Herbert I. , Abbruzzese James L. , Owzar Kouros , Nixon Andrew B.
Published: January 2025

Supplemental Table S1 shows the association of age with biomarker levels.
Supplementary Figure S2 from Characterization of the Biological Variability of the Angiome Biomarkers over Time in Healthy Participants
Authors: Liu Yingmiao , Li Jiatong , Lyu Jing , Howard Lauren E. , Sibley Alexander B. , Starr Mark D. , Brady John C. , Arrowood Christy , Kohn Elise C. , Ivy S. Percy , Hurwitz Herbert I. , Abbruzzese James L. , Owzar Kouros , Nixon Andrew B.
Published: January 2025

Supplementary Figure S2 show intra- and inter-participants variability.
Supplementary Table S3 from Small Area Estimation of PSA Testing in US States and Counties
Authors: Liu Benmei , Pleis John R. , Khan Diba , Parsons Van L. , Lee Richard , Cai Bill , Town Machell , Feuer Eric J. , He Yulei
Published: January 2025

Empirical percentiles for county-level estimates of Prostate Specific Antigen (PSA) screening rate among men aged 55-69. This table presents summary statistics for county-level NHIS direct estimates, BRFSS direct estimates, and final modeled estimated for the two data periods, 2012-2016 and 2018-2019.
Supplementary Figure S4 from Small Area Estimation of PSA Testing in US States and Counties
Authors: Liu Benmei , Pleis John R. , Khan Diba , Parsons Van L. , Lee Richard , Cai Bill , Town Machell , Feuer Eric J. , He Yulei
Published: January 2025

Model diagnosis plots from the 2018-2019 county-level delta model. This figure shows the trace plot, the autocorrelation plot, and the density plot of the MCMC values for the parameter associated with covariate “Dentist rate per 100k population”.
Supplementary Table S2 from Treatment and Survival Differences between Patients with Invasive Lobular Carcinoma Versus Invasive Ductal Carcinoma of the Breast
Authors: Anampa Jesus D. , Lin Shuwen , Obeng-Gyasi Samilia , Xue Xiaonan
Published: January 2025

Supplementary Table S2 shows multivariate analysis of breast cancer-specific survival in the presence of competing events (non-breast cancer death) in patients with invasive ductal carcinoma and invasive lobular carcinoma using Fine and Gray model. Abbreviations: sHR, Subdistribution hazard ratio: CI, confidence interval; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; NH, Non-Hispanic; k, thousand dollars; BCS, breast-conserving surgery; HR, hormone receptor; ER, estrogen receptor; HER2 human epidermal growth factor receptor 2
Supplementary Table S1 from Treatment and Survival Differences between Patients with Invasive Lobular Carcinoma Versus Invasive Ductal Carcinoma of the Breast
Authors: Anampa Jesus D. , Lin Shuwen , Obeng-Gyasi Samilia , Xue Xiaonan
Published: January 2025

Supplementary Table S1 shows multivariable analysis of breast cancer-specific survival in patients with invasive ductal carcinoma and invasive lobular carcinoma using Cox model. Abbreviations: HR, hazard ratio: CI, confidence interval; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; NH, Non-Hispanic; k, thousand dollars; BCS, breast-conserving surgery; HR, hormone receptor; ER, estrogen receptor; HER2 human epidermal growth factor receptor 2
Supplementary Figure S4 from Treatment and Survival Differences between Patients with Invasive Lobular Carcinoma Versus Invasive Ductal Carcinoma of the Breast
Authors: Anampa Jesus D. , Lin Shuwen , Obeng-Gyasi Samilia , Xue Xiaonan
Published: January 2025

Supplementary Figure S4 shows breast cancer-specific survival in patients with hormone positive invasive ductal carcinoma vs. invasive lobular carcinoma. A) Overall. B) Stage I. C) Stage II. D) Stage III. Abbreviations: BCSS, breast cancer specific survival.
Table S7 from Hispanic/Latino Ethnicity Is an Independent Predictor of Worse Survival for Gastric Cancer in a Multicenter Safety-Net Patient Population
Authors: Klingbeil Kyle D. , Dillon Dustin L. , Zarrinkhoo Erfan , Bechay Kirollos , Park Joon Y. , Rook Jordan M. , Mederos Michael A. , Girgis Mark D. , Chen Keren , Chen Kuan-Ting , Bastani Roshan , Manouchehr-Pour Shawdi , Dubé Priyanka , Viragh Karoly , Thomas Mariam , Chiu Victor , Kadera Brian E.
Published: January 2025

Cox model stratified by ‘Treatment Site’, bivariate model
Supplementary Methods from Biomarkers Suitable for Early Detection of Intrathoracic Cancers in Primary Care: A Systematic Review
Authors: Hamad Wasim , Grigore Bogdan , Walford Hugo , Peters Jaime , Alexandris Panos , Bonfield Stefanie , Standen Laura , Boscott Rachel , Behiyat Dawnya , Kuhn Isla , Neal Richard D. , Walter Fiona M. , Calanzani Natalia
Published: January 2025

Search Strategy on Medline and Embase
Table S1 from Hispanic/Latino Ethnicity Is an Independent Predictor of Worse Survival for Gastric Cancer in a Multicenter Safety-Net Patient Population
Authors: Klingbeil Kyle D. , Dillon Dustin L. , Zarrinkhoo Erfan , Bechay Kirollos , Park Joon Y. , Rook Jordan M. , Mederos Michael A. , Girgis Mark D. , Chen Keren , Chen Kuan-Ting , Bastani Roshan , Manouchehr-Pour Shawdi , Dubé Priyanka , Viragh Karoly , Thomas Mariam , Chiu Victor , Kadera Brian E.
Published: January 2025

