Abstract 3387: ctDNA dynamics in early stage node negative lung cancers
Bibliographic record
Abstract
Abstract Introduction: Patients with early-stage lung cancers have a high risk of relapse and death even after curative surgery. Detection of circulating tumour DNA (ctDNA) in plasma perioperatively is associated with shorter recurrence free survival (RFS). ctDNA-Lung-Detect is an investigator-initiated prospective study of ctDNA detection and association with RFS in patients with early stage non-small cell lung cancer (NSCLC). Methods: Patients with clinically staged T<4cm N0 NSCLC planned for surgical resection at the Princess Margaret/University Health Network underwent ctDNA assessment before and after surgery (~1 month and 1 year). ctDNA minimal residual disease (MRD) was detected using the highly sensitive and specific tumor-informed Residual Disease and Recurrence (RaDaR®, Inivata, Cambridge, UK) assay, which can track up to 48 tumor-specific variants in plasma. Results: Since August 2021, 64 eligible patients were enrolled (Table 1). One had isolated metastasis from a remote non-lung primary and subsequently excluded; another had their preoperative sample missed but was included. Preoperative ctDNA was detected in 15/62 (23.8%) patients and 1/63 (1.6%) post-operatively at the 1-month landmark timepoint (occult N2 disease found at surgery). All but one (93.3%) had ctDNA clearance with surgical resection. Two patients have recurred radiographically and in plasma at 1 year post-operatively, 1/15 with preoperative ctDNA detected (6.7%) and 1/47 without preoperative ctDNA detected (2.1%). Patients with higher T stage, tumor size, squamous histology, and without actionable driver alterations were more likely to have ctDNA detected at any timepoint. Conclusions: In patients with early stage NSCLC (small node negative tumors), 24% have detectable ctDNA pre-operatively. Surgical resection led to ctDNA clearance in 93% of cases. Our results support ongoing testing beyond the initial MRD landmark. Assessment of the impact on RFS post resection is ongoing. Citation Format: Jamie Feng, Tom Waddell, Kazuhiro Yasufuku, Andrew Pierre, Shaf Keshavjee, Jonathan Yeung, Marcelo Cypel, Laura Donahoe, Elliot Wakeam, Marc de Perrot, Jennifer Law, Alexandra Salvarrey, Lisa W. Le, Jennifer Lister, Michael Cabanero, Ming Tsao, Christodoulos Pipinikas, Karen Howarth, Natasha Leighl. ctDNA dynamics in early stage node negative lung cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3387.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".