bluuebunny/crossref_metadata_2025 huggingface dataset:
Factors Influencing Nurses Turnover in Saudi Arabia: A Systematic Review
Authors: Albalawi Abdulmajeed M. , Pascua Glezzeelyne P. , Alsaleh Sameer A. , Sabry Walaa , Ahajan Sitti Nursa , Abdulla Jeseela , Abdulalim Amal , Salih Suad S.
Published: July 2024

The turnover of nurses has been an ordeal for healthcare institutions not only locally but also globally. It ranges from changing unit or workplace to changing or leaving the nursing profession. High turnover rates affect care provision and organizational cost. Therefore, this review aims to identify the factors that influence nurse turnover in Saudi Arabia. An in‐depth review of quantitative, qualitative, and mixed‐method studies that includes all studies on nurses who intend to leave the job or profession (resigned or terminated) in any institution within Saudi Arabia was conducted. The grey literature and studies conducted outside of Saudi Arabia were excluded. PubMed, ScienceDirect, EBSCO Medline, ProQuest, and Web of Science were searched for relevant literature from inception to publication. MMAT (Mixed‐Methods Appraisal Tool) is used to assess the risk of bias. A narrative synthesis of the findings is conducted and presented in a table. A total of 1286 articles were retrieved from the initial search and managed using Rayyan software. Out of 1102 articles screened, 44 articles were reviewed for eligibility yielding to 21 articles included in this study. Preliminary screening is performed by two authors by independently scrutinizing the title and abstracts followed by full‐text screening. The data are carefully extracted by the author and validated by a second reviewer. The factors identified to influence nurse turnover in Saudi Arabia are classified into themes, namely, individual‐related factors (demographic characteristics and psychological and personal aspects), work environment, and organizational factors. More studies are needed in all regions using more robust designs. The review protocol is registered in Prospero with ID no. CRD42023433406.
Amelioration of Oxidative Stress in Rats with Chronic Obstructive Pulmonary Disease through Shenqi Huatan Decoction Activation of Peroxisome Proliferator‐Activated Receptor Gamma‐Mediated Activated Protein Kinase/Forkhead Transcription Factor O3a Signaling Pathway
Authors: Chen Jingjing , Qiao Wenxiao , Xue Xiaoming , Li Dian , Zhang Ye , Xie Di , Wang Jinyun , Sun Yaoqin , Yang Shuo , Yang Zhuomin
Published: July 2024

Background. Chronic obstructive pulmonary disease (COPD) is a common respiratory disease. Currently, no specific treatment strategy has been established; therefore, finding new treatment methods is essential. Clinically, Shenqi Huatan Decoction (SQHT) is a traditional Chinese medicinal formula for COPD treatment; however, its mechanism of action in treatment needs to be clarified. Methods. The COPD rat model was replicated by cigarette smoking and tracheal injection using the LPS method. The control group and the SQHT groups were treated with dexamethasone and SQHT by gavage, respectively. After treatment, superoxide dismutase (SOD) serum levels, total antioxidant capacity (TAOC), lipid peroxidation, and malondialdehyde (MDA) were detected by enzyme‐linked immunosorbent assay (ELISA). Activated protein kinase alpha (AMPK‐α), forkhead transcription factor O3a (FOXO3a), manganese SOD (MnSOD), and peroxisome proliferator‐activated receptor gamma (PPARγ) were detected using reverse transcriptase quantitative polymerase chain reaction (RT‐qPCR) and Western blot. Microribonucleic acid and protein expression levels were measured, and pathological changes in lung tissue were observed using hematoxylin and eosin staining. Results. The pathological findings suggested that SQHT substantially affects COPD treatment by enhancing alveolar fusion and reducing emphysema. ELISA results showed that SQHT could lower the blood levels of MDA and lipid peroxide and raise SOD and TAOC levels, suggesting that it could lessen oxidative stress. In the lung tissue of rats with COPD, large doses of SQHT intervention dramatically increased AMPK protein expression, AMPK‐α, FOXO3a, MnSOD, and PPARγ, indicating that SQHT may reduce oxidative stress by activating the PPARγ‐mediated AMPK/FOXO3a signaling pathway. Similar results were obtained using RT‐qPCR. Conclusion. SQHT is effective for COPD treatment. The mechanism of action may be related to the activation of the PPARγ‐mediated AMPK/FOXO3a signaling pathway to improve oxidative stress in lung tissue.
Dravet Syndrome: A Rare Form of Epilepsy
Authors: Hosani Salwa Al , Varghese Sona
Published: July 2024

Dravet syndrome is a rare and severe form of epilepsy that usually emerges in infancy. It is characterized by diverse seizure patterns, cognitive regression, motor impairments, and behavioral abnormalities. The majority of patients with this condition have mutations involving the voltage‐gated sodium channel alpha (I) gene SCN1A. We present a detailed account of a two‐year‐old child with a history of recurrent seizures since the age of 4 months. Genetic testing was performed which revealed a heterozygous pathogenic variant, confirming the diagnosis. The patient was managed successfully by a multidisciplinary approach involving neurologists, developmental specialists, and physical therapists.
Measuring Incompatibilities between Areas of Life in Cancer Survivors: Development and Psychometric Evaluation of the INCOMPAT‐CS Instrument
Authors: Hiltrop Kati , Breidenbach Clara , Degenhardt Marie , Heidkamp Paula , Heier Lina , Kowalski Christoph , Schellack Sophie , Soff Johannes , Ernstmann Nicole
Published: July 2024

Objective. Due to various long‐term consequences of the disease, cancer survivors (CS) can experience incompatibilities between areas of life (work, family, household, leisure time, and disease) that can be associated with reduced satisfaction with these areas of life. To be able to assess such incompatibilities quantitatively, a six‐item instrument (INCOMPAT‐CS) was developed and psychometrically evaluated. Methods. Based on relevant theories, a multidisciplinary team developed the items of the INCOMPAT‐CS. Descriptive analyses, exploratory and confirmatory factor analyses, as well as validity and reliability assessments were conducted using survey data from n = 293 CS collected as part of the CARES study. Incompatibilities between areas of life were measured with the newly developed INCOMPAT‐CS instrument. Results. A one component solution with satisfactory model fit (normed χ2 = 1.90, RMSEA = 0.06, CFI = 0.99, and TLI = 0.97) was identified in exploratory and confirmatory factor analyses. The INCOMPAT‐CS demonstrated acceptable internal consistency (Cronbach’s α = 0.790) and high test‐retest reliability ρ = 0.569 (p < 0.001). Convergent validity could be demonstrated (CR = 0.858 and AVE = 0.506). Hypothesis‐consistent correlations with role and social functioning measures further indicate convergent validity. Conclusions. The INCOMPAT‐CS is useful to assess incompatibilities among areas of life in CS quantitatively. Moreover, it helps to reveal the areas negatively affected due to incompatibilities. This short instrument can be useful in aftercare or psychosocial support interventions to detect incompatibilities and work towards reducing them as a consequence to increase CS’ wellbeing.
TableS5 from CD106 in Tumor-Specific Exhausted CD8+ T Cells Mediates Immunosuppression by Inhibiting TCR Signaling
Authors: Naoi Yuto , Morinaga Takao , Nagasaki Joji , Ariyasu Ryo , Ueda Youki , Yamashita Kazuo , Zhou Wenhao , Kawashima Shusuke , Kawase Katsushige , Honobe-Tabuchi Akiko , Ohnuma Takehiro , Kawamura Tatsuyoshi , Umeda Yoshiyasu , Kawahara Yu , Nakamura Yasuhiro , Kiniwa Yukiko , Yamasaki Osamu , Fukushima Satoshi , Kawazu Masahito , Suzuki Yutaka , Nishikawa Hiroyoshi , Hanazawa Toyoyuki , Ando Mizuo , Inozume Takashi , Togashi Yosuke
Published: July 2024

