Evidence map›Paper›PMID 39732766›Full record

ArticleScientific reports2024

Accelerated growth and local progression of radiorecurrent prostate cancer in an orthotopic bioluminescent mouse model.

Gavin Frame, Xiaoyong Huang, Roni Haas, Kabir A Khan, Hon S Leong, Thomas Kislinger, Paul C Boutros, Michelle Downes, Stanley K Liu

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Gavin FrameDepartment of Medical Biophysics, University of Toronto, Toronto, Canada.
Xiaoyong HuangSunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.
Roni HaasUniversity of California Los Angeles, Los Angeles, USA.
Kabir A KhanSunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.
Hon S LeongDepartment of Medical Biophysics, University of Toronto, Toronto, Canada.
Thomas KislingerDepartment of Medical Biophysics, University of Toronto, Toronto, Canada.
Paul C BoutrosDepartment of Medical Biophysics, University of Toronto, Toronto, Canada.
Michelle DownesDivision of Anatomic Pathology, Precision Diagnostics & Therapeutics Program-Laboratory Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada.
Stanley K LiuDepartment of Medical Biophysics, University of Toronto, Toronto, Canada. Stanley.Liu@sunnybrook.ca.ORCID 0000-0001-6851-0857

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Globally, prostate cancer is the second most common malignancy in males, with over 400 thousand men dying from the disease each year. A common treatment modality for localized prostate cancer is radiotherapy. However, up to half of high-risk patients can relapse with radiorecurrent prostate cancer, the aggressive clinical progression of which remains severely understudied. To address this, we have established an orthotopic mouse model for study that recapitulates the aggressive clinical progression of radiorecurrent prostate cancer. Radiorecurrent DU145 cells which survived conventional fraction (CF) irradiation were orthotopically injected into the prostates of athymic nude mice and monitored with bioluminescent imaging. CF tumours exhibited higher take rates and grew more rapidly than treatment-naïve parental tumours (PAR). Pathohistological analysis revealed extensive seminal vesicle invasion and necrosis in CF tumours, recapitulating the aggressive progression towards locally advanced disease exhibited by radiorecurrent tumours clinically. RNA sequencing of CF and PAR tumours identified ROBO1, CAV1, and CDH1 as candidate targets of radiorecurrent progression associated with biochemical relapse clinically. Together, this study presents a clinically relevant orthotopic model of radiorecurrent prostate cancer progression that will enable discovery of targets for therapeutic intervention to improve outcomes in prostate cancer patients.

Indexed as

Disease Models, AnimalDisease ProgressionMice, NudeNeoplasm Recurrence, LocalProstatic NeoplasmsAnimalsCell Line, TumorHumansLuminescent MeasurementsMaleMice

Identifiers

PMID39732766
PMCPMC11682066

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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.