bluuebunny/crossref_metadata_2025 huggingface dataset:
Supplementary Figure S10 from The N6-methyladenosine Epitranscriptomic Landscape of Lung Adenocarcinoma
Authors: Wang Shiyan , Zeng Yong , Zhu Lin , Zhang Min , Zhou Lei , Yang Weixiong , Luo Weishan , Wang Lina , Liu Yanming , Zhu Helen , Xu Xin , Su Peiran , Zhang Xinyue , Ahmed Musaddeque , Chen Wei , Chen Moliang , Chen Sujun , Slobodyanyuk Mykhaylo , Xie Zhongpeng , Guan Jiansheng , Zhang Wen , Khan Aafaque Ahmad , Sakashita Shingo , Liu Ni , Pham Nhu-An , Boutros Paul C. , Ke Zunfu , Moran Michael F. , Cai Zongwei , Cheng Chao , Yu Jun , Tsao Ming S. , He Housheng H.
Published: November 2024

Analysis of EML4 and METTL3 protein levels in LUAD patient tumors and functional assays of METTL3 perturbation.
Supplementary Figure 3 from The SCRUM-MONSTAR Cancer-Omics Ecosystem: Striving for a Quantum Leap in Precision Medicine
Authors: Hashimoto Tadayoshi , Nakamura Yoshiaki , Fujisawa Takao , Imai Mitsuho , Shibuki Taro , Iida Naoko , Ozaki Hiroshi , Nonomura Norio , Morizane Chigusa , Iwata Hiroji , Okano Susumu , Yamagami Wataru , Yamazaki Naoya , Kadowaki Shigenori , Taniguchi Hiroya , Ueno Makoto , Boku Shogen , Oki Eiji , Komatsu Yoshito , Yuki Satoshi , Makiyama Akitaka , Otsuka Tomoyuki , Hara Hiroki , Okano Naohiro , Nishina Tomohiro , Sakamoto Yasutoshi , Miki Izumi , Kobayashi Shin , Yuda Junichiro , Kageyama Shun-Ichiro , Nagamine Michiko , Sakashita Shingo , Sakamoto Naoya , Yamashita Riu , Koga Yoshikatsu , Bando Hideaki , Ishii Genichiro , Kuwata Takeshi , Park Woong-Yang , Ohtsu Atsushi , Yoshino Takayuki
Published: November 2024

Design of the whole transcriptome sequencing–based clinical trial platform in MONSTAR-SCREEN-3
Supplementary Figure 8 from Rational Protein Engineering to Enhance MHC-Independent T-cell Receptors
Authors: Chang Ju-Fang , Landmann Jack H. , Chang Tien-Ching , Selli Mehmet Emrah , Tenzin Yangdon , Warrington John M. , Ritchey Julie , Hsu Yu-Sung , Slade Michael , Gupta Deepesh Kumar , DiPersio John F. , Holehouse Alex S. , Singh Nathan
Published: November 2024

Supplementary Figure 8 | a, Cytotoxicity, as measured by fold change in Nalm6 growth during co-culture, and b, expansion of human T cells expressing transgenic TCR 1G4, 19/BBζ or 19/28ζ CAR against Nalm6WT or Nalm6triple. c-d, Endogenous expression of 41BBL, CD80 and CD86 in c, Raji and d, K562 cell lines. e, Growth rate of engineered Nalm6 cell lines. f-g, Cytotoxicity of 19/BBζ CAR and mut035 T cells over time during f, acute stimulation and g, re-stimulation. 19/BBζ CAR were initially co-cultured with Nalm6WT and re-stimulated with Nalm6WT; mut035 T cells were initially co-cultured with Nalm641BBL and re-stimulated with Nalm6WT. h, Percent of cells expressing either 19/BBζ CAR or mut035 miTCR on day 0 or after five days of chronic stimulation with various Nalm6 cell lines. i, Expansion of 19/BBζ CAR or mut035 miTCR T cells over the course of chronic stimulation with various Nalm6 cell lines. a-b and f-g Representative data from three independent donors; i, Representative of five independent donors. Statistical significance determined using two-way ANOVA.
Supplementary Figure 5 from Rational Protein Engineering to Enhance MHC-Independent T-cell Receptors
Authors: Chang Ju-Fang , Landmann Jack H. , Chang Tien-Ching , Selli Mehmet Emrah , Tenzin Yangdon , Warrington John M. , Ritchey Julie , Hsu Yu-Sung , Slade Michael , Gupta Deepesh Kumar , DiPersio John F. , Holehouse Alex S. , Singh Nathan
Published: November 2024

Supplementary Figure 5 | a-b, Expression of TCR constant chains on the surface of a, Jurkat and b, human T cells transduced with miTCR variants. c-d, Comparison of surface TCR expression in miTCR1 and miTCR2 variant engineered c, Jurkats and d, human T cells. e, Change in surface expression of TCR constant chains based on presence or absence of PD insertion in the miTCRα chain. f, Expression of NFAT and g, NFκB in reporter Jurkat cells expressing 1G4 or miTCR variants in response to Nalm6. h, Expansion of human T cells expressing miTCR1 and miTCR2 variants upon exposure to Nalm6. d, cumulative data from three independent donors. b,e, representative data from three independent donors. Screening studies in h performed using two independent donors. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 by Student’s t-test (c-e).
Supplementary Figure 2 from Rational Protein Engineering to Enhance MHC-Independent T-cell Receptors
Authors: Chang Ju-Fang , Landmann Jack H. , Chang Tien-Ching , Selli Mehmet Emrah , Tenzin Yangdon , Warrington John M. , Ritchey Julie , Hsu Yu-Sung , Slade Michael , Gupta Deepesh Kumar , DiPersio John F. , Holehouse Alex S. , Singh Nathan
Published: November 2024

Supplementary Figure 2 | a-d, Expression of a, TCR constant chains and mCherry transduction marker, b, FMC63 and mCherry transduction marker, c, FMC63 and TCR constant chains and d, CD3ε and TCR constant chains in primary human T cells engineered with CARs or miTCRs. e, Comparison of surface FMC63 expression in miTCR1 and miTCR2 engineered T cells in n=6 independent donors. f, Median fluorescence intensity (MFI) of APC on T cells that bound soluble CD19-APC conjugate. a-d, Representative of five independent donors. Analysis using Student’s t-test or two-way ANOVA.
Figure 4 from Clinical Validation of a Cell-Free DNA Fragmentome Assay for Augmentation of Lung Cancer Early Detection
Authors: Mazzone Peter J. , Bach Peter B. , Carey Jacob , Schonewolf Caitlin A. , Bognar Katalin , Ahluwalia Manmeet S. , Cruz-Correa Marcia , Gierada David , Kotagiri Sonali , Lloyd Kathryn , Maldonado Fabien , Ortendahl Jesse D. , Sequist Lecia V. , Silvestri Gerard A. , Tanner Nichole , Thompson Jeffrey C. , Vachani Anil , Wong Kwok-Kin , Zaidi Ali H. , Catallini Joseph , Gershman Ariel , Lumbard Keith , Millberg Laurel K. , Nawrocki Jeff , Portwood Carter , Rangnekar Aakanksha , Sheridan Carolina Campos , Trivedi Niti , Wu Tony , Zong Yuhua , Cotton Lindsey , Ryan Allison , Cisar Christopher , Leal Alessandro , Dracopoli Nicholas , Scharpf Robert B. , Velculescu Victor E. , Pike Luke R. G.
Published: November 2024