Frequencies of exhausted CD8+ T cell cluster
TableS4 from CD106 in Tumor-Specific Exhausted CD8+ T Cells Mediates Immunosuppression by Inhibiting TCR Signaling
Authors: Naoi Yuto , Morinaga Takao , Nagasaki Joji , Ariyasu Ryo , Ueda Youki , Yamashita Kazuo , Zhou Wenhao , Kawashima Shusuke , Kawase Katsushige , Honobe-Tabuchi Akiko , Ohnuma Takehiro , Kawamura Tatsuyoshi , Umeda Yoshiyasu , Kawahara Yu , Nakamura Yasuhiro , Kiniwa Yukiko , Yamasaki Osamu , Fukushima Satoshi , Kawazu Masahito , Suzuki Yutaka , Nishikawa Hiroyoshi , Hanazawa Toyoyuki , Ando Mizuo , Inozume Takashi , Togashi Yosuke
Published: July 2024

Sequencing summary
TableS2 from CD106 in Tumor-Specific Exhausted CD8+ T Cells Mediates Immunosuppression by Inhibiting TCR Signaling
Authors: Naoi Yuto , Morinaga Takao , Nagasaki Joji , Ariyasu Ryo , Ueda Youki , Yamashita Kazuo , Zhou Wenhao , Kawashima Shusuke , Kawase Katsushige , Honobe-Tabuchi Akiko , Ohnuma Takehiro , Kawamura Tatsuyoshi , Umeda Yoshiyasu , Kawahara Yu , Nakamura Yasuhiro , Kiniwa Yukiko , Yamasaki Osamu , Fukushima Satoshi , Kawazu Masahito , Suzuki Yutaka , Nishikawa Hiroyoshi , Hanazawa Toyoyuki , Ando Mizuo , Inozume Takashi , Togashi Yosuke
Published: July 2024

Patient characteristics for prognostic analyses
Supplementary Data from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
Authors: White Brian S. , Woo Xing Yi , Koc Soner , Sheridan Todd , Neuhauser Steven B. , Wang Shidan , Evrard Yvonne A. , Chen Li , Foroughi pour Ali , Landua John D. , Mashl R. Jay , Davies Sherri R. , Fang Bingliang , Rosa Maria Gabriela , Evans Kurt W. , Bailey Matthew H. , Chen Yeqing , Xiao Min , Rubinstein Jill C. , Sanderson Brian J. , Lloyd Michael W. , Domanskyi Sergii , Dobrolecki Lacey E. , Fujita Maihi , Fujimoto Junya , Xiao Guanghua , Fields Ryan C. , Mudd Jacqueline L. , Xu Xiaowei , Hollingshead Melinda G. , Jiwani Shahanawaz , Acevedo Saul , Davis-Dusenbery Brandi N. , Robinson Peter N. , Moscow Jeffrey A. , Doroshow James H. , Mitsiades Nicholas , Kaochar Salma , Pan Chong-xian , Carvajal-Carmona Luis G. , Welm Alana L. , Welm Bryan E. , Govindan Ramaswamy , Li Shunqiang , Davies Michael A. , Roth Jack A. , Meric-Bernstam Funda , Xie Yang , Herlyn Meenhard , Ding Li , Lewis Michael T. , Bult Carol J. , Dean Dennis A. , Chuang Jeffrey H.
Published: July 2024

Supplementary Tables
Figure S9 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
Authors: White Brian S. , Woo Xing Yi , Koc Soner , Sheridan Todd , Neuhauser Steven B. , Wang Shidan , Evrard Yvonne A. , Chen Li , Foroughi pour Ali , Landua John D. , Mashl R. Jay , Davies Sherri R. , Fang Bingliang , Rosa Maria Gabriela , Evans Kurt W. , Bailey Matthew H. , Chen Yeqing , Xiao Min , Rubinstein Jill C. , Sanderson Brian J. , Lloyd Michael W. , Domanskyi Sergii , Dobrolecki Lacey E. , Fujita Maihi , Fujimoto Junya , Xiao Guanghua , Fields Ryan C. , Mudd Jacqueline L. , Xu Xiaowei , Hollingshead Melinda G. , Jiwani Shahanawaz , Acevedo Saul , Davis-Dusenbery Brandi N. , Robinson Peter N. , Moscow Jeffrey A. , Doroshow James H. , Mitsiades Nicholas , Kaochar Salma , Pan Chong-xian , Carvajal-Carmona Luis G. , Welm Alana L. , Welm Bryan E. , Govindan Ramaswamy , Li Shunqiang , Davies Michael A. , Roth Jack A. , Meric-Bernstam Funda , Xie Yang , Herlyn Meenhard , Ding Li , Lewis Michael T. , Bult Carol J. , Dean Dennis A. , Chuang Jeffrey H.
Published: July 2024

Figure S9
Figure S5 from CD106 in Tumor-Specific Exhausted CD8+ T Cells Mediates Immunosuppression by Inhibiting TCR Signaling
Authors: Naoi Yuto , Morinaga Takao , Nagasaki Joji , Ariyasu Ryo , Ueda Youki , Yamashita Kazuo , Zhou Wenhao , Kawashima Shusuke , Kawase Katsushige , Honobe-Tabuchi Akiko , Ohnuma Takehiro , Kawamura Tatsuyoshi , Umeda Yoshiyasu , Kawahara Yu , Nakamura Yasuhiro , Kiniwa Yukiko , Yamasaki Osamu , Fukushima Satoshi , Kawazu Masahito , Suzuki Yutaka , Nishikawa Hiroyoshi , Hanazawa Toyoyuki , Ando Mizuo , Inozume Takashi , Togashi Yosuke
Published: July 2024

