Maihi Fujita

ORCID: 0000-0003-2619-1637
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About
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Research Areas
  • AI in cancer detection
  • Radiomics and Machine Learning in Medical Imaging
  • Cancer Genomics and Diagnostics
  • Cancer Cells and Metastasis
  • 3D Printing in Biomedical Research
  • Immunotherapy and Immune Responses
  • HER2/EGFR in Cancer Research
  • Monoclonal and Polyclonal Antibodies Research
  • Cancer Research and Treatments
  • Cancer Treatment and Pharmacology
  • Cell Image Analysis Techniques
  • Radiopharmaceutical Chemistry and Applications
  • Protein Degradation and Inhibitors
  • Nutrition, Genetics, and Disease
  • Macrophage Migration Inhibitory Factor
  • Chemical Reactions and Isotopes
  • Molecular Biology Techniques and Applications
  • Adenosine and Purinergic Signaling
  • Colorectal Cancer Treatments and Studies
  • Digestive system and related health
  • Biomedical Text Mining and Ontologies
  • Cancer Immunotherapy and Biomarkers
  • CRISPR and Genetic Engineering
  • Microtubule and mitosis dynamics

University of Utah
2018-2025

Huntsman Cancer Institute
2016-2024

Models that recapitulate the complexity of human tumors are urgently needed to develop more effective cancer therapies. We report a bank patient-derived xenografts (PDXs) and matched organoid cultures from represent greatest unmet need: endocrine-resistant, treatment-refractory metastatic breast cancers. leverage PDXs PDX-derived organoids (PDxO) for drug screening is feasible cost-effective with in vivo validation. Moreover, we demonstrate feasibility using these models precision oncology...

10.1038/s43018-022-00337-6 article EN cc-by Nature Cancer 2022-02-24
Hua Sun Song Cao R. Jay Mashl Chia-Kuei Mo Simone Zaccaria and 95 more Michael C. Wendl Sherri R. Davies Matthew H. Bailey Tina Primeau Jeremy Hoog Jacqueline L. Mudd Dennis A. Dean Rajesh Patidar Li Chen Matthew A. Wyczalkowski Reyka G. Jayasinghe Fernanda Martins Rodrigues Nadezhda V. Terekhanova Yize Li Kian‐Huat Lim Andrea Wang‐Gillam Brian A. Van Tine X. Cynthia Rebecca Aft Katherine C. Fuh Julie K. Schwarz José P. Zevallos Sidharth V. Puram John F. DiPersio Julie Belmar Jason M. Held Jingqin Luo Brian A. Van Tine Rose Tipton Yige Wu Lijun Yao Daniel Cui Zhou Andrew Butterfield Zhengtao Chu Maihi Fujita Chieh‐Hsiang Yang Emilio Cortes-Sanchez Sandra D. Scherer Ling Zhao Tijana Borovski Vicki Chin John J. DiGiovanna Christian Frech Jeffrey Grover Ryan Jeon Soner Koc Jelena Randjelović Sara Seepo Tamara Stanković Lacey E. Dobrolecki Michael Ittmann Susan G. Hilsenbeck Bert W. O’Malley Nicholas Mitsiades Salma Kaochar Argun Akçakanat Jithesh J. Augustine Huiqin Chen Bingbing Dai Kurt W. Evans Kelly Gale Don L. Gibbons Min Jin Ha V. Behrana Jensen Michael P. Kim Bryce P. Kirby Scott Kopetz Christopher D. Lanier Dali Li Mourad Majidi David G. Menter Ismail M. Meraz Turçin Saridogan Stephen Scott Alexey V. Sorokin Coya Tapia Jing Wang Shannon N. Westin Yuanxin Xi Yi Xu Fei Yang Timothy A. Yap Vashisht G. Yennu-Nanda Erkan Yuca Jianhua Zhang Ran Zhang Xiaoshan Zhang Xiaofeng Zheng Dylan Fingerman Haiyin Lin Qin Liu Andrew V. Kossenkov Vito W. Rebecca Rajasekharan Somasundaram Michae T. Tetzlaff

Abstract Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established landscapes 536 patient-derived xenograft (PDX) models across 25 types, together mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared human tumors, PDXs typically higher purity fit dynamic driver events molecular properties via multiple...

