Circulating cell-free DNA copy-number profiles as a biomarker in melanoma
Session type: Poster / e-Poster / Silent Theatre session
Melanoma is the most aggressive form of skin cancer. By detecting relapses sooner, we could initiate treatments earlier, potentially improving patients’ outcomes. Fragments of tumour-DNA (circulating cell-free DNA, ccfDNA) can be detected in plasma, and genetic-profiles of ccfDNA can potentially be utilised as easily accessible biomarkers of disease.
CcfDNA was extracted from plasma collected from 27 melanoma patients from the Genetics and Epidemiology of Melanoma in Sheffield study (GEMS). Low-coverage genome-wide copy-number profiles were generated using paired-end next-generation sequencing (Illumina Hi-Seq®). Matched genomic-DNA and FFPE- DNA was similarly sequenced. Read-profiles for each ccfDNA sample were normalised against that of the corresponding genomic-DNA, corrected for GC content and log2-transformed to generate copy-number ratios. Z-scores for 1Mb windows were calculated by standardizing to mean copy-number ratios from a cohort of 10 healthy controls. A “genome instability score” (GIS) was then calculated for each ccfDNA sample by summing the square of Z-scores. GIS were compared by t-test.
Of the 27 melanoma cases, 13 had active disease, and 14 had recently-excised disease. All cases with active melanoma had stage IV-disease, while those with resected disease were stage I (7 cases), stage II (4 cases), or stage III (3 cases). On average, 12.6 million reads per sample (range 5.8–32.4) aligned to human reference-genome GRCh38, representing 74.5%-91.9% mapping rates, with a range of 0.1- 0.88 x genome coverage. The mean GIS for the active cases was 7225.3 (SD=12274), and 468.9 (SD=264) for the resected cases (p=0.03).
Our results with this small sample size suggest that the mean GIS for cases with active disease is higher than those with resected disease utilising a cost-effective low-coverage copy-number approach. We are currently analysing the remaining 56 samples from the GEMS study to confirm how sensitive this approach is in differentiating between active and resected disease.