Performance of blood-based lung cancer screening test. A, Sensitivity and specificity of the test in the clinical validation set (N = 382) overall and by clinical subgroup. Point estimates are reported with 95% Wilson confidence intervals. Overall sensitivity and specificity denoted by solid vertical lines. B, Sensitivity of the test in the lung cancer cases in the clinical validation set (N = 248) evaluated across cancer histology, and T, N, and M categories. Point estimates are reported with 95% Wilson confidence intervals. Overall sensitivity of 84% denoted by the solid horizontal line. C, Left, sensitivity of the test in the lung cancer cases in the clinical validation set (N = 246) by cancer group stage. Middle, bar plot showing the stage distribution of lung cancer as observed in populations undergoing lung cancer screening with LDCT (based on NLST study) that are used to weigh observed stage-specific sensitivities. Right, lung cancer screening relevant stage-weighted sensitivity in clinical validation set. D, Comparison of the NNS with LDCT conditioned on test positive or negative result when applied in the lung cancer screening eligible population. Test performance showed consistency across clinical subgroups and expected increased performance with increasing burden of disease (tumor (T), node (N), metastasis (M) and group staging). After weighting, the stage distribution to reflect a screening population, test performance remained high and demonstrated the ability to reliably identify those individuals more likely to have lung cancer detected on LDCT.
Figure 3 from Clinical Validation of a Cell-Free DNA Fragmentome Assay for Augmentation of Lung Cancer Early Detection
Authors: Mazzone Peter J. , Bach Peter B. , Carey Jacob , Schonewolf Caitlin A. , Bognar Katalin , Ahluwalia Manmeet S. , Cruz-Correa Marcia , Gierada David , Kotagiri Sonali , Lloyd Kathryn , Maldonado Fabien , Ortendahl Jesse D. , Sequist Lecia V. , Silvestri Gerard A. , Tanner Nichole , Thompson Jeffrey C. , Vachani Anil , Wong Kwok-Kin , Zaidi Ali H. , Catallini Joseph , Gershman Ariel , Lumbard Keith , Millberg Laurel K. , Nawrocki Jeff , Portwood Carter , Rangnekar Aakanksha , Sheridan Carolina Campos , Trivedi Niti , Wu Tony , Zong Yuhua , Cotton Lindsey , Ryan Allison , Cisar Christopher , Leal Alessandro , Dracopoli Nicholas , Scharpf Robert B. , Velculescu Victor E. , Pike Luke R. G.
Published: November 2024

High-dimensional fragmentation features reflect lung cancer biology and are incorporated in the machine learning classifier. A, Heatmap representation of the deviation of cfDNA fragmentation features across the genome for the classifier training set with lung cancer or noncancer individuals compared with the mean of classifier training noncancer individuals. Each row represents a sample, whereas columns show individual genomic features. The cross-validated DELFI score and clinical characteristics are indicated to the left of the fragmentation deviation heatmap. B, Left, TCGA-derived observations of chromosomal arm gains (red) and losses (blue) in lung adenocarcinoma (LUAD; n = 518) and squamous cell cancer tissues (LUSC; n = 501). Right, the observed chromosome arm gains (red) and losses (blue) in the classifier training individuals separated by histology. C, A heatmap representation of the principal component eigenvectors of the fragmentation profile features. Regression coefficients from the final classifier indicating how the principal components of the fragmentation profiles and z-scores of the chromosomal arms were combined are provided in the top and right margins of the heatmap, respectively. Positive values for the coefficients are represented in red, whereas negative values are represented in blue. Agreement across copy number chromosomal gains and losses in TCGA lung cancers, observed z-scores in the cfDNA of patients with lung cancer, and chromosome arm model coefficients reflect biologic consistency between chromosomal changes in lung cancer, cfDNA fragmentation profiles, and classifier features.
Figure 2 from Clinical Validation of a Cell-Free DNA Fragmentome Assay for Augmentation of Lung Cancer Early Detection
Authors: Mazzone Peter J. , Bach Peter B. , Carey Jacob , Schonewolf Caitlin A. , Bognar Katalin , Ahluwalia Manmeet S. , Cruz-Correa Marcia , Gierada David , Kotagiri Sonali , Lloyd Kathryn , Maldonado Fabien , Ortendahl Jesse D. , Sequist Lecia V. , Silvestri Gerard A. , Tanner Nichole , Thompson Jeffrey C. , Vachani Anil , Wong Kwok-Kin , Zaidi Ali H. , Catallini Joseph , Gershman Ariel , Lumbard Keith , Millberg Laurel K. , Nawrocki Jeff , Portwood Carter , Rangnekar Aakanksha , Sheridan Carolina Campos , Trivedi Niti , Wu Tony , Zong Yuhua , Cotton Lindsey , Ryan Allison , Cisar Christopher , Leal Alessandro , Dracopoli Nicholas , Scharpf Robert B. , Velculescu Victor E. , Pike Luke R. G.
Published: November 2024

Genome-wide fragmentation profiles are altered in patients with cancer and reflect underlying chromatin structure. A, The fragmentation profile (ratio of short to long cfDNA fragments in 5 Mb bins) across the genome was evaluated in the classifier training plasma samples of lung cancer (n = 181) and noncancer individuals (n = 395). The noncancer individuals had similar fragmentation profiles, whereas patients with lung cancer exhibited significant variation. B, Comparison of cfDNA profiles with Hi-C A/B chromatin compartment reference data from lung cancer tissue or peripheral blood cells. Track 1 shows A/B compartments extracted from LUSC cancer tissue (48). Track 2 shows a median lung cancer component extracted from the LUSC plasma samples of 7 patients with lung cancer from the classifier training set with high tumor fraction by ichorCNA (49). The 7 LUSC cases with high ichorCNA have values of 0.051, 0.439, 0.230, 0.259 0.439, 0.167, and 0.057. Track 3 shows the median profile for 10 noncancer plasma samples from the training set. Track 4 shows A/B compartments for lymphoblast cells (48). These four tracks show chromosome 22 as an example, with darker shading indicating informative regions of the genome where the two reference tracks differ in domain (open/closed). C, 100-kb regions were selected using the reference LUSC and lymphoblast A/B tracks as having the same chromatin state or opposite chromatin state. Within these regions, the deviation of the fragmentation value from a noncancer cfDNA reference (n = 10) was plotted per region per individual (noncancer n = 20, LUSC n = 7). Values around 0 have little variation from the noncancer reference. Negative values indicate a region has a more open chromatin state than the reference and positive values indicate a region has a more closed chromatin state than the reference. These data suggest that although cfDNA profiles of healthy individuals reflect the chromatin structure of blood cells, those of patients with lung cancer represent a mixture of cfDNA patterns of chromatin compartments from lung cancer as well as blood cells.
Supplementary Table 2 from ROR2 Regulates Cellular Plasticity in Pancreatic Neoplasia and Adenocarcinoma
Authors: Benitz Simone , Steep Alec , Nasser Malak M. , Preall Jonathan , Mahajan Ujjwal Mukund , McQuithey Holly , Loveless Ian , Davis Erick T. , Wen Hui-Ju , Long Daniel W. , Metzler Thomas , Zwernik Samuel , Louw Michaela , Rempinski Donald , Salas-Escabillas Daniel J. , Brender Sydney M. , Song Linghao , Huang Ling , Theisen Brian K. , Zhang Zhenyu , Steele Nina G. , Regel Ivonne , Bednar Filip , Crawford Howard C.
Published: November 2024

