bluuebunny/crossref_metadata_2025 huggingface dataset:
Detection of Data Integrity Attack Using Model and Data-Driven-Based Approach in CPPS
Authors: Varshini G. Y. Sree , Latha S.
Published: December 2023

The cyber-physical power system (CPPS) is a modern infrastructure utilising information and communication technology that has become more vulnerable to cyberattacks in recent years. The attack magnitude injected by the adversary is stealthier and it cannot be detected using conventional bad data detection techniques. Protecting sensitive data from data integrity attacks (DIA) is essential for ensuring system security and reliability. A tragic event will occur if the attack goes unreported. Therefore, DIA detection is highly vital for the operator in the control centre to make important decisions. This paper addresses the attack impact on WAC applications and attack detection using the model-based method and data-driven-based methods. On the basis of the validation of performance indicators, various detection approaches are simulated and compared to determine the best detection strategy. Simulation results show that in the model-based anomaly detection method, the recursive polynomial model estimator (RPME) has better detection performance than the recursive least square estimator (RLSE). The convolutional neural network- (CNN-) based data-driven anomaly detection technique outperforms other machine learning (ML) techniques such as support vector machine (SVM), K-nearest neighbour (KNN), and random forest (RF). On the WSCC 3 machine 9-bus system, the efficacy of the suggested methods is evaluated.
The One Health Epidemiology of Avian Influenza Infection in Bangladesh: Lessons Learned from the Past 15 Years
Authors: Hassan Mohammad Mahmudul , Dutta Pronesh , Islam Md Mazharul , Ahaduzzaman Md. , Chakma Shovon , Islam Ariful , Magalhaes Ricardo J. Soares
Published: December 2023

Avian influenza viruses (AIVs) are significant transboundary zoonotic pathogens that concern both animal and human. Since the first report of H5N1 AIV in Bangladesh in early 2007, it resulted in numerous outbreaks across the country, hindering the sustainable growth of the poultry industry through economic losses in different production systems (commercial and backyard). Highly pathogenic avian influenza (HPAI) virus and low pathogenic avian influenza (LPAI) virus are currently cocirculating and causing infection in poultry sectors in an endemic manner in Bangladesh as well as in wild bird species. The introduction of multiple clades of H5N1 in different poultry species and the reassortment of AIVs with different patterns of infections have complicated the epidemiological situation for control and created conditions to increase the virulence of the virus, host range, and potential zoonotic transmission. The risk of viral transmission at the human–poultry interface is increasing over time due to inadequate surveillance and early detection strategies and practices, ineffective biosecurity practices among poultry raisers, and the complex supply chains of backyard and commercial poultry and live bird market (LBM) systems. Improving AIV surveillance in poultry flocks and LBMs, vaccination, biosecurity, and awareness among poultry professionals is beneficial to controlling the disease burden in the poultry sector. However, human cases of AIV related to poultry production and marketing chain in Bangladesh suggest a One Health approach engaging various stakeholders from the public and private would be a better option for successfully controlling avian influenza outbreaks in Bangladesh. This review of literature presents the comprehensive overview of AIV infection status in Bangladesh, including a description of pathways for zoonotic transmission at different epidemiological interfaces, the genetic evolution of the virus, and the need for improvement of disease control strategies incorporated with early detection, application of effective vaccines, increases the proper biosecurity practices and improvement of awareness among the poultry raisers, traders and consumers using a One Health approach.
Application of WCA-RBF Neural Network in Fault Diagnosis of Analog Circuits
Authors: Jin Zhiwei
Published: December 2023

With the fast growth of electronic technology today, the hybrid circuit of analog and digital circuits has become a trend in the growth of electronic technology. To address fault diagnosis in analog circuits, Haar wavelet is applied for fault feature extraction. The K-means clustering method and pseudoinverse algorithm were used to optimize the center value and weight value of the radial basis function neural network, respectively. The adaptive step size was improved in the wolf pack algorithm, the parameters of the radial basis function neural network were optimized based on the wolf pack algorithm, and a wolf pack algorithm optimized radial basis function neural network model was constructed. The test results show that this model converges after 40 times of training, with an error value of 10−3 and an average value of the mean squared error of 0.45. Comparing the fault diagnosis rates of the original model, genetic algorithm optimized radial basis function neural network model, and wolf colony algorithm optimized radial basis function neural network model, the last model has the best fault diagnosis rate, reaching 95.52%. The wolf colony algorithm is utilized to optimize the radial basis function neural network model to diagnose the faults in the standard filter circuit, and the fault diagnosis rate reaches 96.17%. The findings express that the radial basis function neural network model optimized by the wolf colony algorithm has a good diagnosis effect for different faults in the analog circuit.
Supplementary Data from Fatty Acid Oxidation Mediated by Malonyl-CoA Decarboxylase Represses Renal Cell Carcinoma Progression
Authors: Zhou Lijie , Luo Yongbo , Liu Yuenan , Zeng Youmiao , Tong Junwei , Li Mengting , Hou Yaxin , Du Kaixuan , Qi Yabin , Pan Wenbang , Liu Yuanhao , Wang Rongli , Tian Fengyan , Gu Chaohui , Chen Ke
Published: December 2023

Supplementary Figure 1-7 and Supplementary Figure legends
Table S4 from Neurotransmitter Receptor HTR2B Regulates Lipid Metabolism to Inhibit Ferroptosis in Gastric Cancer
Authors: Tu Ru-Hong , Wu Sheng-Ze , Huang Ze-Ning , Zhong Qing , Ye Yin-Hua , Zheng Chao-Hui , Xie Jian-Wei , Wang Jia-Bin , Lin Jian-Xian , Chen Qi-Yue , Huang Chang-Ming , Lin Mi , Lu Jun , Cao Long-Long , Li Ping
Published: December 2023

ABCD1 Potential Promoter Regions and Primers used for Chromatin Immunoprecipitation (CHIP) in this study
Supplementary Table S1 from CK1δ and CK1ε Signaling Sustains Mitochondrial Metabolism and Cell Survival in Multiple Myeloma
Authors: Burger Karen L. , Fernandez Mario R. , Meads Mark B. , Sudalagunta Praneeth , Oliveira Paula S. , Renatino Canevarolo Rafael , Alugubelli Raghunandan Reddy , Tungsevik Alexandre , De Avila Gabe , Silva Maria , Graeter Allison I. , Dai Hongyue A. , Vincelette Nicole D. , Prabhu Antony , Magaletti Dario , Yang Chunying , Li Weimin , Kulkarni Amit , Hampton Oliver , Koomen John M. , Roush William R. , Monastyrskyi Andrii , Berglund Anders E. , Silva Ariosto S. , Cleveland John L. , Shain Kenneth H.
Published: December 2023

