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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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A Multicenter Experience: Duration of Mandatory CRS and Icans Monitoring for Myeloma and Lymphoma CAR-T Recipients

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Transplantation and Cellular Therapy 2024年第2期30卷 S207-S208页

作者： Wesson, William, MPH Dima, Danai Davis, James, PharmD, BCOP Rashid, Aliya Furqan, Fateeha, MD Saif, Md Saiful Islam Logan, Emerson, BS McGann, Mary, PharmD, BCOP Tabak, Carine Hazlett, Liz, PharmD Lutfi, Forat Mushtaq, Muhammad Umair, MD Hoffmann, Marc, MD Abdallah, Al-Ola Anwer, Faiz Hamadani, Mehdi McGuirk, Joseph P, DO Hashmi, Hamza Ahmed, Nausheen University of Kansas Medical Center Westwood KS US Myeloma Innovations Research Collaborative (USMIRC) Kansas City KS Rochester General Hospital Rochester NY Department of Biostatistics & Data Science University of Kansas Medical Center Kansas City KS University of Kansas School of Medicine Kansas City KS Department of Pharmacy Medical University of South Carolina (MUSC) Charleston SC University of Kansas Cancer Center Kansas City KS Division of Hematologic Malignancies and Cellular Therapeutics Kansas University Medical Center Kansas City KS Division of Hematologic Malignancies & Cellular Therapeutics University of Kansas Medical Center Westwood KS Blood and Marrow Transplant Program Department of Hematology and Medical Oncology Taussig Cancer Institute Cleveland Clinic Cleveland OH BMT & Cellular Therapy Program Medical College of Wisconsin Milwaukee WI The Medical University of South Carolina (MUSC) Charleston SC

BackgroundThe US Food and Drug Administration implemented a Risk Evaluation and Mitigation Strategy/REMS program for all currently approved BCMA and CD19-directed chimeric antigen receptor T-cell (CAR-T) therapies to mitigate the risks of CRS and ICANS by requiring patients (pts) to stay in proximity of the authorized treating center (ATC) for a period of 4 weeks. Relocation and long monitoring period may present a potential financial challenge and increase disparities in access. Defining the optimal duration of monitoring after treatment, has not been described previously. We therefore evaluated real-world safety outcomes of BCMA and CD-19 CAR-T recipients for multiple myeloma (MM) and non-Hodgkin lymphoma (NHL). MethodsPts with MM and NHL who received BCMA CART: idecabtagene vicleucel (ide-cel) and ciltacabtagene autoleucel (cilta-cel), or CD19 CART: axicabtagene ciloleucel (axi-cel), tisagenlecleucel (tisa-cel), lisocabtagene maraleucel (liso-cel) and brexucabtagene autoleucel (brexu-cel) respectively, from 12/2017 to 06/2023, at four centers: University of Kansas Medical Center, Medical University of South Carolina, University of Wisconsin Health University Hospital, and the Cleveland Clinic Foundation were included. There were two groups: those with no CRS/ICANS or first CRS/ICANS before Day 7 (Events< D7) and those with CRS/ICANS ≥ Day 7 (Events ≥ D7). Chi-square test and proportion tests were calculated. Results256 pts including 106 MM (86% ide-cel, 14% cilta-cel) and 150 NHL pts (61% axi-cel, 21% tisa-cel, 12% liso-cel, 7% brexu-cel) were included. Most patients (64%) lived beyond 30 minutes of the ATC. Most (85%) were in the Events

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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201P Phase II study of taminadenant (A2AR antagonist) + spartalizumab (anti PD-1 antibody) in patients with triple-negative breast cancer (TNBC)

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Immuno-Oncology and Technology 2022年 16卷 100312-100312页

