Noninvasive Therapeutic Monitoring of Circulating Tumor DNA in BRAF‐Mutant Metastatic Colon Cancer Using Droplet Digital PCR, Next‐Generation Sequencing, and Fragmentomics
Bibliographic record
Abstract
Purpose BRAFV600E ‐mutated metastatic colorectal cancers (mCRCs) are associated with poorer prognosis. We present a case, in which noninvasive therapeutic monitoring was performed on a patient with BRAF ‐mutant mCRC, aiming to track disease progression and elucidate the mechanisms of response and resistance towards anti‐ BRAF therapy. Methods A 40‐year‐old man diagnosed with metastatic BRAFV600E mutant sigmoid adenocarcinoma received multiple lines of treatment, including first‐line chemotherapy + bevacizumab and targeted therapy of cetuximab, encorafenib ± binimetinib. Noninvasive therapeutic monitoring was performed on ctDNA using our in‐house designed droplet digital PCR assay and fragmentomics. We also performed serial and paired analyses of tissue, liquid biopsy, and in vitro studies at different multiple timepoints. Results ctDNA and fragmentomics biomarkers were concordant with, and even preceded traditional serological and radiological biomarkers in predicting disease progression. Molecular analyses and drug testing also revealed mutations that are either potentially targetable or account for resistance, which guided the subsequent treatment regimen. Conclusion This case demonstrates the potential application of ctDNA and fragmentomics biomarkers, molecular analyses, and drug testing in noninvasive therapeutic monitoring of BRAFV600E mutant mCRC. These illustrate the potential application of such noninvasive therapeutic monitoring in larger scale cohorts of patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".