Table S1
Table S3 from Neurotransmitter Receptor HTR2B Regulates Lipid Metabolism to Inhibit Ferroptosis in Gastric Cancer
Authors: Tu Ru-Hong , Wu Sheng-Ze , Huang Ze-Ning , Zhong Qing , Ye Yin-Hua , Zheng Chao-Hui , Xie Jian-Wei , Wang Jia-Bin , Lin Jian-Xian , Chen Qi-Yue , Huang Chang-Ming , Lin Mi , Lu Jun , Cao Long-Long , Li Ping
Published: December 2023

Si-RNA used in this study
Table S2 from Neurotransmitter Receptor HTR2B Regulates Lipid Metabolism to Inhibit Ferroptosis in Gastric Cancer
Authors: Tu Ru-Hong , Wu Sheng-Ze , Huang Ze-Ning , Zhong Qing , Ye Yin-Hua , Zheng Chao-Hui , Xie Jian-Wei , Wang Jia-Bin , Lin Jian-Xian , Chen Qi-Yue , Huang Chang-Ming , Lin Mi , Lu Jun , Cao Long-Long , Li Ping
Published: December 2023

Primers used in this study
Table S1 from Neurotransmitter Receptor HTR2B Regulates Lipid Metabolism to Inhibit Ferroptosis in Gastric Cancer
Authors: Tu Ru-Hong , Wu Sheng-Ze , Huang Ze-Ning , Zhong Qing , Ye Yin-Hua , Zheng Chao-Hui , Xie Jian-Wei , Wang Jia-Bin , Lin Jian-Xian , Chen Qi-Yue , Huang Chang-Ming , Lin Mi , Lu Jun , Cao Long-Long , Li Ping
Published: December 2023

Neurotransmitter receptors analyzed in this study
Supplementary Figure S5 Part 1 from CK1δ and CK1ε Signaling Sustains Mitochondrial Metabolism and Cell Survival in Multiple Myeloma
Authors: Burger Karen L. , Fernandez Mario R. , Meads Mark B. , Sudalagunta Praneeth , Oliveira Paula S. , Renatino Canevarolo Rafael , Alugubelli Raghunandan Reddy , Tungsevik Alexandre , De Avila Gabe , Silva Maria , Graeter Allison I. , Dai Hongyue A. , Vincelette Nicole D. , Prabhu Antony , Magaletti Dario , Yang Chunying , Li Weimin , Kulkarni Amit , Hampton Oliver , Koomen John M. , Roush William R. , Monastyrskyi Andrii , Berglund Anders E. , Silva Ariosto S. , Cleveland John L. , Shain Kenneth H.
Published: December 2023

Figure S5 Part 1
Supplementary Data 5 from Neurotransmitter Receptor HTR2B Regulates Lipid Metabolism to Inhibit Ferroptosis in Gastric Cancer
Authors: Tu Ru-Hong , Wu Sheng-Ze , Huang Ze-Ning , Zhong Qing , Ye Yin-Hua , Zheng Chao-Hui , Xie Jian-Wei , Wang Jia-Bin , Lin Jian-Xian , Chen Qi-Yue , Huang Chang-Ming , Lin Mi , Lu Jun , Cao Long-Long , Li Ping
Published: December 2023

Whole transcriptome sequencing between HGC-27 cells treated with 5-HT and control.
Supplementary Data 1 from Neurotransmitter Receptor HTR2B Regulates Lipid Metabolism to Inhibit Ferroptosis in Gastric Cancer
Authors: Tu Ru-Hong , Wu Sheng-Ze , Huang Ze-Ning , Zhong Qing , Ye Yin-Hua , Zheng Chao-Hui , Xie Jian-Wei , Wang Jia-Bin , Lin Jian-Xian , Chen Qi-Yue , Huang Chang-Ming , Lin Mi , Lu Jun , Cao Long-Long , Li Ping
Published: December 2023

Supplementary Data 1-Whole-transcriptome sequencing of 49 human gastric tumor tissues and adjacent non-tumor tissues in our center
Figure S6 from CK1δ and CK1ε Signaling Sustains Mitochondrial Metabolism and Cell Survival in Multiple Myeloma
Authors: Burger Karen L. , Fernandez Mario R. , Meads Mark B. , Sudalagunta Praneeth , Oliveira Paula S. , Renatino Canevarolo Rafael , Alugubelli Raghunandan Reddy , Tungsevik Alexandre , De Avila Gabe , Silva Maria , Graeter Allison I. , Dai Hongyue A. , Vincelette Nicole D. , Prabhu Antony , Magaletti Dario , Yang Chunying , Li Weimin , Kulkarni Amit , Hampton Oliver , Koomen John M. , Roush William R. , Monastyrskyi Andrii , Berglund Anders E. , Silva Ariosto S. , Cleveland John L. , Shain Kenneth H.
Published: December 2023

Supplementary Figure S6
SUPPLEMENTARY FIGURE LEGENDS from Neurotransmitter Receptor HTR2B Regulates Lipid Metabolism to Inhibit Ferroptosis in Gastric Cancer
Authors: Tu Ru-Hong , Wu Sheng-Ze , Huang Ze-Ning , Zhong Qing , Ye Yin-Hua , Zheng Chao-Hui , Xie Jian-Wei , Wang Jia-Bin , Lin Jian-Xian , Chen Qi-Yue , Huang Chang-Ming , Lin Mi , Lu Jun , Cao Long-Long , Li Ping
Published: December 2023

SUPPLEMENTARY FIGURE LEGENDS
Figure S4 from Neurotransmitter Receptor HTR2B Regulates Lipid Metabolism to Inhibit Ferroptosis in Gastric Cancer
Authors: Tu Ru-Hong , Wu Sheng-Ze , Huang Ze-Ning , Zhong Qing , Ye Yin-Hua , Zheng Chao-Hui , Xie Jian-Wei , Wang Jia-Bin , Lin Jian-Xian , Chen Qi-Yue , Huang Chang-Ming , Lin Mi , Lu Jun , Cao Long-Long , Li Ping
Published: December 2023

