Predictors for detecting circulating tumor DNA (ctDNA) in metastatic colorectal cancer (mCRC).
Bibliographic record
Abstract
634 Background: Utilization of ctDNA has been rapidly adopted as a predictive diagnostic in advanced NSCLC and indications in GI cancers may be emerging. While tissue-based assays have yields above 90%, there is less known about factors that influence the sensitivity of ctDNA for detecting mutations. Methods: We retrospectively evaluated mCRC patients (pts) who had plasma-derived NGS utilizing a highly-sensitive 68-73-gene assay. Tissue from prior resections or biopsies underwent concurrent 46-gene sequencing. In a case-control design, pts with a known mutation on tissue and radiologic evidence of metastatic disease but no detectable ctDNA mutation were matched 1:3 with randomly selected pts with detectable mutations and compared according to clinical, laboratory, and radiologic characteristics. A binary logistic regression was performed and Kaplan-Meier and Cox-proportional hazards models were used to compare overall (OS) and progression-free (PFS) survivals. Results: Of 427 mCRC pts who underwent ctDNA testing, 416 pts met inclusion criteria. Plasma-derived NGS did not find tumor mutations in 66 cases (15.9%); 198 pts with detectable alterations were selected as controls. After multivariate analysis, the lack of detection of ctDNA was associated with decreasing age (OR 0.94; 95%CI 0.91-0.98; p = 0.004), absence of liver (OR 0.19; 95%CI 0.08-0.45; p < 0.001) and lymph node metastases (OR 0.28; 95%CI 0.12-0.70; p = 0.006). A key determinant was timing of collection relative to disease status: plasma collected after evidence of progression was substantially more likely to have detectable alterations (OR 10.95; 95%CI 4.23-28.33; p < .001); in these pts, the modeled rate of detection was 98%, which increased to > 99% if the pts also had either liver or nodal disease. Pts with no detectable ctDNA had better OS (HR 0.38; 95%CI 0.21-0.69, p = 0.002) and PFS (HR 0.52; 95%CI 0.32-0.85; p = 0.009). Conclusions: When limited to ctDNA collected in newly diagnosed or recently progressing pts, the yield of ctDNA is 98%, which equals or exceeds the yield of tissue based testing. Our findings support the notion that ctDNA testing, when appropriately utilized, can replace tissue based testing and may provide prognostic information.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".