Additional CD106 functional analysis data
Figure S7 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
Authors: White Brian S. , Woo Xing Yi , Koc Soner , Sheridan Todd , Neuhauser Steven B. , Wang Shidan , Evrard Yvonne A. , Chen Li , Foroughi pour Ali , Landua John D. , Mashl R. Jay , Davies Sherri R. , Fang Bingliang , Rosa Maria Gabriela , Evans Kurt W. , Bailey Matthew H. , Chen Yeqing , Xiao Min , Rubinstein Jill C. , Sanderson Brian J. , Lloyd Michael W. , Domanskyi Sergii , Dobrolecki Lacey E. , Fujita Maihi , Fujimoto Junya , Xiao Guanghua , Fields Ryan C. , Mudd Jacqueline L. , Xu Xiaowei , Hollingshead Melinda G. , Jiwani Shahanawaz , Acevedo Saul , Davis-Dusenbery Brandi N. , Robinson Peter N. , Moscow Jeffrey A. , Doroshow James H. , Mitsiades Nicholas , Kaochar Salma , Pan Chong-xian , Carvajal-Carmona Luis G. , Welm Alana L. , Welm Bryan E. , Govindan Ramaswamy , Li Shunqiang , Davies Michael A. , Roth Jack A. , Meric-Bernstam Funda , Xie Yang , Herlyn Meenhard , Ding Li , Lewis Michael T. , Bult Carol J. , Dean Dennis A. , Chuang Jeffrey H.
Published: July 2024

Figure S7
Figure S1 from CD106 in Tumor-Specific Exhausted CD8+ T Cells Mediates Immunosuppression by Inhibiting TCR Signaling
Authors: Naoi Yuto , Morinaga Takao , Nagasaki Joji , Ariyasu Ryo , Ueda Youki , Yamashita Kazuo , Zhou Wenhao , Kawashima Shusuke , Kawase Katsushige , Honobe-Tabuchi Akiko , Ohnuma Takehiro , Kawamura Tatsuyoshi , Umeda Yoshiyasu , Kawahara Yu , Nakamura Yasuhiro , Kiniwa Yukiko , Yamasaki Osamu , Fukushima Satoshi , Kawazu Masahito , Suzuki Yutaka , Nishikawa Hiroyoshi , Hanazawa Toyoyuki , Ando Mizuo , Inozume Takashi , Togashi Yosuke
Published: July 2024

Additional scRNA-seq data
Figure S2 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
Authors: White Brian S. , Woo Xing Yi , Koc Soner , Sheridan Todd , Neuhauser Steven B. , Wang Shidan , Evrard Yvonne A. , Chen Li , Foroughi pour Ali , Landua John D. , Mashl R. Jay , Davies Sherri R. , Fang Bingliang , Rosa Maria Gabriela , Evans Kurt W. , Bailey Matthew H. , Chen Yeqing , Xiao Min , Rubinstein Jill C. , Sanderson Brian J. , Lloyd Michael W. , Domanskyi Sergii , Dobrolecki Lacey E. , Fujita Maihi , Fujimoto Junya , Xiao Guanghua , Fields Ryan C. , Mudd Jacqueline L. , Xu Xiaowei , Hollingshead Melinda G. , Jiwani Shahanawaz , Acevedo Saul , Davis-Dusenbery Brandi N. , Robinson Peter N. , Moscow Jeffrey A. , Doroshow James H. , Mitsiades Nicholas , Kaochar Salma , Pan Chong-xian , Carvajal-Carmona Luis G. , Welm Alana L. , Welm Bryan E. , Govindan Ramaswamy , Li Shunqiang , Davies Michael A. , Roth Jack A. , Meric-Bernstam Funda , Xie Yang , Herlyn Meenhard , Ding Li , Lewis Michael T. , Bult Carol J. , Dean Dennis A. , Chuang Jeffrey H.
Published: July 2024

Figure S2
Figure S14 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
Authors: White Brian S. , Woo Xing Yi , Koc Soner , Sheridan Todd , Neuhauser Steven B. , Wang Shidan , Evrard Yvonne A. , Chen Li , Foroughi pour Ali , Landua John D. , Mashl R. Jay , Davies Sherri R. , Fang Bingliang , Rosa Maria Gabriela , Evans Kurt W. , Bailey Matthew H. , Chen Yeqing , Xiao Min , Rubinstein Jill C. , Sanderson Brian J. , Lloyd Michael W. , Domanskyi Sergii , Dobrolecki Lacey E. , Fujita Maihi , Fujimoto Junya , Xiao Guanghua , Fields Ryan C. , Mudd Jacqueline L. , Xu Xiaowei , Hollingshead Melinda G. , Jiwani Shahanawaz , Acevedo Saul , Davis-Dusenbery Brandi N. , Robinson Peter N. , Moscow Jeffrey A. , Doroshow James H. , Mitsiades Nicholas , Kaochar Salma , Pan Chong-xian , Carvajal-Carmona Luis G. , Welm Alana L. , Welm Bryan E. , Govindan Ramaswamy , Li Shunqiang , Davies Michael A. , Roth Jack A. , Meric-Bernstam Funda , Xie Yang , Herlyn Meenhard , Ding Li , Lewis Michael T. , Bult Carol J. , Dean Dennis A. , Chuang Jeffrey H.
Published: July 2024

Figure S14
Table S7 from Single-Cell and Spatial Transcriptome Profiling Identifies the Transcription Factor BHLHE40 as a Driver of EMT in Metastatic Colorectal Cancer
Authors: Yang Sheng , Zhang Dongsheng , Sun Qingyang , Nie Hongxu , Zhang Yue , Wang Xiaowei , Huang Yuanjian , Sun Yueming
Published: July 2024

Supplementary Table S7: Relationship between the clinical and pathologic features of colorectal cancer patients and the expression of the BHLHE40 protein.
Figure S13 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
Authors: White Brian S. , Woo Xing Yi , Koc Soner , Sheridan Todd , Neuhauser Steven B. , Wang Shidan , Evrard Yvonne A. , Chen Li , Foroughi pour Ali , Landua John D. , Mashl R. Jay , Davies Sherri R. , Fang Bingliang , Rosa Maria Gabriela , Evans Kurt W. , Bailey Matthew H. , Chen Yeqing , Xiao Min , Rubinstein Jill C. , Sanderson Brian J. , Lloyd Michael W. , Domanskyi Sergii , Dobrolecki Lacey E. , Fujita Maihi , Fujimoto Junya , Xiao Guanghua , Fields Ryan C. , Mudd Jacqueline L. , Xu Xiaowei , Hollingshead Melinda G. , Jiwani Shahanawaz , Acevedo Saul , Davis-Dusenbery Brandi N. , Robinson Peter N. , Moscow Jeffrey A. , Doroshow James H. , Mitsiades Nicholas , Kaochar Salma , Pan Chong-xian , Carvajal-Carmona Luis G. , Welm Alana L. , Welm Bryan E. , Govindan Ramaswamy , Li Shunqiang , Davies Michael A. , Roth Jack A. , Meric-Bernstam Funda , Xie Yang , Herlyn Meenhard , Ding Li , Lewis Michael T. , Bult Carol J. , Dean Dennis A. , Chuang Jeffrey H.
Published: July 2024