Supplementary Figure 4. HTR2B attenuates lipid peroxidation in gastric cancer. (A-B) HGC-27 cells were treated with vehicle or SB204741 (100 μM) for 48 h. (A) Representative confocal images of HGC-27 cells visualized by DCFH-DA dye-based fluorescence imaging. (B) Intracellular ROS levels in HGC-27 cells at indicated times were quantified by DCFH-DA fluorescence (n = 3). **p < 0.01. (C) Representative confocal images of AGS cells stained with C11-BODIPY dye after treatment with vehicle or SB204741 (100 μM) for 48 h. (D-F) HGC-27 and AGS cells in sh-HTR2B or sh-vector groups were subjected to SB204741 treatment (100 μM) for 48 h. (D) Representative confocal images showing fluorescence staining in AGS cells using C11 BODIPY dye. (E) Levels of intracellular lipid ROS in AGS and HGC-27 cells were quantified by C11-BODIPY fluorescence (n = 3). ***p < 0.001. (F) Representative confocal images showing fluorescence staining in HGC-27 cells using C11-BODIPY dye.
Figure S3 from Neurotransmitter Receptor HTR2B Regulates Lipid Metabolism to Inhibit Ferroptosis in Gastric Cancer
Authors: Tu Ru-Hong , Wu Sheng-Ze , Huang Ze-Ning , Zhong Qing , Ye Yin-Hua , Zheng Chao-Hui , Xie Jian-Wei , Wang Jia-Bin , Lin Jian-Xian , Chen Qi-Yue , Huang Chang-Ming , Lin Mi , Lu Jun , Cao Long-Long , Li Ping
Published: December 2023

Supplementary Figure 3. HTR2B gene silencing or treatment with antagonist reduced the gastric tumor burden in vivo. (A) HE and IHC staining (CK-pan, CD45) were performed to verify the nature of tumor tissues harvested from patient-derived xenograft (PDX) mice as gastric cancer. (B and C) Subcutaneous xenograft tumors harvested after 3 weeks of SB204741 treatment. *p < 0.05. (D) 5-HT levels in the culture supernatants of AGS, HGC-27 and GES-1 cells measured with an enzyme-linked immunosorbent assay (ELISA) kit (n = 3). (E) Plasma concentrations of 5-HT in nu/nu nude mice quantified using an ELISA kit (n = 10). (F and G) Representative IHC images of proliferation markers (Ki-67, PCNA) in subcutaneous xenograft tumors. (H-K) Annexin V-PE/7-AAD apoptosis flow assay in HGC-27 (H and I) and AGS (J and K) cells in the presence of vehicle or 5-HT (10 μM) for 48 h. PE Annexin V and 7-AAD negative (viable or no measurable apoptosis); PE Annexin V positive and 7-AAD negative (early apoptosis, membrane integrity present); and PE Annexin V and 7-AAD positive (end apoptosis and death).
Supplementary figure S1-4 from The Tumor Immune Microenvironment Architecture Correlates with Risk of Recurrence in Head and Neck Squamous Cell Carcinoma
Authors: Weed Donald T. , Zilio Serena , McGee Christie , Marnissi Boutheina , Sargi Zoukaa , Franzmann Elizabeth , Thomas Giovana , Leibowitz Jason , Nicolli Elizabeth , Arnold David , Bicciato Silvio , Serafini Paolo
Published: December 2023

supplementary figure S1-4
Supplementary Table S8 from The BRD4–NUT Fusion Alone Drives Malignant Transformation of NUT Carcinoma
Authors: Durall R. Taylor , Huang Julianna , Wojenski Luke , Huang Yeying , Gokhale Prafulla C. , Leeper Brittaney A. , Nash Joshua O. , Ballester Pedro L. , Davidson Scott , Shlien Adam , Sotirakis Emmanuel , Bertaux Fabien , Dubus Vincent , Luo Jia , Wu Catherine J. , Keskin Derin B. , Eagen Kyle P. , Shapiro Geoffrey I. , French Christopher A.
Published: December 2023

Supplementary Table S8. Confocal laser power and detector settings
Supplementary Table S6 from The BRD4–NUT Fusion Alone Drives Malignant Transformation of NUT Carcinoma
Authors: Durall R. Taylor , Huang Julianna , Wojenski Luke , Huang Yeying , Gokhale Prafulla C. , Leeper Brittaney A. , Nash Joshua O. , Ballester Pedro L. , Davidson Scott , Shlien Adam , Sotirakis Emmanuel , Bertaux Fabien , Dubus Vincent , Luo Jia , Wu Catherine J. , Keskin Derin B. , Eagen Kyle P. , Shapiro Geoffrey I. , French Christopher A.
Published: December 2023

Supplementary Table S6. antibodies used for immunoblotting.
Supplementary Table S12 from The BRD4–NUT Fusion Alone Drives Malignant Transformation of NUT Carcinoma
Authors: Durall R. Taylor , Huang Julianna , Wojenski Luke , Huang Yeying , Gokhale Prafulla C. , Leeper Brittaney A. , Nash Joshua O. , Ballester Pedro L. , Davidson Scott , Shlien Adam , Sotirakis Emmanuel , Bertaux Fabien , Dubus Vincent , Luo Jia , Wu Catherine J. , Keskin Derin B. , Eagen Kyle P. , Shapiro Geoffrey I. , French Christopher A.
Published: December 2023

Supplementary Table S12. OTTER scores for mNC tumor tissue, including all cluster with score >0.25.
Supplementary Table S11 from The BRD4–NUT Fusion Alone Drives Malignant Transformation of NUT Carcinoma
Authors: Durall R. Taylor , Huang Julianna , Wojenski Luke , Huang Yeying , Gokhale Prafulla C. , Leeper Brittaney A. , Nash Joshua O. , Ballester Pedro L. , Davidson Scott , Shlien Adam , Sotirakis Emmanuel , Bertaux Fabien , Dubus Vincent , Luo Jia , Wu Catherine J. , Keskin Derin B. , Eagen Kyle P. , Shapiro Geoffrey I. , French Christopher A.
Published: December 2023

Supplementary Table S11. OTTER scores for hNC cell lines, including all cluster with score > 0.05.
Supplementary Fig. S1 from The BRD4–NUT Fusion Alone Drives Malignant Transformation of NUT Carcinoma
Authors: Durall R. Taylor , Huang Julianna , Wojenski Luke , Huang Yeying , Gokhale Prafulla C. , Leeper Brittaney A. , Nash Joshua O. , Ballester Pedro L. , Davidson Scott , Shlien Adam , Sotirakis Emmanuel , Bertaux Fabien , Dubus Vincent , Luo Jia , Wu Catherine J. , Keskin Derin B. , Eagen Kyle P. , Shapiro Geoffrey I. , French Christopher A.
Published: December 2023

