bluuebunny/crossref_metadata_2025 huggingface dataset:
Typology of Tomato Cropping Systems and Determinants of Preharvest Losses in Western Cameroon
Authors: Titti Roland Wilfried , Etoga Anne Stéphanie , Ntsoli Pierre Germain , Etame Georges Marius Kossi , Chotangui Asafor Henry , Bikomo Réné Mbonomo , Yaouba Aoudou
Published: November 2024

Preharvest losses, which are often neglected, limit the availability of fresh tomato fruit to varying degrees in tomato‐based cropping systems in Cameroon. Increasing tomato yields requires identifying, understanding, and controlling the factors responsible for preharvest losses in the identified cropping systems. Field surveys were conducted in three production areas of western Cameroon (Foumbot, Bansoa, and Dschang) to characterize cropping systems and growers, quantify production losses, and identify causal factors associated with losses and proffer solutions. One hundred and ninety‐six growers were interviewed in 13 villages, using purposive sampling to select zones and simple random sampling to select growers. Factor analysis for mixed data (FAMD) and canonical discriminant analysis, combined with multinomial logistic regression, were used to analyze the collected data. The FAMD results indicated that 72% of the farm variability was expressed through technical route, preharvest losses, and technical mastery. The tomato‐based cropping systems were classified into three types: (1) a pure cultivation system that consumes excessive synthetic fertilizers and results in significant losses; (2) a pure cultivation system that consumes high amounts of synthetic fertilizers and moderate amounts of organic fertilizers with moderate losses; and (3) a cultivation system that involves moderate synthetic fertilizer consumption, extremely low organic amendment, and low losses. Preharvest losses can be significantly affected by the unregulated use of synthetic fertilizers. The study’s implications are many, affecting agricultural practices, policy, economic stability, and food security in Cameroon. A more sustainable and productive tomato industry can be achieved by addressing preharvest losses through informed strategies. To address this issue, it is crucial to establish fertilization protocols that consider the soil’s fertility status and the tomato’s essential macro‐ and micronutrient requirements.
Hepatitis D Virus Status Among People With Hepatitis B Virus Infection: A Disconnect Between Guidelines and Practice
Authors: Jack Kathryn , Jackson Rachel , Irving William Lucien
Published: November 2024

Background: Hepatitis D virus (HDV) infection is an important cause of chronic liver disease yet remains a poorly met clinical challenge. The current European and UK guidelines recommend that all patients with hepatitis B virus (HBV) are tested for HDV, but emerging UK data indicates that only approximately 20% have been tested. The World Health Organization viral hepatitis elimination strategy first requires the systematic testing of people with relevant risk factors, so there is a need to understand factors contributing to low anti‐HDV rates of testing.

Methods: An audit was conducted to ascertain the perspectives of patients and healthcare professionals about identifying HDV infection. During a 4‐month period, 39 hepatology healthcare professionals and 70 patients with chronic HBV were recruited. The attitudes of healthcare professionals about HDV testing were surveyed according to the seven constructs of the theoretical framework of acceptability (TFA). Patients were surveyed by telephone about their knowledge of HDV and their own testing status.

Results: Among the 39 healthcare professionals, only 66.6% and 53.8% were able to correctly cite EASL (European) HDV and NICE (UK) testing guidelines, respectively, although there were high levels of anti‐HDV testing acceptability according to the TFA. Among the patients, 95.7% (67/70) had been tested for anti‐HDV and all were seronegative. Only 23% (16/70) knew of the existence of HDV. There was extensive ethnic heterogeneity with 23 countries of birth and 24 different primary languages.

Conclusion: Some clinicians lack familiarity with clinical guideline recommendations for universal testing of all HBsAg‐positive patients. Similarly, most patients lack knowledge about HDV. Staff and patient education are required to increase HDV diagnosis.
The Role of Industrial Designers in Achieving the Green Economy Through Recycling
Authors: Yousif Tamer , Moalosi Richie
Published: November 2024

In recent years, the circular economy has gained prominence as a means to achieve social and environmental benefits and economic growth simultaneously. The carrying capacity of the Earth is in danger due to recent rates of consumption and pollution. The environment is changing drastically because of the significant increase in trash production and pollution. As a result, waste management programs now heavily emphasize recycling and upcycling to achieve positive environmental effects. The green economy offers a compelling paradigm for creating societies that are more socially inclusive, more resource‐efficient, low carbon, and less harmful to the environment. Industrial design plays a crucial role in advancing the principles of the circular economy by promoting system changes that foster sustainability, optimal resource use, and waste reduction. Industrial design can also promote a green economy by promoting resource recycling. The goal of the study was to inspire greater awareness, advocacy, and action toward building a more environmentally conscious and resource‐efficient society. The study adopted a case study because it is a learning technique in which students can be presented with real‐world business challenges and asked to solve them. The findings indicate that students developed a mindset and transformative shift for the positive environmental change that recycling can bring to the environment by designing circular production and consumption processes that resonate with the principles of the circular economy. Educating future designers about circular principles ensures that a new generation of professionals will drive positive change and preserve the environment for future generations.
Innovative Approaches to Suppressing Pseudomonas aeruginosa Growth and Virulence: Current Status and Future Directions
Authors: Patil Sandip , Chen Xiaowen , Wen Feiqiu
Published: November 2024

Pseudomonas aeruginosa, an antibiotic‐resistant opportunistic pathogen, poses significant challenges in treating infections, particularly in immunocompromised individuals. This review explores current and future innovative approaches to suppress its growth and virulence. We delve into the bacterium’s virulence factors, discussing existing strategies like antibiotics, bacteriophages, probiotics, and small‐molecule inhibitors. Additionally, novel approaches, including RNA interference, CRISPR‐Cas systems, and nanotechnology, show promise in preclinical studies. Despite advancements, challenges persist, prompting the need for a multifaceted approach targeting various aspects of P. aeruginosa pathogenesis for effective infection management. This review provides a comprehensive perspective on the status and future directions of innovative strategies against P. aeruginosa.
Monocyte CD36 Expression Predicts Disease Activity in Patients With Crohn’s Disease
Authors: Zhu Jiejie , Zhu Nannan , Wang Jiren , Liu Qiuyuan , Mei Qiao
Published: November 2024

Background: Crohn’s disease (CD) is a chronic intestinal inflammatory disease associated with genetic, environmental, and other unknown factors. Cluster of differentiation 36 (CD36) plays an important role in cancer, inflammation, and metabolic diseases. Although CD36 has recently been implicated in various diseases, its role in CD is still unclear.

Methods: Blood samples were collected from patients with CD and healthy volunteers. Peripheral blood mononuclear cells (PBMCs) were isolated by density gradient centrifugation over Ficoll–Paque and labeled with monoclonal antibodies (CD14‐APC and CD36‐PE). Flow cytometer CytoFlex is used for analysis.