作者： Jerusalem, G. De Braud, F.G.M. de Jonge, M.J.A. Grell, P. Gonçalves, A. Tan, T.J.Y. Greil, R. Yap, T.A. Lin, C-C. Kornbluth, K.A. Yang, X. Vong, C. Choudhury, S. Mataraza, J. Lee, L.H. Otero, J.A. Garralda, E. Medical Oncology CHU Liege and Liege University Liège Belgium Medical Oncology & Haemathology Department University of Milan and Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy Medical Oncology Department Erasmus MC Cancer Institute Rotterdam Netherlands Medical Oncology Masaryk Memorial Cancer Institute Brno Czech Republic Medical Oncology Department Institut Paoli-Calmettes and Aix-Marseille University Marseille France Medical Oncology Department National Cancer Centre Singapore Medical Oncology Paracelsus Medical University Hospital Cancer Research Institute-Center for Clinical Cancer and Immunology Trials Gmbh and Cancer Cluster Salzburg Austria Investigational Cancer Therapeutics Dept. (Phase I Program) The University of Texas M. D. Anderson Cancer Center Houston TX USA Department of Oncology National Taiwan University Hospital Taipei Taiwan Translational Clinical Oncology Novartis Institutes for BioMedical Research Cambridge MA USA Early Development Biostatistics Novartis Institutes for BioMedical Research Cambridge MA USA Early Development Analytics Novartis Institutes for BioMedical Research Basel Switzerland Clinical Pharmacology Novartis Institutes for BioMedical Research Cambridge MA USA Oncology Novartis Institutes for BioMedical Research Cambridge MA USA Oncology Data Science Novartis Institutes for BioMedical Research Cambridge MA USA Early Drug Development Group Hospital Universitari Vall d'Hebron Barcelona Spain

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MB-94 IMMUNOLOGIC PROFILE OF PATIENTS WITH NEWLY-DIAGNOSED MEDULLOBLASTOMA AT INITIAL DIAGNOSIS AND DURING STANDARD RADIATION AND CHEMOTHERAPY (PBTC-N11)

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Neuro-Oncology 2016年第SUPPL_3期18卷 iii118-iii118页

作者： Sridharan Gururangan Elizabeth Reap Renee Reynolds Gerald Grant Arzu Onar-Thomas Mehmet Kodak Patricia Baxter Ian Pollack Peter Phillips James Boyett Maryam Fouladi Duane Mitchell Preston A. Wells Center for Brain Tumor Therapy University of Florida Gainesville FL USA Immunotherapy Program Duke University Medical Center Durham NC USA Department of Neurosurgery State University of New York at Buffalo Buffalo NY USA Department of Neurosurgery Stanford University Palo Alto CA USA Operations Biostatistics and Data Management Center The Pediatric Brain Tumor Consortium Memphis TN USA University of Tennessee Health Science Center Memphis TN USA Texas Children's Cancer Center Houston TX USA Department of Neurosurgery Children's Hospital of Pittsburgh Pittsburgh PA USA Neuro-Oncology Program Children's Hospital of Philadelphia Philadelphia PA USA Cincinnati Children's Hospital Medical Center Cincinnati OH USA

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47P Phase I study to assess the safety and efficacy of P-BCMA-ALLO1: A fully allogeneic CAR-T therapy, in patients with relapsed/refractory multiple myeloma (RRMM)

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Immuno-Oncology and Technology 2022年 16卷 100152-100152页

作者： M.H. Kocoglu A. Asch A. Ramakrishnan C. Bachier T. Martin T. Rodriguez K. McArthur C. Martin H. Namini E. Ostertag M. Spear E. Christie R. Belani M. Zhang S. Cranert J. Coronella D. Shedlock C. Costello Hematology-Oncology University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center Baltimore MD USA Hematology-Oncology Stephenson Cancer Center Oklahoma University Health Science Center Oklahoma City OK USA Hematology-Oncology Sarah Cannon Blood Cancer Center at St. David's South Austin Austin TX USA Hematology-Oncology Sarah Cannon Transplant and Cellular Therapy Program Methodist Hospital San Antonio TX USA Hematology-Oncology UCSF Medical Center at Parnassus San Francisco CA USA Bone Marrow Transplantation Advocate Lutheran General Hospital Park Ridge IL USA Clinical Operations Poseida Therapeutics San Diego CA USA Immunology Poseida Therapeutics San Diego CA USA Data Science Poseida Therapeutics San Diego CA USA Corporate Affairs Poseida Therapeutics San Diego CA USA Clinical Sciences Poseida Therapeutics San Diego CA USA Immuno-Oncology Poseida Therapeutics San Diego CA USA Cell Therapy Poseida Therapeutics San Diego CA USA Hematology-Oncology UCSD Moores Cancer Center La Jolla CA USA

