Evidence map›Paper›PMID 38820127›Full record

ArticleMolecular cancer research : MCR2024

Clinically Relevant Humanized Mouse Models of Metastatic Prostate Cancer Facilitate Therapeutic Evaluation.

Raymond J Kostlan, John T Phoenix, Audris Budreika, Marina G Ferrari, Neetika Khurana, Jae E Choi, Kristin Juckette, Somnath Mahapatra, Brooke L McCollum, Russell Moskal and 5 more

Abstract read
In one paragraph

Article in Molecular cancer research : MCR, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Humanized immune system animal models and their recent applications.Animal models and experimental medicine · 2026
    Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Raymond J KostlanDepartment of Cancer Biology, Loyola University Chicago, Maywood, Illinois.ORCID 0000-0002-0417-0731
John T PhoenixDepartment of Cancer Biology, Loyola University Chicago, Maywood, Illinois.ORCID 0000-0001-7954-4059
Audris BudreikaDepartment of Cancer Biology, Loyola University Chicago, Maywood, Illinois.ORCID 0009-0009-9810-4775
Marina G FerrariDepartment of Cancer Biology, Loyola University Chicago, Maywood, Illinois.ORCID 0000-0001-6295-5695
Neetika KhuranaDepartment of Cancer Biology, Loyola University Chicago, Maywood, Illinois.ORCID 0000-0003-3964-3217
Jae E ChoiMichigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan.ORCID 0009-0008-4744-3833
Kristin JucketteMichigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan.ORCID 0009-0000-0955-0592
Somnath MahapatraMichigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan.ORCID 0000-0001-9490-6353
Brooke L McCollumMichigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan.ORCID 0009-0005-7580-3670
Russell MoskalDepartment of Cancer Biology, Loyola University Chicago, Maywood, Illinois.ORCID 0009-0008-6882-9479
Rahul MannanMichigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan.ORCID 0000-0002-6642-0468
Yuanyuan QiaoMichigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan.ORCID 0000-0002-1178-3480
Donald J Vander GriendDepartment of Pathology, University of Illinois at Chicago, Chicago, Illinois.ORCID 0000-0003-4421-5698
Arul M ChinnaiyanMichigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan.ORCID 0000-0001-9282-3415
Steven KregelDepartment of Cancer Biology, Loyola University Chicago, Maywood, Illinois.ORCID 0000-0002-0847-2657

Funding

Training Program in Tumor MicroenvironmentT32CA009676 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Maria G Castro, Analisa Virginia DiFeo · 1992 to 2026
$7.3M
Exploring Precision Oncology: From Gene Fusions to lncRNAsR35CA231996 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CHINNAIYAN, ARUL M · 2018 to 2024
$6.4M
Targeting the MLL complex in Castration Resistant Prostate CancerR01CA200660 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CHINNAIYAN, ARUL M, GREMBECKA, JOLANTA · 2016 to 2020
$1.9M
DOD Prostate Cancer Research Program (PCRP) W81XWH-17-1-0155Howard Hughes Medical InstituteHoward Hughes Medical Institute (HHMI) Howard Hughes Medical Institute InvestigatorNational Cancer Institute (NCI) T32-CA09676NCI NIH HHS R01 CA200660NCI NIH HHS R35 CA231996NCI NIH HHS T32 CA009676
6 · The paper itself

Abstract

There is tremendous need for improved prostate cancer models. Anatomically and developmentally, the mouse prostate differs from the human prostate and does not form tumors spontaneously. Genetically engineered mouse models lack the heterogeneity of human cancer and rarely establish metastatic growth. Human xenografts are an alternative but must rely on an immunocompromised host. Therefore, we generated prostate cancer murine xenograft models with an intact human immune system (huNOG and huNOG-EXL mice) to test whether humanizing tumor-immune interactions would improve modeling of metastatic prostate cancer and the impact of androgen receptor-targeted and immunotherapies. These mice maintain multiple human immune cell lineages, including functional human T-cells and myeloid cells. Implications: To the best of our knowledge, results illustrate the first model of human prostate cancer that has an intact human immune system, metastasizes to clinically relevant locations, responds appropriately to standard-of-care hormonal therapies, and can model both an immunosuppressive and checkpoint-inhibition responsive immune microenvironment.

Indexed as

Disease Models, AnimalProstatic NeoplasmsAnimalsCell Line, TumorHumansImmunotherapyMaleMiceNeoplasm MetastasisTumor MicroenvironmentXenograft Model Antitumor Assays

Identifiers

PMID38820127
PMCPMC11372372

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.