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A T cell resilience model associated with response to immunotherapy in multiple tumor types (May, 10.1038/s41591-022-01799-y, 2022)

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NATURE MEDICINE 2022年第10期28卷 2219-2219页

作者： Zhang, Yu Vu, Trang Palmer, Douglas C. Kishton, Rigel J. Gong, Lanqi Huang, Jiao Nguyen, Thanh Chen, Zuojia Smith, Cari Livak, Ferenc Paul, Rohit Day, Chi-Ping Wu, Chuan Merlino, Glenn Aldape, Kenneth Guan, Xin-yuan Jiang, Peng Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA Department of Clinical Oncology The University of Hong Kong Hong Kong China Sun Yat-sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangzhou China Surgery Branch National Cancer Institute National Institutes of Health Bethesda MD USA Laboratory of Pathology Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA Experimental Immunology Branch Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA Laboratory Animal Science Program Leidos Biomedical Research Inc Frederick MD USA Flow Cytometry Core Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA Office of the Director Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA Laboratory of Cancer Biology and Genetics Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA

Despite breakthroughs in cancer immunotherapy, most tumor-reactive T cells cannot persist in solid tumors due to an immunosuppressive environment. We developed Tres (tumor-resilient T cell), a computational model utilizing single-cell transcriptomic data to identify signatures of T cells that are resilient to immunosuppressive signals, such as transforming growth factor-β1, tumor necrosis factor-related apoptosis-inducing ligand and prostaglandin E2. Tres reliably predicts clinical responses to immunotherapy in melanoma, lung cancer, triple-negative breast cancer and B cell malignancies using bulk T cell transcriptomic data from pre-treatment tumors from patients who received immune-checkpoint inhibitors (n= 38), infusion products for chimeric antigen receptor T cell therapies (n= 34) and pre-manufacture samples for chimeric antigen receptor T cell or tumor-infiltrating lymphocyte therapies (n= 84). Further, Tres identifiedFIBP, whose functions are largely unknown, as the top negative marker of tumor-resilient T cells across many solid tumor *** in murine and human donor CD8+T cells significantly enhanced T cell-mediated cancer killing in in vitro co-cultures. Further,Fibpknockout in murine T cells potentiated the in vivo efficacy of adoptive cell transfer in the B16 tumor *** T cells exhibit reduced cholesterol metabolism, which inhibits effector T cell function. These results demonstrate the utility of Tres in identifying biomarkers of T cell effectiveness and potential therapeutic targets for immunotherapies in solid tumors.

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Identify Non-mutational p53 Functional Deficiency in Human cancers

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Genomics, Proteomics & Bioinformatics 2024年第5期22卷 85-98页

作者： Qianpeng Li Yang Zhang Sicheng Luo Zhang Zhang Ann L.Oberg David E.Kozono Hua Lu Jann N.Sarkaria Lina Ma Liguo Wang National Genomics Data Center China National Center for BioinformationBeijing 100101China Beijing Institute of Genomics Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Division of Computational Biology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Department of Radiation Oncology Dana-Farber Cancer Institute and Brigham and Women's HospitalBostonMA 02215USA Department of Biochemistry&Molecular Biology and Tulane Cancer Center Tulane University School of MedicineNew OrleansLA 70112USA Department of Radiation Oncology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Department of Biochemistry and Molecular Biology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Bioinformatics and Computational Biology Graduate Program University of Minnesota RochesterRochesterMN 55904USA

An accurate assessment of p53's functional statuses is critical for cancer genomic ***,there is a significant challenge in identifying tumors with non-mutational p53 inactivation which is not detectable through DNA *** undetected cases are often misclassified as p53-normal,leading to inaccurate prognosis and downstream association *** address this issue,we built the support vector machine(SVM)models to systematically reassess p53's functional statuses in TP53 wild-type(TP53^(WT))tumors from multiple The cancer Genome Atlas(TCGA)***-validation demonstrated the good performance of the SVM models with a mean area under the receiver operating characteristic curve(AUROC)of 0.9822,precision of 0.9747,and recall of *** study revealed that a significant proportion(87%-99%)of TP53^(WT) tumors actually had compromised p53 *** analyses uncovered that these genetically intact but functionally impaired(termed as predictively reduced function of p53 or TP53^(WT)-pRF)tumors exhibited genomic and pathophysiologic features akin to TP53-mutant tumors:heightened genomic instability and elevated levels of ***,patients with TP53^(WT)-pRF tumors experienced significantly shortened overall survival or progression-free survival compared to those with predictively normal function of p53(TP53^(WT)-pN)tumors,and these patients also displayed increased sensitivity to platinum-based chemotherapy and radiation therapy.

