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Record W2501435685 · doi:10.1158/1538-7445.am2016-3141

Abstract 3141: Pro-Seq: A novel method to improve sequencing accuracy for liquid biopsy of ctDNA from healthy individuals and cancer patients

2016· article· en· W2501435685 on OpenAlexaff
Andre Marziali

Bibliographic record

VenueCancer Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDNA sequencingDNA sequencerComputational biologyIllumina dye sequencingDeep sequencingDNAMassive parallel sequencingPolymerase chain reactionSingle cell sequencingSequence analysisSequencing by hybridizationSequence (biology)BiologyComputer scienceGeneticsGeneExome sequencingGenomeMutation

Abstract

fetched live from OpenAlex

Abstract A significant barrier to widespread clinical deployment of sensitive circulating tumor DNA (ctDNA) assays (liquid biopsy) is the high assay cost compared to potential reimbursement. Assay cost is currently dominated by the large amount of DNA sequencing required to achieve coverage of a broad gene panel, and by the high read depth required for high clinical sensitivity. Attaching unique molecular barcodes to ctDNA fragments for the purpose of error reduction further increases sequencing requirements, making liquid biopsy commercialization in many clinical applications impractical. We present a novel library construction process for NGS sequencing that increases the accuracy of the combined library construction and sequencing process by an order of magnitude. Named Proximity-Sequencing (Pro-Seq), the method duplicates the sequence information in each original DNA strand prior to the bulk of library construction in such a way as to provide redundant, linked templates to the sequencer. The redundant templates remain linked through the library construction process, allowing detection of PCR errors as sequence disagreement between the two strands. The linked templates are amplified in a single sequencing reaction, such that base quality and incorporation information can be used to determine which bases of the sequence were corrupted during PCR amplification steps. Since both strands are amplified as part of the same sequencing read, sequencing accuracy is improved without requiring use of additional reads on the sequencer. A key element of this process is a novel linked-linear amplification in which DNA primers linked by a short molecule amplify a single strand in the same sense, ensuring twin copies in the same sense that remain physically linked. The method is entirely based on novel reagents and can be implemented without additional instrumentation beyond standard NGS equipment. The method is expected to have significant utility in any applications that require detection of rare sequence variants, including analysis of cell free DNA for liquid biopsy applications. We demonstrate the ability to achieve sequence accuracy similar to barcoded sequencing methods, without the additional sequencing burden required by such methods. We present the method using an Illumina platform, and present data from sequencing of bacterial and human cell free DNA that demonstrate error rates that are improved by an order of magnitude over the current state of the art NGS chemistries. The addition of Pro-Seq library construction to NGS assays enables lower cost, high sensitivity, and high specificity liquid biopsy tests to be developed, enabling commercialization in applications with limited reimbursement, potentially including early cancer detection. Citation Format: Andre Marziali. Pro-Seq: A novel method to improve sequencing accuracy for liquid biopsy of ctDNA from healthy individuals and cancer patients. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3141.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.082
GPT teacher head0.432
Teacher spread0.351 · 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
GenreMethods

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
Published2016
Admission routes1
Has abstractyes

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