Mutation profiling of tumor DNA from plasma and tumor tissue of colorectal cancer patients with a novel, high-sensitivity multiplexed mutation detection platform
Bibliographic record
Abstract
// Evelyn Kidess 1 , Kyra Heirich 1 , Matthew Wiggin 2 , Valentina Vysotskaia 2 , Brendan C. Visser 1 , Andre Marziali 2 , Bertram Wiedenmann 3 , Jeffrey A. Norton 1 , Mark Lee 2 , Stefanie S. Jeffrey 1 and George A. Poultsides 1 1 Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA 2 Boreal Genomics, Mountain View, CA, USA and Vancouver, BC, Canada 3 Deparment of Medicine, Division of Hepatology and Gastroenterology, Charité University Hospital, Berlin, Germany Correspondence: Stefanie S. Jeffrey, email: // George A. Poultsides, email: // Keywords : biomarker, circulating tumor DNA, colon cancer, ctDNA, hepatic metastasis Received : September 04, 2014 Accepted : December 09, 2014 Published : December 10, 2014 Abstract BACKROUND: Circulating tumor DNA (ctDNA) holds promise as a non-invasive means for tumor monitoring in solid malignancies. Assays with high sensitivity and multiplexed analysis of mutations are needed to enable broad application. METHODS: We developed a new assay based on sequence-specific synchronous coefficient of drag alteration (SCODA) technology, which enriches for mutant DNA to achieve high sensitivity and specificity. This assay was applied to plasma and tumor tissue from non-metastatic and metastatic colorectal cancer (CRC) patients, including patients undergoing surgical resection for CRC liver metastases. RESULTS: Across multiple characterization experiments, the assay demonstrated a limit of detection of 0.001% (1 molecule in 100,000) for the majority of the 46 mutations in the panel. In CRC patient samples (n=38), detected mutations were concordant in tissue and plasma for 93% of metastatic patients versus 54% of non-metastatic patients. For three patients, ctDNA identified additional mutations not detected in tumor tissue. In patients undergoing liver metastatectomy, ctDNA anticipated tumor recurrence earlier than carcinoembryonic antigen (CEA) value or imaging. CONCLUSIONS: The multiplexed SCODA mutation enrichment and detection method can be applied to mutation profiling and quantitation of ctDNA, and is likely to have particular utility in the metastatic setting, including patients undergoing metastatectomy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".