Clinical Trial Protocol for ProBio: An Outcome-adaptive and Randomised Multiarm Biomarker-driven Study in Patients with Metastatic Prostate Cancer

Toward Informed Selection and Interpretation of Clinical Genomic Tests in Prostate Cancer

Clinical genomic testing of patient germline, tumor tissue, or plasma cell-free DNA can enable a personalized approach to cancer management and treatment. In prostate cancer (PCa), broad genotyping tests are now widely used to identify germline and/or somatic alterations in BRCA2 and other DNA damage repair genes. Alterations in these genes can confer cancer sensitivity to poly (ADP-ribose) polymerase inhibitors, are linked with poor prognosis, and can have potential hereditary cancer implications for family members. However, there is huge variability in genomic tests and reporting standards, meaning that for successful implementation of testing in clinical practice, end users must carefully select the most appropriate test for a given patient and critically interpret the results. In this white paper, we outline key pre- and post-test considerations for choosing a genomic test and evaluating reported variants, specifically for patients with advanced PCa. Test choice must be based on clinical context and disease state, availability and suitability of tumor tissue, and the genes and regions that are covered by the test. We describe strategies to recognize false positives or negatives in test results, including frameworks to assess low tumor fraction, subclonal alterations, clonal hematopoiesis, and pathogenic versus nonpathogenic variants. We assume that improved understanding among health care professionals and researchers of the nuances associated with genomic testing will ultimately lead to optimal patient care and clinical decision making.

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

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.

Cell-Free DNA Genomic Profiling and Its Clinical Implementation in Advanced Prostate Cancer

Most men with prostate cancer (PCa), despite potentially curable localized disease at initial diagnosis, progress to metastatic disease. Despite numerous treatment options, choosing the optimal treatment for individual patients remains challenging. Biomarkers guiding treatment sequences in an advanced setting are lacking. To estimate the diagnostic potential of liquid biopsies in guiding personalized treatment of PCa, we evaluated the utility of a custom-targeted next-generation sequencing (NGS) panel based on the AmpliSeq HD Technology. Ultra-deep sequencing on plasma circulating free DNA (cfDNA) samples of 40 metastatic castration-resistant PCa (mCRPC) and 28 metastatic hormone-naive PCa (mCSPC) was performed. CfDNA somatic mutations were detected in 48/68 (71%) patients. Of those 68 patients, 42 had matched tumor and cfDNA samples. In 21/42 (50%) patients, mutations from the primary tumor tissue were detected in the plasma cfDNA. In 7/42 (17%) patients, mutations found in the primary tumor were not detected in the cfDNA. Mutations from primary tumors were detected in all tested mCRPC patients (17/17), but only in 4/11 with mCSPC. AR amplifications were detected in 12/39 (31%) mCRPC patients. These results indicate that our targeted NGS approach has high sensitivity and specificity for detecting clinically relevant mutations in PCa.

Flexible evaluation of surrogacy in platform studies

Trial-level surrogates are useful tools for improving the speed and cost effectiveness of trials but surrogates that have not been properly evaluated can cause misleading results. The evaluation procedure is often contextual and depends on the type of trial setting. There have been many proposed methods for trial-level surrogate evaluation, but none, to our knowledge, for the specific setting of platform studies. As platform studies are becoming more popular, methods for surrogate evaluation using them are needed. These studies also offer a rich data resource for surrogate evaluation that would not normally be possible. However, they also offer a set of statistical issues including heterogeneity of the study population, treatments, implementation, and even potentially the quality of the surrogate. We propose the use of a hierarchical Bayesian semiparametric model for the evaluation of potential surrogates using nonparametric priors for the distribution of true effects based on Dirichlet process mixtures. The motivation for this approach is to flexibly model relationships between the treatment effect on the surrogate and the treatment effect on the outcome and also to identify potential clusters with differential surrogate value in a data-driven manner so that treatment effects on the surrogate can be used to reliably predict treatment effects on the clinical outcome. In simulations, we find that our proposed method is superior to a simple, but fairly standard, hierarchical Bayesian method. We demonstrate how our method can be used in a simulated illustrative example (based on the ProBio trial), in which we are able to identify clusters where the surrogate is, and is not useful. We plan to apply our method to the ProBio trial, once it is completed.