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Role of Stem-like Cells in Chemotherapy Resistance and Relapse in Pediatric T Cell Acute Lymphoblastic Leukemia (T-ALL)

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Blood 2024年 144卷 314-314页

作者： Julia Costea Kerstin K. Rauwolf Pietro Zafferani Tobias Rausch Anna Mathioudaki Judith Zaugg Martin Schrappe Cornelia Eckert Gabriele Escherich Jean-Pierre Bourquin Beat Bornhauser Andreas E. Kulozik Jan O. Korbel Molecular Medicine Partnership Unit European Molecular Biology Laboratory (EMBL) and University Hospital Heidelberg Heidelberg Germany European Molecular Biology Laboratory (EMBL) Genome Biology Unit Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Department of Pediatric Oncology University Children's Hospital Zurich Zurich Switzerland Molecular Biosciences/Cancer Biology Program Heidelberg University and German Cancer Research Center (DKFZ) Heidelberg Germany European Molecular Biology Laboratory (EMBL) Genomics Core Facility Heidelberg Germany European Molecular Biology Laboratory (EMBL) Structural and Computational Biology Unit Heidelberg Germany University Hospital Schleswig-Holstein Department of Pediatrics Campus Kiel Kiel Germany Charité Universitätsmedizin Berlin Department of Hematology and Oncology Berlin Germany German Cancer Consortium (DKTK) Heidelberg Germany German Cancer Research Center (DKFZ) Heidelberg Germany University Medical Center Hamburg-Eppendorf Hamburg Germany Hopp-Kindertumorzentrum Heidelberg (KiTZ) Heidelberg Germany Clinical Cooperation Unit Pediatric Leukemia German Cancer Research Center (DKFZ) Heidelberg Germany Department of Pediatric Oncology Hematology and Immunology Heidelberg University Heidelberg Germany Genome Biology Unit European Molecular Biology Laboratory Heidelberg Germany Bridging Research Division on Mechanisms of Genomic Variation and Data Science German Cancer Research Center (DKFZ) Heidelberg Germany

T-ALL is an aggressive leukemia predominantly affecting adolescents and young adults. T-ALL relapses are characterized by chemotherapy resistance, cellular heterogeneity and dismal outcome (PMID: 33925883). While mutations, changes in gene expression and epigenetic dysregulations have been documented (PMID: 26294725; 27655895; 35585141; 23023710; 27623214), a unifying mechanism explaining the development of relapse remains unknown. To comprehensively investigate the heterogeneity of T-ALL and gain a better understanding of mechanisms driving T-ALL relapse, we conducted single-cell full-length RNA sequencing utilizing VASA-seq (PMID: 35760914). This analysis included 13 matched pediatric T-ALL patient-derived xenografts (PDX) samples obtained at initial diagnosis and relapse, generating the to date most comprehensive longitudinal single cell study in paired T-ALL samples, along with 5 non-relapsing PDX samples collected at initial diagnosis. Our dataset encompasses 11 TAL1- 3 TLX1/2-, 2 NKX2- and 2 HOXA-driven T-ALLs patients. While the predominant cell populations in these patients exhibit substantial interpatient heterogeneity, our data unveil a previously elusive subpopulation of T-ALL cells that converges at a gene-regulatory network shared between the majority of patients. This subpopulation is characterized by a molecular stem-like cell phenotype including immaturity in differentiation, persistence in the G1 cell cycling phase, metabolic quiescence and increased cell adhesion. Among the upregulated genes, we identified several surface markers previously recognized for their role in T-ALL relapse (PMID: 21487112; 20231613) or in stem-like cell maintenance in a murine T-ALL model (PMID: 29781813; 38553571), such as CD44, CD226, CD52, CD97, CD27 and ITGB7. We further identified significant enrichment of the anti-apoptotic proteins BCL-6, BCL-2 and MCL1. By contrast, NOTCH1 is among the downregulated genes in the stem-like cell population when compared to the other

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Allogeneic Hematopoietic Cell Transplantation (HCT) Vs. Non-HCT Consolidation Therapies in Acute Myeloid Leukemia (AML) Patients 60-75 Years of Age in First Complete Remission (CR1): An Alliance (A151509), SWOG, ECOG-ACRIN and CIBMTR Study

