Cell-free DNA (cfDNA) in the blood provides a noninvasive diagnostic avenue for patients with cancer1. However, characteristics of the origins and molecular features of cfDNA are poorly understood. We developed an approach to evaluate fragmentation patterns of cfDNA across the genome and found that cfDNA profiles of healthy individuals reflected nucleosomal patterns of white blood cells, while patients with cancer had altered fragmentation profiles. We applied this method to analyze fragmentation profiles of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or bile duct cancer and 245 healthy individuals. A machine learning model incorporating genome-wide fragmentation features had sensitivities of detection ranging from 57% to >99% among the seven cancer types at 98% specificity, with an overall AUC of 0.94. Fragmentation profiles could be used to identify the tissue of origin of the cancers to a limited number of sites in 75% of cases. Combining our approach with mutation based cfDNA analyses detected 91% of cancer patients. The results of these analyses highlight important properties of cfDNA and provide a proof of principle approach for screening, early detection, and monitoring of human cancer.

This cohort study investigates the use of longitudinal data from ultradeep sequencing of cell-free DNA in Danish patients with colorectal cancer.

Non-invasive approaches for cell-free DNA (cfDNA) assessment provide an opportunity for cancer detection and intervention. Here, we use a machine learning model for detecting tumor-derived cfDNA through genome-wide analyses of cfDNA fragmentation in a prospective study of 365 individuals at risk for lung cancer. We validate the cancer detection model using an independent cohort of 385 non-cancer individuals and 46 lung cancer patients. Combining fragmentation features, clinical risk factors, and CEA levels, followed by CT imaging, detected 94% of patients with cancer across stages and subtypes, including 91% of stage I/II and 96% of stage III/IV, at 80% specificity. Genome-wide fragmentation profiles across ~13,000 ASCL1 transcription factor binding sites distinguished individuals with small cell lung cancer from those with non-small cell lung cancer with high accuracy (AUC = 0.98). A higher fragmentation score represented an independent prognostic indicator of survival. This approach provides a facile avenue for non-invasive detection of lung cancer.

Statement of translational relevanceSensitive methods for recurrence risk stratification, monitoring therapeutic efficacy, and early recurrence detection may have a major impact on treatment decisions and outcomes for stage III colorectal cancer patients. Circulating tumor DNA assessments performed postoperative, postadjuvant, and serially during surveillance all allowed stratification of patients into high and low risk groups. CtDNA detected recurrence with a significant leadtime compared to CT-imaging and ctDNA growth rates were prognostic of survival.Treatment of ctDNA positive patients with standard adjuvant therapy prevented recurrence in only 20% of patients. Accordingly, further studies exploring the optimal treatment for ctDNA positive patients are needed, as well as interventional studies assessing the clinical utility of ctDNA-based risk-stratification. A promising opportunity is risk-stratified allocation of surveillance resources, which may improve both the cost-effectiveness and the overall clinical outcome of surveillance. Finally, ctDNA growth rates may identify patients who could benefit from immediate therapeutic intervention compared to awaiting recurrence.Research.

BackgroundEarly detection plays an essential role to reduce colorectal cancer (CRC) mortality. While current screening methods suffer from poor compliance, liquid biopsy-based strategies for cancer detection is rapidly gaining promise. Here, we describe the development of TriMeth, a minimal-invasive blood-based test for detection of early-stage colorectal cancer. The test is based on assessment of three tumour-specific DNA methylation markers in circulating cell-free DNA.ResultsA thorough multi-step biomarker discovery study based on DNA methylation profiles of more than 5000 tumours and blood cell populations identified CRC-specific DNA methylation markers. The DNA methylation patterns of biomarker candidates were validated by bisulfite sequencing and methylation-specific droplet digital PCR in CRC tumour tissue and peripheral blood leucocytes. The three best performing markers were first applied to plasma from 113 primarily early-stage CRC patients and 87 age- and gender-matched colonoscopy-verified controls. Based on this, the test scoring algorithm was locked, and then TriMeth was validated in an independent cohort comprising 143 CRC patients and 91 controls. Three DNA methylation markers, C9orf50, KCNQ5, and CLIP4, were identified, each capable of discriminating plasma from colorectal cancer patients and healthy individuals (areas under the curve 0.86, 0.91, and 0.88). When combined in the TriMeth test, an average sensitivity of 85% (218/256) was observed (stage I: 80% (33/41), stage II: 85% (121/143), stage III: 89% (49/55), and stage IV: 88% (15/17)) at 99% (176/178) specificity in two independent plasma cohorts.ConclusionTriMeth enables detection of early-stage colorectal cancer with high sensitivity and specificity. The reported results underline the potential utility of DNA methylation-based detection of circulating tumour DNA in the clinical management of colorectal cancer.

QOC in PUB has improved substantially in Denmark, but the 30-day mortality remains high. Future initiatives to improve outcomes may include earlier endoscopy, having fully trained endoscopists on call, and increased focus on managing coexisting disease.

This nationwide quality improvement initiative was associated with reduced preoperative delay and improved perioperative monitoring in patients with PPU. A non-significant improvement was seen in 30-day mortality.

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