Primer sequences.
Supplementary Figure 6 from ROR2 Regulates Cellular Plasticity in Pancreatic Neoplasia and Adenocarcinoma
Authors: Benitz Simone , Steep Alec , Nasser Malak M. , Preall Jonathan , Mahajan Ujjwal Mukund , McQuithey Holly , Loveless Ian , Davis Erick T. , Wen Hui-Ju , Long Daniel W. , Metzler Thomas , Zwernik Samuel , Louw Michaela , Rempinski Donald , Salas-Escabillas Daniel J. , Brender Sydney M. , Song Linghao , Huang Ling , Theisen Brian K. , Zhang Zhenyu , Steele Nina G. , Regel Ivonne , Bednar Filip , Crawford Howard C.
Published: November 2024

ROR2 promotes EMT and stemness.
Supplementary Figure 4 from ROR2 Regulates Cellular Plasticity in Pancreatic Neoplasia and Adenocarcinoma
Authors: Benitz Simone , Steep Alec , Nasser Malak M. , Preall Jonathan , Mahajan Ujjwal Mukund , McQuithey Holly , Loveless Ian , Davis Erick T. , Wen Hui-Ju , Long Daniel W. , Metzler Thomas , Zwernik Samuel , Louw Michaela , Rempinski Donald , Salas-Escabillas Daniel J. , Brender Sydney M. , Song Linghao , Huang Ling , Theisen Brian K. , Zhang Zhenyu , Steele Nina G. , Regel Ivonne , Bednar Filip , Crawford Howard C.
Published: November 2024

Ablation of Ror2 promotes tissue transformation.
Supplemental Table 6 from Mechanisms of Resistance to Oncogenic KRAS Inhibition in Pancreatic Cancer
Authors: Dilly Julien , Hoffman Megan T. , Abbassi Laleh , Li Ziyue , Paradiso Francesca , Parent Brendan D. , Hennessey Connor J. , Jordan Alexander C. , Morgado Micaela , Dasgupta Shatavisha , Uribe Giselle A. , Yang Annan , Kapner Kevin S. , Hambitzer Felix P. , Qiang Li , Feng Hanrong , Geisberg Jacob , Wang Junning , Evans Kyle E. , Lyu Hengyu , Schalck Aislyn , Feng Ningping , Lopez Anastasia M. , Bristow Christopher A. , Kim Michael P. , Rajapakshe Kimal I. , Bahrambeigi Vahid , Roth Jennifer A. , Garg Kavita , Guerrero Paola A. , Stanger Ben Z. , Cristea Simona , Lowe Scott W. , Baslan Timour , Van Allen Eliezer M. , Mancias Joseph D. , Chan Emily , Anderson Abraham , Katlinskaya Yuliya V. , Shalek Alex K. , Hong David S. , Pant Shubham , Hallin Jill , Anderes Kenna , Olson Peter , Heffernan Timothy P. , Chugh Seema , Christensen James G. , Maitra Anirban , Wolpin Brian M. , Raghavan Srivatsan , Nowak Jonathan A. , Winter Peter S. , Dougan Stephanie K. , Aguirre Andrew J.
Published: November 2024

Mutation and sequence alterations from whole exome sequencing of in vitro models of acquired resistance to MRTX1133.
Supplemental Table 5 from Mechanisms of Resistance to Oncogenic KRAS Inhibition in Pancreatic Cancer
Authors: Dilly Julien , Hoffman Megan T. , Abbassi Laleh , Li Ziyue , Paradiso Francesca , Parent Brendan D. , Hennessey Connor J. , Jordan Alexander C. , Morgado Micaela , Dasgupta Shatavisha , Uribe Giselle A. , Yang Annan , Kapner Kevin S. , Hambitzer Felix P. , Qiang Li , Feng Hanrong , Geisberg Jacob , Wang Junning , Evans Kyle E. , Lyu Hengyu , Schalck Aislyn , Feng Ningping , Lopez Anastasia M. , Bristow Christopher A. , Kim Michael P. , Rajapakshe Kimal I. , Bahrambeigi Vahid , Roth Jennifer A. , Garg Kavita , Guerrero Paola A. , Stanger Ben Z. , Cristea Simona , Lowe Scott W. , Baslan Timour , Van Allen Eliezer M. , Mancias Joseph D. , Chan Emily , Anderson Abraham , Katlinskaya Yuliya V. , Shalek Alex K. , Hong David S. , Pant Shubham , Hallin Jill , Anderes Kenna , Olson Peter , Heffernan Timothy P. , Chugh Seema , Christensen James G. , Maitra Anirban , Wolpin Brian M. , Raghavan Srivatsan , Nowak Jonathan A. , Winter Peter S. , Dougan Stephanie K. , Aguirre Andrew J.
Published: November 2024

Supplemental Table 5A: Drug sensitivity metrics from MRTX1133-treated KRASG12D patient-derived organoids. Supplemental Table 5B: MRTX1133 dose response across KRASG12D mutated patient-derived organoids.
Supplemental Table 4 from Mechanisms of Resistance to Oncogenic KRAS Inhibition in Pancreatic Cancer
Authors: Dilly Julien , Hoffman Megan T. , Abbassi Laleh , Li Ziyue , Paradiso Francesca , Parent Brendan D. , Hennessey Connor J. , Jordan Alexander C. , Morgado Micaela , Dasgupta Shatavisha , Uribe Giselle A. , Yang Annan , Kapner Kevin S. , Hambitzer Felix P. , Qiang Li , Feng Hanrong , Geisberg Jacob , Wang Junning , Evans Kyle E. , Lyu Hengyu , Schalck Aislyn , Feng Ningping , Lopez Anastasia M. , Bristow Christopher A. , Kim Michael P. , Rajapakshe Kimal I. , Bahrambeigi Vahid , Roth Jennifer A. , Garg Kavita , Guerrero Paola A. , Stanger Ben Z. , Cristea Simona , Lowe Scott W. , Baslan Timour , Van Allen Eliezer M. , Mancias Joseph D. , Chan Emily , Anderson Abraham , Katlinskaya Yuliya V. , Shalek Alex K. , Hong David S. , Pant Shubham , Hallin Jill , Anderes Kenna , Olson Peter , Heffernan Timothy P. , Chugh Seema , Christensen James G. , Maitra Anirban , Wolpin Brian M. , Raghavan Srivatsan , Nowak Jonathan A. , Winter Peter S. , Dougan Stephanie K. , Aguirre Andrew J.
Published: November 2024

