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Record W4293094498 · doi:10.1200/jco.22.00364

Circulating Tumor DNA Identifies Diverse Landscape of Acquired Resistance to Anti–Epidermal Growth Factor Receptor Therapy in Metastatic Colorectal Cancer

2022· article· en· W4293094498 on OpenAlexafffund
James T. Topham, Chris J. O’Callaghan, Harriet Feilotter, Hagen F. Kennecke, Young S. Lee, Weimin Li, Kimberly C. Banks, Katie Quinn, Daniel J. Renouf, Derek J. Jonker, Dongsheng Tu, Eric X. Chen, Jonathan M. Loree

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsPrincess Margaret Cancer CentreUniversity of OttawaOttawa HospitalQueen's UniversityUniversity of British Columbia
FundersBC Cancer FoundationMichael Smith Health Research BCTerry Fox Research InstituteAstraZeneca
KeywordsMedicineKRASNeuroblastoma RAS viral oncogene homologColorectal cancerEpidermal growth factor receptorInternal medicinePanitumumabOncologyTargeted therapyCancer researchCetuximabEGFR inhibitorsCancer

Abstract

fetched live from OpenAlex

PURPOSE Anti–epidermal growth factor receptor (EGFR) antibodies are effective treatments for metastatic colorectal cancer. Improved understanding of acquired resistance mechanisms may facilitate circulating tumor DNA (ctDNA) monitoring, anti-EGFR rechallenge, and combinatorial strategies to delay resistance. METHODS Patients with treatment-refractory metastatic colorectal cancer (n = 169) enrolled on the CO.26 trial had pre–anti-EGFR tissue whole-exome sequencing (WES) compared with baseline and week 8 ctDNA assessments with the GuardantOMNI assay. Acquired alterations were compared between patients with prior anti-EGFR therapy (n = 66) and those without. Anti-EGFR therapy occurred a median of 111 days before ctDNA assessment. RESULTS ctDNA identified 12 genes with increased mutation frequency after anti-EGFR therapy, including EGFR ( P = .0007), KRAS ( P = .0017), LRP1B ( P = .0046), ZNF217 ( P = .0086), MAP2K1 ( P = .018), PIK3CG ( P = .018), BRAF ( P = .048), and NRAS ( P = .048). Acquired mutations appeared as multiple concurrent subclonal alterations, with most showing decay over time. Significant increases in copy-gain frequency were noted in 29 genes after anti-EGFR exposure, with notable alterations including EGFR ( P < .0001), SMO ( P < .0001), BRAF ( P < .0001), MET ( P = .0002), FLT3 ( P = .0002), NOTCH4 ( P = .0006), ERBB2 ( P = .004), and FGFR1 ( P = .006). Copy gains appeared stable without decay 8 weeks later. There were 13 gene fusions noted among 11 patients, all but one of which was associated with prior anti-EGFR therapy. Polyclonal resistance was common with acquisition of ≥ 10 resistance related alterations noted in 21% of patients with previous anti-EGFR therapy compared with 5% in those without ( P = .010). Although tumor mutation burden (TMB) did not differ pretreatment ( P = .63), anti-EGFR exposure increased TMB ( P = .028), whereas lack of anti-EGFR exposure resulted in declining TMB ( P = .014). CONCLUSION Paired tissue and ctDNA sequencing identified multiple novel mutations, copy gains, and fusions associated with anti-EGFR therapy that frequently co-occur as subclonal alterations in the same patient.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.060
GPT teacher head0.385
Teacher spread0.325 · 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 teacher head, 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

Citations67
Published2022
Admission routes2
Has abstractyes

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