Supplementary Fig. S1. Creation of an inducible, highly penetrant genetically engineered mouse model of NUT carcinoma.
AACR Project GENIE Consortium Members from Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal
Authors: de Bruijn Ino , Kundra Ritika , Mastrogiacomo Brooke , Tran Thinh Ngoc , Sikina Luke , Mazor Tali , Li Xiang , Ochoa Angelica , Zhao Gaofei , Lai Bryan , Abeshouse Adam , Baiceanu Diana , Ciftci Ersin , Dogrusoz Ugur , Dufilie Andrew , Erkoc Ziya , Garcia Lara Elena , Fu Zhaoyuan , Gross Benjamin , Haynes Charles , Heath Allison , Higgins David , Jagannathan Prasanna , Kalletla Karthik , Kumari Priti , Lindsay James , Lisman Aaron , Leenknegt Bas , Lukasse Pieter , Madela Divya , Madupuri Ramyasree , van Nierop Pim , Plantalech Oleguer , Quach Joyce , Resnick Adam C. , Rodenburg Sander Y.A. , Satravada Baby A. , Schaeffer Fedde , Sheridan Robert , Singh Jessica , Sirohi Rajat , Sumer Selcuk Onur , van Hagen Sjoerd , Wang Avery , Wilson Manda , Zhang Hongxin , Zhu Kelsey , Rusk Nicole , Brown Samantha , Lavery Jessica A. , Panageas Katherine S. , Rudolph Julia E. , LeNoue-Newton Michele L. , Warner Jeremy L. , Guo Xindi , Hunter-Zinck Haley , Yu Thomas V. , Pilai Shirin , Nichols Chelsea , Gardos Stuart M. , Philip John , Kehl Kenneth L. , Riely Gregory J. , Schrag Deborah , Lee Jocelyn , Fiandalo Michael V. , Sweeney Shawn M. , Pugh Trevor J. , Sander Chris , Cerami Ethan , Gao Jianjiong , Schultz Nikolaus
Published: December 2023

List of AACR Project GENIE Consortium Members
AACR Project GENIE BPC Core Team Members from Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal
Authors: de Bruijn Ino , Kundra Ritika , Mastrogiacomo Brooke , Tran Thinh Ngoc , Sikina Luke , Mazor Tali , Li Xiang , Ochoa Angelica , Zhao Gaofei , Lai Bryan , Abeshouse Adam , Baiceanu Diana , Ciftci Ersin , Dogrusoz Ugur , Dufilie Andrew , Erkoc Ziya , Garcia Lara Elena , Fu Zhaoyuan , Gross Benjamin , Haynes Charles , Heath Allison , Higgins David , Jagannathan Prasanna , Kalletla Karthik , Kumari Priti , Lindsay James , Lisman Aaron , Leenknegt Bas , Lukasse Pieter , Madela Divya , Madupuri Ramyasree , van Nierop Pim , Plantalech Oleguer , Quach Joyce , Resnick Adam C. , Rodenburg Sander Y.A. , Satravada Baby A. , Schaeffer Fedde , Sheridan Robert , Singh Jessica , Sirohi Rajat , Sumer Selcuk Onur , van Hagen Sjoerd , Wang Avery , Wilson Manda , Zhang Hongxin , Zhu Kelsey , Rusk Nicole , Brown Samantha , Lavery Jessica A. , Panageas Katherine S. , Rudolph Julia E. , LeNoue-Newton Michele L. , Warner Jeremy L. , Guo Xindi , Hunter-Zinck Haley , Yu Thomas V. , Pilai Shirin , Nichols Chelsea , Gardos Stuart M. , Philip John , Kehl Kenneth L. , Riely Gregory J. , Schrag Deborah , Lee Jocelyn , Fiandalo Michael V. , Sweeney Shawn M. , Pugh Trevor J. , Sander Chris , Cerami Ethan , Gao Jianjiong , Schultz Nikolaus
Published: December 2023

List of AACR Project GENIE BPC Core Team Members
Table S3 from Combinatorial Treatment with PARP and MAPK Inhibitors Overcomes Phenotype Switch-Driven Drug Resistance in Advanced Melanoma
Authors: Ferretti Lorenza P. , Böhi Flurina , Leslie Pedrioli Deena M. , Cheng Phil F. , Ferrari Elena , Baumgaertner Petra , Alvarado-Diaz Abdiel , Sella Federica , Cereghetti Alessandra , Turko Patrick , Wright Roni H. , De Bock Katrien , Speiser Daniel E. , Ferrari Roberto , Levesque Mitchell P. , Hottiger Michael O.
Published: December 2023

LGX818 versus LGX818/TAL LFQ proteome analyses.
Supplementary Tables S5-8 from EZH2 Cooperates with BRD4-NUT to Drive NUT Carcinoma Growth by Silencing Key Tumor Suppressor Genes
Authors: Huang Yeying , Durall R. Taylor , Luong Nhi M. , Hertzler Hans J. , Huang Julianna , Gokhale Prafulla C. , Leeper Brittaney A. , Persky Nicole S. , Root David E. , Anekal Praju V. , Montero Llopis Paula D.L.M. , David Clement N. , Kutok Jeffery L. , Raimondi Alejandra , Saluja Karan , Luo Jia , Zahnow Cynthia A. , Adane Biniam , Stegmaier Kimberly , Hawkins Catherine E. , Ponne Christopher , Le Quan , Shapiro Geoffrey I. , Lemieux Madeleine E. , Eagen Kyle P. , French Christopher A.
Published: December 2023

Supplementary Tables S5-8
Supplementary Fig. S6 from EZH2 Cooperates with BRD4-NUT to Drive NUT Carcinoma Growth by Silencing Key Tumor Suppressor Genes
Authors: Huang Yeying , Durall R. Taylor , Luong Nhi M. , Hertzler Hans J. , Huang Julianna , Gokhale Prafulla C. , Leeper Brittaney A. , Persky Nicole S. , Root David E. , Anekal Praju V. , Montero Llopis Paula D.L.M. , David Clement N. , Kutok Jeffery L. , Raimondi Alejandra , Saluja Karan , Luo Jia , Zahnow Cynthia A. , Adane Biniam , Stegmaier Kimberly , Hawkins Catherine E. , Ponne Christopher , Le Quan , Shapiro Geoffrey I. , Lemieux Madeleine E. , Eagen Kyle P. , French Christopher A.
Published: December 2023