Supplemental Table 4A: Differential gene expression analysis between MRTX1133 sensitive and resistant KRASG12D cell lines. Supplemental Table 4B: Analysis of Copy number and RPPA datasets between MRTX1133 sensitive and resistant KRASG12D cell lines.
Supplemental Table 1 from Mechanisms of Resistance to Oncogenic KRAS Inhibition in Pancreatic Cancer
Authors: Dilly Julien , Hoffman Megan T. , Abbassi Laleh , Li Ziyue , Paradiso Francesca , Parent Brendan D. , Hennessey Connor J. , Jordan Alexander C. , Morgado Micaela , Dasgupta Shatavisha , Uribe Giselle A. , Yang Annan , Kapner Kevin S. , Hambitzer Felix P. , Qiang Li , Feng Hanrong , Geisberg Jacob , Wang Junning , Evans Kyle E. , Lyu Hengyu , Schalck Aislyn , Feng Ningping , Lopez Anastasia M. , Bristow Christopher A. , Kim Michael P. , Rajapakshe Kimal I. , Bahrambeigi Vahid , Roth Jennifer A. , Garg Kavita , Guerrero Paola A. , Stanger Ben Z. , Cristea Simona , Lowe Scott W. , Baslan Timour , Van Allen Eliezer M. , Mancias Joseph D. , Chan Emily , Anderson Abraham , Katlinskaya Yuliya V. , Shalek Alex K. , Hong David S. , Pant Shubham , Hallin Jill , Anderes Kenna , Olson Peter , Heffernan Timothy P. , Chugh Seema , Christensen James G. , Maitra Anirban , Wolpin Brian M. , Raghavan Srivatsan , Nowak Jonathan A. , Winter Peter S. , Dougan Stephanie K. , Aguirre Andrew J.
Published: November 2024

Genetic and copy number variants at resistance to adagrasib or sotorasib across the novel cohort of PDAC and GI cancer patients.
Supplemental Figures 1-11 from Mechanisms of Resistance to Oncogenic KRAS Inhibition in Pancreatic Cancer
Authors: Dilly Julien , Hoffman Megan T. , Abbassi Laleh , Li Ziyue , Paradiso Francesca , Parent Brendan D. , Hennessey Connor J. , Jordan Alexander C. , Morgado Micaela , Dasgupta Shatavisha , Uribe Giselle A. , Yang Annan , Kapner Kevin S. , Hambitzer Felix P. , Qiang Li , Feng Hanrong , Geisberg Jacob , Wang Junning , Evans Kyle E. , Lyu Hengyu , Schalck Aislyn , Feng Ningping , Lopez Anastasia M. , Bristow Christopher A. , Kim Michael P. , Rajapakshe Kimal I. , Bahrambeigi Vahid , Roth Jennifer A. , Garg Kavita , Guerrero Paola A. , Stanger Ben Z. , Cristea Simona , Lowe Scott W. , Baslan Timour , Van Allen Eliezer M. , Mancias Joseph D. , Chan Emily , Anderson Abraham , Katlinskaya Yuliya V. , Shalek Alex K. , Hong David S. , Pant Shubham , Hallin Jill , Anderes Kenna , Olson Peter , Heffernan Timothy P. , Chugh Seema , Christensen James G. , Maitra Anirban , Wolpin Brian M. , Raghavan Srivatsan , Nowak Jonathan A. , Winter Peter S. , Dougan Stephanie K. , Aguirre Andrew J.
Published: November 2024

Supplemental Figure 1: Acquired resistance to KRASG12C inhibition in PDAC and other GI cancers. Supplemental Figure 2: MRTX1133 sensitivity across KRASG12D mutant in vitro models of PDAC. Supplemental Figure 3: Isogenic models of acquired resistance to MRTX1133. Supplemental Figure 4: In vivo treatment and tumor monitoring for the KPC PDAC mouse model. Supplemental Figure 5: Genomic characterization of KPC tumors. Supplemental Figure 6: snRNA-seq quality metrics and description of the tumor microenvironment. Supplemental Figure 7: Identification and characterization of malignant cell populations. Supplemental Figure 8: Characterization of malignant metaprograms in KPC tumors. Supplemental Figure 9: Treatment with MRTX1133 induces modest changes in the immune microenvironment following tumor regression. Supplemental Figure 10: Treatment of 6694C2-LM tumors with MRTX1133 reduces granulocytes but has little effect on T cells. Supplemental Figure 11: Neoadjuvant and adjuvant therapy in a metastatic model of PDAC.
Supplementary Table 4 from T-cell Responses to Individualized Neoantigen Therapy mRNA-4157 (V940) Alone or in Combination with Pembrolizumab in the Phase 1 KEYNOTE-603 Study
Authors: Gainor Justin F. , Patel Manish R. , Weber Jeffrey S. , Gutierrez Martin , Bauman Julie E. , Clarke Jeffrey M. , Julian Ricklie , Scott Aaron J. , Geiger Jessica L. , Kirtane Kedar , Robert-Tissot Celine , Coder Brandon , Tasneem Moomal , Sun Jing , Zheng Wei , Gerbereux Lauren , Laino Andressa , Porichis Filippos , Pollard Jack Russella , Hou Peijie , Sehgal Vasudha , Chen Xing , Morrissey Manju , Daghestani Hikmat N. , Feldman Igor , Srinivasan Lakshmi , Frederick Joshua P. , Brown Michelle , Aanur Praveen , Meehan Robert , Burris Howard A.
Published: November 2024

Supplementary Table 4. Cellular markers used for ex vivo immune phenotyping.
Supplementary Table 2 from T-cell Responses to Individualized Neoantigen Therapy mRNA-4157 (V940) Alone or in Combination with Pembrolizumab in the Phase 1 KEYNOTE-603 Study
Authors: Gainor Justin F. , Patel Manish R. , Weber Jeffrey S. , Gutierrez Martin , Bauman Julie E. , Clarke Jeffrey M. , Julian Ricklie , Scott Aaron J. , Geiger Jessica L. , Kirtane Kedar , Robert-Tissot Celine , Coder Brandon , Tasneem Moomal , Sun Jing , Zheng Wei , Gerbereux Lauren , Laino Andressa , Porichis Filippos , Pollard Jack Russella , Hou Peijie , Sehgal Vasudha , Chen Xing , Morrissey Manju , Daghestani Hikmat N. , Feldman Igor , Srinivasan Lakshmi , Frederick Joshua P. , Brown Michelle , Aanur Praveen , Meehan Robert , Burris Howard A.
Published: November 2024