10.1038/s41467-021-25177-3 article EN cc-by Nature Communications 2021-08-24

Abstract Background Metastatic breast cancer (MBC) is incurable, with a 5-year survival rate of 28%. In the USA, more than 42,000 patients die from MBC every year. The most common type estrogen receptor-positive (ER+), and ER+ any other subtype. tumors can be successfully treated hormone therapy, but many acquire endocrine resistance, at which point treatment options are limited. There an urgent need for model systems that better represent human in vivo, where metastasize. Patient-derived...

10.1186/s13058-021-01476-x article EN cc-by Breast Cancer Research 2021-10-30

Abstract Background Targeted therapies for triple-negative breast cancer (TNBC) are limited; however, the epidermal growth factor receptor (EGFR) represents a potential target, as majority of TNBC express EGFR. The purpose these studies was to evaluate effectiveness two EGFR-targeted antibody-drug conjugates (ADC: ABT-414; ABBV-321) in combination with navitoclax, an antagonist anti-apoptotic BCL-2 and BCL-X L proteins, order assess translational relevance combinations TNBC. Methods...

10.1186/s13058-020-01374-8 article EN cc-by Breast Cancer Research 2020-11-30

We created the PDX Network (PDXNet) portal (https://portal.pdxnetwork.org/) to centralize access National Cancer Institute-funded PDXNet consortium resources, facilitate collaboration among researchers and make these data easily available for research. The includes sections analysis results, metrics activities, processing protocols training materials data. Currently, contains model information resources from 334 new models across 33 cancer types. Tissue samples of were deposited in NCI's...

10.1093/narcan/zcac014 article EN cc-by NAR Cancer 2022-04-08

Abstract Model systems that recapitulate the complexity of human tumors and reality variable treatment responses are urgently needed to better understand cancer biology develop more effective therapies. Here we report development characterization a large bank patient-derived xenografts (PDX) matched organoid cultures from represent some greatest unmet needs in breast research treatment. These include endocrine-resistant, treatment-refractory, metastatic cancers and, cases, multiple tumor...

10.1101/2021.02.28.433268 preprint EN bioRxiv (Cold Spring Harbor Laboratory) 2021-03-02

Patient-derived xenografts (PDX) model human intra- and intertumoral heterogeneity in the context of intact tissue immunocompromised mice. Histologic imaging via hematoxylin eosin (H&E) staining is routinely performed on PDX samples, which could be harnessed for computational analysis. Prior studies large clinical H&E image repositories have shown that deep learning analysis can identify intercellular morphologic signals correlated with disease phenotype therapeutic response. In this study,...

10.1158/0008-5472.can-23-1349 article EN cc-by-nc-nd Cancer Research 2024-07-02

<div>Abstract<p>Patient-derived xenografts (PDX) model human intra- and intertumoral heterogeneity in the context of intact tissue immunocompromised mice. Histologic imaging via hematoxylin eosin (H&E) staining is routinely performed on PDX samples, which could be harnessed for computational analysis. Prior studies large clinical H&E image repositories have shown that deep learning analysis can identify intercellular morphologic signals correlated with disease phenotype...

10.1158/0008-5472.c.7311385 preprint EN 2024-07-02

<div>Abstract<p>Patient-derived xenografts (PDX) model human intra- and intertumoral heterogeneity in the context of intact tissue immunocompromised mice. Histologic imaging via hematoxylin eosin (H&E) staining is routinely performed on PDX samples, which could be harnessed for computational analysis. Prior studies large clinical H&E image repositories have shown that deep learning analysis can identify intercellular morphologic signals correlated with disease phenotype...

10.1158/0008-5472.c.7311385.v1 preprint EN 2024-07-02
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