Nature Communications (Feb 2024)

Prediction of plasma ctDNA fraction and prognostic implications of liquid biopsy in advanced prostate cancer

  • Nicolette M. Fonseca,
  • Corinne Maurice-Dror,
  • Cameron Herberts,
  • Wilson Tu,
  • William Fan,
  • Andrew J. Murtha,
  • Catarina Kollmannsberger,
  • Edmond M. Kwan,
  • Karan Parekh,
  • Elena Schönlau,
  • Cecily Q. Bernales,
  • Gráinne Donnellan,
  • Sarah W. S. Ng,
  • Takayuki Sumiyoshi,
  • Joanna Vergidis,
  • Krista Noonan,
  • Daygen L. Finch,
  • Muhammad Zulfiqar,
  • Stacy Miller,
  • Sunil Parimi,
  • Jean-Michel Lavoie,
  • Edward Hardy,
  • Maryam Soleimani,
  • Lucia Nappi,
  • Bernhard J. Eigl,
  • Christian Kollmannsberger,
  • Sinja Taavitsainen,
  • Matti Nykter,
  • Sofie H. Tolmeijer,
  • Emmy Boerrigter,
  • Niven Mehra,
  • Nielka P. van Erp,
  • Bram De Laere,
  • Johan Lindberg,
  • Henrik Grönberg,
  • Daniel J. Khalaf,
  • Matti Annala,
  • Kim N. Chi,
  • Alexander W. Wyatt

DOI
https://doi.org/10.1038/s41467-024-45475-w
Journal volume & issue
Vol. 15, no. 1
pp. 1 – 16

Abstract

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Abstract No consensus strategies exist for prognosticating metastatic castration-resistant prostate cancer (mCRPC). Circulating tumor DNA fraction (ctDNA%) is increasingly reported by commercial and laboratory tests but its utility for risk stratification is unclear. Here, we intersect ctDNA%, treatment outcomes, and clinical characteristics across 738 plasma samples from 491 male mCRPC patients from two randomized multicentre phase II trials and a prospective province-wide blood biobanking program. ctDNA% correlates with serum and radiographic metrics of disease burden and is highest in patients with liver metastases. ctDNA% strongly predicts overall survival, progression-free survival, and treatment response independent of therapeutic context and outperformed established prognostic clinical factors. Recognizing that ctDNA-based biomarker genotyping is limited by low ctDNA% in some patients, we leverage the relationship between clinical prognostic factors and ctDNA% to develop a clinically-interpretable machine-learning tool that predicts whether a patient has sufficient ctDNA% for informative ctDNA genotyping (available online: https://www.ctDNA.org ). Our results affirm ctDNA% as an actionable tool for patient risk stratification and provide a practical framework for optimized biomarker testing.