Molecular Hallmarks of Prostate-specific Membrane Antigen in Treatment-naïve Prostate Cancer.

Biomarkers Gene expression Gene expression profiling Humans Prognosis Prostatic neoplasms/genetics Prostatic neoplasms/pathology Tumor

Journal

European urology
ISSN: 1873-7560
Titre abrégé: Eur Urol
Pays: Switzerland
ID NLM: 7512719

Informations de publication

Date de publication:
17 Sep 2024
Historique:
received: 16 05 2024
revised: 22 08 2024
accepted: 05 09 2024
medline: 19 9 2024
pubmed: 19 9 2024
entrez: 18 9 2024
Statut: aheadofprint

Résumé

We characterized tumor prostate-specific membrane antigen (PSMA) levels as a reflection of cancer biology and treatment sensitivities for treatment-naïve prostate cancer. We first correlated PSMA positron emission tomography (PET) maximum standardized uptake values (SUVmax) in primary prostate cancer with tumor FOLH1 (PSMA RNA abundance) to establish RNA as a proxy (n = 55). We then discovered and validated molecular pathways associated with PSMA RNA levels in two large primary tumor cohorts. We validated those associations in independent cohorts (18 total; 5684 tumor samples) to characterize the pathways and treatment responses associated with PSMA. PSMA RNA abundance correlates moderately with SUVmax (ρ = 0.41). In independent cohorts, androgen receptor signaling is more active in tumors with high PSMA. Accordingly, patients with high PSMA tumors experienced longer cancer-specific survival when managed with androgen deprivation therapy for biochemical recurrence (adjusted hazard ratio [AHR] 0.54 [0.34-0.87]; n = 174). PSMA low tumors possess molecular markers of resistance to radiotherapy. Consistent with this, patients with high PSMA tumors experience longer time to recurrence following primary radiotherapy (AHR 0.50 [0.28-0.90]; n = 248). In the SAKK09/10 trial (n = 224), patients with high PSMA tumors who were managed with salvage radiotherapy experienced longer time to progression in the 64-Gy arm (restricted mean survival time [RMST] +7.60 [0.05-15.16]), but this effect was mitigated in the 70-Gy arm (RMST 3.52 [-3.30 to 10.33]). Limitations include using PSMA RNA as a surrogate for PET SUVmax. PSMA levels in treatment-naïve prostate cancer differentiate tumor biology and treatment susceptibilities. These results warrant validation using PET metrics to substantiate management decisions based on imaging.

Sections du résumé

BACKGROUND AND OBJECTIVE OBJECTIVE
We characterized tumor prostate-specific membrane antigen (PSMA) levels as a reflection of cancer biology and treatment sensitivities for treatment-naïve prostate cancer.
METHODS METHODS
We first correlated PSMA positron emission tomography (PET) maximum standardized uptake values (SUVmax) in primary prostate cancer with tumor FOLH1 (PSMA RNA abundance) to establish RNA as a proxy (n = 55). We then discovered and validated molecular pathways associated with PSMA RNA levels in two large primary tumor cohorts. We validated those associations in independent cohorts (18 total; 5684 tumor samples) to characterize the pathways and treatment responses associated with PSMA.
KEY FINDINGS AND LIMITATIONS UNASSIGNED
PSMA RNA abundance correlates moderately with SUVmax (ρ = 0.41). In independent cohorts, androgen receptor signaling is more active in tumors with high PSMA. Accordingly, patients with high PSMA tumors experienced longer cancer-specific survival when managed with androgen deprivation therapy for biochemical recurrence (adjusted hazard ratio [AHR] 0.54 [0.34-0.87]; n = 174). PSMA low tumors possess molecular markers of resistance to radiotherapy. Consistent with this, patients with high PSMA tumors experience longer time to recurrence following primary radiotherapy (AHR 0.50 [0.28-0.90]; n = 248). In the SAKK09/10 trial (n = 224), patients with high PSMA tumors who were managed with salvage radiotherapy experienced longer time to progression in the 64-Gy arm (restricted mean survival time [RMST] +7.60 [0.05-15.16]), but this effect was mitigated in the 70-Gy arm (RMST 3.52 [-3.30 to 10.33]). Limitations include using PSMA RNA as a surrogate for PET SUVmax.
CONCLUSIONS AND CLINICAL IMPLICATIONS CONCLUSIONS
PSMA levels in treatment-naïve prostate cancer differentiate tumor biology and treatment susceptibilities. These results warrant validation using PET metrics to substantiate management decisions based on imaging.

