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.

Multiregion sampling of de novo metastatic prostate cancer reveals complex polyclonality and augments clinical genotyping.

De novo metastatic prostate cancer is highly aggressive, but the paucity of routinely collected tissue has hindered genomic stratification and precision oncology. Here, we leveraged a rare study of surgical intervention in 43 de novo metastatic prostate cancers to assess somatic genotypes across 607 synchronous primary and metastatic tissue regions plus circulating tumor DNA. Intra-prostate heterogeneity was pervasive and impacted clinically relevant genes, resulting in discordant genotypes between select primary restricted regions and synchronous metastases. Additional complexity was driven by polyclonal metastatic seeding from phylogenetically related primary populations. When simulating clinical practice relying on a single tissue region, genomic heterogeneity plus variable tumor fraction across samples caused inaccurate genotyping of dominant disease; however, pooling extracted DNA from multiple biopsy cores before sequencing can rescue misassigned somatic genotypes. Our results define the relationship between synchronous treatment-sensitive primary and metastatic lesions in men with de novo metastatic prostate cancer and provide a framework for implementing genomics-guided patient management.

A generalizable machine learning framework for classifying DNA repair defects using ctDNA exomes.

Specific classes of DNA damage repair (DDR) defect can drive sensitivity to emerging therapies for metastatic prostate cancer. However, biomarker approaches based on DDR gene sequencing do not accurately predict DDR deficiency or treatment benefit. Somatic alteration signatures may identify DDR deficiency but historically require whole-genome sequencing of tumour tissue. We assembled whole-exome sequencing data for 155 high ctDNA fraction plasma cell-free DNA and matched leukocyte DNA samples from patients with metastatic prostate or bladder cancer. Labels for DDR gene alterations were established using deep targeted sequencing. Per sample mutation and copy number features were used to train XGBoost ensemble models. Naive somatic features and trinucleotide signatures were associated with specific DDR gene alterations but insufficient to resolve each class. Conversely, XGBoost-derived models showed strong performance including an area under the curve of 0.99, 0.99 and 1.00 for identifying BRCA2, CDK12, and mismatch repair deficiency in metastatic prostate cancer. Our machine learning approach re-classified several samples exhibiting genomic features inconsistent with original labels, identified a metastatic bladder cancer sample with a homozygous BRCA2 copy loss, and outperformed an existing exome-based classifier for BRCA2 deficiency. We present DARC Sign (DnA Repair Classification SIGNatures); a public machine learning tool leveraging clinically-practical liquid biopsy specimens for simultaneously identifying multiple types of metastatic prostate cancer DDR deficiencies. We posit that it will be useful for understanding differential responses to DDR-directed therapies in ongoing clinical trials and may ultimately enable prospective identification of prostate cancers with phenotypic evidence of DDR deficiency.

Early On-treatment Changes in Circulating Tumor DNA Fraction and Response to Enzalutamide or Abiraterone in Metastatic Castration-Resistant Prostate Cancer.

PURPOSE: Androgen receptor pathway inhibitors (ARPI) are standard of care for treatment-naïve metastatic castration-resistant prostate cancer (mCRPC), but rapid resistance is common. Early identification of resistance will improve management strategies. We investigated whether changes in circulating tumor DNA (ctDNA) fraction during ARPI treatment are linked with mCRPC clinical outcomes. EXPERIMENTAL DESIGN: Plasma cell-free DNA was collected from 81 patients with mCRPC at baseline and after 4 weeks of first-line ARPI treatment during two prospective multicenter observational studies (NCT02426333; NCT02471469). ctDNA fraction was calculated from somatic mutations in targeted sequencing and genome copy-number profiles. Samples were classified into detected versus undetected ctDNA. Outcome measurements were progression-free survival (PFS) and overall survival (OS). Nondurable treatment response was defined as PFS ≤6 months. RESULTS: ctDNA was detected in 48/81 (59%) baseline and 29/81 (36%) 4-week samples. ctDNA fraction for samples with detected ctDNA was lower at 4 weeks versus baseline (median 5.0% versus 14.5%, P = 0.017). PFS and OS were shortest for patients with persistent ctDNA at 4 weeks (univariate HR, 4.79; 95% CI, 2.62-8.77 and univariate HR, 5.49; 95% CI, 2.76-10.91, respectively), independent of clinical prognostic factors. For patients exhibiting change from detected to undetected ctDNA by 4 weeks, there was no significant PFS difference versus patients with baseline undetected ctDNA. ctDNA change had a positive predictive value of 88% and negative predictive value of 92% for identifying nondurable responses. CONCLUSIONS: Early changes in ctDNA fraction are strongly linked to duration of first-line ARPI treatment benefit and survival in mCRPC and may inform early therapy switches or treatment intensification. See related commentary by Sartor, p. 2745.

