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Record W4213286840 · doi:10.3390/curroncol29020094

Cell-Free Tumor DNA (ctDNA) Utility in Detection of Original Sensitizing and Resistant EGFR Mutations in Non-Small Cell Lung Cancer (NSCLC)

2022· article· en· W4213286840 on OpenAlexaffvenueabout
Jason Agulnik, Andreas I. Papadakis, Carmela Pepe, Lama Sakr, David Small, Hangjun Wang, Goulnar Kasymjanova, Alan Spatz, Victor Cohen

Bibliographic record

VenueCurrent Oncology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsMcGill University Health CentreMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsT790MMedicineLung cancerInternal medicineOncologyMutationBiomarkerLiquid biopsyCell-free fetal DNAResistance mutationCancerGeneCancer researchAdenocarcinomaPolymerase chain reactionGeneticsReverse transcriptaseBiologyROS1

Abstract

fetched live from OpenAlex

Background: Recent studies have demonstrated the utility of cell-free tumor DNA (ctDNA) from plasma as an alternative source of genomic material for detection of sensitizing and resistance mutations in NSCLC. We hypothesized that the plasma level of ctDNA is an effective biomarker to provide a non-invasive and thus a less risky method to determine new resistance mutations and to monitor response to treatment and tumor progression in lung cancer patients. Methods: This prospective cohort study was approved and conducted at the Peter Brojde Lung Cancer Centre, Montreal. Blood was collected in STRECK tubes at four time points. DNA was extracted from plasma, and ctDNA was analyzed for the presence of mutations in the EGFR gene using the COBAS® EGFR v2 qPCR (Roche) test. Results: Overall, 75 pts were enrolled in the study. In total, 23 pts were TKI-naïve, and 52 were already receiving first-line TKI treatment. ctDNA detected the original mutations (OM) in 35/75 (48%) patients. Significantly higher detection rates were observed in TKI-naïve patients compared to the TKI-treated group, 70% versus 37%, respectively (p = 0.012). The detection of the original mutation at the study baseline was a negative predictor of progression-free survival (PFS) and overall survival (OS). The resistance mutation (T790M) was detected in 32/74 (43%) patients. In 27/32 (84%), the T790M was detected during treatment with TKI: in 25/27 patients, T790M was detected at the time of radiologic progression, in one patient, T790M was detected before radiologic progression, and in one patient, T790M was detected four weeks after starting systemic chemotherapy post progression on TKI. At the time of progression, the detection of T790M significantly correlates with the re-appearance of OM (p = 0.001). Conclusion: Plasma ctDNA is a noninvasive patient-friendly test that can be used to monitor response to treatment, early progression, and detection of acquired resistant mutations. Monitoring of clearance and re-emergence of driver mutations during TKI treatment effectively identifies progression of the disease. As larger NGS panels are available for ctDNA testing, these findings may also have implications for other biomarkers. The results from ongoing and prospective studies will further determine the utility of plasma testing to diagnose, monitor for disease progression, and guide treatment decisions in NSCLC.

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)0.002
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
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.307
Teacher spread0.286 · 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".

Quick stats

Citations8
Published2022
Admission routes3
Has abstractyes

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