Droplet Digital PCR Improves Detection of BRCA1/2 Copy Number Variants in Advanced Prostate Cancer

TL;DR

Droplet digital PCR improves detection of BRCA1/2 copy number variants in advanced prostate cancer compared to MLPA.

Contribution

Droplet digital PCR provides more precise classification of BRCA1/2 CNVs in heterogeneous prostate cancer samples.

Findings

ddPCR effectively classified normal and deletion CNV groups in prostate cancer samples.

ddPCR reclassified ambiguous MLPA cases using optimal cutoff values of 1.35 for BRCA1 and 1.55 for BRCA2.

ddPCR shows potential as a more sensitive biomarker tool for targeted therapy in advanced prostate cancer.

Abstract

BRCA1 and BRCA2 are associated with advanced prostate cancer progression and poor prognosis. Copy number variants (CNVs) of these genes play a crucial role in guiding targeted treatments, particularly for patients receiving PARP inhibitors. However, CNV detection using multiplex ligation-dependent probe amplification (MLPA) is often limited by tumor heterogeneity, leading to ambiguous results. This study therefore aimed to evaluate BRCA1/2 CNVs in advanced prostate cancer patients using droplet digital PCR (ddPCR) and compare the results with MLPA. DNA from 11 advanced prostate cancer tissues was analyzed using both methods, in parallel with four cell lines and seven healthy volunteers. Our findings revealed that ddPCR effectively classified normal CNV groups—including normal control cell lines, healthy volunteers, and samples with normal MLPA final ratios—from deletion groups, which included deletion control cell lines, samples with deletion final ratios from MLPA, and cases with previously ambiguous results. Interestingly, two cases involving BRCA1 and one case involving BRCA2 exhibited ambiguous results using MLPA; however, ddPCR enabled more precise classification by applying the Youden Index from ROC analysis and identifying optimal cutoff values of 1.35 for BRCA1 and 1.55 for BRCA2. These optimal thresholds allowed ddPCR to effectively reclassify the ambiguous MLPA cases into the deletion group. Overall, ddPCR could offer a more sensitive and reliable approach for CNV detection in heterogeneous tissue samples and demonstrates strong potential as a biomarker tool for guiding targeted therapy in advanced prostate cancer patients. However, further validation in larger cohorts is necessary to optimize cutoff precision, confirm diagnostic performance, and evaluate the full clinical utility of ddPCR.

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