Figure S13
Figure S11 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
Authors: White Brian S. , Woo Xing Yi , Koc Soner , Sheridan Todd , Neuhauser Steven B. , Wang Shidan , Evrard Yvonne A. , Chen Li , Foroughi pour Ali , Landua John D. , Mashl R. Jay , Davies Sherri R. , Fang Bingliang , Rosa Maria Gabriela , Evans Kurt W. , Bailey Matthew H. , Chen Yeqing , Xiao Min , Rubinstein Jill C. , Sanderson Brian J. , Lloyd Michael W. , Domanskyi Sergii , Dobrolecki Lacey E. , Fujita Maihi , Fujimoto Junya , Xiao Guanghua , Fields Ryan C. , Mudd Jacqueline L. , Xu Xiaowei , Hollingshead Melinda G. , Jiwani Shahanawaz , Acevedo Saul , Davis-Dusenbery Brandi N. , Robinson Peter N. , Moscow Jeffrey A. , Doroshow James H. , Mitsiades Nicholas , Kaochar Salma , Pan Chong-xian , Carvajal-Carmona Luis G. , Welm Alana L. , Welm Bryan E. , Govindan Ramaswamy , Li Shunqiang , Davies Michael A. , Roth Jack A. , Meric-Bernstam Funda , Xie Yang , Herlyn Meenhard , Ding Li , Lewis Michael T. , Bult Carol J. , Dean Dennis A. , Chuang Jeffrey H.
Published: July 2024

Figure S11
Table S4 from Single-Cell and Spatial Transcriptome Profiling Identifies the Transcription Factor BHLHE40 as a Driver of EMT in Metastatic Colorectal Cancer
Authors: Yang Sheng , Zhang Dongsheng , Sun Qingyang , Nie Hongxu , Zhang Yue , Wang Xiaowei , Huang Yuanjian , Sun Yueming
Published: July 2024

Supplementary Table S4: CNV score and CNV status of all EPCs.
Supplementary Figure Legends from Single-Cell and Spatial Transcriptome Profiling Identifies the Transcription Factor BHLHE40 as a Driver of EMT in Metastatic Colorectal Cancer
Authors: Yang Sheng , Zhang Dongsheng , Sun Qingyang , Nie Hongxu , Zhang Yue , Wang Xiaowei , Huang Yuanjian , Sun Yueming
Published: July 2024

Supplementary Figure Legends
Figure S8 from Single-Cell and Spatial Transcriptome Profiling Identifies the Transcription Factor BHLHE40 as a Driver of EMT in Metastatic Colorectal Cancer
Authors: Yang Sheng , Zhang Dongsheng , Sun Qingyang , Nie Hongxu , Zhang Yue , Wang Xiaowei , Huang Yuanjian , Sun Yueming
Published: July 2024

Figure S8. Immunofluorescence assay of LoVo and HCT116 cells. Scale bar: 20 µm.
Figure S5 from Single-Cell and Spatial Transcriptome Profiling Identifies the Transcription Factor BHLHE40 as a Driver of EMT in Metastatic Colorectal Cancer
Authors: Yang Sheng , Zhang Dongsheng , Sun Qingyang , Nie Hongxu , Zhang Yue , Wang Xiaowei , Huang Yuanjian , Sun Yueming
Published: July 2024

Figure S5. Spatial transcriptome and spatial trajectory of malignant EPCs_8 and CXCL1+ CAFs. (A–C) Spatial feature plot displaying possible expression distributions of BHLHE40, SMAD3, and SMAD4. (D) Cluster plot of 0–15 subgroups clustered by K-means. (E–G) Spatial feature plot displaying possible expression distributions of malignant EPCs_8, CXCL1+ CAFs, and SFRP2+ CAFs in another sample predicted by SPOTlight. (H) Spatial feature plot displaying possible expression distributions of BHLHE40 in another sample. (I) Possible spatial differentiation trajectory from malignant EPCs_8 to CXCL1+ CAFs in another sample.
Figure S4 from Single-Cell and Spatial Transcriptome Profiling Identifies the Transcription Factor BHLHE40 as a Driver of EMT in Metastatic Colorectal Cancer
Authors: Yang Sheng , Zhang Dongsheng , Sun Qingyang , Nie Hongxu , Zhang Yue , Wang Xiaowei , Huang Yuanjian , Sun Yueming
Published: July 2024

Figure S4. GSEA analysis of BHLHE40 in GSE39582 and cell–cell communication of CXCL1+ CAFs and SFRP2+ CAFs. (A) Enrichment plot of the EMT signaling, the angiogenesis, and the TGFβ signaling of the BHLHE40 high and BHLHE40 low groups in GSE39582. (B) Dot plot of expression of top 30 active ligands in CXCL1+ CAFs and SFRP2+ CAFs. (C) Heatmap of top 30 active ligands in SFRP2+ CAFs sorted by AUPR values. (D) Heatmap of top 30 active ligands and predicted target genes in CXCL1+ CAFs. (E) Heatmap of top 30 active ligands and predicted receptors in CXCL1+ CAFs.
Figure S1 from Single-Cell and Spatial Transcriptome Profiling Identifies the Transcription Factor BHLHE40 as a Driver of EMT in Metastatic Colorectal Cancer
Authors: Yang Sheng , Zhang Dongsheng , Sun Qingyang , Nie Hongxu , Zhang Yue , Wang Xiaowei , Huang Yuanjian , Sun Yueming
Published: July 2024

Figure S1. Display of Leiden clusters and different cluster marker genes. (A) UMAP of all the 166,180 single cells in 22 primary colorectal cancers, annotated by Leiden clusters. (B) Dot plot of common cell markers in different Leiden clusters.
Supplementary Figure 4 from GPR1 and CMKLR1 Control Lipid Metabolism to Support the Development of Clear Cell Renal Cell Carcinoma
Authors: Wang Dazhi , Mahmud Iqbal , Thakur Vijay S. , Tan Sze Kiat , Isom Daniel G. , Lombard David B. , Gonzalgo Mark L. , Kryvenko Oleksandr N. , Lorenzi Philip L. , Tcheuyap Vanina T. , Brugarolas James , Welford Scott M.
Published: July 2024

Supplementary Figure 4
Supplementary Figure 3 from GPR1 and CMKLR1 Control Lipid Metabolism to Support the Development of Clear Cell Renal Cell Carcinoma
Authors: Wang Dazhi , Mahmud Iqbal , Thakur Vijay S. , Tan Sze Kiat , Isom Daniel G. , Lombard David B. , Gonzalgo Mark L. , Kryvenko Oleksandr N. , Lorenzi Philip L. , Tcheuyap Vanina T. , Brugarolas James , Welford Scott M.
Published: July 2024

Supplementary Figure 3
Supplementary Figure 2 from GPR1 and CMKLR1 Control Lipid Metabolism to Support the Development of Clear Cell Renal Cell Carcinoma
Authors: Wang Dazhi , Mahmud Iqbal , Thakur Vijay S. , Tan Sze Kiat , Isom Daniel G. , Lombard David B. , Gonzalgo Mark L. , Kryvenko Oleksandr N. , Lorenzi Philip L. , Tcheuyap Vanina T. , Brugarolas James , Welford Scott M.
Published: July 2024