Definitions of study variables
Data from Metabolic Syndrome and Risks of Breast Cancer Outcomes for Luminal, Triple-Negative, and HER2-Overexpressing Subtypes
Authors: Loroña Nicole C. , Othus Megan , Malone Kathleen E. , Linden Hannah M. , Tang Mei-Tzu C. , Li Christopher I.
Published: January 2025

AbstractBackground:
We evaluated the association between metabolic syndrome (MetS; obesity plus two metabolic risk factors) and breast cancer outcomes according to molecular subtype.
Methods:
This population-based prospective cohort consisted of 3,267 women ages 20 to 69 years diagnosed with a first primary invasive breast cancer from 2004 to 2015 in the Seattle–Puget Sound region. Breast cancer was categorized into three subtypes based on estrogen receptor (ER), progesterone receptor, and HER2 expression: luminal (ER+), triple-negative (ER−/progesterone receptor negative/HER2−), and HER2-overexpressing (H2E; ER−/HER2+) subtypes. We used time-varying Cox models to assess the association of prevalent and incident MetS with risks of recurrence, breast cancer–specific mortality (BCSM), and all-cause mortality (ACM).
Results:
MetS was associated with a greater risk of recurrence [HR, 3.24; 95% confidence interval (CI), 1.13–9.33] and BCSM (HR, 5.34; 95% CI, 2.32–12.31) only for the H2E subtype and greater risks of ACM for luminal (HR, 1.92; 95% CI, 1.37–2.68), H2E (HR, 5.09; 95% CI, 2.51–10.32), and all cases combined (HR, 1.90; 95% CI, 1.42–2.53). We also observed heterogeneity in recurrence and mortality outcomes across specific components of MetS and molecular subtypes.
Conclusions:
MetS is associated with ACM among women with breast cancer and with BCSM among women with the H2E subtype.
Impact:
These results highlight the importance of managing comorbidities to decrease the risk for adverse outcomes among breast cancer survivors.
Data from The Influence of DNA Repair Genes and Prenatal Tobacco Exposure on Risk of Childhood Acute Lymphoblastic Leukemia: A Gene–Environment Interaction Study
Authors: Wang Xinran , Zhong Charlie , Ma Xiaomei , Metayer Catherine , Mancuso Nicolas , Gauderman W. James , Wiemels Joseph L.
Published: January 2025

AbstractBackground:
Acute lymphoblastic leukemia (ALL) is the most common type of cancer among children. Tobacco exposure during gestation has been investigated as a potential risk factor, but its role remains undefined. Given tobacco’s toxicologic profile as a DNA-damaging agent, we examined the impact of DNA repair gene variability as a source of vulnerability to tobacco exposure risk for ALL.
Methods:
Leveraging demographic and genotype data from two large California-based ALL epidemiology studies, we used logistic regression, MinimumP (minP) statistical method, and permutation tests to examine interactions between DNA repair genes and prenatal tobacco exposure.
Results:
We found statistically significant interactions between prenatal tobacco exposure and DNA repair genes RECQL (minP = 1.00 × 10−4, FDR-P value = 1.86 × 10−2) and TDG (minP = 1.00 × 10−4, FDR-P value = 1.86 × 10−2) with regard to childhood ALL risk. Notable interactions in the homologous recombination pathway were observed among Latino children, whereas non-Latino White children displayed significant interactions in the base excision repair and nucleotide excision repair pathways.
Conclusions:
Our study highlights the significance of DNA repair genes and pathways when evaluating environmental exposure to tobacco smoke, suggesting that genetic variability within these pathways could impact vulnerability in the development of childhood ALL.
Impact:
This study highlights the significant impact of genetic variation interacting with prenatal tobacco exposure on ALL risk. Further research is needed to understand these interactions and their implications for ALL etiology. Expanding studies to other gene–environment interactions will aid in developing targeted prevention, diagnosis, and treatment strategies for pediatric oncology.
Data from Changes in Breast Cancer Screening Prevalence in the United States during the COVID-19 Pandemic, 2018 to 2022
Authors: Ashad-Bishop Kilan C. , Star Jessica , Giaquinto Angela N. , Smith Robert A. , Jemal Ahmedin , Bandi Priti
Published: January 2025

