Comprehensive Genomic Profiling of Circulating Tumor DNA in Patients with Previously Treated Metastatic Colorectal Cancer: Analysis of a Real-World Healthcare Claims Database
Bibliographic record
Abstract
We used a real-world database (GuardantINFORMTM) to analyze the treatment choices for patients with mCRC who underwent next-generation sequencing of circulating tumor DNA (ctDNA) using a commercially available test (Guardant360®) after first- or second-line therapy. From 18,875 patients with claims for CRC, 1064 had confirmed metastatic disease and sufficient histories for analysis (median age 59 years, 44.8% female, 44.5% left-sided). ctDNA was detectable for 997/1064 (93.7%) patients. Clinically actionable molecular profiles were present for 507/1064 (47.7%) patients, including those who had not received targeted therapy in the previous line (410/926, 44.3%). Second- or third-line targeted therapies were administered to 338/1064 patients (31.8%) and were considered matched for 193/338 (57.1%) patients. Therapies administered after testing were informed by the ctDNA results in 56.7% of patients overall (603/1064). Time to treatment discontinuation was most favorable for patients with a clinically actionable ctDNA profile who received matched therapy. This analysis demonstrates the real-world clinical value of plasma-based comprehensive genomic profiling for selecting appropriate molecular-targeted therapies in mCRC patients with disease progression after first- or second-line therapy.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".