Results: Twenty‐nine patients with CD in remission, 42 patients with active CD, and 23 healthy volunteers were included in the study. Our results showed that the frequency of the CD14+CD36+ monocyte subset was increased in PBMCs from patients with active CD compared with patients in remission and healthy controls. However, CD36 on monocytes was lower in CD compared with the healthy controls. CD36 expression was decreased in patients with active CD compared with that of patients with CD in remission and healthy control subjects, but no difference was found between patients with CD in remission and healthy controls. Interestingly, we found negative correlations of CD36 with HBI, SES‐CD, C‐reactive protein, and neutrophil‐to‐lymphocyte ratio.

Conclusions: These data indicate that monocyte CD36 associates with disease activity in CD and might be a potential biomarker for assessing the activity of CD.
Table 2 from Associations between Dietary Patterns and Incident Colorectal Cancer in 114,443 Individuals from the UK Biobank: A Prospective Cohort Study
Authors: Skulsky Samuel L. , Koutoukidis Dimitrios A. , Carter Jennifer L. , Piernas Carmen , Jebb Susan A. , Gao Min , Astbury Nerys M.
Published: November 2024

Associations of DP1 and DP2 with incident colorectal cancer by sex subgroups.
Table 1 from Associations between Dietary Patterns and Incident Colorectal Cancer in 114,443 Individuals from the UK Biobank: A Prospective Cohort Study
Authors: Skulsky Samuel L. , Koutoukidis Dimitrios A. , Carter Jennifer L. , Piernas Carmen , Jebb Susan A. , Gao Min , Astbury Nerys M.
Published: November 2024

Colorectal cancer outcomes, baseline characteristics, and dietary intakes of participants by DP quintiles.
Supplementary Table S5 from Associations between Dietary Patterns and Incident Colorectal Cancer in 114,443 Individuals from the UK Biobank: A Prospective Cohort Study
Authors: Skulsky Samuel L. , Koutoukidis Dimitrios A. , Carter Jennifer L. , Piernas Carmen , Jebb Susan A. , Gao Min , Astbury Nerys M.
Published: November 2024

Table S5 Hazard ratios and 95% confidence intervals of all variables in the fully-adjusted model for DP1
Figure 3 from Associations between Dietary Patterns and Incident Colorectal Cancer in 114,443 Individuals from the UK Biobank: A Prospective Cohort Study
Authors: Skulsky Samuel L. , Koutoukidis Dimitrios A. , Carter Jennifer L. , Piernas Carmen , Jebb Susan A. , Gao Min , Astbury Nerys M.
Published: November 2024

HRs (95% CIs) of continuous form. A, DP1 z-scores and B, DP2 z-scores for the risk of incident colorectal cancer. HRs (solid black line) and 95% CIs (gray shading) were derived from spline regression models to evaluate for potential nonlinear associations between DP1 scores and incident colorectal cancer. The reference point of the DP z-scores was set to the fifth percentile values. X-axis represents DP z-scores.
Figure 2 from Associations between Dietary Patterns and Incident Colorectal Cancer in 114,443 Individuals from the UK Biobank: A Prospective Cohort Study
Authors: Skulsky Samuel L. , Koutoukidis Dimitrios A. , Carter Jennifer L. , Piernas Carmen , Jebb Susan A. , Gao Min , Astbury Nerys M.
Published: November 2024

Shape plot of the HRs and 95% CIs denoting incident colorectal cancer risk by DP1 quintiles in the fully adjusted model; DP1 quintile 1 is shown as the reference. CIs were obtained using the floating absolute risk method (34).
Figure 1 from Associations between Dietary Patterns and Incident Colorectal Cancer in 114,443 Individuals from the UK Biobank: A Prospective Cohort Study
Authors: Skulsky Samuel L. , Koutoukidis Dimitrios A. , Carter Jennifer L. , Piernas Carmen , Jebb Susan A. , Gao Min , Astbury Nerys M.
Published: November 2024

Sequential stratification/adjustments to the model comparing the risk of incident colorectal cancer associated with DP1 and DP2. Top, sequential changes to the HRs for quintiles 5 versus 1 for DP1 and DP2. Bottom, sequential changes to the HRs for DP1 and DP2 z-scores in continuous form. The X-axis represents HRs on the log-scale. CIs were obtained using the floating absolute risk method (34). χ2 and P values were calculated by likelihood ratio tests to assess the heterogeneity in the associations with sequential adjustments for covariates. Behavioral factors included smoking status, physical activity level (MET-hours/week), and total energy intake (ln-kJ). SES* comprises TDI and educational attainment. Models were stratified by covariates violating the proportional hazards assumption: physical activity, family history of colorectal cancer, education, and BMI. SES, socioeconomic status.
Supplementary Table 9 from Prostate Cancer Disparities in Clinical Characteristics and Survival among Black and Latino Patients Considering Nativity: Findings from the California Cancer Registry
Authors: Freedland Alexis R. , Sanchez Mendez Joel , Liu Lihua , Hamilton Ann S. , Zhang Juanjuan , Hwang Amie E. , Ballas Leslie , Abreu Andre Luis , Deapen Dennis , Stern Mariana C.
Published: November 2024

Risk of dying of prostate cancer, among non-Latino White, non-Latino Black, US-born Latino, and non-US-born Latino patients in California by time of follow-up (2004-2021).
Supplementary Table S3 from Development of a Breast Cancer Risk Prediction Model Integrating Monogenic, Polygenic, and Epidemiologic Risk
Authors: Kalia Sarah S. , Boddicker Nicholas J. , Yadav Siddhartha , Huang Hongyan , Na Jie , Hu Chunling , Ambrosone Christine B. , Yao Song , Haiman Christopher A. , Chen Fei , John Esther M. , Kurian Allison W. , Guo Boya , Lindstrӧm Sara , Auer Paul , Lacey James V. , Neuhausen Susan L. , Martinez Maria Elena , Sandler Dale P. , O’Brien Katie M. , Taylor Jack A. , Teras Lauren R. , Hodge James M. , Lori Adriana , Bodelon Clara , Trentham-Dietz Amy , Burnside Elizabeth S. , Vachon Celine M. , Winham Stacey J. , Goldgar David E. , Domchek Susan M. , Nathanson Katherine L. , Weitzel Jeffrey N. , Couch Fergus J. , Kraft Peter
Published: November 2024

Supplementary Table S3 shows univariate associations with odds of any breast cancer and invasive breast cancer only
Supplemental Table 3 from Chronic Health Conditions, Disability, and Physical and Cognitive Limitations among LGBTQ+ Cancer Survivors
Authors: Waters Austin R. , Jin Mu , Jones Shaun R. , Datta Geetanjali D. , Butler Eboneé N. , Kent Erin E. , Nichols Hazel B. , Tan Kelly
Published: November 2024

