Allele-informed copy number evaluation of plasma DNA samples from metastatic prostate cancer patients: the PCF_SELECT consortium assay

Abstract

Sequencing of cell-free DNA (cfDNA) in cancer patients’ plasma offers a minimally-invasive solution to detect tumor cell genomic alterations to aid real-time clinical decision-making. The reliability of copy number detection decreases at lower cfDNA tumor fractions, limiting utility at earlier stages of the disease. To test a novel strategy for detection of allelic imbalance, we developed a prostate cancer bespoke assay, PCF_SELECT, that includes an innovative sequencing panel covering ∼25 000 high minor allele frequency SNPs and tailored analytical solutions to enable allele-informed evaluation. First, we assessed it on plasma samples from 50 advanced prostate cancer patients. We then confirmed improved detection of genomic alterations in samples with <10% tumor fractions when compared against an independent assay. Finally, we applied PCF_SELECT to serial plasma samples intensively collected from three patients previously characterized as harboring alterations involving DNA repair genes and consequently offered PARP inhibition. We identified more extensive pan-genome allelic imbalance than previously recognized in prostate cancer. We confirmed high sensitivity detection of BRCA2 allelic imbalance with decreasing tumor fractions resultant from treatment and identified complex ATM genomic states that may be incongruent with protein losses. Overall, we present a framework for sensitive detection of allele-specific copy number changes in cfDNA.

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

“…However, these ultrasensitive assays would add operational complexity (i.e., archival tissue block retrieval and cancer genotyping) and exclude patients for whom tumor tissue is unavailable; ultimately it is plausible that more granular stratification of the ctDNA < 2% subgroup may offer diminishing returns for prognostication in advanced disease where ctDNA is relatively abundant. Conversely, measuring ctDNA% in the range of 2–100% is readily achieved via a variety of existing panel-based de novo genotyping strategies 23 , 29 , 57 , 58 , including assays that have been deployed across recent phase II-III mCRPC trials 16 , 30 , 59 – 61 . In future, continued improvements in methodological transparency and acknowledgement of test limitations will be important for truly optimized ctDNA% testing, and ultimately end-users should still be aware of factors such as clonal-hematopoiesis and tumor aneuploidy that can cause erroneous ctDNA% readouts 26 , 33 .…”

“…In current clinically-validated biomarker tests, 30% ctDNA is the approximate limit of detection for deletions which constitute a major class of genomic eligibility for targeted drugs in prostate cancer (e.g., BRCA2 and PARP inhibitors, PTEN and AKT/PI3K inhibitors) 23 , 61 , 63 . While more sensitive tumor-agnostic ctDNA sequencing approaches can largely overcome low ctDNA% for de novo detection of single nucleotide polymorphisms and indels 41 , 53 , 54 , sensitivity for focal deletions cannot generally be improved beyond a fundamental lower limit of ~5-15% ctDNA 26 , 58 . Although the interval between sample requisition and results reporting is typically longer for tissue than ctDNA 40 , 64 , potential delays are more tolerable for patients with excellent prognosis (i.e., ctDNA < 2%) where urgency of biomarker-informed clinical management is ostensibly lower.…”

Analysis of Genetic Ancestry from NGS Data Using EthSEQ

“…(2020), the EthSEQ approach was extended to exploit three PCA dimensions instead of two, improving its sensitivity and precision. In addition, due to its application across different scenarios (Beltran et al., 2015; Gandellini et al., 2019; Huang et al., 2017; Orlando et al., 2022; Sailer et al., 2019; Valentini et al., 2022), several computational and reporting improvements have been implemented. Specifically, EthSEQ version 3 requires 10 times less memory and provides a much larger collection of reference models across different human reference genome assemblies.…”