Supplementary Figure 2
Supplementary Figure 13 from GPR1 and CMKLR1 Control Lipid Metabolism to Support the Development of Clear Cell Renal Cell Carcinoma
Authors: Wang Dazhi , Mahmud Iqbal , Thakur Vijay S. , Tan Sze Kiat , Isom Daniel G. , Lombard David B. , Gonzalgo Mark L. , Kryvenko Oleksandr N. , Lorenzi Philip L. , Tcheuyap Vanina T. , Brugarolas James , Welford Scott M.
Published: July 2024

Supplementary Figure 13
Supplementary Figure 1 from GPR1 and CMKLR1 Control Lipid Metabolism to Support the Development of Clear Cell Renal Cell Carcinoma
Authors: Wang Dazhi , Mahmud Iqbal , Thakur Vijay S. , Tan Sze Kiat , Isom Daniel G. , Lombard David B. , Gonzalgo Mark L. , Kryvenko Oleksandr N. , Lorenzi Philip L. , Tcheuyap Vanina T. , Brugarolas James , Welford Scott M.
Published: July 2024

Supplementary Figure 1
Sequences and antibodies from GPR1 and CMKLR1 Control Lipid Metabolism to Support the Development of Clear Cell Renal Cell Carcinoma
Authors: Wang Dazhi , Mahmud Iqbal , Thakur Vijay S. , Tan Sze Kiat , Isom Daniel G. , Lombard David B. , Gonzalgo Mark L. , Kryvenko Oleksandr N. , Lorenzi Philip L. , Tcheuyap Vanina T. , Brugarolas James , Welford Scott M.
Published: July 2024

Primers, gRNA sequences, and antibodies used
Supplementary Methods from A Tetravalent Bispecific Antibody Selectively Inhibits Diverse FGFR3 Oncogenic Variants
Authors: Yang Yan , Suhasini Avvaru N. , Jiang Zaoli , Liu Nina , Rosconi Michael , Zhang Bojie , Li Yinyin , Dudgeon Drew , Seong Changhyun , Kim Steven , Rafique Ashique , Huang Tammy , Bhosle Sangram , Krueger Pamela , Ullman Erica , Olson William , Lin John C. , Shen Yang , Daly Christopher
Published: July 2024

Supplementary Methods
Supplementary Fig S9 from A Tetravalent Bispecific Antibody Selectively Inhibits Diverse FGFR3 Oncogenic Variants
Authors: Yang Yan , Suhasini Avvaru N. , Jiang Zaoli , Liu Nina , Rosconi Michael , Zhang Bojie , Li Yinyin , Dudgeon Drew , Seong Changhyun , Kim Steven , Rafique Ashique , Huang Tammy , Bhosle Sangram , Krueger Pamela , Ullman Erica , Olson William , Lin John C. , Shen Yang , Daly Christopher
Published: July 2024

Supplementary Fig S9
Supplementary Data from Adhesion GPCR ADGRE2 Maintains Proteostasis to Promote Progression in Acute Myeloid Leukemia
Authors: Huang Deyu , Yu Zebin , Lu Huan , Jiang Penglei , Qian Xinyue , Han Yingli , Qian Pengxu
Published: July 2024

These supplementary data include Figure S1–S7 and Table S1–S4.
Supplementary Fig S4 from A Tetravalent Bispecific Antibody Selectively Inhibits Diverse FGFR3 Oncogenic Variants
Authors: Yang Yan , Suhasini Avvaru N. , Jiang Zaoli , Liu Nina , Rosconi Michael , Zhang Bojie , Li Yinyin , Dudgeon Drew , Seong Changhyun , Kim Steven , Rafique Ashique , Huang Tammy , Bhosle Sangram , Krueger Pamela , Ullman Erica , Olson William , Lin John C. , Shen Yang , Daly Christopher
Published: July 2024

Supplementary Fig S4
Supplementary Fig S3 from A Tetravalent Bispecific Antibody Selectively Inhibits Diverse FGFR3 Oncogenic Variants
Authors: Yang Yan , Suhasini Avvaru N. , Jiang Zaoli , Liu Nina , Rosconi Michael , Zhang Bojie , Li Yinyin , Dudgeon Drew , Seong Changhyun , Kim Steven , Rafique Ashique , Huang Tammy , Bhosle Sangram , Krueger Pamela , Ullman Erica , Olson William , Lin John C. , Shen Yang , Daly Christopher
Published: July 2024

Supplementary Fig S3
Figure S8 from Omental Preadipocytes Stimulate Matrix Remodeling and IGF Signaling to Support Ovarian Cancer Metastasis
Authors: Waters Jennifer A. , Robinson Mikella , Lujano-Olazaba Omar , Lucht Cassidy , Gilbert Samuel F. , House Carrie D.
Published: July 2024

IL-6 and CCL-2 stimulation
Figure S7 from Omental Preadipocytes Stimulate Matrix Remodeling and IGF Signaling to Support Ovarian Cancer Metastasis
Authors: Waters Jennifer A. , Robinson Mikella , Lujano-Olazaba Omar , Lucht Cassidy , Gilbert Samuel F. , House Carrie D.
Published: July 2024

Direct coculture images
Figure S1 from Omental Preadipocytes Stimulate Matrix Remodeling and IGF Signaling to Support Ovarian Cancer Metastasis
Authors: Waters Jennifer A. , Robinson Mikella , Lujano-Olazaba Omar , Lucht Cassidy , Gilbert Samuel F. , House Carrie D.
Published: July 2024

Characterization of preadipocytes
Data from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
Authors: White Brian S. , Woo Xing Yi , Koc Soner , Sheridan Todd , Neuhauser Steven B. , Wang Shidan , Evrard Yvonne A. , Chen Li , Foroughi pour Ali , Landua John D. , Mashl R. Jay , Davies Sherri R. , Fang Bingliang , Raso Maria Gabriela , Evans Kurt W. , Bailey Matthew H. , Chen Yeqing , Xiao Min , Rubinstein Jill C. , Sanderson Brian J. , Lloyd Michael W. , Domanskyi Sergii , Dobrolecki Lacey E. , Fujita Maihi , Fujimoto Junya , Xiao Guanghua , Fields Ryan C. , Mudd Jacqueline L. , Xu Xiaowei , Hollingshead Melinda G. , Jiwani Shahanawaz , Acevedo Saul , Davis-Dusenbery Brandi N. , Robinson Peter N. , Moscow Jeffrey A. , Doroshow James H. , Mitsiades Nicholas , Kaochar Salma , Pan Chong-xian , Carvajal-Carmona Luis G. , Welm Alana L. , Welm Bryan E. , Govindan Ramaswamy , Li Shunqiang , Davies Michael A. , Roth Jack A. , Meric-Bernstam Funda , Xie Yang , Herlyn Meenhard , Ding Li , Lewis Michael T. , Bult Carol J. , Dean Dennis A. , Chuang Jeffrey H.
Published: July 2024