Prevalence estimates excluding skin cancer survivors
Supplementary figure S5 from Lung Microbial and Host Genomic Signatures as Predictors of Prognosis in Early-Stage Adenocarcinoma
Authors: Tsay Jun-Chieh J. , Darawshy Fares , Wang Chan , Kwok Benjamin , Wong Kendrew K. , Wu Benjamin G. , Sulaiman Imran , Zhou Hua , Isaacs Bradley , Kugler Matthias C. , Sanchez Elizabeth , Bain Alexander , Li Yonghua , Schluger Rosemary , Lukovnikova Alena , Collazo Destiny , Kyeremateng Yaa , Pillai Ray , Chang Miao , Li Qingsheng , Vanguri Rami S. , Becker Anton S. , Moore William H. , Thurston George , Gordon Terry , Moreira Andre L. , Goparaju Chandra M. , Sterman Daniel H. , Tsirigos Aristotelis , Li Huilin , Segal Leopoldo N. , Pass Harvey I.
Published: November 2024

Supplementary Figure S5 shows Differential expression of taxa between tumor and unaffected lung samples.
Supplementary Table 3 from Upgrading of Grade Group 1 Prostate Cancer at Prostatectomy: Germline Risk Factors in a Prospective Cohort
Authors: Liss Michael A. , Zeltser Nicole , Zheng Yingye , Lopez Camden , Liu Menghan , Patel Yash , Yamaguchi Takafumi N. , Eng Stefan E. , Tian Mao , Semmes Oliver J. , Lin Daniel W. , Brooks James D. , Wei John T. , Klein Eric A. , Tewari Ashutosh K. , Mosquera Juan Miguel , Khani Francesca , Robinson Brian D. , Aasad Muhammad , Troyer Dean A. , Kagan Jacob , Sanda Martin G. , Thompson Ian M. , Boutros Paul C. , Leach Robin J.
Published: November 2024

Supplementary Table 3 reports per-sample genetic feature information in the genetic analysis cohort.
Supplementary figure S3 from Lung Microbial and Host Genomic Signatures as Predictors of Prognosis in Early-Stage Adenocarcinoma
Authors: Tsay Jun-Chieh J. , Darawshy Fares , Wang Chan , Kwok Benjamin , Wong Kendrew K. , Wu Benjamin G. , Sulaiman Imran , Zhou Hua , Isaacs Bradley , Kugler Matthias C. , Sanchez Elizabeth , Bain Alexander , Li Yonghua , Schluger Rosemary , Lukovnikova Alena , Collazo Destiny , Kyeremateng Yaa , Pillai Ray , Chang Miao , Li Qingsheng , Vanguri Rami S. , Becker Anton S. , Moore William H. , Thurston George , Gordon Terry , Moreira Andre L. , Goparaju Chandra M. , Sterman Daniel H. , Tsirigos Aristotelis , Li Huilin , Segal Leopoldo N. , Pass Harvey I.
Published: November 2024

Supplementary Figure S3 shows Alpha and Beta diversity among different baseline characteristic
Supplementary figure S1 from Lung Microbial and Host Genomic Signatures as Predictors of Prognosis in Early-Stage Adenocarcinoma
Authors: Tsay Jun-Chieh J. , Darawshy Fares , Wang Chan , Kwok Benjamin , Wong Kendrew K. , Wu Benjamin G. , Sulaiman Imran , Zhou Hua , Isaacs Bradley , Kugler Matthias C. , Sanchez Elizabeth , Bain Alexander , Li Yonghua , Schluger Rosemary , Lukovnikova Alena , Collazo Destiny , Kyeremateng Yaa , Pillai Ray , Chang Miao , Li Qingsheng , Vanguri Rami S. , Becker Anton S. , Moore William H. , Thurston George , Gordon Terry , Moreira Andre L. , Goparaju Chandra M. , Sterman Daniel H. , Tsirigos Aristotelis , Li Huilin , Segal Leopoldo N. , Pass Harvey I.
Published: November 2024

Supplementary Figure S1 shows Bacterial load
Supplementary Figure 1 from Upgrading of Grade Group 1 Prostate Cancer at Prostatectomy: Germline Risk Factors in a Prospective Cohort
Authors: Liss Michael A. , Zeltser Nicole , Zheng Yingye , Lopez Camden , Liu Menghan , Patel Yash , Yamaguchi Takafumi N. , Eng Stefan E. , Tian Mao , Semmes Oliver J. , Lin Daniel W. , Brooks James D. , Wei John T. , Klein Eric A. , Tewari Ashutosh K. , Mosquera Juan Miguel , Khani Francesca , Robinson Brian D. , Aasad Muhammad , Troyer Dean A. , Kagan Jacob , Sanda Martin G. , Thompson Ian M. , Boutros Paul C. , Leach Robin J.
Published: November 2024

Supplementary Figure 1 displays sequencing, variant capture, and imputation quality control metrics.
Supplementary Table S4 from Lung Microbial and Host Genomic Signatures as Predictors of Prognosis in Early-Stage Adenocarcinoma
Authors: Tsay Jun-Chieh J. , Darawshy Fares , Wang Chan , Kwok Benjamin , Wong Kendrew K. , Wu Benjamin G. , Sulaiman Imran , Zhou Hua , Isaacs Bradley , Kugler Matthias C. , Sanchez Elizabeth , Bain Alexander , Li Yonghua , Schluger Rosemary , Lukovnikova Alena , Collazo Destiny , Kyeremateng Yaa , Pillai Ray , Chang Miao , Li Qingsheng , Vanguri Rami S. , Becker Anton S. , Moore William H. , Thurston George , Gordon Terry , Moreira Andre L. , Goparaju Chandra M. , Sterman Daniel H. , Tsirigos Aristotelis , Li Huilin , Segal Leopoldo N. , Pass Harvey I.
Published: November 2024

Supplementary Table S4: MIRKAT analyses for beta diversity measures associated with time to recurrence
Supplementary Table S2 from Lung Microbial and Host Genomic Signatures as Predictors of Prognosis in Early-Stage Adenocarcinoma
Authors: Tsay Jun-Chieh J. , Darawshy Fares , Wang Chan , Kwok Benjamin , Wong Kendrew K. , Wu Benjamin G. , Sulaiman Imran , Zhou Hua , Isaacs Bradley , Kugler Matthias C. , Sanchez Elizabeth , Bain Alexander , Li Yonghua , Schluger Rosemary , Lukovnikova Alena , Collazo Destiny , Kyeremateng Yaa , Pillai Ray , Chang Miao , Li Qingsheng , Vanguri Rami S. , Becker Anton S. , Moore William H. , Thurston George , Gordon Terry , Moreira Andre L. , Goparaju Chandra M. , Sterman Daniel H. , Tsirigos Aristotelis , Li Huilin , Segal Leopoldo N. , Pass Harvey I.
Published: November 2024

