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Record W2739996467 · doi:10.1158/1538-7445.am2017-2746

Abstract 2746: Detection of tumor-specific mutations in plasma DNA: A potential esophageal adenocarcinoma biomarker

2017· article· en· W2739996467 on OpenAlexaff
Matthew Egyud, Jennifer Jackson, Emiko Yamada, Anders Ståhlberg, Paul M. Krzyzanowski, Virginia R. Litle, Lincoln Stein, Tony E. Godfrey

Bibliographic record

VenueCancer Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsOntario Institute for Cancer Research
Fundersnot available
KeywordsLiquid biopsyCOLD-PCRBiomarkerPolymerase chain reactionDigital polymerase chain reactionBiologyMultiplexMutationPoint mutationCancer researchCancerGeneGenetics

Abstract

fetched live from OpenAlex

Abstract BACKGROUND: Recent studies have shown that tumor-specific DNA from multiple types of tumors can be detected circulating in plasma and this has raised the possibility of “liquid biopsies” using mutated tumor DNA as a potential diagnostic and prognostic biomarker. Detection of mutations with allele frequencies below 0.1% remains challenging however given that circulating cell-free DNA is highly degraded and in low abundance. Detection of multiple different mutations in the same sample presents an additional challenge particularly when the mutation panel may change from patient to patient. We have developed a novel approach, called SimSen-Seq, to introduce molecular barcodes into sequencing libraries with DNA inputs as low as 5ng. Barcodes enable differentiation of true mutants from background noise introduced by Taq polymerase errors and permits detection of variant alleles with frequencies below 0.1%. The barcodes are protected from mis-priming using a hairpin structure which permits a high degree of multiplexing and flexibility for detection of multiple mutations from one plasma sample. We are using this technology to test the utility of liquid biopsy as a biomarker for esophageal adenocarcinoma (EAC) diagnosis and disease monitoring. METHODS: Blood samples were obtained at a single time point from patients with various stages of EAC and longitudinal blood samples were also collected from patients undergoing neoadjuvant therapy followed by surgery. Tumor samples were obtained from biopsy or resection specimens. Tumor DNA was sequenced using a targeted EAC panel to identify mutations in each tumor. SimSen-Seq assays were designed to identify these mutations in plasma, and hairpin barcodes were attached. Sequencing libraries were generated from circulating DNA, sequenced and analyzed using the barcodes to reduce background noise. RESULTS: Mutations were identified in tumor samples from 37 patients. To date, 29 patients have had plasma analyzed; 5 stage I, 6 stage II, 13 stage III, and 5 stage IV. Of these 29, the same mutations have been identified in 15 plasma DNA samples (20% stage I, 50% stage II, 54% stage III, 80% stage IV. Six patients demonstrated multiple mutant alleles in plasma DNA. One patient with detectable pre-treatment ctDNA underwent serial blood draws during their treatment course, and post-operative detection of tumor-specific markers preceded physical evidence of disease recurrence. DISCUSSION: SimSen-Seq shows promise as a novel ultra-sensitive, highly multiplexed sequencing method for identifying rare circulating mutations. Possible applications include prognostication in early stage patients and rapid monitoring of therapeutic response and recurrence. Further work is to evaluate this is ongoing. Citation Format: Matthew Egyud, Jennifer Jackson, Emiko Yamada, Anders Ståhlberg, Paul Krzyzanowski, Virginia Litle, Lincoln Stein, Tony Godfrey. Detection of tumor-specific mutations in plasma DNA: A potential esophageal adenocarcinoma biomarker [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2746. doi:10.1158/1538-7445.AM2017-2746

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.047
GPT teacher head0.350
Teacher spread0.303 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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