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Blood 2018年 132卷 2170-2170页

作者： Celalettin Ustun Jennifer Le-Rademacher Hai-Lin Wang Megan Othus Zhuoxin Sun Brittny Major Mei-Jie Zhang Elizabeth Storrick Jacqueline M Lafky Selina Chow Krzysztof Mrózek Eyal C. Attar Clara D. Bloomfield Larry D Cripe Richard A. Larson Guido Marcucci Gail J. Roboz Geoffrey L. Uy Richard M. Stone Aminah Jatoi Andrew S Artz Blood and Marrow Transplant Program University of Minnesota Minneapolis MN Dept of Biomedical Statistics and Informatics Mayo Clinic Rochester MN CIBMTR (Center for International Blood and Marrow Transplant Research) Department of Medicine Medical College of Wisconsin Milwaukee WI Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle Harvard T.H. Chan School of Public Health Boston Department of Health Sciences Research Mayo Clinic Rochester Alliance Statistics and Data Center Mayo Clinic Rochester MN Mayo Clinic Rochester University of Chicago Comprehensive Cancer Center Chicago The Ohio State University Comprehensive Cancer Center Columbus OH Massachusetts General Hospital Boston Agios Pharmaceuticals Inc. Cambridge MA Comprehensive Cancer Center The Ohio State University Columbus OH Indiana University Simon Cancer Center Indianapolis Department of Medicine and Comprehensive Cancer Center University of Chicago Chicago IL Department of Hematological Malignancies Translational Science Gehr Family Center for Leukemia Research Hematologic Malignancies and Stem Cell Transplantation Institute Beckman Research Institute City of Hope Medical Center Duarte CA Weill Cornell Medicine and The Weill Medical College of Cornell University New York NY Washington University School of Medicine Saint Louis MO Dana-Farber Cancer Institute Boston MA Department of Medicine Section of Hematology/Oncology University of Chicago Chicago IL

Introduction: The preferred post-remission therapy for older patients (pts) with AML remains uncertain. We compared outcomes for older AML pts in CR1 receiving HCT reported to the CIBMTR to older AML pts achieving CR1 on National Clinical Trials Network induction and non-HCT consolidation therapy (CT) trials. Methods: This study focused on pts 60-75 years of age treated between 2004 to 2013. CT pts (n=211) underwent induction and consolidation on Alliance for Clinical Trials in Oncology, ECOG-ACRIN or SWOG clinical trials for initial therapy for newly diagnosed AML; the CIBMTR provided data for HCT pts (n=431). CT patients received at least one cycle of CT on study and were excluded if HCT occurred at any time. Time to event started at CR1 and pts entered at CT or HCT, respectively, using left-truncation to account for differential entry times. Results: For the CT cohort, first consolidation included standard therapy (e.g., cytarabine or a hypomethylating agent) and additional study drug (e.g., bortezomib, dasatinib, sorafenib, and Zosuquidar, gemtuzumab) or tipifarnib alone. Among HCT pts, the donor was a HLA-matched sibling or unrelated donor (URD) in 66% and the others were partially HLA-matched/mismatched URD (10%) or cord blood (24%). HCT pts were younger and more frequently had high-risk AML (high WBC, secondary AML and unfavorable cytogenetics) (Table). The median time from CR1 to HCT and CT was 3.2 and 0.5 months, respectively. Allogeneic HCT showed worse overall survival (OS) (HR=1.52, p=0.02) prior to 9 months and better OS thereafter (HR= 0.53, p <0.0001) relative to CT (figure 1A). Treatment-related mortality (TRM) was worse after HCT in the first 9 months (HR=2.8, CI: 1.5 -5.2, p =.0009), while relapse was less frequent beyond 9 months after treatment (HR = 0.42, CI: 0.29 to 0.61, p <.0001). Despite higher early TRM, HCT recipients went on to manifest superior OS [5 year OS: HCT 29% (24-34%), CT 13.8% (9 -22%)] (Figure 1A). The benefit of HCT for surviv

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Author Correction: Comprehensive mutational scanning of EGFR reveals TKI sensitivities of extracellular domain mutants

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Nature communications 2024年第1期15卷 3273页