Supplementary Table S2: Taxonomic differential abundance between tumor and lung samples
Supplement S1 from Recalibrating the Genetics and Epidemiology of Colorectal Cancer Consortium Environmental Risk Score for Use in US Veterans
Authors: Williams April R. , Redding Thomas S. , Sullivan Brian A. , Baidya Robin N. , Ear Belinda , Cho Kelly , Ivey Kerry L. , Williams Christina D. , Dominitz Jason A. , Lieberman David , Hauser Elizabeth R.
Published: November 2024

Supplement S1: This document lists acknowledgements for the VA Million Veteran Program Core used in publications as of March 2024
Supplementary Table S16 from Lung Microbial and Host Genomic Signatures as Predictors of Prognosis in Early-Stage Adenocarcinoma
Authors: Tsay Jun-Chieh J. , Darawshy Fares , Wang Chan , Kwok Benjamin , Wong Kendrew K. , Wu Benjamin G. , Sulaiman Imran , Zhou Hua , Isaacs Bradley , Kugler Matthias C. , Sanchez Elizabeth , Bain Alexander , Li Yonghua , Schluger Rosemary , Lukovnikova Alena , Collazo Destiny , Kyeremateng Yaa , Pillai Ray , Chang Miao , Li Qingsheng , Vanguri Rami S. , Becker Anton S. , Moore William H. , Thurston George , Gordon Terry , Moreira Andre L. , Goparaju Chandra M. , Sterman Daniel H. , Tsirigos Aristotelis , Li Huilin , Segal Leopoldo N. , Pass Harvey I.
Published: November 2024

Supplementary Table S16: Absolute loadings for the identified components integrated by microbiome (ASV) and transcriptome (genes annotated to pathways) in unaffected lung samples with recurrence
Supplementary Table S11 from Lung Microbial and Host Genomic Signatures as Predictors of Prognosis in Early-Stage Adenocarcinoma
Authors: Tsay Jun-Chieh J. , Darawshy Fares , Wang Chan , Kwok Benjamin , Wong Kendrew K. , Wu Benjamin G. , Sulaiman Imran , Zhou Hua , Isaacs Bradley , Kugler Matthias C. , Sanchez Elizabeth , Bain Alexander , Li Yonghua , Schluger Rosemary , Lukovnikova Alena , Collazo Destiny , Kyeremateng Yaa , Pillai Ray , Chang Miao , Li Qingsheng , Vanguri Rami S. , Becker Anton S. , Moore William H. , Thurston George , Gordon Terry , Moreira Andre L. , Goparaju Chandra M. , Sterman Daniel H. , Tsirigos Aristotelis , Li Huilin , Segal Leopoldo N. , Pass Harvey I.
Published: November 2024

Supplementary Table S11: Genomic differential abundance between recurrence and no recurrence lung samples
Data from Prostate Cancer Disparities in Clinical Characteristics and Survival among Black and Latino Patients Considering Nativity: Findings from the California Cancer Registry
Authors: Freedland Alexis R. , Sanchez Mendez Joel , Liu Lihua , Hamilton Ann S. , Zhang Juanjuan , Hwang Amie E. , Ballas Leslie , Abreu Andre Luis , Deapen Dennis , Stern Mariana C.
Published: November 2024

AbstractBackground:
We investigated clinical characteristics and prostate cancer survival patterns among Latino patients considering nativity compared with non-Latino Black (NLB) and non-Latino White (NLW) patients.
Methods:
We used data from the California Cancer Registry (1995–2021), which included 347,540 NLW, 50,032 NLB, and 75,238 Latino patients with prostate cancer. Frequencies of sociodemographic and clinical variables were assessed using χ2 tests. Multivariable regression models were fitted to evaluate determinants of treatment reception, Gleason upgrade, and survival differences. Exploratory analyses were conducted grouping Latino cases into US born and non–US born by country of origin.
Results:
Compared with NLW, NLB cases had the greatest proportion of younger patients, whereas non–US-born Latino patients had the greatest proportion of low socioeconomic status and uninsured patients. Non–US-born Latinos showed a greater proportion of diagnoses completed with <6 core biopsies, Gleason >8, stage IV tumors, and metastasis. Multivariable analyses showed that compared with NLW, Latino patients were as likely to receive treatment, whereas NLB cases were less likely (OR = 0.81; 95% confidence interval, 0.67–0.98; P = 0.029). Compared with NLW, non–US-born Latino cases were less likely to die of prostate cancer (HR = 0.78; 95% confidence interval, 0.64–0.94; P = 0.011), with no difference reported for NLB cases.
Conclusions:
Considering sociodemographic and clinical characteristics, non–US-born Latino patients with prostate cancer had better survival than NLW. This highlights the need to identify key determinants of these survival differences and the importance of sociodemographic and clinical determinants in survival disparities.
Impact:
Our study emphasizes the importance of considering nativity among Latino patients to understand prostate cancer disparities and outcomes in this population.
The Context of Poverty and Cancer: Denying Human Potential
Authors: Doose Michelle , Kennedy Amy E. , Williams Shanita D. , Srinivasan Shobha
Published: November 2024

Poverty is a carcinogen and a leading cause of cancer disparities and overall mortality in the United States. Poverty is often viewed as an individual failure for “being poor,” but in fact, poverty is structurally driven, intergenerational, and place-based that socially deprives and denies human potential. Disparities in timely cancer prevention, diagnosis, treatment, survivorship, and survival disproportionally impact people living in poverty and especially in persistent poverty areas, an extreme form of place-based poverty that affects communities over multiple generations. There has been some progress made to address place-based conditions that exacerbate poverty, such as the NCI’s initiative on persistent poverty. However, gross inequality and cancer disparities continue to exist and persist. The time is now to accelerate the development of research-informed strategies and solutions with communities along with multisectoral collaborations with education, housing, occupation/workforce, foster care, criminal justice, transportation, and data collection systems. This commentary discusses the structural, place-based, and generational context of poverty, illustrates how entrenched inequities shape poor cancer outcomes, and describes opportunities for future research.
Supplementary Table S1 from Targeted Therapies, Novel Antibodies, and Immunotherapies in Advanced Non–Small Cell Lung Cancer: Clinical Evidence and Drug Approval Patterns
Authors: Koban Marén U. , Hartmann Markus , Amexis Georgios , Franco Pedro , Huggins Laura , Shah Imran , Karachaliou Niki
Published: November 2024

Pharmacological class and mechanism of new drugs approved by the U.S. FDA for use in NSCLC, January 2011 to December 2023
Supplementary Table 6 from Clinical and Genomic Features of Classical and Basal Transcriptional Subtypes in Pancreatic Cancer
Authors: Singh Harshabad , Xiu Joanne , Kapner Kevin S. , Yuan Chen , Narayan Raja R. , Oberley Matthew , Farrell Alex , Surana Rishi , Huffman Brandon M. , Perez Kimberly , Cleary James M. , Jordan Alexander C. , Dias Costa Andressa , Williams Hannah L. , Raghavan Srivatsan , Weinberg Benjamin , Pishvaian Michael J. , Shroff Rachna T. , Goel Sanjay , Dougan Stephanie K. , Nowak Jonathan A. , Spetzler David , Sledge George , Wolpin Brian M. , Aguirre Andrew J.
Published: November 2024

