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Publications

Journal Article Ahead of print

PubMed: 42848319 DOI: 10.1158/1078-0432.CCR-26-2269

Multi-institutional Patient-level Comparison of Decipher Genomic Classifier and Artera Multimodal Artificial Intelligence (MMAI) in Prostate Cancer.

Clin Cancer Res

Authors: Boon Hao Hong, Adeniyi A Olabumuyi, Shuang G Zhao, Purvish Trivedi, Ryan Putney, Esther Katende, Amparo Serna, Riley Smith, Jasreman Dhillon, Jong Y Park, Rodrigo Pessoa, Michael Poch, Daniel Grass, Julio Pow-Sang, Alexander Hakansson, Yang Liu, Elai Davicioni, Yi Ren, Siyi Tang, Danielle Croucher, Huei-Chung Huang, S Laura Chang, Mary Feng, Rikiya Yamashita, Enya H W Ong, Kae Jack Tay, Kah Min Tan, Jeffrey K L Tuan, Michael L C Wang, Li Yan Khor, Olivia G Jordan, Jinhee Chang, Claudia Datnow-Martinez, Xiaolei Shi, Amol Shetty, Zaker H Rana, Jason K Molitoris, Mark V Mishra, Young Kwok, Yang Song, Matthew Ramotar, Alejandro Berlin, Ozan Guler, Cem Onal, Loig Vaugier, Audrey Blanc-Lapierre, Stéphane Supiot, Carole Mercier, Piet Dirix, Piet Ost … and 25 other authors

Affiliations

  • National Cancer Centre Singapore Singapore Singapore.
  • University of Maryland Medical Center Baltimore, MD United States.
  • University of Wisconsin-Madison Madison, Wisconsin United States.
  • Moffitt Cancer Center Tampa United States.
  • H. Lee Moffitt Cancer Center & Research Institute Tampa, FL United States.
  • Moffitt Cancer Center Tampa, FL United States.
  • Moffitt Cancer Center Tampa, Florida United States.
  • H. Lee Moffitt Cancer center and research Institute Tampa, FL United States.
  • Veracyte San Diego United States.
  • Veracyte (United States) United States.
  • Veracyte (United States) San Diego, California United States.
  • Artera Los Altos, California United States.
  • University of California, San Francisco San Francisco, CA United States.
  • Singapore General Hospital Singapore.
  • University of Maryland Medical Center United States.
  • Michigan State University East Lansing, MI United States.
  • University of Maryland, Baltimore United States.
  • University of Maryland, Baltimore United States.
  • University of Maryland School of Medicine Baltimore, MD United States.
  • Princess Margaret Cancer Centre Toronto, Ontario, Canada.
  • University of Toronto Canada.
  • Rutgers, The State University of New Jersey Adana Turkey.
  • Baskent University Turkey.
  • Institut de Cancérologie de l'Ouest Saint Herblain France.
  • Institut de Cancérologie de l'Ouest Saint-Herblain France.
  • Centre de Recherche en Cancérologie Immunologie de Nantes Angers Nantes cedex 1 France.
  • Iridium Kankernetwerk Belgium.
  • Ghent University Gent Belgium.

Abstract

IMPORTANCE

The Decipher Prostate Genomic Classifier (GC) and ArteraAI Multimodal Artificial Intelligence (MMAI) platform are widely used prognostic tools for localized prostate cancer (PCa), yet no direct comparison has been performed within the same patient cohort.

METHODS

We sought to evaluate the concordance and prognostic value of GC and MMAI across multi-institutional cohorts totaling 688 patients with localized and oligometastatic PCa. These included populations enriched for African American and East Asian patients. GC and MMAI scores were stratified into risk groups based on specimen type (biopsy or radical prostatectomy (RP)). The primary endpoint was distant metastasis-free survival (DMFS), measured from diagnosis to last follow-up or event.

RESULTS

GC and MMAI scores were significantly correlated across multiple cohorts. In Moffitt RP, biopsy, and NCCS cohorts, both MMAI and GC were prognostic alone and after adjusting for clinical variables. Multivariable analysis including both biomarkers and clinical variables showed that MMAI was significant even accounting for GC in Moffitt RP, and borderline in Moffitt biopsy. Analysis of concordant/discordant cases showed that patients who were high-risk for both GC and MMAI did qualitatively worse. In pathway analysis, concordance with GC and MMAI was high for Moffitt RP and oligometastatic patients. Across both biopsy cohorts, while most biological pathways were concordant, there were consistent discordant signaling, metabolic, and DNA repair pathways.

CONCLUSIONS

In the first direct patient-level comparison of GC and MMAI in PCa, these biomarkers were found to demonstrate moderate correlation, and both biomarkers demonstrated prognostic value across diverse populations and disease states.

MeSH

Intelligence, Neoplasms, Multiple Chronic Conditions, Prostate, Biopsy, Biomarkers, Prostatic Neoplasms, Artificial Intelligence