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Perakis, S.

Publications and source records attributed to Perakis, S..

2 recordsLinked to original sources

Comprehensive characterisation of cell-free tumour DNA in plasma and urine of patients with renal tumours

Cell-free tumour-derived DNA (ctDNA) allows non-invasive monitoring of cancers but its utility in renal cell cancer (RCC) has not been established. Here, untargeted and targeted sequencing methods, applied to two independent cohorts of renal tumour patients (n=90), were used to determine ctDNA content in plasma and urine. Our data revealed lower plasma ctDNA levels in RCC relative to other cancers, with untargeted detection of [~]33%. A sensitive personalised approach, applied to plasma and urine from select patients improved detection to [~]50%, including in patients with early-stage and even benign lesions.\n\nA machine-learning based model predicted detection, potentially offering a means of triaging samples for personalised analysis. In addition, with limited data we observed that plasma, and for the first time, urine ctDNA may better represent tumour heterogeneity than tissue biopsy. Furthermore, longitudinal sampling of >200 plasma samples revealed that ctDNA can track disease course. Additional datasets will be required to validate these findings.\n\nOverall, our data highlight RCC as a ctDNA-low malignancy, but indicate potential clinical utility provided improvement in detection approaches.\n\nOne sentence summaryComplementary sequencing methods show that cell-free tumour DNA levels are low in renal cancer though, via various strategies, may still be informative.

cancer biology↗

Inference of tumor cell-specific transcription factor binding from cell-free DNA

Alterations in transcription factors are important drivers of tumorigenesis, but non-invasive assays for assessing transcription factor activity are lacking. Here, we evaluated the feasibility of inferring transcription factor binding in solid tumors from their nucleosome footprint in circulating cell-free DNA. We developed a novel analysis pipeline to determine accessibility of transcription factor binding sites and applied it to 244 cell-free DNA samples from patients with prostate, breast and colon cancer. We observed patient-specific as well as tumor-specific patterns. Specifically, inferred binding patterns for the transcription factors AR, HOXB13, and NKX3-1 allowed us to classify patients by tumor type, including subtypes of prostate cancer, which has clinical implications for the management of patients. Our approach for mapping tumor-specific transcription factor binding in vivo based on blood samples makes a key part of the noncoding genome amenable for clinical analysis.

cancer biology↗