AbstractBackground:
Annual mammography screening declined year-on-year during the COVID-19 pandemic through 2021. This study examined changes in 2022 compared with 2018 in the national prevalence of self-reported up-to-date mammography.
Methods:
Using 2018 to 2022 data from the Center for Disease Control and Prevention’s Behavioral Risk Factor Surveillance System, we assess relative changes defined as annual prevalence ratios (aPR) in the SR receipt of past-year and up-to-date (UTD) breast cancer screening (biannual mammography in women of ages 50–74 years) during the third year of the COVID-19 pandemic overall and by sociodemographic characteristics.
Results:
UTD breast cancer screening declined for the first time since 2018 [2018 compared with 2022, from 78.7%–76.6%; aPR, 0.97; 95% confidence interval (CI), 0.96–0.98] despite a small increase in past-year breast cancer screening from 2020 to 2022 (57.9%–59.6%; aPR, 1.03; 95% CI, 1.01–1.05). This translated to 747,791 fewer women reporting UTD with recommended breast cancer screening in 2022 versus 2018. UTD breast cancer screening declines between 2018 and 2022 were largest for American Indian/Alaska Native women (74.8%–62.2%; aPR, 0.83; 95% CI, 0.74–0.93), women with less formal educational attainment (< high school: 73.1%–65.5%; aPR, 0.9; 95% CI, 0.85–0.95), and women without a usual source of care (48%–42.9%; aPR, 0.85; 95% CI, 0.78–0.92).
Conclusions:
Previously noted pandemic-related declines in past-year breast cancer screening now reflect in women reporting being UTD, with the largest declines in American Indian/Alaska Native women and those with lower socioeconomic status.
Impact:
Future studies should monitor screening prevalence in relation to breast cancer diagnostic stage overall and by sociodemographic groups.
Data from Characterization of the Biological Variability of the Angiome Biomarkers over Time in Healthy Participants
Authors: Liu Yingmiao , Li Jiatong , Lyu Jing , Howard Lauren E. , Sibley Alexander B. , Starr Mark D. , Brady John C. , Arrowood Christy , Kohn Elise C. , Ivy S. Percy , Hurwitz Herbert I. , Abbruzzese James L. , Owzar Kouros , Nixon Andrew B.
Published: January 2025

AbstractBackground:
Biomarker analyses are an integral part of cancer research. Despite the intense efforts to identify and characterize biomarkers in patients with cancer, little is known regarding the natural variation of biomarkers in healthy populations. Here we conducted a clinical study to evaluate the natural variability of biomarkers over time in healthy participants.
Methods:
The angiome multiplex array, a panel of 25 circulating protein biomarkers, was assessed in 28 healthy participants across eight timepoints over the span of 60 days. We utilized the intraclass correlation coefficient (ICC) to quantify the reliability of the biomarkers. Adjusted ICC values were calculated under the framework of a linear mixed-effects model, taking into consideration age, sex, body mass index, fasting status, and sampling factors.
Results:
ICC was calculated to determine the reliability of each biomarker. Hepatocyte growth factor was the most stable marker (ICC = 0.973), while platelet-derived growth factor (PDGF)-BB was the most variable marker (ICC = 0.167). In total, ICC analyses revealed that 22 out of 25 measured biomarkers display good (≥0.4) to excellent (>0.75) ICC values. Three markers (PDGF-BB, TGFβ1, PDGF-AA) had ICC values <0.4. Greater age was associated with higher IL6 (P = 0.0114). Higher body mass index was associated with higher levels of IL6 (P = 0.0003) and VEGF-R3 (P = 0.0045).
Conclusions:
Of the 25 protein biomarkers measured over this short time period, 22 markers were found to have good or excellent ICC values, providing additional validation for this biomarker assay.
Impact:
These data further support the validation of the angiome biomarker assay and its application as an integrated biomarker in clinical trial testing.
Data from Treatment and Survival Differences between Patients with Invasive Lobular Carcinoma Versus Invasive Ductal Carcinoma of the Breast
Authors: Anampa Jesus D. , Lin Shuwen , Obeng-Gyasi Samilia , Xue Xiaonan
Published: January 2025

AbstractBackground:
Invasive lobular carcinoma (ILC) exhibits differences in molecular and biological characteristics compared with invasive ductal carcinoma (IDC). We aim to compare breast cancer–specific survival (BCSS) between patients with ILC and IDC.
Methods:
We used data from the Surveillance, Epidemiology, and End Results database (1992–2020). Logistic regression analyses were conducted to identify factors associated with treatment modalities. We examined BCSS at different time points using a cox regression model with time-dependent coefficient.
Results:
A total of 343,397 patients with IDC and 39,859 patients with ILC were included. Patients with ILC had more advanced-stage disease (stage II, 35% vs. 34%; stage III, 16% vs. 11%) and a higher rate of hormone receptor–positive disease (97% vs. 81%). Compared with patients with IDC, patients with ILC had better BCSS in the first five years (HR = 0.71; P < 0.001) but worse BCSS in later years (HR = 1.30, P < 0.001 in years 6–10; HR = 1.75, P < 0.001 in years 11–15; and HR = 2.17, P < 0.001 in years 16–20).
Conclusions:
Patients with ILC survive better in early years but worse in later years compared with patients with IDC. Future studies are required to identify patients with ILC who are at risk of late recurrence or mortality.
Impact:
The results of this study add to the currently conflicting literature of survival of ILC and demonstrate the importance of evaluating novel therapeutic approaches and extended therapy for patients with ILC.
Data from Hispanic/Latino Ethnicity Is an Independent Predictor of Worse Survival for Gastric Cancer in a Multicenter Safety-Net Patient Population
Authors: Klingbeil Kyle D. , Dillon Dustin L. , Zarrinkhoo Erfan , Bechay Kirollos , Park Joon Y. , Rook Jordan M. , Mederos Michael A. , Girgis Mark D. , Chen Keren , Chen Kuan-Ting , Bastani Roshan , Manouchehr-Pour Shawdi , Dubé Priyanka , Viragh Karoly , Thomas Mariam , Chiu Victor , Kadera Brian E.
Published: January 2025