作者： Tikvah K Hayes Elisa Aquilanti Nicole S Persky Xiaoping Yang Erica E Kim Lisa Brenan Amy B Goodale Douglas Alan Ted Sharpe Robert E Shue Lindsay Westlake Lior Golomb Brianna R Silverman Myshal D Morris Ty Running Fisher Eden Beyene Yvonne Y Li Andrew D Cherniack Federica Piccioni J Kevin Hicks Andrew S Chi Daniel P Cahill Jorg Dietrich Tracy T Batchelor David E Root Cory M Johannessen Matthew Meyerson Department of Medical Oncology Dana-Farber Cancer Institute & Harvard Medical School Boston MA USA. Cancer Program The Broad Institute of M.I.T. and Harvard Cambridge MA USA. Department of Molecular and Medical Pharmacology University of California Los Angeles CA USA. Genetic Perturbation Platform The Broad Institute of M.I.T. and Harvard Cambridge MA USA. Aera Therapeutics Cambridge MA USA. Data Science Platform The Broad Institute of M.I.T. and Harvard Cambridge Cambridge MA USA. Summer Honors Undergraduate Research Program Harvard Medical School Boston MA USA. Merck Research Laboratories Cambridge MA USA. Department of Pathology H. Lee Moffitt Cancer Center and Research Institute Tampa FL USA. Center for Neuro-Oncology Division of Neuro-Oncology Massachusetts General Hospital Boston MA USA. Department of Neurology Division of Neuro-Oncology Massachusetts General Hospital Boston MA USA. Department of Neurology Brigham and Women's Hospital & Harvard Medical School Boston MA USA. Department of Oncology Novartis Institutes for Biomedical Research Cambridge MA USA. Department of Medical Oncology Dana-Farber Cancer Institute & Harvard Medical School Boston MA USA. matthew_meyerson@dfci.harvard.edu. Cancer Program The Broad Institute of M.I.T. and Harvard Cambridge MA USA. matthew_meyerson@dfci.harvard.edu.

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66O Association of a blood T cell fitness gene signature with clinical benefit from ImmTAC bispecific T cell engagers

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Annals of Oncology 2024年 35卷 S239-S240页

作者： J.J. Sacco R.D. Carvajal O. Hamid P.B. Kirk L. Collins M.O. Butler A.N. Shoushtari J.C. Hassel A.P. Ikeguchi L. Hernandez-Aya P. Nathan J.M. Piulats Rodriguez H. Shaw D. Davar C. Han M.R. Middleton F. Thistlethwaite F. Peck K. Ranade T. Sato Medical Oncology Department The Clatterbridge Cancer Centre and University of Liverpool Wirral UK Medical Oncology Northwell Health Cancer Institute New York NY USA Melanoma Center and Phase I Immuno-Oncology Program The Angeles Clinic and Research Institute a Cedars-Sinai Affiliate Los Angeles CA USA Translational Medicine Immunocore Limited Abingdon-on-Thames UK Bioinformatics and Data Science Immunocore Limited Abingdon-on-Thames UK Department of Medical Oncology and Hematology Princess Margaret Cancer Center Toronto ON Canada Medical Oncology Memorial Sloan Kettering Cancer Center New York NY USA Department of Dermatology and National Center for Tumor Diseases University Hospital Heidelberg Heidelberg Germany Department of Medicine Stephenson Cancer Center/University of Oklahoma Oklahoma City OK USA Cutaneous Oncology University of Miami Sylvester Comprehensive Cancer Center Miami Beach FL USA Medical Oncology Department Mount Vernon Cancer Centre Northwood UK Medical Oncology Department ICO - Institut Català d'Oncologia l'Hospitalet (Hospital Duran i Reynals) Barcelona Spain Medical Oncology UCLH - University College London Hospitals NHS Foundation Trust London UK Medical Oncology University of Pittsburgh Cancer Institute Pittsburgh PA USA Faculty of Medical & Health Sciences University of Auckland Auckland New Zealand Department of Oncology University of Oxford Oxford UK Medical Oncology Department The Christie NHS Foundation Trust Manchester UK Clinical Development Immunocore Limited Abingdon-on-Thames UK Translational Medicine Department Immunocore LLC Rockville MD USA Sidney Kimmel Cancer Center Jefferson University Philadelphia PA USA

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