Supplementary Fig. S6
Supplementary Fig. S4 from EZH2 Cooperates with BRD4-NUT to Drive NUT Carcinoma Growth by Silencing Key Tumor Suppressor Genes
Authors: Huang Yeying , Durall R. Taylor , Luong Nhi M. , Hertzler Hans J. , Huang Julianna , Gokhale Prafulla C. , Leeper Brittaney A. , Persky Nicole S. , Root David E. , Anekal Praju V. , Montero Llopis Paula D.L.M. , David Clement N. , Kutok Jeffery L. , Raimondi Alejandra , Saluja Karan , Luo Jia , Zahnow Cynthia A. , Adane Biniam , Stegmaier Kimberly , Hawkins Catherine E. , Ponne Christopher , Le Quan , Shapiro Geoffrey I. , Lemieux Madeleine E. , Eagen Kyle P. , French Christopher A.
Published: December 2023

Supplementary Fig. S4
Supplementary Data from The Next-Generation Oral Selective Estrogen Receptor Degrader Camizestrant (AZD9833) Suppresses ER+ Breast Cancer Growth and Overcomes Endocrine and CDK4/6 Inhibitor Resistance
Authors: Lawson Mandy , Cureton Natalie , Ros Susana , Cheraghchi-Bashi Azadeh , Urosevic Jelena , D'Arcy Sophie , Delpuech Oona , DuPont Michelle , Fisher David I. , Gangl Eric T. , Lewis Hilary , Trueman Dawn , Wali Neha , Williamson Stuart C. , Moss Jennifer , Montaudon Elodie , Derrien Heloise , Marangoni Elisabetta , Miragaia Ricardo J. , Gagrica Sladjana , Morentin-Gutierrez Pablo , Moss Thomas A. , Maglennon Gareth , Sutton Daniel , Polanski Radoslaw , Rosen Alan , Cairns Jonathan , Zhang Pei , Sánchez-Guixé Mònica , Serra Violeta , Critchlow Susan E. , Scott James S. , Lindemann Justin P.O. , Barry Simon T. , Klinowska Teresa , Morrow Christopher J. , S Carnevalli Larissa
Published: December 2023

Supplementary file containing all supplementary tables, text and figures
Data from Neurotransmitter Receptor HTR2B Regulates Lipid Metabolism to Inhibit Ferroptosis in Gastric Cancer
Authors: Tu Ru-Hong , Wu Sheng-Ze , Huang Ze-Ning , Zhong Qing , Ye Yin-Hua , Zheng Chao-Hui , Xie Jian-Wei , Wang Jia-Bin , Lin Jian-Xian , Chen Qi-Yue , Huang Chang-Ming , Lin Mi , Lu Jun , Cao Long-Long , Li Ping
Published: December 2023

Abstract
Nerves can support tumor development by secreting neurotransmitters that promote cancer cell proliferation and invasion. 5-Hydroxytryptamine (5-HT) is a critical neurotransmitter in the gastrointestinal nervous system, and 5-HT signaling has been shown to play a role in tumorigenesis. Here, we found that expression of the 5-HT receptor HTR2B was significantly elevated in human gastric adenocarcinoma tissues compared with nontumor tissues, and high HTR2B expression corresponded to shorter patient survival. Both 5-HT and a specific HTR2B agonist enhanced gastric adenocarcinoma cell viability under metabolic stress, reduced cellular and lipid reactive oxygen species, and suppressed ferroptosis; conversely, HTR2B loss or inhibition with a selective HTR2B antagonist yielded the inverse tumor suppressive effects. In a patient-derived xenograft tumor model, HTR2B-positive tumors displayed accelerated growth, which was inhibited by HTR2B antagonists. Single-cell analysis of human gastric adenocarcinoma tissues revealed enrichment of PI3K/Akt/mTOR and fatty acid metabolism–related gene clusters in cells expressing HTR2B compared with HTR2B-negative cells. Mechanistically, HTR2B cooperated with Fyn to directly regulate p85 activity and trigger the PI3K/Akt/mTOR signaling pathway, which led to increased expression of HIF1α and ABCD1 along with decreased levels of lipid peroxidation and ferroptosis. Together, these findings demonstrate that HTR2B activity modulates PI3K/Akt/mTOR signaling to stimulate gastric cancer cell survival and indicate that HTR2B expression could be a potential prognostic biomarker in patients with gastric cancer.
Significance:
Nerve cancer cross-talk mediated by HTR2B inhibits lipid peroxidation and ferroptosis in gastric cancer cells and promotes viability under metabolic stress, resulting in increased tumor growth and decreased patient survival.
Data from CK1δ and CK1ε Signaling Sustains Mitochondrial Metabolism and Cell Survival in Multiple Myeloma
Authors: Burger Karen L. , Fernandez Mario R. , Meads Mark B. , Sudalagunta Praneeth , Oliveira Paula S. , Renatino Canevarolo Rafael , Alugubelli Raghunandan Reddy , Tungsevik Alexandre , De Avila Gabe , Silva Maria , Graeter Allison I. , Dai Hongyue A. , Vincelette Nicole D. , Prabhu Antony , Magaletti Dario , Yang Chunying , Li Weimin , Kulkarni Amit , Hampton Oliver , Koomen John M. , Roush William R. , Monastyrskyi Andrii , Berglund Anders E. , Silva Ariosto S. , Cleveland John L. , Shain Kenneth H.
Published: December 2023