Abstract
Patient-derived xenografts (PDX) model human intra- and intertumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histologic imaging via hematoxylin and eosin (H&E) staining is routinely performed on PDX samples, which could be harnessed for computational analysis. Prior studies of large clinical H&E image repositories have shown that deep learning analysis can identify intercellular and morphologic signals correlated with disease phenotype and therapeutic response. In this study, we developed an extensive, pan-cancer repository of >1,000 PDX and paired parental tumor H&E images. These images, curated from the PDX Development and Trial Centers Research Network Consortium, had a range of associated genomic and transcriptomic data, clinical metadata, pathologic assessments of cell composition, and, in several cases, detailed pathologic annotations of neoplastic, stromal, and necrotic regions. The amenability of these images to deep learning was highlighted through three applications: (i) development of a classifier for neoplastic, stromal, and necrotic regions; (ii) development of a predictor of xenograft-transplant lymphoproliferative disorder; and (iii) application of a published predictor of microsatellite instability. Together, this PDX Development and Trial Centers Research Network image repository provides a valuable resource for controlled digital pathology analysis, both for the evaluation of technical issues and for the development of computational image–based methods that make clinical predictions based on PDX treatment studies.

Significance: A pan-cancer repository of >1,000 patient-derived xenograft hematoxylin and eosin–stained images will facilitate cancer biology investigations through histopathologic analysis and contributes important model system data that expand existing human histology repositories.
Data from GPR1 and CMKLR1 Control Lipid Metabolism to Support the Development of Clear Cell Renal Cell Carcinoma
Authors: Wang Dazhi , Mahmud Iqbal , Thakur Vijay S. , Tan Sze Kiat , Isom Daniel G. , Lombard David B. , Gonzalgo Mark L. , Kryvenko Oleksandr N. , Lorenzi Philip L. , Tcheuyap Vanina T. , Brugarolas James , Welford Scott M.
Published: July 2024

Abstract
Clear cell renal cell carcinoma (ccRCC), the most common type of kidney cancer, is largely incurable in the metastatic setting. ccRCC is characterized by excessive lipid accumulation that protects cells from stress and promotes tumor growth, suggesting that the underlying regulators of lipid storage could represent potential therapeutic targets. Here, we evaluated the regulatory roles of GPR1 and CMKLR1, two G protein–coupled receptors of the protumorigenic adipokine chemerin that is involved in ccRCC lipid metabolism. Both genetic and pharmacologic suppression of either receptor suppressed lipid formation and induced multiple forms of cell death, including apoptosis, ferroptosis, and autophagy, thereby significantly impeding ccRCC growth in cell lines and patient-derived xenograft models. Comprehensive lipidomic and transcriptomic profiling of receptor competent and depleted cells revealed overlapping and unique signaling of the receptors granting control over triglyceride synthesis, ceramide production, and fatty acid saturation and class production. Mechanistically, both receptors enforced suppression of adipose triglyceride lipase, but each receptor also demonstrated distinct functions, such as the unique ability of CMKLR1 to control lipid uptake through regulation of sterol regulatory element–binding protein 1c and the CD36 scavenger receptor. Treating patient-derived xenograft models with the CMKLR1-targeting small molecule 2-(α-naphthoyl) ethyltrimethylammonium iodide (α-NETA) led to a dramatic reduction in tumor growth, lipid storage, and clear-cell morphology. Together, these findings provide mechanistic insights into lipid regulation in ccRCC and identify a targetable axis at the core of the histologic definition of this tumor that could be exploited therapeutically.

Significance: Extracellular control of lipid accumulation via G protein receptor–mediated cell signaling is a metabolic vulnerability in clear cell renal cell carcinoma, which depends on lipid storage to avoid oxidative toxicity.
Data from A Tetravalent Bispecific Antibody Selectively Inhibits Diverse FGFR3 Oncogenic Variants
Authors: Yang Yan , Suhasini Avvaru N. , Jiang Zaoli , Liu Nina , Rosconi Michael , Zhang Bojie , Li Yinyin , Dudgeon Drew , Seong Changhyun , Kim Steven , Rafique Ashique , Huang Tammy , Bhosle Sangram , Krueger Pamela , Ullman Erica , Olson William , Lin John C. , Shen Yang , Daly Christopher
Published: July 2024

Abstract
The receptor tyrosine kinase FGFR3 is frequently mutated in bladder cancer and is a validated therapeutic target. Although pan-FGFR tyrosine kinase inhibitors (TKI) have shown clinical efficacy, toxicity and acquired resistance limit the benefit of these agents. While antibody-based therapeutics can offer superior selectivity than TKIs, conventional ligand-blocking antibodies are usually ineffective inhibitors of constitutively active receptor tyrosine kinases. Furthermore, the existence of multiple oncogenic variants of FGFR3 presents an additional challenge for antibody-mediated blockade. Here, we developed a tetravalent FGFR3×FGFR3 bispecific antibody that inhibited FGFR3 point mutants and fusion proteins more effectively than any of the conventional FGFR3 antibodies that we produced. Each arm of the bispecific antibody contacted two distinct epitopes of FGFR3 through a cis mode of binding. The antibody blocked dimerization of the most common FGFR3 oncogenic variant (S249C extracellular domain mutation) and inhibited the function of FGFR3 variants that are resistant to pan-FGFR TKIs. The antibody was highly effective in suppressing growth of FGFR3-driven tumor models, providing efficacy comparable to that of the FDA-approved TKI erdafitinib. Thus, this bispecific antibody may provide an effective approach for broad and highly selective inhibition of oncogenic FGFR3 variants.

Significance: Development of a bispecific antibody that broadly inhibits gain-of-function FGFR3 variants provides a therapeutic strategy to target tumors with oncogenic FGFR3 point mutations and fusions, a particularly difficult case for antibody blockade.
Data from Adhesion GPCR ADGRE2 Maintains Proteostasis to Promote Progression in Acute Myeloid Leukemia
Authors: Huang Deyu , Yu Zebin , Lu Huan , Jiang Penglei , Qian Xinyue , Han Yingli , Qian Pengxu
Published: July 2024

Abstract
Acute myeloid leukemia (AML) is an aggressive and heterogeneous hematologic malignancy. In elderly patients, AML incidence is high and has a poor prognosis due to a lack of effective therapies. G protein–coupled receptors (GPCR) play integral roles in physiologic processes and human diseases. Particularly, one third of adhesion GPCRs, the second largest group of GPCRs, are highly expressed in hematopoietic stem and progenitor cells or lineage cells. Here, we investigate the role of adhesion GPCRs in AML and whether they could be harnessed as antileukemia targets. Systematic screening of the impact of adhesion GPCRs on AML functionality by bioinformatic and functional analyses revealed high expression of ADGRE2 in AML, particularly in leukemic stem cells, which is associated with poor patient outcomes. Silencing ADGRE2 not only exerts antileukemic effects in AML cell lines and cells derived from patients with AML in vitro, but also delays AML progression in xenograft models in vivo. Mechanistically, ADGRE2 activates phospholipase Cβ/protein kinase C/MEK/ERK signaling to enhance the expression of AP1 and transcriptionally drive the expression of DUSP1, a protein phosphatase. DUSP1 dephosphorylates Ser16 in the J-domain of the co-chaperone DNAJB1, which facilitates the DNAJB1–HSP70 interaction and maintenance of proteostasis in AML. Finally, combined inhibition of MEK, AP1, and DUSP1 exhibits robust therapeutic efficacy in AML xenograft mouse models. Collectively, this study deciphers the roles and mechanisms of ADGRE2 in AML and provides a promising therapeutic strategy for treating AML.