关键词： cancer Composite expression DNA mutation Machine learning p53 deficiency

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Response to Dr. Wang's comment

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Journal of clinical monitoring and computing 2025年 1-2页

作者： Juan P Cata Bhavin Soni Shreyas Bhavsar Jose Soliz Jeffrey Siewerdsen Department of Anaesthesiology and Perioperative Medicine The University of Texas - MD Anderson Cancer Center Houston USA. jcata@***. Anesthesiology and Surgical Oncology Research Group (ASORG) Houston USA. jcata@***. Surgical Data Science Program Institute for Data Science in Oncology (IDSO) The University of Texas MD Anderson Cancer Center Houston USA. jcata@***. Department of Imaging Physics The University of Texas MD Anderson Cancer Center Houston USA. Department of Anaesthesiology and Perioperative Medicine The University of Texas - MD Anderson Cancer Center Houston USA.

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Privacy-Preserving Dashboard for F.A.I.R Head and Neck cancer data supporting multi-centered collaborations 14

Privacy-Preserving Dashboard for F.A.I.R Head and Neck Cance...

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14th International Conference on Semantic Web Applications and Tools for Health Care and Life sciences, SWAT4HCLS 2023

作者： Gouthamchand, Varsha Choudhury, Ananya Hoebers, Frank Wesseling, Frederik Welch, Mattea Kim, Sejin Haibe-Kains, Benjamin Kazmierska, Joanna Dekker, Andre van Soest, Johan Wee, Leonard GROW School of Oncology and Reproduction Maastricht University Medical Centre+ Maastricht Netherlands Clinical Data Science Faculty of Health Medicine and Life Sciences Maastricht University Maastricht Netherlands Radiation Medicine Program Princess Margaret Cancer Centre University Health Network TorontoON Canada Dept of Radiation Oncology Greater Poland Cancer Centre II Poznan Poland Brightlands Institute for Smart Society Faculty of Science and Engineering Maastricht University Heerlen Netherlands

Research in modern healthcare requires vast volumes of data from various healthcare centers across the globe. It is not always feasible to centralize clinical data without compromising privacy. A tool addressing these issues and facilitating reuse of clinical data is the need of the hour. The Federated Learning approach, governed in a set of agreements such as the Personal Health Train (PHT) manages to tackle these concerns by distributing models to the data centers instead of the traditional approach of centralizing datasets. One of the prerequisites of PHT is using semantically interoperable datasets for the models to be able to find them. FAIR (Findable, Accessible, Interoperable, Reusable) principles help in building interoperable and reusable data by adding knowledge representation and providing descriptive metadata. However, the process of making data FAIR is not always easy and straight-forward. Our main objective is to disentangle this process by using domain and technical expertise and get data prepared for federated learning. This paper introduces applications that are easily deployable as Docker containers, which will automate parts of the aforementioned process and significantly simplify the task of creating FAIR clinical data. Our method bypasses the need for clinical researchers to have a high degree of technical skills. We demonstrate the FAIR-ification process by applying it to five Head and Neck cancer datasets (four public and one private). The PHT paradigm is explored by building a distributed visualization dashboard from the aggregated summaries of the FAIR-ified datasets. Using the PHT infrastructure for exchanging only statistical summaries or model coefficients allows researchers to explore data from multiple centers without breaching privacy. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Knowledge representation

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Molecular Mechanisms Promoting Long-Term Cytopenia after BCMA CAR-T Therapy in Multiple Myeloma

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BLOOD 2024年 144卷 4798-4799页

作者： [Anonymous] Hematology and Cell Therapy Department. Clinica Universidad de Navarra. IdiSNA. Pamplona Spain Hemato-Oncology Program. Cima Universidad de Navarra. IdiSNA. Pamplona Spain Computational Biology Program. Cima Universidad de Navarra. IdiSNA. Pamplona Spain Immunology and Immunotherapy Department. Clinica Universidad de Navarra. Pamplona Spain Centro de Investigación Biomedica en Red de Cancer (CIBERONC). Madrid Spain Cancer Center Clinica Universidad de Navarra (CCUN). Pamplona Spain Cancer Center Clinica Universidad de Navarra (CCUN) Pamplona Spain Immunology and Immunotherapy Program. Cima Universidad de Navarra. IdiSNA. Pamplona Spain Center for Data Science and Artificial Intelligence Institute (DATAI) Universidad de Navarra Pamplona Pamplona Spain Hematology and Cell Therapy Department Clinica Universidad de Navarra IdiSNA Pamplona Spain Centro de Investigación Biomedica en Red de Cancer (CIBERONC) Madrid Spain