AbstractBackground:
Various population-based studies have shown Hispanic/Latino ethnicity is a risk factor for worse survival in patients with gastric cancer linked to disparate access to care. We aimed to address whether Hispanic patients treated within safety-net hospital systems continue to experience this survival deficit compared to non-Hispanic patients.
Methods:
We performed a retrospective cohort study comparing survival between Hispanic and non-Hispanic patients diagnosed with gastric adenocarcinoma between January 1, 2016, and December 31, 2020, within Los Angeles County’s safety-net hospital system. Gastric cancer–specific survival was compared between the two cohorts using the Kaplan–Meier estimate and Cox proportional-hazards regression model.
Results:
In total, 448 patients who received care from five medical centers were included; 348 (77.7%) patients self-identified as Hispanic and 100 (22.3%) as non-Hispanic. Mean follow-up time was 2.0 years (median 0.91 years, IQR, 0.34–2.5 years). Hispanic patients were found to be diagnosed at a younger age (55.6 vs. 60.7 years, P < 0.01), demonstrate higher state area deprivation index (6.4 vs. 5.0, P < 0.01), and present with metastatic disease (59.8% vs. 45%, P = 0.04). After adjusting social and oncologic variables, Hispanic ethnicity remained an independent risk factor for worse survival [HR 1.56, (95% CI, 1.06–2.28); P = 0.02].
Conclusions:
Hispanic patients treated within a large, multicenter safety-net hospital system experience worse survival compared to non-Hispanic patients. This suggests ethnic disparities exist within safety-net hospital systems, independent of known clinicopathologic factors.
Impact:
Improving outcomes for Hispanic patients with gastric cancer requires future efforts aimed at defining and addressing these unidentified barriers to care.

See related In the Spotlight, p. 12
Data from Biomarkers Suitable for Early Detection of Intrathoracic Cancers in Primary Care: A Systematic Review
Authors: Hamad Wasim , Grigore Bogdan , Walford Hugo , Peters Jaime , Alexandris Panos , Bonfield Stefanie , Standen Laura , Boscott Rachel , Behiyat Dawnya , Kuhn Isla , Neal Richard D. , Walter Fiona M. , Calanzani Natalia
Published: January 2025

Abstract
Intrathoracic cancers, including lung cancer, mesothelioma, and thymoma, present diagnostic challenges in primary care. Biomarkers could resolve some challenges. We synthesized evidence on biomarker performance for intrathoracic cancer detection in low-prevalence settings. A search in Embase and MEDLINE included studies that recruited participants with suspected intrathoracic cancer and reported on at least one diagnostic measure for a validated, noninvasive biomarker. Studies were excluded if participants were recruited based on a preestablished diagnosis. A total of 52 studies were included, reporting on 108 individual biomarkers and panels. Carcinoembryonic antigen, CYFRA 21-1, and VEGF were evaluated for lung cancer and mesothelioma. For lung cancer, carcinoembryonic antigen and CYFRA 21-1 were the most studied, with AUCs of 0.48 to –0.90 and 0.48 to –0.83, respectively. Pro–gastrin-releasing peptide (Pro-GRP) and neuron-specific enolase (NSE) had the highest negative predictive values (NPV) (98.2% and 96.9%, respectively), whereas Early Cancer Detection Test - Lung (Early CDT) and miRNA signature classifier panels showed NPVs of 99.3% and 99.0%, respectively, in smokers. For mesothelioma, fibrillin-3 and mesothelin plus osteopontin had AUCs of 0.93 and 0.91, respectively. Thymoma panels (binding AcHR + StrAb and binding AcHR + modulating AcHR + StrAb) had 100% NPVs in patients with myasthenia gravis. The review highlights the performance of some biomarkers. However, few were evaluated in low-prevalence settings. Further evaluation is necessary before implementing these biomarkers for intrathoracic cancers in primary care.
Figure S8 from Transient EZH2 Suppression by Tazemetostat during In Vitro Expansion Maintains T-Cell Stemness and Improves Adoptive T-Cell Therapy
Authors: Hou Yingqin , Zak Jaroslav , Shi Yujie , Pratumchai Isaraphorn , Dinner Brandon , Wang Wenjian , Qin Ke , Weber Evan W. , Teijaro John R. , Wu Peng
Published: January 2025

Figure S8 Phenotype analysis of OT-I cells from B16-OVA tumor and EZH2 knockout provided a similar phenotype of OT-I cells as tazemetostat inhibition but impaired survival upon restimulation.
Figure S13 from Transient EZH2 Suppression by Tazemetostat during In Vitro Expansion Maintains T-Cell Stemness and Improves Adoptive T-Cell Therapy
Authors: Hou Yingqin , Zak Jaroslav , Shi Yujie , Pratumchai Isaraphorn , Dinner Brandon , Wang Wenjian , Qin Ke , Weber Evan W. , Teijaro John R. , Wu Peng
Published: January 2025

Figure S12 The effect of tazemetostat on H3K27me3 levels and associated gene expression.
Figure S10 from Transient EZH2 Suppression by Tazemetostat during In Vitro Expansion Maintains T-Cell Stemness and Improves Adoptive T-Cell Therapy
Authors: Hou Yingqin , Zak Jaroslav , Shi Yujie , Pratumchai Isaraphorn , Dinner Brandon , Wang Wenjian , Qin Ke , Weber Evan W. , Teijaro John R. , Wu Peng
Published: January 2025

Figure S10 Transient inhibition of EZH2 by tazemetostat increased chemokine production of OT-I cells.
Supplementary Figure 6 from Complement Factor H Is an ICOS Ligand Modulating Tregs in the Glioma Microenvironment
Authors: Smolag Karolina I. , Olszowka Jakub , Rosberg Rebecca , Johansson Elinn , Marinko Elisabet , Leandersson Karin , O’Connell David J. , Governa Valeria , Tuysuz Emre Can , Belting Mattias , Pietras Alexander , Martin Myriam , Blom Anna M.
Published: January 2025