Supplementary Table 6
Supplementary Table S4 from Exploiting WEE1 Kinase Activity as FUS::DDIT3-Dependent Therapeutic Vulnerability in Myxoid Liposarcoma
Authors: Heinst Lorena , Lee Kwang Seok , Berthold Ruth , Isfort Ilka , Wosnig Svenja , Kuntze Anna , Hafner Susanne , Altvater Bianca , Rossig Claudia , Åman Pierre , Wardelmann Eva , Scholl Claudia , Hartmann Wolfgang , Fröhling Stefan , Trautmann Marcel
Published: November 2024

Supplementary Table S4. Statistical correlation analysis by means of χ².
Supplementary Table S2 from Exploiting WEE1 Kinase Activity as FUS::DDIT3-Dependent Therapeutic Vulnerability in Myxoid Liposarcoma
Authors: Heinst Lorena , Lee Kwang Seok , Berthold Ruth , Isfort Ilka , Wosnig Svenja , Kuntze Anna , Hafner Susanne , Altvater Bianca , Rossig Claudia , Åman Pierre , Wardelmann Eva , Scholl Claudia , Hartmann Wolfgang , Fröhling Stefan , Trautmann Marcel
Published: November 2024

Supplementary Table S2. Clinicopathological characteristics of patients with myxoid liposarcoma (n=49).
Supplementary Figure S4 from Exploiting WEE1 Kinase Activity as FUS::DDIT3-Dependent Therapeutic Vulnerability in Myxoid Liposarcoma
Authors: Heinst Lorena , Lee Kwang Seok , Berthold Ruth , Isfort Ilka , Wosnig Svenja , Kuntze Anna , Hafner Susanne , Altvater Bianca , Rossig Claudia , Åman Pierre , Wardelmann Eva , Scholl Claudia , Hartmann Wolfgang , Fröhling Stefan , Trautmann Marcel
Published: November 2024

Supplementary Figure S4. Concurrent expression of WEE 1 signaling pathway and G 1 /S effectors and correlation of P Thr 160 CDK 2 with the histological grade of MLS.
Supplementary Figure S2 from Exploiting WEE1 Kinase Activity as FUS::DDIT3-Dependent Therapeutic Vulnerability in Myxoid Liposarcoma
Authors: Heinst Lorena , Lee Kwang Seok , Berthold Ruth , Isfort Ilka , Wosnig Svenja , Kuntze Anna , Hafner Susanne , Altvater Bianca , Rossig Claudia , Åman Pierre , Wardelmann Eva , Scholl Claudia , Hartmann Wolfgang , Fröhling Stefan , Trautmann Marcel
Published: November 2024

Supplementary Figure S2. Dose dependent sensitivity of MLS cells to pharmacologic inhibition of WEE 1 activity and cellular localization.
Supplementary Figure S2 from A Histopathologic Correlation Study Evaluating Glymphatic Function in Brain Tumors by Multiparametric MRI
Authors: Gao Min , Liu Zhengliang , Zang Hongjing , Wu Xiong , Yan Yizhong , Lin Hai , Yuan Jianmin , Liu Tianming , Zhou Yu , Liu Jun
Published: November 2024

Supplementary Figure S2: Box plot of comparisons of MTP quantitative parameters among different brain tumors.
Supplementary Figure S3 from Understanding and Overcoming Resistance to Selective FGFR Inhibitors across FGFR2-Driven Malignancies
Authors: Facchinetti Francesco , Loriot Yohann , Brayé Floriane , Vasseur Damien , Bahleda Rastislav , Bigot Ludovic , Barbé Rémy , Nobre Catline , Combarel David , Michiels Stefan , Italiano Antoine , Smolenschi Cristina , Tselikas Lambros , Scoazec Jean-Yves , Ponce-Aix Santiago , Besse Benjamin , André Fabrice , Olaussen Ken A. , Hollebecque Antoine , Friboulet Luc
Published: November 2024

Clinico-radiological and molecular evolution of patient MR408, suffering from a FGFR2-rearranged intrahepatic cholangiocarcinoma.
Supplementary Tables 1 from Clonal Hematopoiesis and Clinical Outcomes in Metastatic Castration-Resistant Prostate Cancer Patients Given Androgen Receptor Pathway Inhibitors (Alliance A031201)
Authors: Jensen Jeffrey L. , Bobek Olivia , Chan Irenaeus C. C. , Miller Brian C. , Hillman David W. , Heller Glenn , Druley Todd , Armstrong Andrew J. , Morris Michael J. , Milowsky Matthew I. , Beltran Himisha , Bolton Kelly L. , Coombs Catherine C.
Published: November 2024

Supplementary Tables
Supplementary Figure S7 from The Immune-Related 27-Gene Signature DetermaIO Predicts Response to Neoadjuvant Atezolizumab plus Chemotherapy in Triple-Negative Breast Cancer
Authors: Dugo Matteo , Huang Chiun-Sheng , Egle Daniel , Bermejo Begoña , Zamagni Claudio , Seitz Robert S. , Nielsen Tyler J. , Thill Marc , Antón-Torres Antonio , Russo Stefania , Ciruelos Eva Maria , Schweitzer Brock L. , Ross Douglas T. , Galbardi Barbara , Greil Richard , Semiglazov Vladimir , Gyorffy Balázs , Colleoni Marco , Kelly Catherine M. , Mariani Gabriella , Del Mastro Lucia , Blasi Olivia , Callari Maurizio , Pusztai Lajos , Valagussa Pinuccia , Viale Giuseppe , Gianni Luca , Bianchini Giampaolo
Published: November 2024

Supplementary Figure 7. Association of TNBC subtypes and IO classifications. A) Frequency of RT-qPCR DetermaIO class by TNBC subtype; B) Frequency of RNAseq IO classes by TNBC subtype. C) Multivariable logistic regression model with TNBC subtypes and RT-qPCR IO score for association with pCR by treatment arm. Odds ratio (OR) and 95% confidence intervals are shown. D) Multivariable logistic regression model with TNBC subtypes and RNA-Seq IO score for association with pCR by treatment arm. Odds ratio (OR) and 95% confidence intervals are shown.
Supplementary Data 1 from Clonal Hematopoiesis and Clinical Outcomes in Metastatic Castration-Resistant Prostate Cancer Patients Given Androgen Receptor Pathway Inhibitors (Alliance A031201)
Authors: Jensen Jeffrey L. , Bobek Olivia , Chan Irenaeus C. C. , Miller Brian C. , Hillman David W. , Heller Glenn , Druley Todd , Armstrong Andrew J. , Morris Michael J. , Milowsky Matthew I. , Beltran Himisha , Bolton Kelly L. , Coombs Catherine C.
Published: November 2024