Hematologic toxicity, specifically prolonged cytopenia, is a common side effect associated with CAR-T therapies, being particularly severe in patients with relapsed/refractory Multiple Myeloma (MM) (1). However, to date, its etiology is poorly understood. Thus, the main objective of this study was to identify and understand the underlying mechanisms of prolonged cytopenias associated with CAR-T therapy targeting BCMA. In our work, we combined the retrospective analysis of a cohort of 48 patients treated with BCMA CAR-T cells at Clinica Universidad de Navarra, with ex vivo transcriptomic analysis using single-cell RNA sequencing, to characterize the kinetics of cytopenia, identify predictive factors and determine potential mechanism underlying these toxicities. The overall incidence of cytopenia was 95.7%, and grade>3 thrombocytopenia and neutropenia one month after infusion, was observed in 57% and 53% of the patients, being still present after one year in 4 and 3 patients respectively. Presence of cytopenia at baseline and high peak inflammatory markers highly correlated with cytopenia persisting up to three months. To determine potential mechanisms underpinning cytopenias, we evaluated the paracrine effect of BCMA CAR-T cells on HSPCs differentiation, using an ex vivo myeloid differentiation model (2). Thus, supernatants from CAR-T cells or control T lymphocytes, co-cultured with MM tumor cell line U266, were added to the differentiation process. Phenotypic analysis by flow cytometry showed that supernatants from activated CAR-T cells (spCAR) halted HSPCs differentiation, promoting more immature phenotypes, with reduced expression of granulocytic, monocytic, and erythroid maturation markers, suggesting a paracrine effect related to CAR-T cells activation. Interestingly, this immature phenotype could be prevented by a combination of IFNγ, TNFα/β, TGFβ, IL-6, and IL-17 inhibitors, indicating the essential role of these cytokines and providing potential targets to pr

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CAR-T and CAR-NK as cellular cancer immunotherapy for solid tumors

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Cellular & Molecular Immunology 2024年第10期21卷 1089-1108页

作者： Lei Peng Giacomo Sferruzza Luojia Yang Liqun Zhou Sidi Chen Department of Genetics Yale University School of MedicineNew HavenCTUSA System Biology Institute Yale UniversityWest HavenCTUSA Combined Program in the Biological and Biomedical Sciences Yale UniversityNew HavenCTUSA Molecular Cell Biology Geneticsand Development ProgramYale UniversityNew HavenCTUSA Immunobiology Program Yale UniversityNew HavenCTUSA Yale Comprehensive Cancer Center Yale University School of MedicineNew HavenCTUSA Department of Neurosurgery Yale University School of MedicineNew HavenCTUSA Yale Stem Cell Center Yale University School of MedicineNew HavenCTUSA Yale Liver Center Yale University School of MedicineNew HavenCTUSA Yale Center for Biomedical Data Science Yale University School of MedicineNew HavenCTUSA Yale Center for RNA Science and Medicine Yale University School of MedicineNew HavenCTUSA

In the past decade,chimeric antigen receptor(CAR)-T cell therapy has emerged as a promising immunotherapeutic approach for combating cancers,demonstrating remarkable efficacy in relapsed/refractory hematological malignancies in both pediatric and adult ***-natural killer(CAR-NK)cell complements CAR-T cell therapy by offering several distinct ***-NK cells do not require HLA compatibility and exhibit low safety ***,CAR-NK cells are conducive to“off-the-shelf”therapeutics,providing significant logistic advantages over CAR-T *** CAR-T and CAR-NK cells have shown consistent and promising results in hematological ***,their efficacy against solid tumors remains limited due to various obstacles including limited tumor trafficking and infiltration,as well as an immuno-suppressive tumor *** this review,we discuss the recent advances and current challenges of CAR-T and CAR-NK cell immunotherapies,with a specific focus on the obstacles to their application in solid *** also analyze in depth the advantages and drawbacks of CAR-NK cells compared to CAR-T cells and highlight CAR-NK CAR ***,we explore future perspectives of these adoptive immunotherapies,highlighting the increasing contribution of cutting-edge biotechnological tools in shaping the next generation of cellular immunotherapy.

