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Evaluating the use of circulating tumor DNA (ctDNA) in patients with urothelial cancer in the context of FGFR-targeted therapy.

2023· article· en· W4379281611 on OpenAlexafffund
Elena Schönlau, David C. Müller, Gillian Vandekerkhove, Andrew J. Murtha, Jack V. W. Bacon, Connor Wells, Kimia Rostin, Sunil Parimi, Krista Noonan, Naveen S. Basappa, Jenny J. Ko, Daygen L. Finch, Nimira Alimohamed, Tarek A. Bismar, Lucia Nappi, Matti Annala, Cecily Q. Bernales, Kim N., Alexander W. Wyatt, Bernhard J. Eigl

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of AlbertaAbbotsford Veterinary ClinicSurrey Place CentreKelowna General HospitalUniversity of British Columbia
FundersBladder Cancer Canada
KeywordsMedicineFibroblast growth factor receptorSomatic cellTargeted therapyOncologyCancerCancer researchContext (archaeology)Cell-free fetal DNAInternal medicineFibroblast growth factorGeneBiologyReceptorGenetics

Abstract

fetched live from OpenAlex

4577 Background: Fibroblast growth factor receptor (FGFR) inhibitors (e.g., erdafitinib) are increasingly important in the management of FGFR-mutated urothelial carcinoma. FDA-approved archival tissue testing for specific FGFR alterations was implemented as a companion diagnostic for erdafitinib. However, longitudinal sequencing studies indicate variable tumor FGFR status over time, and erdafitinib resistance mechanisms in metastatic urothelial carcinoma (mUC) are underreported. This ongoing study aims to evaluate the accuracy of cell-free DNA (cfDNA) compared to archival tissue testing in mUC for FGFR alterations detection, and to evaluate genomic mechanisms of erdafitinib resistance in cfDNA at progression. Methods: Patients with progressing mUC who were undergoing archival tissue testing for FGFR1-3 mutations and/or fusions and who had blood samples drawn during the management of their metastatic disease were eligible. Plasma cfDNA and matched leukocyte DNA were subjected to deep targeted sequencing with a custom panel including UC-specific gene loci and all clinically approved hotspots in FGFR1+2 and all exons and introns of FGFR3. Results: As of January 2023, 109 patients from 6 sites were enrolled. Median age at diagnosis was 71, 33% had upper urinary tract primaries, and 76% were male. Tissue and cfDNA results for comparison were available for 69 patients to date. Actionable somatic FGFR alterations were found in the tissue of 15 patients (31%); the most common alteration was the FGFR3 p.S249C mutation (67%). 50 of the analyzed cfDNA samples had detectable somatic circulating tumor DNA (ctDNA) variant allele fraction of 0.5% (72%). Of those, 49 had an evaluable tissue test result. Analysis of this subset revealed high concordance (92%) between the two test methods. With the assumption that archival tissue testing is considered the ‘gold standard’, sensitivity is 93% and specificity is 91%. The four discordant results comprised one cfDNA test with undetectable FGFR3-TACC3 fusion, which was detected in tissue and three positive ctDNA test results in patients with FGFR wild-type tissue tests. In one case at erdafitinib progression, ctDNA revealed multiple subclonal populations with distinct FGFR3 gatekeeper mutations suggesting polyclonal resistance. Conclusions: This ongoing study suggests cfDNA is a valuable minimally invasive adjunct to tissue-based assays for the detection of FGFR alterations to identify patients for FGFR inhibitor therapy and to monitor for mechanisms of resistance.

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)0.003
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.187
GPT teacher head0.456
Teacher spread0.269 · 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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Citations1
Published2023
Admission routes2
Has abstractyes

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