ABSTRACT
Existing methods to improve detection of circulating tumor DNA (ctDNA) have focused on genomic alterations but have rarely considered the biological properties of plasma cell-free DNA (cfDNA). We hypothesized that differences in fragment lengths of circulating DNA could be exploited to enhance sensitivity for detecting the presence of ctDNA and for noninvasive genomic analysis of cancer. We surveyed ctDNA fragment sizes in 344 plasma samples from 200 patients with cancer using low-pass whole-genome sequencing (0.4×). To establish the size distribution of mutant ctDNA, tumor-guided personalized deep sequencing was performed in 19 patients. We detected enrichment of ctDNA in fragment sizes between 90 and 150 bp and developed methods for in vitro and in silico size selection of these fragments. Selecting fragments between 90 and 150 bp improved detection of tumor DNA, with more than twofold median enrichment in >95% of cases and more than fourfold enrichment in >10% of cases. Analysis of size-selected cfDNA identified clinically actionable mutations and copy number alterations that were otherwise not detected. Identification of plasma samples from patients with advanced cancer was improved by predictive models integrating fragment length and copy number analysis of cfDNA, with area under the curve (AUC) >0.99 compared to AUC <0.80 without fragmentation features. Increased identification of cfDNA from patients with glioma, renal, and pancreatic cancer was achieved with AUC > 0.91 compared to AUC < 0.5 without fragmentation features. Fragment size analysis and selective sequencing of specific fragment sizes can boost ctDNA detection and could complement or provide an alternative to deeper sequencing of cfDNA.
Subject(s)
Circulating Tumor DNA/analysis , Circulating Tumor DNA/chemistry , Animals , Circulating Tumor DNA/blood , DNA Copy Number Variations/genetics , Genome, Human , Humans , Machine Learning , Mice , Mutation/genetics , Whole Genome SequencingABSTRACT
AIM: To develop and validate a biomarker-based index to optimize referral and diagnosis of patients with suspected ovarian cancer. Furthermore, to compare this new index with the Risk of Malignancy Index (RMI) and Risk of Ovarian Malignancy Algorithm (ROMA). PATIENTS AND METHODS: A training study, consisting of patients with benign ovarian disease (n=809) and ovarian cancer (n=246), was used to develop the Copenhagen Index (CPH-I) utilizing the variables serum HE4, serum CA125 and patient age. Eight international studies provided the validation population; comprising 1060 patients with benign ovarian masses and 550 patients with ovarian cancer. RESULTS: Overall, 2665 patients were included. CPH-I was highly significant in discriminating benign from malignant ovarian disease. At the defined cut-off of 0.070 for CPH-I the sensitivity and specificity were 95.0% and 78.4% respectively in the training cohort and 82.0% and 88.4% in the validation cohort. Comparison of CPH-I, ROMA and RMI demonstrated area-under-curve (AUC) at 0.960, 0.954 and 0.959 respectively in the training study and 0.951, 0.953 and 0.935 respectively in the validation study. Using a sensitivity of 95.0%, the specificities for CPH-I, ROMA and RMI in the training cohort were 78.4%, 71.7% and 81.5% respectively, and in the validation cohort 67.3%, 70.7% and 69.5% respectively. CONCLUSION: All three indices perform well at the clinically relevant sensitivity of 95%, but CPH-I, unlike RMI and ROMA, is independent of ultrasound and menopausal status, and may provide a simple index to optimize referral of women with suspected ovarian cancer.