Supplementary figure 6. FH expression in glioma correlates with pro-tumorigenic markers
Supplementary Figure 5 from Complement Factor H Is an ICOS Ligand Modulating Tregs in the Glioma Microenvironment
Authors: Smolag Karolina I. , Olszowka Jakub , Rosberg Rebecca , Johansson Elinn , Marinko Elisabet , Leandersson Karin , O’Connell David J. , Governa Valeria , Tuysuz Emre Can , Belting Mattias , Pietras Alexander , Martin Myriam , Blom Anna M.
Published: January 2025

Supplementary figure 5. FH, ICOSL and ICOS dependence on glioma patient survival. The survival data of n = 509 patients from TCGA provisional dataset brain lower-grade glioma, analyzed with cBioPortal. Statistical tests: Logrank Test.
Supplementary Figure 2 from Complement Factor H Is an ICOS Ligand Modulating Tregs in the Glioma Microenvironment
Authors: Smolag Karolina I. , Olszowka Jakub , Rosberg Rebecca , Johansson Elinn , Marinko Elisabet , Leandersson Karin , O’Connell David J. , Governa Valeria , Tuysuz Emre Can , Belting Mattias , Pietras Alexander , Martin Myriam , Blom Anna M.
Published: January 2025

Supplementary figure 2. FH in Ntv-a mouse model (A) Mouse FH binds to mouse derived primary Tregs. Tregs were incubated for 2 h at 4oC with fluorescently labeled 25 or 100 μg/mL FH. The binding was detected using flow cytometry. (B) Western blot detecting FH in supernatants of tumor cells isolated from Ntv-a mice. FH was partially depleted with antibody against mouse FH bound to Dynabeads. (C) FH-rendered increase in survival of mouse Tregs. The cells were incubated with tumor cell derived supernatant and FH-depleted supernatant. After 7 days viability was assessed by Annexin V and Via-Probe staining. (D) FH was successfully knockdown with shFH. DF1 cells were transfected with three different shFH constructs and Gl2 shRNA. The knockdown of FH in NIH3T3 cells, transfected with supernatants from the DF1-RCAS-shFH/DF-1-RCAS-shGl2 cells was detected with goat anti-FH antiserum by western blot. (E) Control staining of mouse tumor sections. Samples were incubated with goat anti-FH antibody, goat IgG isotype control, and anti-goat IgG secondary antibody or only secondary antibody. Nucleus was stained with DAPI. Data are means ± SD of (A) n = 4, (C) n = 3 independent experiments. Representative blot (D) of n = 3 and picture (E) of n = 3 independent experiments. Statistical tests: two- Kruskal-Wallis with Dunn´s multiple comparison test (A, C). (*P<0.05, **P<0.01, ***P<0.001, ****P<0.0001, ns -, nonsignificant; CTRL, control.
Figure 7 from Complement Factor H Is an ICOS Ligand Modulating Tregs in the Glioma Microenvironment
Authors: Smolag Karolina I. , Olszowka Jakub , Rosberg Rebecca , Johansson Elinn , Marinko Elisabet , Leandersson Karin , O’Connell David J. , Governa Valeria , Tuysuz Emre Can , Belting Mattias , Pietras Alexander , Martin Myriam , Blom Anna M.
Published: January 2025

FH in glioma correlates with Treg occurrence and increased Treg viability. FH expression positively correlates with infiltration of Tregs in lower-grade glioma (A) and GBM (B). C, Western blot detecting FH in supernatants of FH-transfected glioma cell lines and depletion of FH with MRC-OX24-bound Dynabeads. D, Tregs incubated with supernatants of FH-transfected H4 glioma cell line exhibited increased survival, and the effect is weaker when FH is partially depleted. Correlation analysis of the immune infiltration was performed using TIMER2.0, based on n = 516 samples from lower-grade glioma and n = 599 samples from patients with GBM. Data are means ± SD of n = 4 (D) independent experiments. Representative blot of n = 4 (C) independent experiments. Statistical tests: Spearman’s rho (A and B), Kruskal–Wallis with Dunn’s multiple comparison test (D). ***, P < 0.001; ****, P < 0.0001.
Figure 5 from Complement Factor H Is an ICOS Ligand Modulating Tregs in the Glioma Microenvironment
Authors: Smolag Karolina I. , Olszowka Jakub , Rosberg Rebecca , Johansson Elinn , Marinko Elisabet , Leandersson Karin , O’Connell David J. , Governa Valeria , Tuysuz Emre Can , Belting Mattias , Pietras Alexander , Martin Myriam , Blom Anna M.
Published: January 2025

ICOS is associated with poor prognosis of patients with glioma. A, ICOS expression correlates with decreased survival of patients with glioma. Expression of ICOS correlates with neoplasm histological grade (B), cancer type (C), and EGFR mutations (D). ICOS positively correlates with infiltration of Tregs (E) and negatively with infiltration of CD8+ T cells (F) and CD4+ T cells (G) in lower-grade glioma. H, In a patient cohort stratified based on FH expression, ICOS correlates with worse survival only in the FH-high group. The survival estimates and correlation with other clinical parameters were analyzed in cBioPortal and based on the TCGA provisional dataset brain lower-grade glioma. The cases were set to include samples with mRNA data from n = 508 patients (A–D and H). Correlation analysis of the immune infiltration was performed using TIMER2.0, based on n = 516 samples from patients with lower-grade glioma. Statistical tests: logrank test (A–H), χ2 test (B and C), one-sided Fisher exact test (D), and Spearman’s rho correlation (E–G).
Supplemental Figure 8. from Alternate MHC I Antigen Presentation Pathways Allow CD8+ T-cell Recognition and Killing of Cancer Cells in the Absence of β2M or TAP
Authors: Cruz Freidrich M. , Orellano Laura A. A. , Chan Amanda , Rock Kenneth L.
Published: January 2025

