Longitudinal ultrasensitive ctDNA monitoring for high-resolution lung cancer risk prediction
Bibliographic record
Abstract
Biomarkers accurately informing prognostic assessment and therapeutic strategy are critical for improving patient outcome in oncology. Here, we apply a whole-genome, tumor-informed circulating tumor DNA (ctDNA) detection approach to address this challenge, leveraging 1,800 variants across 2,994 plasma samples from 431 patients with non-small cell lung cancer (NSCLC) from the TRACERx study. We show that ultrasensitive ctDNA detection below 80 parts per million both pre- and postoperatively is highly prognostic, and combinatorial analysis of the pre- and postoperative ctDNA status identifies an intermediate risk group, improving disease stratification. ctDNA kinetics demonstrate clinical utility during adjuvant therapy, where patients that "clear" ctDNA during adjuvant therapy experience improved outcomes. Moreover, characterization of patterns in postoperative ctDNA kinetics reveals insights into the timing, risk, and anatomical pattern of relapses. By incorporating longitudinal ultrasensitive ctDNA detection, we propose a refined schema for guiding the stratification and treatment recommendations in early stage NSCLC.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".