关键词： CAR-T CAR-NK Cell therapy cancer immunotherapy Solid tumor

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Deep-learning triage of 3D pathology data for improved disease detection while reducing pathologist workloads

Deep-learning triage of 3D pathology data for improved disea...

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Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics

作者： Gao, Gan Wang, Fiona Brenes, David Song, Andrew H. Chow, Sarah S.L. Mahmood, Faisal Liu, Jonathan T.C. Department of Mechanical Engineering University of Washington SeattleWA98195 United States Department of Computer Science University of Washington SeattleWA98195 United States Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA02115 United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA02114 United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA02142 United States Data Science Program Dana-Farber Cancer Institute BostonMA02215 United States Department of Bioengineering University of Washington SeattleWA98195 United States Department of Laboratory Medicine & Pathology University of Washington School of Medicine SeattleWA98195 United States

3D pathology can potentially improve disease detection, but the datasets are too large to review. We're developing a deep-learning-based triage method to identify the highest-risk 2D sections within 3D pathology d... 详细信息

关键词： Adversarial machine learning

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BENCHMARKING COMMUNITY DRUG RESPONSE PREDICTION MODELS: dataSETS, MODELS, TOOLS, AND METRICS FOR CROSS-dataSET GENERALIZATION ANALYSIS

arXiv

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arXiv 2025年

作者： Partin, Alexander Vasanthakumari, Priyanka Narykov, Oleksandr Wilke, Andreas Koussa, Natasha Jones, Sara E. Zhu, Yitan Overbeek, Jamie C. Jain, Rajeev Fernando, Gayara Demini Sanchez-Villalobos, Cesar Garcia-Cardona, Cristina Mohd-Yusof, Jamaludin Chia, Nicholas Wozniak, Justin M. Ghosh, Souparno Pal, Ranadip Brettin, Thomas S. Weil, M. Ryan Stevens, Rick L. Division of Data Science and Learning Argonne National Laboratory LemontIL United States Frederick National Laboratory for Cancer Research Cancer Data Science Initiatives Cancer Research Technology Program FrederickMD United States Department of Statistics University of Nebraska–Lincoln LincolnNE United States Department of Electrical & Computer Engineering Texas Tech University LubbockTX United States Division of Computer Computational and Statistical Sciences Los Alamos National Laboratory Los AlamosNM United States Department of Computer Science The University of Chicago ChicagoIL United States

Deep learning (DL) and machine learning (ML) models have shown promise in drug response prediction (DRP), yet their ability to generalize across datasets remains an open question, raising concerns about their real-world applicability. Due to the lack of standardized benchmarking approaches, model evaluations and comparisons often rely on inconsistent datasets and evaluation criteria, making it difficult to assess true predictive capabilities. In this work, we introduce a benchmarking framework for evaluating cross-dataset prediction generalization in DRP models. Our framework incorporates five publicly available drug screening datasets, six standardized DRP models, and a scalable workflow for systematic evaluation. To assess model generalization, we introduce a set of evaluation metrics that quantify both absolute performance (e.g., predictive accuracy across datasets) and relative performance (e.g., performance drop compared to within-dataset results), enabling a more comprehensive assessment of model transferability. Our results reveal substantial performance drops when models are tested on unseen datasets, underscoring the importance of rigorous generalization assessments. While several models demonstrate relatively strong cross-dataset generalization, no single model consistently outperforms across all datasets. Furthermore, we identify CTRPv2 as the most effective source dataset for training, yielding higher generalization scores across target datasets. By sharing this standardized evaluation framework with the community, our study aims to establish a rigorous foundation for model comparison, and accelerate the development of robust DRP models for real-world applications. © 2025, CC BY.

关键词： Prediction models

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Genetic Perturbation of CD70/CD27 Co-Stimulation Promotes the Development of Bcl6-Driven Diffuse Large B-Cell Lymphoma

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BLOOD 2021年 138卷 713-713页

作者： Mandato, Elisa Sun, Yanbo Shanmugam, Vignesh Choi, Il-Kyu Wright, Kyle T. Redd, Robert A. Lawton, Lee N. Neuberg, Donna S. Rodig, Scott J. Zhang, Baochun Shipp, Margaret A. Department of Medical Oncology Dana-Farber Cancer Institute Boston MA Department of Pathology Brigham and Women's Hospital Boston MA Broad Institute of MIT and Harvard Cancer Program Cambridge MA Department of Pathology University of Oklahoma Health Sciences Center Oklahoma City OK Department of Data Science Dana-Farber Cancer Institute Boston MA Department of Data Science Dana-Farber Cancer Institute Boston Department of Pathology Brigham & Women's Hospital Boston MA Department of Medical Oncology Dana Farber Cancer Institute Boston MA

