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ctDNA-Lung-DETECT: ctDNA outcomes for resected early stage non-small cell lung cancers at 12 months.

2024· article· en· W4400109279 on OpenAlexaffabout
Sam Khan, Jamie Feng, Thomas K. Waddell, Kazuhiro Yasufuku, Andrew Pierre, Shaf Keshavjee, Jonathan Yeung, Marcelo Cypel, Laura Donahoe, Elliot Wakeam, Marc de Perrot, Najib Safieddine, Michael L. Ko, David N. Parente, Mary Rose Rabey, Michael Cabanero, Lisa W. Le, Christodoulos Pipinikas, A. Chevalier, Natasha B. Leighl

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of TorontoToronto East General HospitalPrincess Margaret Cancer CentreSt Joseph's Health CentreUniversity Health Network
Fundersnot available
KeywordsMedicineLung cancerStage (stratigraphy)Internal medicineOncologyCirculating tumor DNAProspective cohort studyLungGastroenterologyCancerSurgery

Abstract

fetched live from OpenAlex

8018 Background: ctDNA Lung DETECT is a multicentre investigator initiated prospective study at 3 thoracic surgery centres in the Greater Toronto Area assessing ctDNA detection and association with recurrence free survival (RFS) in patients with early stage non-small cell lung cancer (NSCLC) (NCT05254782). Patients who have ctDNA detected perioperatively are offered ctDNA Lung RCT, a randomized trial investigating the benefit of adjuvant chemo-immunotherapy in patients where the standard of care is observation alone after surgery (NCT04966663). Herein, we report on ctDNA outcomes at 12 months for patients with resected early stage NSCLC. Methods: Patients with stage I (T1-2N0) or multifocal T3-4 < 4cm N0 NSCLC planned for resection at University Health Network consented to plasma ctDNA assessment before and after surgery, and at 12 months post-operatively or relapse using the tumor-informed RaDaR® assay, which detects up to 48 tumor-specific variants in plasma with a Limit of Detection (LoD₉₅) of 0.0011% variant allele fraction. Results: From July 2021 to January 2024, 178 patients were enrolled; 115 had sufficient tissue for assessment. Of these, 68/72 patients have 12 month post-resection ctDNA results available (3 withdrew, 1 sample failed). ctDNA was detected pre-operatively in 18 patients; 99% (71/72) had ctDNA clearance post-operatively, and 93% (62/67) remained ctDNA negative at 12 months. Median follow up time was 18.7 months (range 12.0– 28.3); 8/72 (11%) patients (5 stage I, 3 stage II) experienced lung cancer recurrence. Median time to recurrence was 13.9 months (range 6.2- 24.9). Of these, 3 had ctDNA detected on their preoperative and 12-month or recurrence sample, 1 had ctDNA detected at 12 months prior to relapse, 1 had ctDNA detected at 12 months and recurred around the same time, 2 had negative ctDNA samples and 1 missed sample collection preoperatively. The recurrence rate was 16.7% (3/18, 95% exact CI 3.6-41.4%) in patients with ctDNA detected pre-operatively vs. 7.5% (4/53, CI 2.1-18.2%) in those without. New lung cancers were diagnosed in 5/72 (median time to new primary 15.3 months, range 4.9-14.2) and 2/72 patients had new cancers diagnosed (ovarian/liposarcoma). For those with new lung primaries, 1 had ctDNA detected preoperatively but none had ctDNA detected at time of new primary diagnosis. Of 4 patients who have died, 2 were from recurrent lung cancer and 2 from new primaries (lung/sarcoma). Conclusions: This study represents one of the largest prospective cohorts of ctDNA kinetics in patients with resected lung cancer. Of patients with at least 12 months follow up, 8/72 experienced a lung cancer recurrence with a higher rate in those with pre-operative ctDNA detected (16.7% vs 7.5%). Pre-operative ctDNA detection may help identify patients with resected stage I NSCLC that could benefit from treatment intensification, currently under study in ctDNA Lung RCT (NCT04966663). Clinical trial information: NCT05254782 .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.043
GPT teacher head0.393
Teacher spread0.350 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations5
Published2024
Admission routes2
Has abstractyes

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