Supplementary Table 2. Safety summary for patients who received a dose of mRNA-4157.
Supplementary Table 1 from T-cell Responses to Individualized Neoantigen Therapy mRNA-4157 (V940) Alone or in Combination with Pembrolizumab in the Phase 1 KEYNOTE-603 Study
Authors: Gainor Justin F. , Patel Manish R. , Weber Jeffrey S. , Gutierrez Martin , Bauman Julie E. , Clarke Jeffrey M. , Julian Ricklie , Scott Aaron J. , Geiger Jessica L. , Kirtane Kedar , Robert-Tissot Celine , Coder Brandon , Tasneem Moomal , Sun Jing , Zheng Wei , Gerbereux Lauren , Laino Andressa , Porichis Filippos , Pollard Jack Russella , Hou Peijie , Sehgal Vasudha , Chen Xing , Morrissey Manju , Daghestani Hikmat N. , Feldman Igor , Srinivasan Lakshmi , Frederick Joshua P. , Brown Michelle , Aanur Praveen , Meehan Robert , Burris Howard A.
Published: November 2024

Supplementary Table 1. Representativeness of study participants.
Supplementary Figure 8 from T-cell Responses to Individualized Neoantigen Therapy mRNA-4157 (V940) Alone or in Combination with Pembrolizumab in the Phase 1 KEYNOTE-603 Study
Authors: Gainor Justin F. , Patel Manish R. , Weber Jeffrey S. , Gutierrez Martin , Bauman Julie E. , Clarke Jeffrey M. , Julian Ricklie , Scott Aaron J. , Geiger Jessica L. , Kirtane Kedar , Robert-Tissot Celine , Coder Brandon , Tasneem Moomal , Sun Jing , Zheng Wei , Gerbereux Lauren , Laino Andressa , Porichis Filippos , Pollard Jack Russella , Hou Peijie , Sehgal Vasudha , Chen Xing , Morrissey Manju , Daghestani Hikmat N. , Feldman Igor , Srinivasan Lakshmi , Frederick Joshua P. , Brown Michelle , Aanur Praveen , Meehan Robert , Burris Howard A.
Published: November 2024

Supplementary Figure 8. Study design for Part A and Part D.
Supplementary Figure 4 from T-cell Responses to Individualized Neoantigen Therapy mRNA-4157 (V940) Alone or in Combination with Pembrolizumab in the Phase 1 KEYNOTE-603 Study
Authors: Gainor Justin F. , Patel Manish R. , Weber Jeffrey S. , Gutierrez Martin , Bauman Julie E. , Clarke Jeffrey M. , Julian Ricklie , Scott Aaron J. , Geiger Jessica L. , Kirtane Kedar , Robert-Tissot Celine , Coder Brandon , Tasneem Moomal , Sun Jing , Zheng Wei , Gerbereux Lauren , Laino Andressa , Porichis Filippos , Pollard Jack Russella , Hou Peijie , Sehgal Vasudha , Chen Xing , Morrissey Manju , Daghestani Hikmat N. , Feldman Igor , Srinivasan Lakshmi , Frederick Joshua P. , Brown Michelle , Aanur Praveen , Meehan Robert , Burris Howard A.
Published: November 2024

Supplementary Figure 4. Predicted HLA alleles (A) and their associated immunogenic epitopes (B) in patients treated with mRNA-4157 monotherapy.
Supplementary Figure 3 from T-cell Responses to Individualized Neoantigen Therapy mRNA-4157 (V940) Alone or in Combination with Pembrolizumab in the Phase 1 KEYNOTE-603 Study
Authors: Gainor Justin F. , Patel Manish R. , Weber Jeffrey S. , Gutierrez Martin , Bauman Julie E. , Clarke Jeffrey M. , Julian Ricklie , Scott Aaron J. , Geiger Jessica L. , Kirtane Kedar , Robert-Tissot Celine , Coder Brandon , Tasneem Moomal , Sun Jing , Zheng Wei , Gerbereux Lauren , Laino Andressa , Porichis Filippos , Pollard Jack Russella , Hou Peijie , Sehgal Vasudha , Chen Xing , Morrissey Manju , Daghestani Hikmat N. , Feldman Igor , Srinivasan Lakshmi , Frederick Joshua P. , Brown Michelle , Aanur Praveen , Meehan Robert , Burris Howard A.
Published: November 2024

Supplementary Figure 3. Patient responses to individual neoantigens at pre-treatment and post-treatment for patients who received mRNA-4157 monotherapy (A–C) or combination therapy (D–J) using ELISpot assay.
Supplementary Figure 1 from T-cell Responses to Individualized Neoantigen Therapy mRNA-4157 (V940) Alone or in Combination with Pembrolizumab in the Phase 1 KEYNOTE-603 Study
Authors: Gainor Justin F. , Patel Manish R. , Weber Jeffrey S. , Gutierrez Martin , Bauman Julie E. , Clarke Jeffrey M. , Julian Ricklie , Scott Aaron J. , Geiger Jessica L. , Kirtane Kedar , Robert-Tissot Celine , Coder Brandon , Tasneem Moomal , Sun Jing , Zheng Wei , Gerbereux Lauren , Laino Andressa , Porichis Filippos , Pollard Jack Russella , Hou Peijie , Sehgal Vasudha , Chen Xing , Morrissey Manju , Daghestani Hikmat N. , Feldman Igor , Srinivasan Lakshmi , Frederick Joshua P. , Brown Michelle , Aanur Praveen , Meehan Robert , Burris Howard A.
Published: November 2024

Supplementary Figure 1. Disease status of patients in the study compared with those of broader melanoma populations.
Supplementary Table S1 from The Journey of Antibody–Drug Conjugates: Lessons Learned from 40 Years of Development
Authors: Colombo Raffaele , Tarantino Paolo , Rich Jamie R. , LoRusso Patricia M. , de Vries Elisabeth G.E.
Published: November 2024

Dose, regiment, DAR, payload, target, and indication(s) for auristatin, maytansinoid, camptothecin, and PBD ADCs and corresponding small molecules
Supplementary Table S3 from Mechanisms of Response and Tolerance to Active RAS Inhibition in KRAS-Mutant Non–Small Cell Lung Cancer
Authors: Araujo Haniel A. , Pechuan-Jorge Ximo , Zhou Teng , Do Minh Truong , Hu Xin , Rojas Alvarez Frank R. , Salvatierra Maria E. , Ibarguen Heladio P. , Lee Richard , Raghulan Rashi , Shah Harshit , Moreno Ayala Mariela A. , Chen Kevin , Tovbis Shifrin Nataliya , Wu Shuhong , Solis Soto Luisa M. , Negrao Marcelo V. , Gibbons Don L. , Hong David S. , Roth Jack A. , Heymach John V. , Zhang Jianjun , Jiang Jingjing , Singh Mallika , Smith Jacqueline A.M. , Quintana Elsa , Skoulidis Ferdinandos
Published: November 2024

Supplementary table S3. List of antibodies used for immunohistochemical studies.
Supplementary Figure S4 from Mechanisms of Response and Tolerance to Active RAS Inhibition in KRAS-Mutant Non–Small Cell Lung Cancer
Authors: Araujo Haniel A. , Pechuan-Jorge Ximo , Zhou Teng , Do Minh Truong , Hu Xin , Rojas Alvarez Frank R. , Salvatierra Maria E. , Ibarguen Heladio P. , Lee Richard , Raghulan Rashi , Shah Harshit , Moreno Ayala Mariela A. , Chen Kevin , Tovbis Shifrin Nataliya , Wu Shuhong , Solis Soto Luisa M. , Negrao Marcelo V. , Gibbons Don L. , Hong David S. , Roth Jack A. , Heymach John V. , Zhang Jianjun , Jiang Jingjing , Singh Mallika , Smith Jacqueline A.M. , Quintana Elsa , Skoulidis Ferdinandos
Published: November 2024