Multiple co-stimulatory and co-inhibitory pathways modulate T-cell dependent anti-tumor immune responses in lymphoid malignancies. We recently defined the recurrent genetic alterations and associated substructure of diffuse large B-cell lymphoma (DLBCL), including five distinct clusters (1-5), and identified potential genetic bases for immune evasion [ Nature Medicine 2018; 24:679-690]. In our series, 26% of tumors had inactivating somatic mutations or copy loss of CD70 and likely disruption of CD70/CD27 co-stimulation. CD70 and CD27 are homotrimer type II and homodimer type I transmembrane proteins and members of the TNF and TNF receptor superfamilies, respectively. CD70 is transiently expressed on certain normal B-cell and dendritic cell populations upon activation and constitutively expressed on multiple B-cell tumors. CD70 activation of CD27 + T cells promotes antigen-dependent T-cell expansion and immune surveillance of normal and malignant B cells. Patients with germline deficiencies of either CD70 or CD27 have an increased incidence of EBV-associated lymphoid malignancies, underscoring the importance of this co-stimulatory pathway. In our series, CD70 alterations were most common in Cluster 1 DLBCLs, which also exhibited recurrent BCL6 chromosomal translocations. The co-occurrence of CD70 and BCL6 genetic alterations was noteworthy because of the established role of CD8 + T-cell dependent immune surveillance in murine models of Bcl6 -driven DLBCL [ Nature Medicine 2014; 20:283-290]. To assess the consequences of Cd70 deficiency and perturbed CD70/CD27 co-stimulation on Bcl6 -driven lymphomagenesis, we crossed the previously described Bcl6 tg/+ and Cd70 -/- mice to generate Cd70 -/- ; Bcl6 tg/+ animals. In the aging cohorts, Cd70 -/- ; Bcl6 tg/+ mice were more likely than Bcl6 tg/+ animals (or the Cd70 -/- or wild-type [WT] groups) to be euthanized for symptoms before the study endpoint (18 months [mo]) (5 of 18 Cd70 -/- ; Bcl6 tg/+ euthanized for symptoms pri

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Through the Lens of Patient Partners: Challenges in Accrual of Older Adults to NCI Clinical Trials

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Journal of the National cancer Institute - Monographs 2022年第60期2022卷 125-134页

作者： BrintzenhofeSzoc, Karlynn Canin, Beverly Casas-Silva, Esmeralda Denicoff, Andrea Braun-Inglis, Christa Okado, Izumi Bakos, Alexis Oncology Social Work Kent School of Social Work University of Louisville Louisville KY United States SCOREboard Patient Advocate Board The Cancer and Aging Research Group United States Center for Biomedical Informatics and Information Technology Informatics and Data Science Program National Institutes of Health National Cancer Institute Rockville MD United States Division of Cancer Treatment and Diagnosis Cancer Therapy and Evaluation Program National Institutes of Health National Cancer Institute Rockville MD United States Clinical Faculty UH Nancy Atmospera-Walch School of Nursing University of Hawaii Cancer Center Hawaii M U NCORP Honolulu HI United States University of HawaiiCancer Center Honolulu HI United States Division of Cancer Prevention Community Oncology and Prevention Trials Research Group National Institutes of Health National Cancer Institute Rockville MD United States

The workshop “Engaging Older Adults in cancer Clinical Trials Conducted in the NCI Clinical Trials Network: Challenges and Opportunities” included a Patient Stakeholder Workgroup that explored the needs and concerns of older adults with cancer regarding clinical trials. To accomplish this, the workgroup conducted patient focus groups in which participants were interviewed, recorded conversations were analyzed and coded, and salient themes were identified. The focus groups identified general barriers to accrual such as complex consent forms, general communication, restrictive eligibility, nonreferrals, patient costs, cultural insensitivity, limited accessibility in community settings, and transportation issues. They also identified the influence of knowledgeable information presenters, improved care, family or caregiver support, and the desire to help others as drivers or reasons to participate in clinical trials. The workshop concluded that multi-level interventions could be used to increase the accrual of older adults to National cancer Institute clinical trials as well as others. © The Author(s) 2022. Published by Oxford University Press. All rights reserved.

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