Abstract
Multiple myeloma remains an incurable malignancy due to acquisition of intrinsic programs that drive therapy resistance. Here we report that casein kinase-1δ (CK1δ) and CK1ε are therapeutic targets in multiple myeloma that are necessary to sustain mitochondrial metabolism. Specifically, the dual CK1δ/CK1ε inhibitor SR-3029 had potent in vivo and ex vivo anti–multiple myeloma activity, including against primary multiple myeloma patient specimens. RNA sequencing (RNA-seq) and metabolic analyses revealed inhibiting CK1δ/CK1ε disables multiple myeloma metabolism by suppressing genes involved in oxidative phosphorylation (OxPhos), reducing citric acid cycle intermediates, and suppressing complexes I and IV of the electron transport chain. Finally, sensitivity of multiple myeloma patient specimens to SR-3029 correlated with elevated expression of mitochondrial genes, and RNA-seq from 687 multiple myeloma patient samples revealed that increased CSNK1D, CSNK1E, and OxPhos genes correlate with disease progression and inferior outcomes. Thus, increases in mitochondrial metabolism are a hallmark of multiple myeloma progression that can be disabled by targeting CK1δ/CK1ε.
Significance:
CK1δ and CK1ε are attractive therapeutic targets in multiple myeloma whose expression increases with disease progression and connote poor outcomes, and that are necessary to sustain expression of genes directing OxPhos.
Data from EZH2 Cooperates with BRD4-NUT to Drive NUT Carcinoma Growth by Silencing Key Tumor Suppressor Genes
Authors: Huang Yeying , Durall R. Taylor , Luong Nhi M. , Hertzler Hans J. , Huang Julianna , Gokhale Prafulla C. , Leeper Brittaney A. , Persky Nicole S. , Root David E. , Anekal Praju V. , Montero Llopis Paula D.L.M. , David Clement N. , Kutok Jeffery L. , Raimondi Alejandra , Saluja Karan , Luo Jia , Zahnow Cynthia A. , Adane Biniam , Stegmaier Kimberly , Hawkins Catherine E. , Ponne Christopher , Le Quan , Shapiro Geoffrey I. , Lemieux Madeleine E. , Eagen Kyle P. , French Christopher A.
Published: December 2023

Abstract
NUT carcinoma is an aggressive carcinoma driven by the BRD4-NUT fusion oncoprotein, which activates chromatin to promote expression of progrowth genes. BET bromodomain inhibitors (BETi) are a promising treatment for NUT carcinoma that can impede BRD4-NUT's ability to activate genes, but the efficacy of BETi as monotherapy is limited. Here, we demonstrated that enhancer of zeste homolog 2 (EZH2), which silences genes through establishment of repressive chromatin, is a dependency in NUT carcinoma. Inhibition of EZH2 with the clinical compound tazemetostat potently blocked growth of NUT carcinoma cells. Epigenetic and transcriptomic analysis revealed that tazemetostat reversed the EZH2-specific H3K27me3 silencing mark and restored expression of multiple tumor suppressor genes while having no effect on key oncogenic BRD4-NUT–regulated genes. Indeed, H3K27me3 and H3K27ac domains were found to be mutually exclusive in NUT carcinoma cells. CDKN2A was identified as the only gene among all tazemetostat-derepressed genes to confer resistance to tazemetostat in a CRISPR-Cas9 screen. Combined inhibition of EZH2 and BET synergized to downregulate cell proliferation genes, resulting in more pronounced growth arrest and differentiation than either inhibitor alone. In preclinical models, combined tazemetostat and BETi synergistically blocked tumor growth and prolonged survival of NUT carcinoma–xenografted mice, with complete remission without relapse in one cohort. Identification of EZH2 as a dependency in NUT carcinoma substantiates the reliance of NUT carcinoma tumor cells on epigenetic dysregulation of functionally opposite, yet highly complementary, chromatin regulatory pathways to maintain NUT carcinoma growth.
Significance:
Repression of tumor suppressor genes, including CDKN2A, by EZH2 provides a mechanistic rationale for combining EZH2 and BET inhibitors for the clinical treatment of NUT carcinoma.

See related commentary by Kazansky and Kentsis, p. 3827
Supplemental Table 2 from Unraveling Racial Disparities in Supportive Care Medication Use among End-of-Life Pancreatic Cancer Patients: Focus on Pain Management and Psychiatric Therapies
Authors: Allen John M. , Awunti MegCholack , Guo Yi , Bian Jiang , Rogers Sherise C. , Scarton Lisa , DeRemer David L. , Wilkie Diana J.
Published: December 2023

Supplemental Table 2 shows clinical and demographic characteristics by race and gender for the included study participants
Supplementary Table S1 from Prediction of Risk of Metastatic Recurrence for Female Breast Cancer Patients in the Presence of Competing Causes of Death
Authors: Mariotto Angela B. , Botta Laura , Bernasconi Alice , Zou Zhaohui , Gatta Gemma , Capocaccia Riccardo
Published: December 2023

Supplementary Table S1 is an extended version of Table 1, that includes estimates for 10 and 15 years post-diagnosis.
Table S6 from Cancer Prevalence Projections in Japan and Decomposition Analysis of Changes in Cancer Burden, 2020–2050: A Statistical Modeling Study
Authors: Nguyen Phuong The , Hori Megumi , Matsuda Tomohiro , Katanoda Kota
Published: December 2023

All-site combined projections of 10-yaer prevalence count and rate in 2020 and 2050, by age group
Table S10 from Cancer Prevalence Projections in Japan and Decomposition Analysis of Changes in Cancer Burden, 2020–2050: A Statistical Modeling Study
Authors: Nguyen Phuong The , Hori Megumi , Matsuda Tomohiro , Katanoda Kota
Published: December 2023

Site-specific projections of 10-year prevalence count in 2020, 2030, 2040 and 2050
Figure S7 from Cancer Prevalence Projections in Japan and Decomposition Analysis of Changes in Cancer Burden, 2020–2050: A Statistical Modeling Study
Authors: Nguyen Phuong The , Hori Megumi , Matsuda Tomohiro , Katanoda Kota
Published: December 2023

Scenario-specific projections with sensitivity range in 2020–2050, by cancer site, of male 10-year prevalence count
Notes: (*) the main projection scenario with the dynamic population demographics, incidence rates, and survival rates; Cancer sites are ordered based on sex-specific prevalence count in year 2020 as reading order, with the highest at the top-left and the lowest at the bottom-right corner.
Figure S17 from Cancer Prevalence Projections in Japan and Decomposition Analysis of Changes in Cancer Burden, 2020–2050: A Statistical Modeling Study
Authors: Nguyen Phuong The , Hori Megumi , Matsuda Tomohiro , Katanoda Kota
Published: December 2023

Decomposed absolute changes over 2020–2050 by cancer site, of male 10-year prevalence count
Notes: Prevalence in year 2020 is used as reference year; Absolute changes in a specific year X is calculated as Prevalence in year X – Prevalence in reference year. Cancer sites are ordered based on sex-specific prevalence count in year 2020 as reading order, with the highest at the top-left and the lowest at the bottom-right corner.
Figure S14 from Cancer Prevalence Projections in Japan and Decomposition Analysis of Changes in Cancer Burden, 2020–2050: A Statistical Modeling Study
Authors: Nguyen Phuong The , Hori Megumi , Matsuda Tomohiro , Katanoda Kota
Published: December 2023