AR and PI3K Genomic Profiling of Cell-free DNA Can Identify Poor Responders to Lutetium-177-PSMA Among Patients with Metastatic Castration-resistant Prostate Cancer

Lutetium-177 prostate-specific membrane antigen radioligands (¹⁷⁷Lu-PSMA) are new therapeutic agents for the treatment of metastatic castration-resistant prostate cancer (mCRPC). We evaluated the prognostic value of circulating tumour DNA (ctDNA) profiling in patients with mCRPC starting treatment with ¹⁷⁷Lu-PSMA I&T. Between January 2020 and October 2022, patients with late-stage mCRPC (n = 57) were enrolled in a single-centre observational cohort study. Genomic alterations in the AR gene, PI3K signalling pathway, TP53, and TMPRSS2-ERG were associated with progression-free survival (PFS) on Kaplan-Meier and multivariable Cox regression analyses. Median PFS of 3.84 mo (95% confidence interval [CI] 3.3-5.4) was observed, and 21/56 (37.5%) evaluable patients experienced a prostate-specific antigen response of ≥50% during treatment. Among 46 patients who provided a blood sample for profiling before ¹⁷⁷Lu-PSMA treatment. ctDNA was detected in 39 (84.8%); higher ctDNA was correlated with shorter PFS. Genomic structural rearrangements in the AR gene (hazard ratio [HR] 9.74, 95% confidence interval [CI] 2.4-39.5; p = 0.001) and alterations in the PI3K signalling pathway (HR 3.58, 95% CI 1.41-9.08; p = 0.007) were independently associated with poor ¹⁷⁷Lu-PSMA prognosis on multivariable Cox regression. Prospective evaluation of these associations in biomarker-driven trials is warranted.

Patient summary

We examined cell-free DNA in blood samples from patients with advanced metastatic prostate cancer who started treatment with lutetium-177-PSMA, a new radioligand therapy. We found that patients with genetic alterations in the androgen receptor gene or PI3K pathway genes did not experience a lasting benefit from lutetium-177-PSMA.

Genetic and Genomic Testing for Prostate Cancer: Beyond DNA Repair

Significant progress has been made in genetic and genomic testing for prostate cancer across the disease spectrum. Molecular profiling is increasingly relevant for routine clinical management, fueled in part by advancements in testing technology and integration of biomarkers into clinical trials. In metastatic prostate cancer, defects in DNA damage response genes are now established predictors of benefit to US Food and Drug Administration-approved poly (ADP-ribose) polymerase inhibitors and immune checkpoint inhibitors, and trials are actively investigating these and other targeted treatment strategies in earlier disease states. Excitingly, opportunities for molecularly informed management beyond DNA damage response genes are also maturing. Germline genetic variants (eg, BRCA2 or MSH2/6) and polygenic germline risk scores are being investigated to inform cancer screening and active surveillance in at-risk carriers. RNA expression tests have recently gained traction in localized prostate cancer, enabling patient risk stratification and tailored treatment intensification via radiotherapy and/or androgen deprivation therapy for localized or salvage treatment. Finally, emerging minimally invasive circulating tumor DNA technology promises to enhance biomarker testing in advanced disease pending additional methodological and clinical validation. Collectively, genetic and genomic tests are rapidly becoming indispensable tools for informing the optimal clinical management of prostate cancer.

Towards clinical implementation of circulating tumor DNA in metastatic prostate cancer: Opportunities for integration and pitfalls to interpretation

Plasma circulating tumor DNA (ctDNA) represents short fragments of tumor-derived DNA released into the bloodstream primarily from cancer cells undergoing apoptosis. In metastatic castration-resistant prostate cancer (mCRPC), characterizing genomic alterations in ctDNA identifies mutations, copy number alterations, and structural rearrangements with predictive and prognostic biomarker utility. These associations with clinical outcomes have resulted in ctDNA increasingly incorporated into routine clinical care. In this review, we summarize current and emerging applications for ctDNA analysis in metastatic prostate cancer, including outcome prediction, treatment selection, and characterization of treatment resistance. We also discuss potential pitfalls with interpreting ctDNA findings, namely false negatives arising from low tumor content and optimal assay design, including correction for clonal hematopoiesis of indeterminate potential and germline variants. Understanding the influence of these limitations on interpretation of ctDNA results is necessary to overcome barriers to clinical implementation. Nevertheless, as assay availability and technology continue to improve, recognizing both opportunities and shortcomings of ctDNA analysis will retain relevance with informing the implementation of precision-oncology initiatives for metastatic prostate cancer.

Factors influencing survival in metastatic castration resistant prostate cancer therapy

INTRODUCTION

The number of patients with metastatic castration resistant prostate cancer (mCRPC) is expecting to increase due to the long-life expectancy of those with advanced disease who are also more commonly diagnosed today because of stage migration. Several compounds are available for treating these patients.

AREAS COVERED

We reviewed currently available treatments for mCRPC, their mechanism of action and resistance and we explored possible predictors of treatment success useful to predict survival in mCRPC patients.

EXPERT OPINION

A combination of molecular, clinical, pathological, and imaging features is necessary to correctly estimate patients' risk of death. The combination of these biomarkers may allow clinicians to tailor treatments based on cancer history and patients' features. The search of predictive biomarkers remains an unmet medical need for most patients with mCRPC.