Supplemental Figure 8: Implanted B2M KO Yumm1.7 cells remain MHC-I deficient in vivo.
Supplementary Table S4 from Gefitinib Reverses PD-L1–Mediated Immunosuppression Induced by Long-term Glutamine Blockade in Bladder Cancer
Authors: Ma Guofeng , Jia Huiqing , Li Zhiqiang , Zhang Xiangyan , Wang Liping , Zhang Zhilei , Xiao Yujing , Liang Zhijuan , Li Dan , Chen Yuanbin , Tian Xintao , Wang Yonghua , Liang Ye , Niu Haitao
Published: January 2025

General characteristics of C1 and C2.
Supplementary Table S2 from Gefitinib Reverses PD-L1–Mediated Immunosuppression Induced by Long-term Glutamine Blockade in Bladder Cancer
Authors: Ma Guofeng , Jia Huiqing , Li Zhiqiang , Zhang Xiangyan , Wang Liping , Zhang Zhilei , Xiao Yujing , Liang Zhijuan , Li Dan , Chen Yuanbin , Tian Xintao , Wang Yonghua , Liang Ye , Niu Haitao
Published: January 2025

Antibodies and Oligonucleotides in the Study.
Supplementary Table S1 from Gefitinib Reverses PD-L1–Mediated Immunosuppression Induced by Long-term Glutamine Blockade in Bladder Cancer
Authors: Ma Guofeng , Jia Huiqing , Li Zhiqiang , Zhang Xiangyan , Wang Liping , Zhang Zhilei , Xiao Yujing , Liang Zhijuan , Li Dan , Chen Yuanbin , Tian Xintao , Wang Yonghua , Liang Ye , Niu Haitao
Published: January 2025

405 MIBC samples with both glutamine metabolism subtypes and TCGA molecular subtypes.
Supplementary Fig. S9 from Gefitinib Reverses PD-L1–Mediated Immunosuppression Induced by Long-term Glutamine Blockade in Bladder Cancer
Authors: Ma Guofeng , Jia Huiqing , Li Zhiqiang , Zhang Xiangyan , Wang Liping , Zhang Zhilei , Xiao Yujing , Liang Zhijuan , Li Dan , Chen Yuanbin , Tian Xintao , Wang Yonghua , Liang Ye , Niu Haitao
Published: January 2025

Gefitinib synergizes with glutamine blockade to reduce bladder cancer growth in C57BL/6 mice.
Supplemental Figure 1. from Alternate MHC I Antigen Presentation Pathways Allow CD8+ T-cell Recognition and Killing of Cancer Cells in the Absence of β2M or TAP
Authors: Cruz Freidrich M. , Orellano Laura A. A. , Chan Amanda , Rock Kenneth L.
Published: January 2025

Supplemental Figure 1: Gating Strategy for Flow Cytometry
Supplementary Fig. S4 from Gefitinib Reverses PD-L1–Mediated Immunosuppression Induced by Long-term Glutamine Blockade in Bladder Cancer
Authors: Ma Guofeng , Jia Huiqing , Li Zhiqiang , Zhang Xiangyan , Wang Liping , Zhang Zhilei , Xiao Yujing , Liang Zhijuan , Li Dan , Chen Yuanbin , Tian Xintao , Wang Yonghua , Liang Ye , Niu Haitao
Published: January 2025

Glutamine blockade affects multiple biological processes and causes up-regulation of PD-L1 in bladder cancer cells.
Supplementary Fig. S2 from Gefitinib Reverses PD-L1–Mediated Immunosuppression Induced by Long-term Glutamine Blockade in Bladder Cancer
Authors: Ma Guofeng , Jia Huiqing , Li Zhiqiang , Zhang Xiangyan , Wang Liping , Zhang Zhilei , Xiao Yujing , Liang Zhijuan , Li Dan , Chen Yuanbin , Tian Xintao , Wang Yonghua , Liang Ye , Niu Haitao
Published: January 2025

Glutamine blockade suppresses cell proliferation, cell cycle progression and aerobic glycolysis and promotes apoptosis of bladder cancer cells.
Figure S2 from A Phase I Trial of Trebananib, an Angiopoietin 1 and 2 Neutralizing Peptibody, Combined with Pembrolizumab in Patients with Advanced Ovarian and Colorectal Cancer
Authors: Huffman Brandon M. , Rahma Osama E. , Tyan Kevin , Li Yvonne Y. , Giobbie-Hurder Anita , Schlechter Benjamin L. , Bockorny Bruno , Manos Michael P. , Cherniack Andrew D. , Baginska Joanna , Mariño-Enríquez Adrián , Kao Katrina Z. , Maloney Anna K. , Ferro Allison , Kelland Sarah , Ng Kimmie , Singh Harshabad , Welsh Emma L. , Pfaff Kathleen L. , Giannakis Marios , Rodig Scott J. , Hodi F. Stephen , Cleary James M.
Published: January 2025

