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Identification of immunotherapy early treatment failure in non-small cell lung cancer (NSCLC) using a novel cell-free DNA (cfDNA) tissue-agnostic genome-wide methylome enrichment assay.

2025· article· en· W4410802690 on OpenAlexaff
Tuan Hoang, Yahan Yang, Sally C. M. Lau, Miguel García-Pardo, Collin Melton, Justin Burgener, Ben Brown, Scott V. Bratman, Abigail Williams, Brian Allen, Jing Zhang, Daniel D. De Carvalho, Anne‐Renee Hartman, Lawson Eng, Penelope Ann Bradbury, Frances A. Shepherd, Natasha B. Leighl, Geoffrey Liu, Elena Elimova, Adrian G. Sacher

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineImmunotherapyDNA methylationLung cancerCell-free fetal DNADNAGenomeCancer researchCancerCellComputational biologyGeneOncologyBiologyInternal medicineGeneticsGene expression

Abstract

fetched live from OpenAlex

8550 Background: Immune checkpoint inhibitor treatment failure constitutes a significant clinical challenge in non-small cell lung cancer (NSCLC). Molecular residual disease (MRD) detection in NSCLC may allow earlier detection of disease recurrence/progression and enable early treatment intensification or clinical trial enrolment. We have developed a novel tissue-agnostic genome-wide methylation enrichment platform based on cell free methylated DNA immunoprecipitation and high throughput sequencing (cfMeDIP-seq). Here, we present data on its application as an MRD assay to predict early recurrence or progression in patients (pts) with NSCLC receiving immunotherapy. Methods: The study population consists of pts with stage III/IV NSCLC at the Princess Margaret Cancer Centre, treated with definitive chemoradiation followed by consolidative durvalumab (stage III) or with PD-1 inhibitors +/- chemotherapy (stage IV). Pts underwent serial blood collection prior to initiation of treatment, 2-4 weeks after treatment initiation and approximately 6-8 weeks thereafter until progression. 5-10 ng of cfDNA was isolated from plasma. A classifier was trained on an independent set of lung and non-cancer samples to quantify relative circulating tumor DNA (ctDNA) content. The analysis considered multiple timepoints. Results were considered "positive” if there was a detected result at any follow-up timepoint. Results were considered "negative” if all follow-up timepoints were reported as not detected. Progression-free survival (PFS) was compared between groups using a log-rank test. Hazard ratio (HR) was estimated using Cox proportional hazards model. Results: A total of 187 samples from 63 unique pts (44% stage III and 56% stage IV) were analyzed and correlated with PFS. Pts with a positive MRD test showed significantly worse PFS than those who tested negative (HR 4.8; 95% CI, 2.1-10.8, P<0.0001), sensitivity 80%, specificity 91%. The lead time between MRD positivity and progression was up to 12.6 months, with a mean of 5.1 months. Secondary analysis of pts with stage III NSCLC revealed significantly worse PFS in MRD-positive pts compared to MRD-negative pts (HR 8; 95% CI, 1.4-46.7, P=0.007). Conclusions: MRD detection using genome-wide methylome enrichment correlates strongly with PFS in pts with advanced NSCLC receiving immunotherapy. This tissue agnostic assay shows promise for early identification of treatment failure, enabling timely selection of patients for treatment intensification or clinical trials.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.375
Teacher spread0.346 · 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 designBench or experimental
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

Citations1
Published2025
Admission routes1
Has abstractyes

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