Supplementary Figure S4. Acute inhibition of active RAS in KRASG12C-mutant lung adenocarcinoma.
Supplementary Figure S3 from Mechanisms of Response and Tolerance to Active RAS Inhibition in KRAS-Mutant Non–Small Cell Lung Cancer
Authors: Araujo Haniel A. , Pechuan-Jorge Ximo , Zhou Teng , Do Minh Truong , Hu Xin , Rojas Alvarez Frank R. , Salvatierra Maria E. , Ibarguen Heladio P. , Lee Richard , Raghulan Rashi , Shah Harshit , Moreno Ayala Mariela A. , Chen Kevin , Tovbis Shifrin Nataliya , Wu Shuhong , Solis Soto Luisa M. , Negrao Marcelo V. , Gibbons Don L. , Hong David S. , Roth Jack A. , Heymach John V. , Zhang Jianjun , Jiang Jingjing , Singh Mallika , Smith Jacqueline A.M. , Quintana Elsa , Skoulidis Ferdinandos
Published: November 2024

Supplementary Figure S3. Acute effects of active RAS inhibition in the KP2 and KL2 KrasG12C-mutant lung adenocarcinoma models
Supplementary Figure S15 from Mechanisms of Response and Tolerance to Active RAS Inhibition in KRAS-Mutant Non–Small Cell Lung Cancer
Authors: Araujo Haniel A. , Pechuan-Jorge Ximo , Zhou Teng , Do Minh Truong , Hu Xin , Rojas Alvarez Frank R. , Salvatierra Maria E. , Ibarguen Heladio P. , Lee Richard , Raghulan Rashi , Shah Harshit , Moreno Ayala Mariela A. , Chen Kevin , Tovbis Shifrin Nataliya , Wu Shuhong , Solis Soto Luisa M. , Negrao Marcelo V. , Gibbons Don L. , Hong David S. , Roth Jack A. , Heymach John V. , Zhang Jianjun , Jiang Jingjing , Singh Mallika , Smith Jacqueline A.M. , Quintana Elsa , Skoulidis Ferdinandos
Published: November 2024

Supplementary Figure S15. Clinical implications of mucinous lineage differentiation in human KRAS-mutant NSCLC cohorts
Supplementary Figure S13 from Mechanisms of Response and Tolerance to Active RAS Inhibition in KRAS-Mutant Non–Small Cell Lung Cancer
Authors: Araujo Haniel A. , Pechuan-Jorge Ximo , Zhou Teng , Do Minh Truong , Hu Xin , Rojas Alvarez Frank R. , Salvatierra Maria E. , Ibarguen Heladio P. , Lee Richard , Raghulan Rashi , Shah Harshit , Moreno Ayala Mariela A. , Chen Kevin , Tovbis Shifrin Nataliya , Wu Shuhong , Solis Soto Luisa M. , Negrao Marcelo V. , Gibbons Don L. , Hong David S. , Roth Jack A. , Heymach John V. , Zhang Jianjun , Jiang Jingjing , Singh Mallika , Smith Jacqueline A.M. , Quintana Elsa , Skoulidis Ferdinandos
Published: November 2024

Supplementary Figure S13. Correlates of mucinous markers in human KRAS-mutant NSCLC
Supplementary Figure S12 from Mechanisms of Response and Tolerance to Active RAS Inhibition in KRAS-Mutant Non–Small Cell Lung Cancer
Authors: Araujo Haniel A. , Pechuan-Jorge Ximo , Zhou Teng , Do Minh Truong , Hu Xin , Rojas Alvarez Frank R. , Salvatierra Maria E. , Ibarguen Heladio P. , Lee Richard , Raghulan Rashi , Shah Harshit , Moreno Ayala Mariela A. , Chen Kevin , Tovbis Shifrin Nataliya , Wu Shuhong , Solis Soto Luisa M. , Negrao Marcelo V. , Gibbons Don L. , Hong David S. , Roth Jack A. , Heymach John V. , Zhang Jianjun , Jiang Jingjing , Singh Mallika , Smith Jacqueline A.M. , Quintana Elsa , Skoulidis Ferdinandos
Published: November 2024

Supplementary Figure S12. Evolution and regulatory networks of RASGTP inhibitor-tolerant persister cells.
Supplementary Figure S11 from Mechanisms of Response and Tolerance to Active RAS Inhibition in KRAS-Mutant Non–Small Cell Lung Cancer
Authors: Araujo Haniel A. , Pechuan-Jorge Ximo , Zhou Teng , Do Minh Truong , Hu Xin , Rojas Alvarez Frank R. , Salvatierra Maria E. , Ibarguen Heladio P. , Lee Richard , Raghulan Rashi , Shah Harshit , Moreno Ayala Mariela A. , Chen Kevin , Tovbis Shifrin Nataliya , Wu Shuhong , Solis Soto Luisa M. , Negrao Marcelo V. , Gibbons Don L. , Hong David S. , Roth Jack A. , Heymach John V. , Zhang Jianjun , Jiang Jingjing , Singh Mallika , Smith Jacqueline A.M. , Quintana Elsa , Skoulidis Ferdinandos
Published: November 2024

Supplementary Figure S11. Characterization of tumor-cell transcriptional heterogeneity following RAS inhibition.
Supplementary Table 1 from A Classical Epithelial State Drives Acute Resistance to KRAS Inhibition in Pancreatic Cancer
Authors: Singhal Anupriya , Styers Hannah C. , Rub Jonathan , Li Zhuxuan , Torborg Stefan R. , Kim Jung Yun , Grbovic-Huezo Olivera , Feng Huijin , Tarcan Zeynep Cagla , Sahin Ozkan Hulya , Hallin Jill , Basturk Olca , Yaeger Rona , Christensen James G. , Betel Doron , Yan Yan , Chio Iok In Christine , de Stanchina Elisa , Tammela Tuomas
Published: November 2024

Supplementary Table 1 includes gene set enrichment analysis (GSEA) of KPF mouse PDAC cells comparing MRTX1133-treated vs. vehicle-treated classical and basal cells
Supplementary Table 1 from A Call for Innovative Translational and Clinical Research to Address China’s Unique Cancer Landscape
Authors: Zhang Chaoqi , Wu Peng , Li Dongyu , Gu Xuanyu , Lin Chuqi , Zhou Junhan , Shang Dexin , Liu Jingjing , Ma Ruijie , Zhao Bohui , Sun Nan , He Jie
Published: November 2024