Supplementary Data
Supplementary Table S2 from A Phase 1 First-in-Human Study of the MCL-1 Inhibitor AZD5991 in Patients with Relapsed/Refractory Hematologic Malignancies
Authors: Desai Pinkal , Lonial Sagar , Cashen Amanda , Kamdar Manali , Flinn Ian , O’Brien Susan , Garcia Jacqueline S. , Korde Neha , Moslehi Javid , Wey Margaret , Cheung Patricia , Sharma Shringi , Olabode Damilola , Chen Hong , Ali Syed Firasath , Liu Mary , Saeh Jamal , Andrade-Campos Marcio , Kadia Tapan M. , Blachly James S.
Published: November 2024

Early phase clinical trials with various MCL-1 inhibitors
Supplementary Figure S2 from Multiomics Profiling Distinguishes Sebaceous Carcinoma from Benign Sebaceous Neoplasms and Provides Insight into the Genetic Evolution of Sebaceous Carcinogenesis
Authors: Starrett Gabriel J. , Baikie Brittany C. , Stoff Benjamin K. , Grossniklaus Hans E. , Van Buren Inga , Berry Elizabeth G. , Novoa Roberto A. , Rieger Kerri E. , Sarin Kavita Y. , Lynch Charles F. , Royer Michael C. , Piaskowski Mary L. , Brownell Isaac , Chu Emily Y. , Godse Rama , Chen Suephy C. , Yu Kelly J. , Goldstein Alisa M. , Engels Eric A. , Sargen Michael R.
Published: November 2024

Correlation between tumor purity as determined by pathology versus as calculated by PURPLE.
Supplementary Figure S1 from Multiomics Profiling Distinguishes Sebaceous Carcinoma from Benign Sebaceous Neoplasms and Provides Insight into the Genetic Evolution of Sebaceous Carcinogenesis
Authors: Starrett Gabriel J. , Baikie Brittany C. , Stoff Benjamin K. , Grossniklaus Hans E. , Van Buren Inga , Berry Elizabeth G. , Novoa Roberto A. , Rieger Kerri E. , Sarin Kavita Y. , Lynch Charles F. , Royer Michael C. , Piaskowski Mary L. , Brownell Isaac , Chu Emily Y. , Godse Rama , Chen Suephy C. , Yu Kelly J. , Goldstein Alisa M. , Engels Eric A. , Sargen Michael R.
Published: November 2024

A. PCA plot of all transcriptomes including normal tissues. B. Upset plot of common differentially expressed genes between all histological category comparisons.
Data from The Immune-Related 27-Gene Signature DetermaIO Predicts Response to Neoadjuvant Atezolizumab plus Chemotherapy in Triple-Negative Breast Cancer
Authors: Dugo Matteo , Huang Chiun-Sheng , Egle Daniel , Bermejo Begoña , Zamagni Claudio , Seitz Robert S. , Nielsen Tyler J. , Thill Marc , Antón-Torres Antonio , Russo Stefania , Ciruelos Eva Maria , Schweitzer Brock L. , Ross Douglas T. , Galbardi Barbara , Greil Richard , Semiglazov Vladimir , Gyorffy Balázs , Colleoni Marco , Kelly Catherine M. , Mariani Gabriella , Del Mastro Lucia , Blasi Olivia , Callari Maurizio , Pusztai Lajos , Valagussa Pinuccia , Viale Giuseppe , Gianni Luca , Bianchini Giampaolo
Published: November 2024

AbstractPurpose:
We assessed the 27-gene RT-qPCR–based DetermaIO assay and the same score calculated from RNA sequencing (RNA-seq) data as predictors of sensitivity to immune checkpoint therapy in the neoTRIPaPDL1 randomized trial that compared neoadjuvant carboplatin/nab-paclitaxel chemotherapy (CT) plus atezolizumab with CT alone in stage II/III triple-negative breast cancer. We also assessed the predictive function of the immuno-oncology (IO) score in expression data of patients treated with pembrolizumab plus paclitaxel (N = 29) or CT alone (N = 56) in the I-SPY2 trial.
Experimental Design:
RNA-seq data were obtained from pretreatment core biopsies from 242 (93.8%) of the 258 patients in the per-protocol-population. The DetermaIO RT-qPCR test, performed in the CAP/CLIA-accredited laboratory of Oncocyte Corp., was available for 220 patients (85.3%). A previously established threshold was used to assign DetermaIO-positive versus DetermaIO-negative status. Publicly available microarray data were used from I-SPY2.
Results:
IO scores calculated from RNA-seq and RT-qPCR data were highly concordant. In neoTRIPaPDL1, DetermaIO-positive cancers (N = 92, 41.8%) had pathologic complete response (pCR) rates of 69.8% and 46.9% in the CT + atezolizumab and CT arms, respectively. In DetermaIO-negative cases, pCR rates were similar in both arms (44.6% vs. 49.2%; interaction test P = 0.04). PDL1 protein expression and stromal tumor–infiltrating lymphocyte count were not predictive of differential benefit from atezolizumab. In I-SPY2, IO-positive cancers (45.9%) had pCR rates of 85.7% and 16%, with and without immunotherapy, respectively. In IO-negative cancers, pCR rates were 46.7% versus 16.1%.
Conclusions:
DetermaIO identified patients who benefited from neoadjuvant immunotherapy resulting in improved pCR rate, independently of PDL1.
Supplementary Figure S4 from GRAIL1 Stabilizes Misfolded Mutant p53 through a Ubiquitin Ligase-Independent, Chaperone Regulatory Function
Authors: Ray Paramita , Jaiswal Sangeeta , Ferrer-Torres Daysha , Wang Zhuwen , Nancarrow Derek , Curtin Meghan , Martinho May San , Lacy Shannon M. , Kasturirangan Srimathi , Thomas Dafydd , Spence Jason R. , Truttmann Matthias C. , Lagisetty Kiran H. , Lawrence Theodore S. , Wang Thomas D. , Beer David G. , Ray Dipankar
Published: November 2024

Supplementary Figure S4. Frag J/Jm mediated loss of misfolded Fluc-DM is independent of mutant p53 status.
Supplementary Figure 5 from METTL14-Mediated Bim mRNA m6A Modification Augments Osimertinib Sensitivity in EGFR-Mutant NSCLC Cells
Authors: Fan Siwen , Lv Xinwu , Zhang Chuantao , Zeng Bingbing , Liang Yanqing , Chen Danyang , Xu Zumin , Li Pan , Wu Shanshan , Liu Hao , Luo Kai , Liu Zongcai , Yi Yanmei
Published: November 2024