The 5-Hydroxymethylcytosine Landscape of Prostate Cancer.

Analysis of DNA methylation is a valuable tool to understand disease progression and is increasingly being used to create diagnostic and prognostic clinical biomarkers. While conversion of cytosine to 5-methylcytosine (5mC) commonly results in transcriptional repression, further conversion to 5-hydroxymethylcytosine (5hmC) is associated with transcriptional activation. Here we perform the first study integrating whole-genome 5hmC with DNA, 5mC, and transcriptome sequencing in clinical samples of benign, localized, and advanced prostate cancer. 5hmC is shown to mark activation of cancer drivers and downstream targets. Furthermore, 5hmC sequencing revealed profoundly altered cell states throughout the disease course, characterized by increased proliferation, oncogenic signaling, dedifferentiation, and lineage plasticity to neuroendocrine and gastrointestinal lineages. Finally, 5hmC sequencing of cell-free DNA from patients with metastatic disease proved useful as a prognostic biomarker able to identify an aggressive subtype of prostate cancer using the genes TOP2A and EZH2, previously only detectable by transcriptomic analysis of solid tumor biopsies. Overall, these findings reveal that 5hmC marks epigenomic activation in prostate cancer and identify hallmarks of prostate cancer progression with potential as biomarkers of aggressive disease. SIGNIFICANCE: In prostate cancer, 5-hydroxymethylcytosine delineates oncogene activation and stage-specific cell states and can be analyzed in liquid biopsies to detect cancer phenotypes. See related article by Wu and Attard, p. 3880.

Deep whole-genome ctDNA chronology of treatment-resistant prostate cancer.

Circulating tumour DNA (ctDNA) in blood plasma is an emerging tool for clinical cancer genotyping and longitudinal disease monitoring1. However, owing to past emphasis on targeted and low-resolution profiling approaches, our understanding of the distinct populations that comprise bulk ctDNA is incomplete2-12. Here we perform deep whole-genome sequencing of serial plasma and synchronous metastases in patients with aggressive prostate cancer. We comprehensively assess all classes of genomic alterations and show that ctDNA contains multiple dominant populations, the evolutionary histories of which frequently indicate whole-genome doubling and shifts in mutational processes. Although tissue and ctDNA showed concordant clonally expanded cancer driver alterations, most individual metastases contributed only a minor share of total ctDNA. By comparing serial ctDNA before and after clinical progression on potent inhibitors of the androgen receptor (AR) pathway, we reveal population restructuring converging solely on AR augmentation as the dominant genomic driver of acquired treatment resistance. Finally, we leverage nucleosome footprints in ctDNA to infer mRNA expression in synchronously biopsied metastases, including treatment-induced changes in AR transcription factor signalling activity. Our results provide insights into cancer biology and show that liquid biopsy can be used as a tool for comprehensive multi-omic discovery.

Genomic Features of Lung-Recurrent Hormone-Sensitive Prostate Cancer.

PURPOSE: Pulmonary involvement is rare in metastatic hormone-sensitive prostate cancer (mHSPC) that recurs after treatment for localized disease. Guidelines recommend intensive systemic therapy, similar to patients with liver metastases, but some lung-recurrent mHSPC may have good outcomes. Genomic features of lung metastases may clarify disease aggression, but are poorly understood since lung biopsy is rarely performed. We present a comparative assessment of genomic drivers and heterogeneity in metachronous prostate tumors and lung metastases. METHODS: We leveraged a prospective functional imaging study of 208 biochemically recurrent prostate cancers to identify 10 patients with lung-recurrent mHSPC. Histologic diagnosis was attained via thoracic surgery or fine-needle lung biopsy. We retrieved clinical data and performed multiregion sampling of primary tumors and metastases. Targeted and/or whole-exome sequencing was applied to 46 primary and 32 metastatic foci. RESULTS: Unusually for mHSPC, all patients remained alive despite a median follow-up of 11.5 years. Several patients experienced long-term freedom from systemic treatment. The genomic landscape of lung-recurrent mHSPC was typical of curable prostate cancer with frequent PTEN, SPOP, and chromosome 8p alterations, and there were no deleterious TP53 and DNA damage repair gene mutations that characterize aggressive prostate cancer. Despite a long median time to recurrence (76.8 months), copy number alterations and clonal mutations were highly conserved between metastatic and primary foci, consistent with intrapatient homogeneity and limited genomic evolution. CONCLUSION: In this retrospective hypothesis-generating study, we observed indolent genomic etiology in selected lung-recurrent mHSPC, cautioning against grouping these patients together with liver or bone-predominant mHSPC. Although our data do not generalize to all patients with lung metastases, the results encourage prospective efforts to stratify lung-recurrent mHSPC by genomic features.