Supplementary Table 1. The number of Investigational New Drug (IND) and New Drug Application (NDA) applications retrieved from the Annual Drug Evaluation Report by the Center for Drug Evaluation (CDE) from 2018-2023.
Data from Rational Protein Engineering to Enhance MHC-Independent T-cell Receptors
Authors: Chang Ju-Fang , Landmann Jack H. , Chang Tien-Ching , Selli Mehmet Emrah , Tenzin Yangdon , Warrington John M. , Ritchey Julie , Hsu Yu-Sung , Slade Michael , Gupta Deepesh Kumar , DiPersio John F. , Holehouse Alex S. , Singh Nathan
Published: November 2024

Abstract
Chimeric antigen receptor (CAR)–based therapies have pioneered synthetic cellular immunity but remain limited in their long-term efficacy. Emerging data suggest that dysregulated CAR-driven T-cell activation causes T-cell dysfunction and therapeutic failure. To re-engage the precision of the endogenous T-cell response, we designed MHC-independent T-cell receptors (miTCR) by linking antibody variable domains to T-cell receptor constant chains. Using predictive modeling, we observed that this standard “cut and paste” approach to synthetic protein design resulted in myriad biochemical conflicts at the hybrid variable–constant domain interface. Through iterative modeling and sequence modifications, we developed structure-enhanced miTCRs which significantly improved receptor-driven T-cell function across multiple tumor models. We found that 41BB costimulation specifically prolonged miTCR T-cell persistence and enabled improved leukemic control in vivo compared with classic CAR T cells. Collectively, we have identified core features of hybrid receptor structure responsible for regulating function.

Significance: Improving the durability of engineered T-cell immunotherapies is critical to enhancing efficacy. We used a structure-informed design to evolve improved miTCR function across several models. This work underscores the central role of synthetic receptor structure in T-cell function and provides a framework for improved receptor engineering.
Data from Clinical Validation of a Cell-Free DNA Fragmentome Assay for Augmentation of Lung Cancer Early Detection
Authors: Mazzone Peter J. , Bach Peter B. , Carey Jacob , Schonewolf Caitlin A. , Bognar Katalin , Ahluwalia Manmeet S. , Cruz-Correa Marcia , Gierada David , Kotagiri Sonali , Lloyd Kathryn , Maldonado Fabien , Ortendahl Jesse D. , Sequist Lecia V. , Silvestri Gerard A. , Tanner Nichole , Thompson Jeffrey C. , Vachani Anil , Wong Kwok-Kin , Zaidi Ali H. , Catallini Joseph , Gershman Ariel , Lumbard Keith , Millberg Laurel K. , Nawrocki Jeff , Portwood Carter , Rangnekar Aakanksha , Sheridan Carolina Campos , Trivedi Niti , Wu Tony , Zong Yuhua , Cotton Lindsey , Ryan Allison , Cisar Christopher , Leal Alessandro , Dracopoli Nicholas , Scharpf Robert B. , Velculescu Victor E. , Pike Luke R. G.
Published: November 2024

Abstract
Lung cancer screening via annual low-dose computed tomography has poor adoption. We conducted a prospective case–control study among 958 individuals eligible for lung cancer screening to develop a blood-based lung cancer detection test that when positive is followed by a low-dose computed tomography. Changes in genome-wide cell-free DNA fragmentation profiles (fragmentomes) in peripheral blood reflected genomic and chromatin characteristics of lung cancer. We applied machine learning to fragmentome features to identify individuals who were more or less likely to have lung cancer. We trained the classifier using 576 cases and controls from study samples and validated it in a held-out group of 382 cases and controls. The validation demonstrated high sensitivity for lung cancer and consistency across demographic groups and comorbid conditions. Applying test performance to the screening eligible population in a 5-year model with modest utilization assumptions suggested the potential to prevent thousands of lung cancer deaths.

Significance: Lung cancer screening has poor adoption. Our study describes the development and validation of a novel blood-based lung cancer screening test utilizing a highly affordable, low-coverage genome-wide sequencing platform to analyze cell-free DNA fragmentation patterns. The test could improve lung cancer screening rates leading to substantial public health benefits.

See related commentary by Haber and Skates, p. 2025
Data from T-cell Responses to Individualized Neoantigen Therapy mRNA-4157 (V940) Alone or in Combination with Pembrolizumab in the Phase 1 KEYNOTE-603 Study
Authors: Gainor Justin F. , Patel Manish R. , Weber Jeffrey S. , Gutierrez Martin , Bauman Julie E. , Clarke Jeffrey M. , Julian Ricklie , Scott Aaron J. , Geiger Jessica L. , Kirtane Kedar , Robert-Tissot Celine , Coder Brandon , Tasneem Moomal , Sun Jing , Zheng Wei , Gerbereux Lauren , Laino Andressa , Porichis Filippos , Pollard Jack Russella , Hou Peijie , Sehgal Vasudha , Chen Xing , Morrissey Manju , Daghestani Hikmat N. , Feldman Igor , Srinivasan Lakshmi , Frederick Joshua P. , Brown Michelle , Aanur Praveen , Meehan Robert , Burris Howard A.
Published: November 2024

Abstract
mRNA-4157 (V940) is an individualized neoantigen therapy targeting up to 34 patient-specific tumor neoantigens to induce T-cell responses and potentiate antitumor activity. We report mechanistic insights into the immunogenicity of mRNA-4157 via characterization of T-cell responses to neoantigens from the first-in-human, phase 1, KEYNOTE-603 study (NCT03313778) in patients with resected non–small cell lung cancer (Part A: 1-mg mRNA-4157, n = 4) or resected cutaneous melanoma (Part D: 1-mg mRNA-4157 + 200-mg pembrolizumab, n = 12). Safety, tolerability, and immunogenicity were assessed. All patients experienced ≥1 treatment-emergent adverse event; there were no grade 4/5 adverse events or dose-limiting toxicities. mRNA-4157 alone induced consistent de novo and strengthened preexisting T-cell responses to targeted neoantigens. Following combination therapy, sustained mRNA-4157-induced neoantigen-specific T-cell responses and expansion of cytotoxic CD8 and CD4 T cells were observed. These findings show the potential of a novel mRNA individualized neoantigen therapy approach in oncology.

Significance: The safety and immunogenicity results from this phase 1 study of mRNA-4157 as adjuvant monotherapy or combination therapy with pembrolizumab show generation of de novo and enhancement of existing neoantigen-specific T-cell responses and provide mechanistic proof of concept to support further development of mRNA-4157 for patients with resected solid tumors.

See related commentary by Berraondo et al., p. 2021
Data from The Journey of Antibody–Drug Conjugates: Lessons Learned from 40 Years of Development
Authors: Colombo Raffaele , Tarantino Paolo , Rich Jamie R. , LoRusso Patricia M. , de Vries Elisabeth G.E.
Published: November 2024

Abstract
Antibody–drug conjugates (ADC) represent one of the most rapidly expanding treatment modalities in oncology, with 11 ADCs approved by the FDA and more than 210 currently being tested in clinical trials. Spanning over 40 years, ADC clinical development has enhanced our understanding of the multifaceted mechanisms of action for this class of therapeutics. In this article, we discuss key insights into the toxicity, efficacy, stability, distribution, and fate of ADCs. Furthermore, we highlight ongoing challenges related to their clinical optimization, the development of rational sequencing strategies, and the identification of predictive biomarkers.