S5. METTL14 promotes cell apoptosis in NSCLC cells.
Supplementary Figure 3 from METTL14-Mediated Bim mRNA m6A Modification Augments Osimertinib Sensitivity in EGFR-Mutant NSCLC Cells
Authors: Fan Siwen , Lv Xinwu , Zhang Chuantao , Zeng Bingbing , Liang Yanqing , Chen Danyang , Xu Zumin , Li Pan , Wu Shanshan , Liu Hao , Luo Kai , Liu Zongcai , Yi Yanmei
Published: November 2024

S3. The effect of METTL3 on the osimertinib sensitivity of NSCLC cells.
Table S8 from lncRNA-WAL Promotes Triple-Negative Breast Cancer Aggression by Inducing β-Catenin Nuclear Translocation
Authors: Huang Hongyan , Jin Haiyun , Lei Rong , He Zhanghai , He Shishi , Chen Jiewen , Saw Phei E. , Qiu Zhu , Ren Guosheng , Nie Yan
Published: November 2024

Sequences for siRNAs in vitro.
Table S6 from lncRNA-WAL Promotes Triple-Negative Breast Cancer Aggression by Inducing β-Catenin Nuclear Translocation
Authors: Huang Hongyan , Jin Haiyun , Lei Rong , He Zhanghai , He Shishi , Chen Jiewen , Saw Phei E. , Qiu Zhu , Ren Guosheng , Nie Yan
Published: November 2024

Univariate and multivariate Cox proportional hazard analyses of DFS in 189 patients with TNBC.
Figure S2 from lncRNA-WAL Promotes Triple-Negative Breast Cancer Aggression by Inducing β-Catenin Nuclear Translocation
Authors: Huang Hongyan , Jin Haiyun , Lei Rong , He Zhanghai , He Shishi , Chen Jiewen , Saw Phei E. , Qiu Zhu , Ren Guosheng , Nie Yan
Published: November 2024

Association of lnc-WAL expression with the clinical outcome of patients with multiple cancers.
Figure S4 from hnRNPAB Promotes Pancreatic Ductal Adenocarcinoma Extravasation and Liver Metastasis by Stabilizing MYC mRNA
Authors: Lei Ke , Sun Mingyue , Chen Xianghan , Wang Jia , Liu Xiaolan , Ning Ying , Ping Shuai , Gong Ruining , Zhang Yu , Qing Gong , Zhao Chenyang , Ren He
Published: November 2024

Prediction of hnRNPAB binding to MYC mRNA and effects of hnRNPAB on MYC protein stability
Supplementary Table S2 from Single-Cell View of Tumor Microenvironment Gradients in Pleural Mesothelioma
Authors: Giotti Bruno , Dolasia Komal , Zhao William , Cai Peiwen , Sweeney Robert , Merritt Elliot , Kiner Evgeny , Kim Grace S. , Bhagwat Atharva , Nguyen Thinh , Hegde Samarth , Fitzgerald Bailey G. , Shroff Sanjana , Dawson Travis , Garcia-Barros Monica , Abdul-Ghafar Jamshid , Chen Rachel , Gnjatic Sacha , Soto Alan , Brody Rachel , Kim-Schulze Seunghee , Chen Zhihong , Beaumont Kristin G. , Merad Miriam , Flores Raja M. , Sebra Robert P. , Horowitz Amir , Marron Thomas U. , Tocheva Anna , Wolf Andrea , Tsankov Alexander M.
Published: November 2024

De novo markers discovered for each major cell type annotation in tumor samples
Supplementary Table S1 from Single-Cell View of Tumor Microenvironment Gradients in Pleural Mesothelioma
Authors: Giotti Bruno , Dolasia Komal , Zhao William , Cai Peiwen , Sweeney Robert , Merritt Elliot , Kiner Evgeny , Kim Grace S. , Bhagwat Atharva , Nguyen Thinh , Hegde Samarth , Fitzgerald Bailey G. , Shroff Sanjana , Dawson Travis , Garcia-Barros Monica , Abdul-Ghafar Jamshid , Chen Rachel , Gnjatic Sacha , Soto Alan , Brody Rachel , Kim-Schulze Seunghee , Chen Zhihong , Beaumont Kristin G. , Merad Miriam , Flores Raja M. , Sebra Robert P. , Horowitz Amir , Marron Thomas U. , Tocheva Anna , Wolf Andrea , Tsankov Alexander M.
Published: November 2024

Patients clinical information
Supplementary Figure S3 from Single-Cell View of Tumor Microenvironment Gradients in Pleural Mesothelioma
Authors: Giotti Bruno , Dolasia Komal , Zhao William , Cai Peiwen , Sweeney Robert , Merritt Elliot , Kiner Evgeny , Kim Grace S. , Bhagwat Atharva , Nguyen Thinh , Hegde Samarth , Fitzgerald Bailey G. , Shroff Sanjana , Dawson Travis , Garcia-Barros Monica , Abdul-Ghafar Jamshid , Chen Rachel , Gnjatic Sacha , Soto Alan , Brody Rachel , Kim-Schulze Seunghee , Chen Zhihong , Beaumont Kristin G. , Merad Miriam , Flores Raja M. , Sebra Robert P. , Horowitz Amir , Marron Thomas U. , Tocheva Anna , Wolf Andrea , Tsankov Alexander M.
Published: November 2024

Fetal-like, cancer-specific PLVAP+ endothelial cells associate with angiogenesis
Supplementary Methods from The N6-methyladenosine Epitranscriptomic Landscape of Lung Adenocarcinoma
Authors: Wang Shiyan , Zeng Yong , Zhu Lin , Zhang Min , Zhou Lei , Yang Weixiong , Luo Weishan , Wang Lina , Liu Yanming , Zhu Helen , Xu Xin , Su Peiran , Zhang Xinyue , Ahmed Musaddeque , Chen Wei , Chen Moliang , Chen Sujun , Slobodyanyuk Mykhaylo , Xie Zhongpeng , Guan Jiansheng , Zhang Wen , Khan Aafaque Ahmad , Sakashita Shingo , Liu Ni , Pham Nhu-An , Boutros Paul C. , Ke Zunfu , Moran Michael F. , Cai Zongwei , Cheng Chao , Yu Jun , Tsao Ming S. , He Housheng H.
Published: November 2024

All methods used in this study.
Supplementary Figure S3 from The N6-methyladenosine Epitranscriptomic Landscape of Lung Adenocarcinoma
Authors: Wang Shiyan , Zeng Yong , Zhu Lin , Zhang Min , Zhou Lei , Yang Weixiong , Luo Weishan , Wang Lina , Liu Yanming , Zhu Helen , Xu Xin , Su Peiran , Zhang Xinyue , Ahmed Musaddeque , Chen Wei , Chen Moliang , Chen Sujun , Slobodyanyuk Mykhaylo , Xie Zhongpeng , Guan Jiansheng , Zhang Wen , Khan Aafaque Ahmad , Sakashita Shingo , Liu Ni , Pham Nhu-An , Boutros Paul C. , Ke Zunfu , Moran Michael F. , Cai Zongwei , Cheng Chao , Yu Jun , Tsao Ming S. , He Housheng H.
Published: November 2024