Identifiants

DOI: 10.1016/j.eururo.2024.09.005 PMID: 39294048
pubmed: 39294048
pii: S0302-2838(24)02597-1
doi: 10.1016/j.eururo.2024.09.005
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Auteurs

Adam B Weiner (AB)

Department of Urology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA; Institute for Precision Health, University of California-Los Angeles, Los Angeles, CA, USA; Jonsson Comprehensive Cancer Center, University of California-Los Angeles, Los Angeles, CA, USA. Electronic address: abweiner@mednet.ucla.edu.

Raag Agrawal (R)

Institute for Precision Health, University of California-Los Angeles, Los Angeles, CA, USA; Jonsson Comprehensive Cancer Center, University of California-Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA.

Nicholas K Wang (NK)

Jonsson Comprehensive Cancer Center, University of California-Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA.

Ida Sonni (I)

Department of Radiological Sciences, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA; Department of Clinical and Experimental Medicine, University Magna Graecia, Catanzaro, Italy.

Eric V Li (EV)

Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Jaron Arbet (J)

Department of Urology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA; Institute for Precision Health, University of California-Los Angeles, Los Angeles, CA, USA; Jonsson Comprehensive Cancer Center, University of California-Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA.

J J H Zhang (JJH)

Department of Urology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA.

James A Proudfoot (JA)

Veracyte, Inc, San Diego, CA, USA.

Boon Hao Hong (BH)

Division of Medical Sciences, National Cancer Centre Singapore, Singapore, Singapore.

Elai Davicioni (E)

Veracyte, Inc, San Diego, CA, USA.

Nathanael Kane (N)

Department of Radiation Oncology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA; Radiation Oncology Service, Veteran Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA.

Luca F Valle (LF)

Department of Radiation Oncology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA; Radiation Oncology Service, Veteran Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA.

Amar U Kishan (AU)

Department of Radiation Oncology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA.

Alan Dal Pra (AD)

Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA.

Pirus Ghadjar (P)

Department of Radiation Oncology, Charité Universitätsmedizin Berlin, Berlin, Germany.

Christopher J Sweeney (CJ)

South Australian Immunogenomics Cancer Institute, University of Adelaide, Adelaide, SA, Australia.

Nicholas G Nickols (NG)

Department of Radiation Oncology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA; Radiation Oncology Service, Veteran Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA.

R Jeffrey Karnes (RJ)

Department of Urology, Mayo Clinic, Rochester, MN, USA.

John Shen (J)

Department of Urology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA; Department of Medicine, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA.

Matthew B Rettig (MB)

Department of Urology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA; Department of Medicine, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA.

Johannes Czernin (J)

Ahmanson Translational Theranostics Division, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA.

Ashely E Ross (AE)

Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Melvin Lee Kiang Chua (M)

Divisions of Radiation Oncology and Medical Sciences, National Cancer Centre, Singapore, Singapore; Duke-NUS Medical School, Singapore, Singapore.

Edward M Schaeffer (EM)

Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Jeremie Calais (J)

Ahmanson Translational Theranostics Division, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA.

Paul C Boutros (PC)

Department of Urology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA; Institute for Precision Health, University of California-Los Angeles, Los Angeles, CA, USA; Jonsson Comprehensive Cancer Center, University of California-Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA.

Robert E Reiter (RE)

Department of Urology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA; Jonsson Comprehensive Cancer Center, University of California-Los Angeles, Los Angeles, CA, USA.

Classifications MeSH