Decomposed absolute changes over 2020–2050 by cancer site, of male 5-year prevalence count
Notes: Prevalence in year 2020 is used as reference year; Absolute changes in a specific year X is calculated as Prevalence in year X – Prevalence in reference year. Cancer sites are ordered based on sex-specific prevalence count in year 2020 as reading order, with the highest at the top-left and the lowest at the bottom-right corner.
Figure S1 from Cancer Prevalence Projections in Japan and Decomposition Analysis of Changes in Cancer Burden, 2020–2050: A Statistical Modeling Study
Authors: Nguyen Phuong The , Hori Megumi , Matsuda Tomohiro , Katanoda Kota
Published: December 2023

All-site combined 10-year cancer projections by sex, 2020–2050
Notes: Panel (A): Scenario-specific projections with sensitivity range of prevalence count; (*) the main projection scenario with the dynamic population demographics, incidence rates, and survival rates; the red dashed lines signify the projected peak of prevalence counts (horizontal line) and the corresponding year of the peak (vertical line).
Panel (B): Age distribution of cancer survivor.
Panel (C): Decomposed absolute changes over 2020–2050 in prevalence count; Absolute changes in a specific year X is calculated as Prevalence in year X – Prevalence in reference year. Prevalence in year 2020 is used as reference year.  
Figure S1 from Cancer Incidence and Mortality Estimates in Arab Countries in 2018: A GLOBOCAN Data Analysis
Authors: Al-Muftah Mariam , Al-Ejeh Fares
Published: December 2023

Figure S1: Age-standardized rate (ASR) for cancer incidence and mortality, and mortality-to-incidence ratio (MIR) in both sexes in 2018. (A) Comparison of ASR (per 100,000 people) in Arab countries in the MENA region (right) to the worldwide (global) ASR (left) for cancer incidence (blue) and cancer mortality (red) in 2018 for all ages. ASR is shown for all cancers and each cancer site. Cancer sites are ranked according to ASR incidence in the MENA-Arab region. The ASR for incidence and mortality are shown in blue and red, respectively. Cancer sites that are labelled in red had higher incidence and mortality in the MENA-Arab region than the global rates in 2018. (B) ASR for incidence (blue) and mortality (red) for all cancers in 2018 in both sexes for all ages in 186 countries. Countries were ranked according to the mortality-to-incidence ratio (MIR); mortality ASR divided by incidence ASR (black dots). The 22 Arab countries are labelled in red. (C) Top 15 cancer sites for incidence (B) and mortality (C) in both sexes, in 2018 for all ages, in the MENA region and worldwide. The total number of cases is shown in the middle of the donut graphs; the percentage contribution and the number of cases of each cancer site are shown in the legends.
Table S1 from Genome-Wide Association and Two-Sample Mendelian Randomization Analyses of Plasma Ghrelin and Gastrointestinal Cancer Risk
Authors: Larsson Susanna C. , Höijer Jonas , Sun Jing , Li Xue , Burgess Stephen , Michaëlsson Karl
Published: December 2023

Classification of each gastrointestinal cancer in the UK Biobank and FinnGen
Figure S1 from Genome-Wide Association and Two-Sample Mendelian Randomization Analyses of Plasma Ghrelin and Gastrointestinal Cancer Risk
Authors: Larsson Susanna C. , Höijer Jonas , Sun Jing , Li Xue , Burgess Stephen , Michaëlsson Karl
Published: December 2023

Associations of plasma ghrelin with gastrointestinal cancer risk in two-sample MR analysis using the secondary instrument comprising the single cis-SNP
Figure S3 from Hypothetical Interventions on Diet Quality and Lifestyle Factors to Improve Breast Cancer Survival: The Pathways Study
Authors: Ergas Isaac J. , Bradshaw Patrick T. , Cespedes Feliciano Elizabeth M. , Roh Janise M. , Kwan Marilyn L. , Laraia Barbara , Madsen Kristine A. , Yao Song , Thomsen Catherine , Kushi Lawrence H.
Published: December 2023

A simplified causal diagram of the relationship between time-varying diet quality, physical activity and smoking on survival after breast cancer
Supplemental Figure 5 from Gene–Environment Analyses Reveal Novel Genetic Candidates with Prenatal Tobacco Exposure in Relation to Risk for Childhood Acute Lymphoblastic Leukemia
Authors: Zhong Charlie , Li Shaobo , Arroyo Katti , Morimoto Libby M. , de Smith Adam J. , Metayer Catherine , Ma Xiaomei , Kogan Scott C. , Gauderman W. James , Wiemels Joseph L.
Published: December 2023

QQ-plot for CCRLP study, Hispanic participants. As Supp. Fig. 4, but for the CCRLP Hispanic participant strata.
Supplemental Figure 2 from Gene–Environment Analyses Reveal Novel Genetic Candidates with Prenatal Tobacco Exposure in Relation to Risk for Childhood Acute Lymphoblastic Leukemia
Authors: Zhong Charlie , Li Shaobo , Arroyo Katti , Morimoto Libby M. , de Smith Adam J. , Metayer Catherine , Ma Xiaomei , Kogan Scott C. , Gauderman W. James , Wiemels Joseph L.
Published: December 2023

Manhattan plot of gene-environment associations. The figure displays gene-additive results with environment (tobacco) as the outcome.
Data from Prediction of Risk of Metastatic Recurrence for Female Breast Cancer Patients in the Presence of Competing Causes of Death
Authors: Mariotto Angela B. , Botta Laura , Bernasconi Alice , Zou Zhaohui , Gatta Gemma , Capocaccia Riccardo
Published: December 2023

AbstractBackground:
To estimate risk of recurrence for women diagnosed with nonmetastatic breast cancer considering the risks of other causes mortality.
Methods:
We extend a method based on the diagnosis–metastasis–death pathway to include risks of other causes mortality. We estimate three probabilities as cumulative incidence of: (i) being alive and recurrence-free, (ii) death for other causes before a recurrence, and (iii) recurrence. We apply the method to female breast cancer relative survival from the Surveillance, Epidemiology, and End Results Program registries (2000–2018) data.
Results:
The cumulative incidence of recurrence shows a higher increase with more advanced cancer stage and is less influenced by age at diagnosis. At 5 years from diagnosis, the cumulative incidence of recurrence is less than 3% for those diagnosed with stage I, 10% to 13% for those diagnosed with stage II, and 37% to 47% for those diagnosed with stage III breast cancer. The estimates of recurrence considering versus ignoring the risks of dying from other causes were generally consistent, except for older women with more advanced stage, and longer time since diagnosis. In these groups, the net probability of recurrence, excluding the risks of dying from other causes, were overestimated.
Conclusions:
For patients with cancer who are older or long-term survivors, it is important to include the risks of other cause mortality as the crude cumulative incidence of recurrence is a more appropriate measure.
Impact:
These estimates are important in clinical decision making, as higher competing mortality may preclude the benefits of aggressive treatments.
Data from Genome-Wide Association and Two-Sample Mendelian Randomization Analyses of Plasma Ghrelin and Gastrointestinal Cancer Risk
Authors: Larsson Susanna C. , Höijer Jonas , Sun Jing , Li Xue , Burgess Stephen , Michaëlsson Karl
Published: December 2023