Significance: Increased expression of the adhesion GPCR member ADGRE2 in AML supports leukemia stem cell self-renewal and leukemogenesis by modulating proteostasis via an MEK/AP1/DUSP1 axis, which can be targeted to suppress AML progression.
A Tetravalent Bispecific Antibody Selectively Inhibits Diverse FGFR3 Oncogenic Variants
Authors: Yang Yan , Suhasini Avvaru N. , Jiang Zaoli , Liu Nina , Rosconi Michael , Zhang Bojie , Li Yinyin , Dudgeon Drew , Seong Changhyun , Kim Steven , Rafique Ashique , Huang Tammy , Bhosle Sangram , Krueger Pamela , Ullman Erica , Olson William , Lin John C. , Shen Yang , Daly Christopher
Published: July 2024

The receptor tyrosine kinase FGFR3 is frequently mutated in bladder cancer and is a validated therapeutic target. Although pan-FGFR tyrosine kinase inhibitors (TKI) have shown clinical efficacy, toxicity and acquired resistance limit the benefit of these agents. While antibody-based therapeutics can offer superior selectivity than TKIs, conventional ligand-blocking antibodies are usually ineffective inhibitors of constitutively active receptor tyrosine kinases. Furthermore, the existence of multiple oncogenic variants of FGFR3 presents an additional challenge for antibody-mediated blockade. Here, we developed a tetravalent FGFR3×FGFR3 bispecific antibody that inhibited FGFR3 point mutants and fusion proteins more effectively than any of the conventional FGFR3 antibodies that we produced. Each arm of the bispecific antibody contacted two distinct epitopes of FGFR3 through a cis mode of binding. The antibody blocked dimerization of the most common FGFR3 oncogenic variant (S249C extracellular domain mutation) and inhibited the function of FGFR3 variants that are resistant to pan-FGFR TKIs. The antibody was highly effective in suppressing growth of FGFR3-driven tumor models, providing efficacy comparable to that of the FDA-approved TKI erdafitinib. Thus, this bispecific antibody may provide an effective approach for broad and highly selective inhibition of oncogenic FGFR3 variants.

Significance: Development of a bispecific antibody that broadly inhibits gain-of-function FGFR3 variants provides a therapeutic strategy to target tumors with oncogenic FGFR3 point mutations and fusions, a particularly difficult case for antibody blockade.
The Warburg Effect Revisited through Blood and Electron Flow
Authors: Wang Yahui , Dang Chi V.
Published: July 2024

The Warburg effect describes the propensity of many cancers to consume glucose avidly and convert it to lactate in the presence of oxygen. The benefit of the Warburg effect on cancer cells remains enigmatic, particularly because extracellular disposal of incompletely oxidized lactate is wasteful. However, lactate is not discarded from the body, but rather recycled as pyruvate for metabolism through the tricarboxylic acid cycle in oxidative tissues and cells. Hence, tissue and interorgan metabolism play important roles in tumor metabolism. The production of tumor lactate to be recycled elsewhere parallels the Cori cycle, in which lactate produced by muscle activity is shuttled to the liver, where it is converted to pyruvate and subsequently stored as glucose moieties in glycogen. This perspective will consider this organismal contextwhile discussing how glucose is used in tumors. We highlight several key articles published decades ago in Cancer Research that are foundational to our current understanding of cancer biology and metabolism.
Supplementary Figure S7 from Near-infrared Photoimmunotherapy Targeting Cancer-Associated Fibroblasts in Patient-Derived Xenografts Using a Humanized Anti-Fibroblast Activation Protein Antibody
Authors: Kobayashi Teruki , Noma Kazuhiro , Nishimura Seitaro , Kato Takuya , Nishiwaki Noriyuki , Ohara Toshiaki , Kunitomo Tomoyoshi , Kawasaki Kento , Akai Masaaki , Komoto Satoshi , Kashima Hajime , Kikuchi Satoru , Tazawa Hiroshi , Shirakawa Yasuhiro , Choyke Peter L. , Kobayashi Hisataka , Fujiwara Toshiyoshi
Published: July 2024

Supplementary Figure S7
Supplementary Figure S2 from Near-infrared Photoimmunotherapy Targeting Cancer-Associated Fibroblasts in Patient-Derived Xenografts Using a Humanized Anti-Fibroblast Activation Protein Antibody
Authors: Kobayashi Teruki , Noma Kazuhiro , Nishimura Seitaro , Kato Takuya , Nishiwaki Noriyuki , Ohara Toshiaki , Kunitomo Tomoyoshi , Kawasaki Kento , Akai Masaaki , Komoto Satoshi , Kashima Hajime , Kikuchi Satoru , Tazawa Hiroshi , Shirakawa Yasuhiro , Choyke Peter L. , Kobayashi Hisataka , Fujiwara Toshiyoshi
Published: July 2024

Supplementary Figure S2
Supplementary Materials and Methods from Inhibition of MALT1 and BCL2 Induces Synergistic Antitumor Activity in Models of B-Cell Lymphoma
Authors: Plotnik Joshua P. , Richardson Adam E. , Yang Haopeng , Rojas Estela , Bontcheva Velitchka , Dowell Colleen , Parsons Sydney , Wilson Ashley , Ravanmehr Vida , Will Christine , Jung Paul , Zhu Haizhong , Partha Sarathy Karunan , Panchal Sanjay C. , Mali Raghuveer Singh , Kohlhapp Frederick J. , McClure Ryan A. , Ramathal Cyril Y. , George Mariam D. , Jhala Manisha , Elsen Nathaniel L. , Qiu Wei , Judge Russell A. , Pan Chin , Mastracchio Anthony , Henderson Jared , Meulbroek Jonathan A. , Green Michael R. , Pappano William N.
Published: July 2024