Significance: The development and utilization of ADCs have allowed for relevant improvements in the prognosis of multiple cancer types. Concomitantly, the rise of ADCs in oncology has produced several challenges, including the prediction of their activity, their utilization in sequence, and minimization of their side effects, that still too often resemble those of the cytotoxic molecule that they carry. In this review, we retrace 40 years of development in the field of ADCs and delve deep into the mechanisms of action of these complex therapeutics and reasons behind the many achievements and failures observed in the field to date.
Data from A Call for Innovative Translational and Clinical Research to Address China’s Unique Cancer Landscape
Authors: Zhang Chaoqi , Wu Peng , Li Dongyu , Gu Xuanyu , Lin Chuqi , Zhou Junhan , Shang Dexin , Liu Jingjing , Ma Ruijie , Zhao Bohui , Sun Nan , He Jie
Published: November 2024

Summary:
The Chinese government has, in recent decades, implemented various administrative laws and regulatory policies to expedite cancer therapeutic development, boosting research and development pipelines for domestic pharmaceutical companies and clinical trials; however, China faces unique challenges given the high prevalence of certain cancer types and distinct disease burdens, some of which are frequently overlooked by international pharmaceutical companies. Given the substantial unmet need for China-specific cancer care, it is crucial to promote the development of innovative pharmaceutical and clinical research in China, with a particular emphasis on addressing tumors most prevalent in its population.
Supplementary Table 5 from Therapy-Related Myeloid Neoplasms: Complex Interactions among Cytotoxic Therapies, Genetic Factors, and Aberrant Microenvironment
Authors: Singhal Deepak , Kutyna Monika M. , Hahn Christopher N. , Shah Mithun Vinod , Hiwase Devendra K.
Published: November 2024

Supplementary Table 5 shows distribution of somatic mutations across different studies in therapy-related acute myeloid leukemia (t-AML) and primary AML
Supplementary Data from Tracking Rare Single Donor and Recipient Immune and Leukemia Cells after Allogeneic Hematopoietic Cell Transplantation Using Mitochondrial DNA Mutations
Authors: Penter Livius , Cieri Nicoletta , Maurer Katie , Kwok Marwan , Lyu Haoxiang , Lu Wesley S. , Oliveira Giacomo , Gohil Satyen H. , Leshchiner Ignaty , Lareau Caleb A. , Ludwig Leif S. , Neuberg Donna S. , Kim Haesook T. , Li Shuqiang , Bullinger Lars , Ritz Jerome , Getz Gad , Garcia Jacqueline S. , Soiffer Robert J. , Livak Kenneth J. , Wu Catherine J.
Published: November 2024

Supplementary Figures 1-20 and Supplementary Tables 1-10, Supplementary notes
Suppl. Table 9 from Tracking Rare Single Donor and Recipient Immune and Leukemia Cells after Allogeneic Hematopoietic Cell Transplantation Using Mitochondrial DNA Mutations
Authors: Penter Livius , Cieri Nicoletta , Maurer Katie , Kwok Marwan , Lyu Haoxiang , Lu Wesley S. , Oliveira Giacomo , Gohil Satyen H. , Leshchiner Ignaty , Lareau Caleb A. , Ludwig Leif S. , Neuberg Donna S. , Kim Haesook T. , Li Shuqiang , Bullinger Lars , Ritz Jerome , Getz Gad , Garcia Jacqueline S. , Soiffer Robert J. , Livak Kenneth J. , Wu Catherine J.
Published: November 2024

Mutations targeted using single cell DNA sequencing in AML1026.
Suppl. Table 8 from Tracking Rare Single Donor and Recipient Immune and Leukemia Cells after Allogeneic Hematopoietic Cell Transplantation Using Mitochondrial DNA Mutations
Authors: Penter Livius , Cieri Nicoletta , Maurer Katie , Kwok Marwan , Lyu Haoxiang , Lu Wesley S. , Oliveira Giacomo , Gohil Satyen H. , Leshchiner Ignaty , Lareau Caleb A. , Ludwig Leif S. , Neuberg Donna S. , Kim Haesook T. , Li Shuqiang , Bullinger Lars , Ritz Jerome , Getz Gad , Garcia Jacqueline S. , Soiffer Robert J. , Livak Kenneth J. , Wu Catherine J.
Published: November 2024

Mutations targeted using single cell DNA sequencing in AML1012.
Figure S5 from Repurposing NAMPT Inhibitors for Germinal Center B Cell–Like Diffuse Large B-Cell Lymphoma
Authors: Scuoppo Claudio , Cai Bowen , Ofori Kenneth , Scholze Hanna , Kumar Rahul , D’Alessandro Angelo , Basso Katia , Pasqualucci Laura , Dalla-Favera Riccardo
Published: November 2024

Figure S5 shows the in vivo antitumor activity of KPT-9724 in K422 xenotransplants
Figure S4 from Repurposing NAMPT Inhibitors for Germinal Center B Cell–Like Diffuse Large B-Cell Lymphoma
Authors: Scuoppo Claudio , Cai Bowen , Ofori Kenneth , Scholze Hanna , Kumar Rahul , D’Alessandro Angelo , Basso Katia , Pasqualucci Laura , Dalla-Favera Riccardo
Published: November 2024

Figure S4 shows the NAMPT, PARP1 and NAMPT/PARP ratio in COO-classified DLBCL cases
Suppl. Table 5 from Tracking Rare Single Donor and Recipient Immune and Leukemia Cells after Allogeneic Hematopoietic Cell Transplantation Using Mitochondrial DNA Mutations
Authors: Penter Livius , Cieri Nicoletta , Maurer Katie , Kwok Marwan , Lyu Haoxiang , Lu Wesley S. , Oliveira Giacomo , Gohil Satyen H. , Leshchiner Ignaty , Lareau Caleb A. , Ludwig Leif S. , Neuberg Donna S. , Kim Haesook T. , Li Shuqiang , Bullinger Lars , Ritz Jerome , Getz Gad , Garcia Jacqueline S. , Soiffer Robert J. , Livak Kenneth J. , Wu Catherine J.
Published: November 2024

Immunosuppression tapering cohort.
Figure S3 from Repurposing NAMPT Inhibitors for Germinal Center B Cell–Like Diffuse Large B-Cell Lymphoma
Authors: Scuoppo Claudio , Cai Bowen , Ofori Kenneth , Scholze Hanna , Kumar Rahul , D’Alessandro Angelo , Basso Katia , Pasqualucci Laura , Dalla-Favera Riccardo
Published: November 2024

Figure S3 shows the transcript levels of NAD synthetic and degradation enzymes in DLBCL lines.
Figure S1 from Repurposing NAMPT Inhibitors for Germinal Center B Cell–Like Diffuse Large B-Cell Lymphoma
Authors: Scuoppo Claudio , Cai Bowen , Ofori Kenneth , Scholze Hanna , Kumar Rahul , D’Alessandro Angelo , Basso Katia , Pasqualucci Laura , Dalla-Favera Riccardo
Published: November 2024

Figure S1 shows the subtype specificity of drugs in the sensitivity screen