ASCO 2024: A Clinical-Genetic ctDNA-Based Prognostic Model for Predicting OS in Men with mCRPC Treated with Potent Androgen Receptor Inhibition (Alliance)

(UroToday.com) The 2024 American Society of Clinical Oncology (ASCO) annual meeting featured a session on prostate cancer, and a presentation by Dr. Susan Halabi discussing a clinical-genetic circulating tumor DNA (ctDNA)-based prognostic model for predicting overall survival in men with metastatic castrate-resistant prostate cancer (mCRPC) treated with potent androgen receptor inhibition. Androgen receptor pathway inhibitor therapy improves overall survival in the first line treatment versus ADT alone, but survival varies based on known clinical factors. Dr. Halabi and colleagues previously developed and validated a clinical prognostic model of overall survival in mCRPC men that included the following variables: performance status, disease site, opioid analgesic use, lactate dehydrogenase, albumin, hemoglobin, prostate specific antigen, and alkaline phosphatase.¹ Additionally, there are several other genetic factors associated with shorter overall survival and response, including:

Circulating tumor cell enumeration and ctDNA fraction
Tumor genetic subtypes derived from ctDNA, AR and non-AR ctDNA alterations such as TP53 and RB1 loss, DNA repair deficiency, and PTEN loss

Furthermore, prior studies have been based on small numbers of patients. The goal of this analysis presented at the ASCO 2024 annual meeting was to improve upon the clinical model of overall survival by incorporating ctDNA pathogenic genetic alterations.

Data from the A031201 phase 3 trial of enzalutamide +/- abiraterone² was used to develop and validate the clinical genetic model of overall survival. The trial design for A031201 is as follows:

Key eligibility criteria included (i) treatment for first line mCRPC, (ii) ECOG performance status 0-1, (iii) no prior taxane therapy for metastatic disease (the trial design preceded results from the CHAARTED and STAMPEDE trials in mHSPC), (iv) no prior enzalutamide or abiraterone acetate and no prior ketoconazole, and (v) no prior history of stroke or CNS metastases. Cell-free DNA was isolated from plasma and analyzed using a 69-gene targeted DNA-sequencing assay for the detection of ctDNA pathogenic genetic alterations. Genetic features were identified based on feature importance using a random survival forest, and the final clinical genetic model was trained including clinical and selected genetic factors. A complete list of the gene panel is as follows:

With regards to model building, this included a random survival forest to develop a clinical-genetic model of overall survival. The first step was to identify the most important genetic variables using feature important scores, and the second step was to incorporate clinical variables, ctDNA aneuploidy fraction, and BRCA2 loss. Next, Monte Carlo cross-validation 100 splits in 4:1 training was undertaken for testing the allocation ratio. Finally, time dependent area under the receiver operating characteristic curve assessed predictive accuracy.

Data for this analysis were available for 776 patients. As follows is the CONSORT diagram and patient characteristics stratified by cfDNA versus no cfDNA:

The correlation of ctDNA aneuploidy with clinical risk scores and the Kaplan Meier overall survival by ctDNA aneuploidy fraction is highlighted below:

In addition to clinical variables, the model included in this order: gains in AR and the AR enhancer, MYC, RSPO2, and losses and/or pathogenic genetic alterations of ZBTB16, PTEN, MSH6, PPP2R2A, NKX3-1, TP53, FANCA, RB1, APC, CHD1, and BRCA2, and ichorCNA tumor fraction:

Time-dependent area under the receiver operating characteristic curves in clinical and clinical genetic models were 0.72 (95% CI 0.72-0.73) and 0.77 (95% CI 0.76-0.77), respectively:

The Kaplan Meier overall survival curves by the top six genetic variants is as follows:

The Kaplan Meier overall survival curves by key clinical variables include:

Finally, the median overall survival and the hazard ratios by the three- and four- prognostic risk groups are presented:

The strengths of this study include the use of small amounts of plasma (1-3 mL), this is the largest number of patients with detectable ctDNA and long term follow-up, and the presence of ctDNA allows noninvasive testing of genetic alterations. Limitations include the model needing to be externally validated in the context of therapies, such as AR/PARP inhibitor trials, in the second line setting after androgen receptor pathway inhibitor/ADT in the mHSPC setting, and in androgen receptor pathway inhibitor/LuPSMA trials

Dr. Halabi concluded her presentation by discussing a clinical-genetic ctDNA-based prognostic model for predicting overall survival in men with mCRPC treated with potent androgen receptor inhibition with the following take home messages:

A clinical + genetic prognostic model for overall survival in first line mCRPC developed and validated using data from Alliance Phase 3 Trial (A031201) assessing enzalutamide versus enzalutamide + abiraterone
The final model includes 16 ctDNA genetic factors. The top predictors of overall survival are: gains in AR and the AR enhancer, MYC, and RSPO2; losses of ZBTB16, PTEN, and MSH6
Hemoglobin and alkaline phosphates are the most influential clinical predictors
The clinical + genetic model is more predictive than the clinical-only model

Presented by: Susan Halabi, PhD, Duke Cancer Institute Center for Prostate and Urologic Cancers, Duke University School of Medicine, Durham, NC

Written by: Zachary Klaassen, MD, MSc – Urologic Oncologist, Associate Professor of Urology, Georgia Cancer Center, Wellstar MCG Health, @zklaassen_md on Twitter during the 2024 American Society of Clinical Oncology (ASCO) Annual Meeting, Chicago, IL, Fri, May 31 – Tues, June 4, 2024.

Related content: Integrating Genetic Variants and Clinical Factors for Improved Prostate Cancer Prognostication - Susan Halabi

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