Differences between m6A level-derived patient subtypes.
Supplementary Figure 2 from The SCRUM-MONSTAR Cancer-Omics Ecosystem: Striving for a Quantum Leap in Precision Medicine
Authors: Hashimoto Tadayoshi , Nakamura Yoshiaki , Fujisawa Takao , Imai Mitsuho , Shibuki Taro , Iida Naoko , Ozaki Hiroshi , Nonomura Norio , Morizane Chigusa , Iwata Hiroji , Okano Susumu , Yamagami Wataru , Yamazaki Naoya , Kadowaki Shigenori , Taniguchi Hiroya , Ueno Makoto , Boku Shogen , Oki Eiji , Komatsu Yoshito , Yuki Satoshi , Makiyama Akitaka , Otsuka Tomoyuki , Hara Hiroki , Okano Naohiro , Nishina Tomohiro , Sakamoto Yasutoshi , Miki Izumi , Kobayashi Shin , Yuda Junichiro , Kageyama Shun-Ichiro , Nagamine Michiko , Sakashita Shingo , Sakamoto Naoya , Yamashita Riu , Koga Yoshikatsu , Bando Hideaki , Ishii Genichiro , Kuwata Takeshi , Park Woong-Yang , Ohtsu Atsushi , Yoshino Takayuki
Published: November 2024

Comparison of overall survival between patients receiving matched therapy vs. those not receiving matched therapy among patients with any targets recommended for matched therapy.
Supplementary Figure 1 from The SCRUM-MONSTAR Cancer-Omics Ecosystem: Striving for a Quantum Leap in Precision Medicine
Authors: Hashimoto Tadayoshi , Nakamura Yoshiaki , Fujisawa Takao , Imai Mitsuho , Shibuki Taro , Iida Naoko , Ozaki Hiroshi , Nonomura Norio , Morizane Chigusa , Iwata Hiroji , Okano Susumu , Yamagami Wataru , Yamazaki Naoya , Kadowaki Shigenori , Taniguchi Hiroya , Ueno Makoto , Boku Shogen , Oki Eiji , Komatsu Yoshito , Yuki Satoshi , Makiyama Akitaka , Otsuka Tomoyuki , Hara Hiroki , Okano Naohiro , Nishina Tomohiro , Sakamoto Yasutoshi , Miki Izumi , Kobayashi Shin , Yuda Junichiro , Kageyama Shun-Ichiro , Nagamine Michiko , Sakashita Shingo , Sakamoto Naoya , Yamashita Riu , Koga Yoshikatsu , Bando Hideaki , Ishii Genichiro , Kuwata Takeshi , Park Woong-Yang , Ohtsu Atsushi , Yoshino Takayuki
Published: November 2024

Conceptual framework for virtual sequencing using deep learning.
Supplementary Figure 1 from Rational Protein Engineering to Enhance MHC-Independent T-cell Receptors
Authors: Chang Ju-Fang , Landmann Jack H. , Chang Tien-Ching , Selli Mehmet Emrah , Tenzin Yangdon , Warrington John M. , Ritchey Julie , Hsu Yu-Sung , Slade Michael , Gupta Deepesh Kumar , DiPersio John F. , Holehouse Alex S. , Singh Nathan
Published: November 2024

Supplementary Figure 1 | a-b, Expression of NFAT and NFκB in reporter Jurkat cells expressing CARs or miTCRs in response to a, CD19+ Nalm6 or CD19- Molm14. b, Representative flow cytometry plot of TCR constant chain expression after dual TRAC and TRBC disruption. c, Summary data of TCR surface expression after dual TRAC and TRBC disruption in n=8 individual donors. Analysis using two-sided Student’s t-test.
Supplementary Figure 3 from ROR2 Regulates Cellular Plasticity in Pancreatic Neoplasia and Adenocarcinoma
Authors: Benitz Simone , Steep Alec , Nasser Malak M. , Preall Jonathan , Mahajan Ujjwal Mukund , McQuithey Holly , Loveless Ian , Davis Erick T. , Wen Hui-Ju , Long Daniel W. , Metzler Thomas , Zwernik Samuel , Louw Michaela , Rempinski Donald , Salas-Escabillas Daniel J. , Brender Sydney M. , Song Linghao , Huang Ling , Theisen Brian K. , Zhang Zhenyu , Steele Nina G. , Regel Ivonne , Bednar Filip , Crawford Howard C.
Published: November 2024

Ror2 is expressed in pancreatic meta/neoplastic lesions with a gastric neck/SPEM cell-like identity.
Supplementary Figure 1 from ROR2 Regulates Cellular Plasticity in Pancreatic Neoplasia and Adenocarcinoma
Authors: Benitz Simone , Steep Alec , Nasser Malak M. , Preall Jonathan , Mahajan Ujjwal Mukund , McQuithey Holly , Loveless Ian , Davis Erick T. , Wen Hui-Ju , Long Daniel W. , Metzler Thomas , Zwernik Samuel , Louw Michaela , Rempinski Donald , Salas-Escabillas Daniel J. , Brender Sydney M. , Song Linghao , Huang Ling , Theisen Brian K. , Zhang Zhenyu , Steele Nina G. , Regel Ivonne , Bednar Filip , Crawford Howard C.
Published: November 2024

Using snATAC-seq to study early transformation of the pancreas.
Supplemental Table 8 from Mechanisms of Resistance to Oncogenic KRAS Inhibition in Pancreatic Cancer
Authors: Dilly Julien , Hoffman Megan T. , Abbassi Laleh , Li Ziyue , Paradiso Francesca , Parent Brendan D. , Hennessey Connor J. , Jordan Alexander C. , Morgado Micaela , Dasgupta Shatavisha , Uribe Giselle A. , Yang Annan , Kapner Kevin S. , Hambitzer Felix P. , Qiang Li , Feng Hanrong , Geisberg Jacob , Wang Junning , Evans Kyle E. , Lyu Hengyu , Schalck Aislyn , Feng Ningping , Lopez Anastasia M. , Bristow Christopher A. , Kim Michael P. , Rajapakshe Kimal I. , Bahrambeigi Vahid , Roth Jennifer A. , Garg Kavita , Guerrero Paola A. , Stanger Ben Z. , Cristea Simona , Lowe Scott W. , Baslan Timour , Van Allen Eliezer M. , Mancias Joseph D. , Chan Emily , Anderson Abraham , Katlinskaya Yuliya V. , Shalek Alex K. , Hong David S. , Pant Shubham , Hallin Jill , Anderes Kenna , Olson Peter , Heffernan Timothy P. , Chugh Seema , Christensen James G. , Maitra Anirban , Wolpin Brian M. , Raghavan Srivatsan , Nowak Jonathan A. , Winter Peter S. , Dougan Stephanie K. , Aguirre Andrew J.
Published: November 2024

List of genes in each NMF metaprograms