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Clinical utility of circulating tumor DNA (ctDNA) in resectable pancreatic ductal adenocarcinoma (PDAC).

2016· article· en· W2590145929 on OpenAlexaff
Hui‐Li Wong, Kevin Bushell, Joanna M. Karasinska, Sarah E. Arthur, Ryan D. Morin, David F. Schaeffer, Daniel J. Renouf

Bibliographic record

VenueJournal of Clinical Oncology · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsVancouver General HospitalUniversity of British ColumbiaPancreas Centre (Canada)Simon Fraser UniversityBC Cancer Agency
Fundersnot available
KeywordsKRASMedicineBiomarkerLiquid biopsyInternal medicineCirculating tumor DNAOncologyMultiplexMinimal residual diseaseDigital polymerase chain reactionPancreatic cancerMutationCancer researchCancerBioinformaticsGenePolymerase chain reactionColorectal cancerBiologyGenetics

Abstract

fetched live from OpenAlex

247 Background: ctDNA is emerging as a promising biomarker, with potential utility in screening, detecting minimal residual disease after curative resection and monitoring treatment response or resistance in advanced disease. Most PDAC studies to date have focused on identifying mutant KRAS ctDNA in metastatic disease. Here we perform sequential ctDNA quantification in patients (pts) with resectable PDAC using a novel and highly sensitive multiplex technology to explore the clinical utility of ctDNA as a diagnostic and prognostic biomarker. Methods: Banked plasma and tumor samples from 18 pts with resected PDAC were retrieved. Plasma samples were collected 0-28 days before, and 28-70 days after surgery. DNA was extracted using standard protocols and analyzed using the OnTarget system, which enriches for DNA molecules containing hot spot mutations prior to sequencing. A 96-plex panel that includes the most prevalent mutations in KRAS, PIK3CA and TP53 was used. Results: 16 pts (89%) had at least 1 mutation detected by OnTarget in the tumor sample, most frequently in KRAS codon 12 (n = 14). ctDNA was detected in the pre-operative blood sample in 7/16 pts with tumor mutations (sensitivity 44%) and 0/2 pts without detectable tumor mutations (specificity 100%). Of the 10 pts with available post-operative blood samples, 1 did not have a tumor mutation. 4 pts had detectable ctDNA, 3 of whom have recurred. In contrast, 0 of the 5 pts without detectable post-operative ctDNA have recurred. At median follow-up of 37 weeks, recurrence-free survival (RFS) was significantly longer in pts without detectable ctDNA after surgery (median not reached vs 9 weeks, p = 0.022). Of 11 plasma samples with detectable ctDNA, 3 harbored mutations that were not detected in the primary tumor, including 2 non-KRAS mutations (GNAS R201H and PIK3CA E542K). Conclusions: Pre-operative ctDNA has low sensitivity, suggesting limited utility in PDAC screening. RFS was significantly longer in pts without detectable post-operative ctDNA; however this analysis is limited by small numbers and short follow-up. Discordance in hot spot mutations detected in tumor and matched plasma was observed in 27% of samples, possibly related to intratumoral heterogeneity.

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: Observational · Consensus signal: Observational
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.087
GPT teacher head0.420
Teacher spread0.333 · 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
Published2016
Admission routes1
Has abstractyes

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