AbstractBackground:
Observational studies have suggested that the gut hormone ghrelin is an early marker of future risk of developing gastrointestinal cancer. However, whether ghrelin is a causal risk factor remains unclear. We conducted a genome-wide association study (GWAS) of plasma ghrelin and used Mendelian randomization (MR) to investigate the possible causal association between ghrelin and gastrointestinal cancer risk.
Methods:
Genetic variants associated with plasma ghrelin were identified in a GWAS comprising 10,742 Swedish adults in the discovery (N = 6,259) and replication (N = 4,483) cohorts. The association between ghrelin and gastrointestinal cancer was examined through a two-sample MR analysis using the identified genetic variants as instruments and GWAS data from the UK Biobank, FinnGen, and a colorectal cancer consortium.
Results:
GWAS found associations between multiple genetic variants within ±200 kb of the GHRL gene and plasma ghrelin. A two-sample MR analysis revealed that genetically predicted higher plasma ghrelin levels were associated with a lower risk of gastrointestinal cancer in UK Biobank and in a meta-analysis of the UK Biobank and FinnGen studies. The combined OR per approximate doubling of genetically predicted plasma ghrelin was 0.91 (95% confidence interval, 0.85–0.99; P = 0.02). Colocalization analysis revealed limited evidence of shared causal variants for plasma ghrelin and gastrointestinal cancer at the GHRL locus (posterior probability H4 = 24.5%); however, this analysis was likely underpowered.
Conclusions:
Our study provides evidence in support of a possible causal association between higher plasma ghrelin levels and a reduced risk of gastrointestinal cancer.
Impact:
Elevated plasma ghrelin levels might reduce the risk of gastrointestinal cancer.
Data from Hypothetical Interventions on Diet Quality and Lifestyle Factors to Improve Breast Cancer Survival: The Pathways Study
Authors: Ergas Isaac J. , Bradshaw Patrick T. , Cespedes Feliciano Elizabeth M. , Roh Janise M. , Kwan Marilyn L. , Laraia Barbara , Madsen Kristine A. , Yao Song , Thomsen Catherine , Kushi Lawrence H.
Published: December 2023

AbstractBackground:
The number of breast cancer survivors is increasing, yet evidence to inform dietary and lifestyle guidelines is limited.
Methods:
This analysis included 3,658 participants from the Pathways Study, a prospective cohort of women diagnosed with invasive breast cancer. A healthy plant-based dietary index score (hPDI), an American Cancer Society (ACS) nutrition guidelines score, a 2015 Healthy Eating Index score (HEI), hours per week of moderate to vigorous physical activity (PA), and lifetime cumulative pack-years of cigarette smoking (SM) were each measured at diagnosis, 6, 24, and 72 months. Using g-computation, 5- and 10-year risk ratios (RR), risk differences, and 95% confidence intervals (CI) for all-cause mortality under hypothetical interventions on diet quality, PA, and SM, compared with the natural course (no intervention) were calculated.
Results:
Hypothetical moderate to extreme interventions on hPDI, ACS, and HEI, each in combination with PA and SM, showed 11% to 56%, 9% to 38%, and 9% to 49% decreases in 5-year risks of all-cause mortality compared with no intervention, respectively [(hPDI: RRmoderate = 0.89, 95% CI: 0.82–0.94; RRextreme = 0.44, 95% CI: 0.26–0.67), (ACS: RRmoderate = 0.91, 95% CI: 0.85–0.96; RRextreme = 0.62, 95% CI: 0.43–0.82), (HEI: RRmoderate = 0.91, 95% CI: 0.84–0.95; RRextreme = 0.51, 95% CI: 0.33–0.72)]. While 10-year relative risks were slightly attenuated, absolute risk reductions were more pronounced.
Conclusions:
Interventions to improve diet quality, increase PA, or reduce SM at the time of diagnosis may improve survival among breast cancer survivors.
Impact:
We estimate that over 10% of deaths could be delayed by even moderate adoption of these behaviors.
Data from Gene–Environment Analyses Reveal Novel Genetic Candidates with Prenatal Tobacco Exposure in Relation to Risk for Childhood Acute Lymphoblastic Leukemia
Authors: Zhong Charlie , Li Shaobo , Arroyo Katti , Morimoto Libby M. , de Smith Adam J. , Metayer Catherine , Ma Xiaomei , Kogan Scott C. , Gauderman W. James , Wiemels Joseph L.
Published: December 2023

AbstractBackground:
Associations between maternal tobacco exposure during pregnancy and childhood acute lymphoblastic leukemia (ALL) have yielded mixed results. This may be due to biases in self-reported smoking or other differences in individual-level risk factors. We utilized a biological marker of maternal tobacco exposure to evaluate the association between maternal tobacco exposure during pregnancy, genetics, and subsequent childhood ALL risk in two large population-based studies of childhood ALL in California.
Methods:
Maternal exposure to tobacco smoke was assessed with a validated methylation marker (cg05575921) of the aryl hydrocarbon receptor repressor (AHRR) gene in newborn dried blood spots. We adjusted for sex, birthweight, gestational age, mode of delivery, year of birth, AHRR quantitative trait locus (mQTL) rs77111113, and a polygenetic risk score for childhood ALL. We additionally adjusted for principal components in a gene–environment interaction testing method that incorporates gene-only and environment-only effects along with interactions.
Results:
AHRR hypomethylation overall was not associated with childhood ALL. In gene–environment interaction testing, several genetic variants displayed significant interaction with AHRR hypomethylation and childhood ALL.
Conclusions:
Our results suggest that novel candidates in PTPRK and DPP6 may play a role in tobacco-related leukemogenesis. Further research is necessary to better understand the effects of tobacco and these variants on childhood ALL risk.
Impact:
Despite the lack of an overall “main effect,” tobacco exposure during pregnancy affects childhood ALL risk depending on specific genetic variants.