Supplementary Figure S2: Treatment history of each patient with metastatic CRC with ERBB2 amplification and durable response to trial therapy in patient 32 (a) and patient 5 (b).
Figure S1 from A Phase I Trial of Trebananib, an Angiopoietin 1 and 2 Neutralizing Peptibody, Combined with Pembrolizumab in Patients with Advanced Ovarian and Colorectal Cancer
Authors: Huffman Brandon M. , Rahma Osama E. , Tyan Kevin , Li Yvonne Y. , Giobbie-Hurder Anita , Schlechter Benjamin L. , Bockorny Bruno , Manos Michael P. , Cherniack Andrew D. , Baginska Joanna , Mariño-Enríquez Adrián , Kao Katrina Z. , Maloney Anna K. , Ferro Allison , Kelland Sarah , Ng Kimmie , Singh Harshabad , Welsh Emma L. , Pfaff Kathleen L. , Giannakis Marios , Rodig Scott J. , Hodi F. Stephen , Cleary James M.
Published: January 2025

Supplementary Figure S1: Efficacy data of pembrolizumab and trebananib.
Figure S3 from CD27-Armored BCMA CAR T-cell Therapy (CBG-002) for Relapsed and Refractory Multiple Myeloma: A Phase I Clinical Trial
Authors: Xu Yang , Zhang Xuzhao , Xin Dijia , Zhang Jiawei , Wang Luyao , Fan Yili , Chen Boxiao , Lei Wen , Qiu Xi , Jiang Huawei , Xiao Xibin , Huang Liansheng , Yu Jiandong , Yang Xin , Yang Wenjun , Zhu Jiangao , Qian Wenbin
Published: January 2025

Median levels of serum CRP (A), IL-6 (B), IL-10 (C), TNFα (D) and IFNγ (E) during the CRS phase were measured in serial clinical samples stratified by dose cohorts.
Figure S1 from CD27-Armored BCMA CAR T-cell Therapy (CBG-002) for Relapsed and Refractory Multiple Myeloma: A Phase I Clinical Trial
Authors: Xu Yang , Zhang Xuzhao , Xin Dijia , Zhang Jiawei , Wang Luyao , Fan Yili , Chen Boxiao , Lei Wen , Qiu Xi , Jiang Huawei , Xiao Xibin , Huang Liansheng , Yu Jiandong , Yang Xin , Yang Wenjun , Zhu Jiangao , Qian Wenbin
Published: January 2025

Figure S1. A, control CAR (HBsAg CAR T), BCMA CAR and BCMA CAR-CD27 constructs were transduced into T cells, and CAR expression was detected by protein L assay. B, mock T, BCMA CAR and BCMA CAR-CD27 T cells were stained with anti-human CD27 antibodies. C, mock T and BCMA CAR-CD27 T cells were stained with fluorescence dye-labeled recombinant protein L and BCMA (rp), respectively. D, flow cytometry was used for detection of BCMA and CD70 on the cell surface of multiple cell lines. E, cell coculture was set-up as described in materials and methods. At Day 1 and 2 after coculture, supernatants were harvested for IFNγ measurement by ELISA. F, the transduced or non-transduced T cells were co-cultured with medium in the presence of anti-human CD107a for 1h, monensin was then added; and after 3h, cells were stained for CD3/CD4/CD8 antibodies and recombinant BCMA protein, and detected by flow cytometry. G and H, on day 15 after transduction, CAR-T cells were harvested and stained with CD4/CD8/ki67 antibodies and recombinant BCMA protein, and analyzed using flow cytometry (E); the percentage of Ki76+ cells in CD4+ and CD8+ CAR T cells were shown (F). * P<0.05.
Figure S4 from CD49a Targeting Enhances NK Cell Function and Antitumor Immunity
Authors: Zhang Yu , Li Yangyang , Zhang Zhengfeng , Zheng Xiaodong , Peng Hui , Tian Zhigang , Sun Rui , Sun Haoyu
Published: January 2025

CD49a deficiency prevents tumor growth and prolongs survival in various tumor models.
Figure S3 from CD49a Targeting Enhances NK Cell Function and Antitumor Immunity
Authors: Zhang Yu , Li Yangyang , Zhang Zhengfeng , Zheng Xiaodong , Peng Hui , Tian Zhigang , Sun Rui , Sun Haoyu
Published: January 2025

CD49a deficiency prevents the exhaustion of tumor-infiltrating CD8+ T cells in experimental lung metastasis model.
Figure S8 from Deleting Trim33 in Myeloid Cells Improves the Efficiency of Radiotherapy through an IFNβ-Dependent Antitumor Immune Response
Authors: Assouvie Anaïs , Gerbé-de-Thoré Marine , Torres Claire , Ménard Véronique , Alfaro Alexia , Deutsch Eric , Mondini Michele , Rousselet Germain
Published: January 2025