Supplementary Materials and Methods
Supplementary File 2 from Inhibition of MALT1 and BCL2 Induces Synergistic Antitumor Activity in Models of B-Cell Lymphoma
Authors: Plotnik Joshua P. , Richardson Adam E. , Yang Haopeng , Rojas Estela , Bontcheva Velitchka , Dowell Colleen , Parsons Sydney , Wilson Ashley , Ravanmehr Vida , Will Christine , Jung Paul , Zhu Haizhong , Partha Sarathy Karunan , Panchal Sanjay C. , Mali Raghuveer Singh , Kohlhapp Frederick J. , McClure Ryan A. , Ramathal Cyril Y. , George Mariam D. , Jhala Manisha , Elsen Nathaniel L. , Qiu Wei , Judge Russell A. , Pan Chin , Mastracchio Anthony , Henderson Jared , Meulbroek Jonathan A. , Green Michael R. , Pappano William N.
Published: July 2024

Excel Workbook RNASeq
Supplementary Figure 7 from Inhibition of MALT1 and BCL2 Induces Synergistic Antitumor Activity in Models of B-Cell Lymphoma
Authors: Plotnik Joshua P. , Richardson Adam E. , Yang Haopeng , Rojas Estela , Bontcheva Velitchka , Dowell Colleen , Parsons Sydney , Wilson Ashley , Ravanmehr Vida , Will Christine , Jung Paul , Zhu Haizhong , Partha Sarathy Karunan , Panchal Sanjay C. , Mali Raghuveer Singh , Kohlhapp Frederick J. , McClure Ryan A. , Ramathal Cyril Y. , George Mariam D. , Jhala Manisha , Elsen Nathaniel L. , Qiu Wei , Judge Russell A. , Pan Chin , Mastracchio Anthony , Henderson Jared , Meulbroek Jonathan A. , Green Michael R. , Pappano William N.
Published: July 2024

MALT1 and BCL2 inhibition do not antagonize each other in monotherapy sensitive patient derived xenografts
Supplementary Figure 4 from Inhibition of MALT1 and BCL2 Induces Synergistic Antitumor Activity in Models of B-Cell Lymphoma
Authors: Plotnik Joshua P. , Richardson Adam E. , Yang Haopeng , Rojas Estela , Bontcheva Velitchka , Dowell Colleen , Parsons Sydney , Wilson Ashley , Ravanmehr Vida , Will Christine , Jung Paul , Zhu Haizhong , Partha Sarathy Karunan , Panchal Sanjay C. , Mali Raghuveer Singh , Kohlhapp Frederick J. , McClure Ryan A. , Ramathal Cyril Y. , George Mariam D. , Jhala Manisha , Elsen Nathaniel L. , Qiu Wei , Judge Russell A. , Pan Chin , Mastracchio Anthony , Henderson Jared , Meulbroek Jonathan A. , Green Michael R. , Pappano William N.
Published: July 2024

Efficacy of ABBV-MALT1 across non-GCB PDX models
Figure S3 from The CDK4/6 Inhibitor Palbociclib Synergizes with ATRA to Induce Differentiation in AML
Authors: Hu Linhui , Li Qian , Wang Jiyu , Wang Huiping , Ren Xiyang , Huang Keke , Wang Yangyang , Liang Xue , Pu Lianfang , Xiong Shudao , Zhai Zhimin
Published: July 2024

Figure S3. Knockdown CDK4 and CDK6 via siRNA in HL-60 and molm13 cells (A). The effect of palbociclib and ATRA combination on cell cycle(B), cell proliferation (C), and cell apoptosis (D). * Represent when it compares with the control group, P<0.05. # Represent when it compares with the ATRA group, P<0.05.
Figure S2 from The CDK4/6 Inhibitor Palbociclib Synergizes with ATRA to Induce Differentiation in AML
Authors: Hu Linhui , Li Qian , Wang Jiyu , Wang Huiping , Ren Xiyang , Huang Keke , Wang Yangyang , Liang Xue , Pu Lianfang , Xiong Shudao , Zhai Zhimin
Published: July 2024

Figure S2. The comparison of the expression of CDK4/6-cyclin D pathway genes based on age (A) and risk stratification (B). The Kaplan-Meier curve for CDK6 (C). The calibration curve of the nomogram for 1-year and 2-year overall survival (D).
Supplementary Figure S4 from The Novel ATR Inhibitor M1774 Induces Replication Protein Overexpression and Broad Synergy with DNA-targeted Anticancer Drugs
Authors: Jo Ukhyun , Arakawa Yasuhiro , Zimmermann Astrid , Taniyama Daiki , Mizunuma Makito , Jenkins Lisa M. , Maity Tapan , Kumar Suresh , Zenke Frank T. , Takebe Naoko , Pommier Yves
Published: July 2024

This figure shows profiling of cell viability after M1774 treatment in H146 and H82 cells.
Figure S1 from Assessment of Patient-Derived Xenograft Growth and Antitumor Activity: The NCI PDXNet Consensus Recommendations
Authors: Meric-Bernstam Funda , Lloyd Michael W. , Koc Soner , Evrard Yvonne A. , McShane Lisa M. , Lewis Michael T. , Evans Kurt W. , Li Dali , Rubinstein Lawrence , Welm Alana , Dean Dennis A. , Srivastava Anuj , Grover Jeffrey W. , Ha Min J. , Chen Huiqin , Huang Xuelin , Varadarajan Kaushik , Wang Jing , Roth Jack A. , Welm Bryan , Govinden Ramaswamy , Ding Li , Kaochar Salma , Mitsiades Nicholas , Carvajal-Carmona Luis , Herylyn Meenhard , Davies Michael A. , Shapiro Geoffrey I. , Fields Ryan , Trevino Jose G. , Harrell Joshua C. , Doroshow James H. , Chuang Jeffrey H. , Moscow Jeffrey A.
Published: July 2024

Supplementary Figure 1 shows (A) spider plots of Log 2 of individual tumor volumes, (B) growth curves representing average Log 2 tumor volumes, (C) spider plots of Log 2 of proportional changes in tumor volume, and (D) growth curves showing Log2 proportional changes in tumor volume of the data shown in Figures 1 and 2.
Table S1 from Optimizing the Design and Geometry of T Cell–Engaging Bispecific Antibodies Targeting CEA in Colorectal Cancer
Authors: Elsayed Abdullah , Plüss Louis , Nideroest Larissa , Rotta Giulia , Thoma Marina , Zangger Nathan , Peissert Frederik , Pfister Stefanie K. , Pellegrino Christian , Dakhel Plaza Sheila , De Luca Roberto , Manz Markus G. , Oxenius Annette , Puca Emanuele , Halin Cornelia , Neri Dario
Published: July 2024

Supplementary Table S1
Movie S2 from Optimizing the Design and Geometry of T Cell–Engaging Bispecific Antibodies Targeting CEA in Colorectal Cancer
Authors: Elsayed Abdullah , Plüss Louis , Nideroest Larissa , Rotta Giulia , Thoma Marina , Zangger Nathan , Peissert Frederik , Pfister Stefanie K. , Pellegrino Christian , Dakhel Plaza Sheila , De Luca Roberto , Manz Markus G. , Oxenius Annette , Puca Emanuele , Halin Cornelia , Neri Dario
Published: July 2024

Supplementary Movie 2