Unleashing the potential of liquid biopsy: allele-informed evaluation of plasma samples for cancer patients management

Liquid biopsy and next-generation sequencing of cell-free DNA (cfDNA) in cancer patients’ plasma offer a minimally-invasive solution to detect tumor cell genomic information to aid real-time clinical decision-making. Reliability and sensitivity in the detection of genomic alterations is crucial for patient management and it is particularly relevant in the context of targeted therapies. However, biological and technical factors, such as low cfDNA tumor fraction and sequencing errors, affect the correct interpretation of genomic data limiting the utility of non-invasive cfDNA-based tumor profiling. To address these issues, we designed 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 of patients’ tumor. The framework also implements ABEMUS, an ad-hoc computational procedure we specifically designed for cfDNA samples to accurately detect somatic point mutations that could emerge under treatment pressure and as drug resistance mechanism. When applied on serial plasma samples from three patients receiving PARP inhibition harboring DNA repair gene aberrations, PCF_SELECT demonstrated high sensitivity in detecting BRCA2 allelic imbalance with decreasing tumor fractions resultant from treatment and identified complex ATM genomic states that may be incongruent with protein losses. As a step towards a more sensitive detection of tumor features in circulation of cancer patients, we next hypothesized that recent WGS-based approaches exploiting cfDNA fragments characteristics could be extrapolated for targeted sequencing data and that gene-region specific measures could improve detection metrics. Preliminary results suggest an increased sensitivity compared to copy-number-based methods supporting the integration at no extra cost in our targeted assay. Overall, this work is relevant to the context of precision oncology. It provides an innovative platform for the management of cancer patients, delivering detailed patient-specific molecular information which could be applied to guide treatment and improve clinical outcomes.