Immunofluorescence analysis of CD11b and CD3 in irradiated MC-38 tumors
Figure S6 from Deleting Trim33 in Myeloid Cells Improves the Efficiency of Radiotherapy through an IFNβ-Dependent Antitumor Immune Response
Authors: Assouvie Anaïs , Gerbé-de-Thoré Marine , Torres Claire , Ménard Véronique , Alfaro Alexia , Deutsch Eric , Mondini Michele , Rousselet Germain
Published: January 2025

gating strategies for B16 and MC-38 tumors cytometric analyses
Figure S3 from Deleting Trim33 in Myeloid Cells Improves the Efficiency of Radiotherapy through an IFNβ-Dependent Antitumor Immune Response
Authors: Assouvie Anaïs , Gerbé-de-Thoré Marine , Torres Claire , Ménard Véronique , Alfaro Alexia , Deutsch Eric , Mondini Michele , Rousselet Germain
Published: January 2025

fractionated irradiation in sub-cutaneous MC-38 or TC1/Luc models
Supplementary Tables from Preclinical Development of T Cells Engineered to Express a T-Cell Antigen Coupler Targeting Claudin 18.2–Positive Solid Tumors
Authors: Xu Stacey X. , Wang Ling , Ip Philbert , Randhawa Ritu R. , Benatar Tania , Prosser Suzanna L. , Lal Prabha , Khan Alima Naim , Nitya-Nootan Thanyashanthi , Thakor Gargi , MacGregor Heather , Hayes Danielle L , Vucicevic Andrea , Mathew Princy , Sengupta Sadhak , Helsen Christopher W. , Bader Andreas G.
Published: January 2025

Supplementary Tables 1-9
Supplementary Figure 9 from Preclinical Development of T Cells Engineered to Express a T-Cell Antigen Coupler Targeting Claudin 18.2–Positive Solid Tumors
Authors: Xu Stacey X. , Wang Ling , Ip Philbert , Randhawa Ritu R. , Benatar Tania , Prosser Suzanna L. , Lal Prabha , Khan Alima Naim , Nitya-Nootan Thanyashanthi , Thakor Gargi , MacGregor Heather , Hayes Danielle L , Vucicevic Andrea , Mathew Princy , Sengupta Sadhak , Helsen Christopher W. , Bader Andreas G.
Published: January 2025

Supplementary Figure 9: Serum cytokine levels in mice treated with TAC01-CLDN18.2.
Supplementary Figure 7 from Preclinical Development of T Cells Engineered to Express a T-Cell Antigen Coupler Targeting Claudin 18.2–Positive Solid Tumors
Authors: Xu Stacey X. , Wang Ling , Ip Philbert , Randhawa Ritu R. , Benatar Tania , Prosser Suzanna L. , Lal Prabha , Khan Alima Naim , Nitya-Nootan Thanyashanthi , Thakor Gargi , MacGregor Heather , Hayes Danielle L , Vucicevic Andrea , Mathew Princy , Sengupta Sadhak , Helsen Christopher W. , Bader Andreas G.
Published: January 2025

Supplementary Figure 7: N87CLDN18.2 tumor growth during primary and secondary tumor challenge in DKO mice treated with non-transduced T cells (NTD).
Supplementary Figure 10 from Preclinical Development of T Cells Engineered to Express a T-Cell Antigen Coupler Targeting Claudin 18.2–Positive Solid Tumors
Authors: Xu Stacey X. , Wang Ling , Ip Philbert , Randhawa Ritu R. , Benatar Tania , Prosser Suzanna L. , Lal Prabha , Khan Alima Naim , Nitya-Nootan Thanyashanthi , Thakor Gargi , MacGregor Heather , Hayes Danielle L , Vucicevic Andrea , Mathew Princy , Sengupta Sadhak , Helsen Christopher W. , Bader Andreas G.
Published: January 2025

Supplementary Figure 10: Histology of murine stomach tissues.
Supplementary Figure 1 from Preclinical Development of T Cells Engineered to Express a T-Cell Antigen Coupler Targeting Claudin 18.2–Positive Solid Tumors
Authors: Xu Stacey X. , Wang Ling , Ip Philbert , Randhawa Ritu R. , Benatar Tania , Prosser Suzanna L. , Lal Prabha , Khan Alima Naim , Nitya-Nootan Thanyashanthi , Thakor Gargi , MacGregor Heather , Hayes Danielle L , Vucicevic Andrea , Mathew Princy , Sengupta Sadhak , Helsen Christopher W. , Bader Andreas G.
Published: January 2025

Supplementary Figure 1: Specificity of recombinant hVH6-GFP protein in a protein array study encompassing >6,000 human membrane proteins.
Figure 6 from Preclinical Development of T Cells Engineered to Express a T-Cell Antigen Coupler Targeting Claudin 18.2–Positive Solid Tumors
Authors: Xu Stacey X. , Wang Ling , Ip Philbert , Randhawa Ritu R. , Benatar Tania , Prosser Suzanna L. , Lal Prabha , Khan Alima Naim , Nitya-Nootan Thanyashanthi , Thakor Gargi , MacGregor Heather , Hayes Danielle L , Vucicevic Andrea , Mathew Princy , Sengupta Sadhak , Helsen Christopher W. , Bader Andreas G.
Published: January 2025

TAC01-CLDN18.2 lacks activity against primary cells representing CLDN18.2-negative normal human tissues. Top, CLDN18.2 mRNA analysis by ddPCR in cancer and human primary cells, as indicated. Data are presented as percent relative to levels in N87CLDN18.2 cells (100%). Center/bottom, Flow cytometry analysis measuring proportions of T cells expressing surface CD69 (center) and intracellular cytokines (bottom) determined after a 4-hour coculture of CLDN18.2-TAC T cells with target cells. Data are normalized to data obtained from NTD T cells cocultures to correct for background activity and are plotted relative to values in N87CLDN18.2 cells (100%). Data are shown as averages with SDs derived from experiments done with four TAC01-CLDN18.2 products manufactured from three separate donors. CD69 and all cytokines were significantly elevated in T cells cocultured with OE19 target cells compared with the N87 negative controls or all other human cell models. There was no significant difference between the N87 negative control and any of the other human cell models (statistical data are shown in Supplementary Table S9). ND, not detected.