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Utility of plasma cell-free DNA and tissue next generation sequencing (NGS) in detecting genetic mutations in non-small cell lung cancer (NSCLC).

2025· article· en· W4410805160 on OpenAlexaffabout
Andreas I. Papadakis, Goulnar Kasymjanova, Carmela Pepe, Lama Sakr, Jennifer Friedmann, Dahlia Leibovich, Hangjun Wang, Kalyani Rajalingham, Victor Cohen, Alan Spatz, Jason Agulnik

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMedicineDNA sequencingLung cancerDNACell-free fetal DNACancerCellCancer researchLungMutationPathologyComputational biologyGeneBiologyGeneticsInternal medicine

Abstract

fetched live from OpenAlex

e20514 Background: NSCLC is defined by molecular alterations driving tumor progression and therapeutic response. Genetic profiling traditionally relies on tissue biopsies, often hindered by insufficient samples, tumor heterogeneity, or inability to perform repeat biopsies. ctDNA analysis has emerged as a complementary approach. The Oncomine Pan-Cancer Cell-Free Assay is a robust NGS platform detecting diverse genetic alterations in ctDNA. This study evaluated the utility and concordance of the Oncomine ctDNA NGS assay in suspected advanced NSCLC cases and its role when tissue-based testing could not be performed. Methods: A prospective cohort study analyzed ctDNA from patients with suspected advanced NSCLC using the Oncomine Pan-Cancer Cell-Free Assay. Conducted at the Anna and Peter Brojde Lung Cancer Centre with support from the McGill Rossy Cancer Network. Results: Of 68 patients tested with ctDNA NGS Oncomine, 44/68 (65%) were positive, 15/68 (22%) negative for mutations, and 9/68 (13%) inconclusive, likely due to low ctDNA fractions or technical sensitivity. Among positive results, 19/44 (43%) had targetable mutations, including EGFR (12/19), KRAS (6/19), and BRAF (1/19), while 25/44 (57%) were non-targetable. Inconclusive cases revealed 6 targetable mutations via SOC NGS: BRAF (2), EGFR (2), ERBB2 (1), and KRAS (1). The correlation of mutation detection between the ctDNA NGS test and the standard-of-care (SOC) tissue NGS test (Table 1) was assessed on 57/68 cases as for 11/68 cases the SOC genetic tissue testing was not performed: 5 were SCLC, and 6 had other diagnoses. Among cases tested on both 42/57 (74%) were highly concordant: 27/57 (47%) were positive and 15/57 (26%) were negative on both tests. Conversely 15 (26%) cases were discordant: 9 of SOC-negative cases were found to be positive by Oncomine with 2/9 (both were EGFR) being targetable. Another 6 cases were negative on Oncomine but positive on SOC NGS. Conclusions: This study highlights the complementary benefit of incorporating ctDNA to SOC NGS in the initial diagnosis of advanced NSCLC. Oncomine ctDNA was able to identify mutations in 9 patients with negative SOC NGS molecular testing. Similarly, SOC NGS identified 9 pts with mutations that were negative on Oncomine ctDNA. In addition, there are still inconclusive results from the ctDNA – oncomine testing, thus requiring SOC tissue NGS for results. Further advances in technology may improve ctDNA sensitivity to eventually surpass tissue SOC NGS. Concordance between ctDNA and SOC tissue NGS tests. SOC NGS Oncomine-ctDNA Positive Negative Total P value Positive 27 (43%) 9 (14%) 36 (57%) <0.001 Negative 6 (10%) 15 (23%) 21 (33%) Inconclusive* 6 (10%) 0 (0%) 6 (10%) Total 39 (63%) 24 (37%) 63 (100%) *6/9 inconclusive ctDNA had SOC NGS done.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.059
GPT teacher head0.382
Teacher spread0.323 · 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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Citations0
Published2025
Admission routes2
Has abstractyes

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