MétaCan
Menu
← Back to cohort
Record W2485297274 · doi:10.1158/1538-7445.am2016-846

Abstract 846: Improving T-cell receptor clonotyping of T-cell lymphomas using hybrid-Capture and next-generation sequencing

2016· article· en· W2485297274 on OpenAlexaff
Etienne Mahé, David T. Mulder, Mark Dowar, Mahadeo A. Sukhai, Linh T. Nguyen, Pamela S. Ohashi, Jan Delabie, Tracy Stockley, Trevor J. Pugh, Suzanne Kamel‐Reid

Bibliographic record

VenueCancer Research · 2016
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsToronto General HospitalPrincess Margaret Cancer CentreUniversity Health NetworkCalgary Laboratory Services
Fundersnot available
KeywordsT-cell receptorBiologySanger sequencingT cellMolecular biologyDNA sequencingGene rearrangementComputational biologyGeneticsGene

Abstract

fetched live from OpenAlex

Abstract Background T-cell clonality assays (TCAs) support a variety of clinical and research interests including T-cell malignancy clone identification, minimal-residual disease (MRD) testing and T-cell receptor (TCR) repertoire characterization for the purposes of immunotherapy. Unfortunately, many TCAs are unable to interrogate alpha, beta, gamma, and delta TCR loci simultaneously. Aiming to overcome these limitations, we designed a T-cell clonality assay (the “NTRA”), capable of identifying T-cell gene rearrangements from all four TCR loci, using hybrid-capture followed by deep next-generation sequencing (NGS). A novel informatics package exploiting the Burrows Wheeler Alignment algorithm and finding of CDR3 seed sequences was also deployed. We then validated the assay using orthogonal methods and in a series of clinical T-cell malignancy specimens. Methods We used DNA probes for hybrid-capture of all V and J segments in the TCR loci, followed by NGS on the Illumina NextSeq 500 platform. Analytical validation used a series of 10 specimens, including 6 flow-cytometry characterized T-cell specimens of variable degrees of immunophenotypic uniformity and 4 cell-lines with known TCR rearrangements. PCR/gel electrophoresis and Sanger sequencing were used for orthogonal validation, using primer sets designed to test each specimen for all 90th-centile V-J configurations. Subsequently, 61 clinical T-cell lymphoma specimens were tested, each previously assessed for T-cell clonality using the clinical gold standard (i.e. PCR/ electrophoresis using the BIOMED-2 consensus primers for the TCR-beta (TRB) and TCR-gamma (TRG) loci). Results PCR confirmed the NTRA-identified V-J configurations with an area-under-the-curve (AUC) by receiver-operator characteristic (ROC) analysis of 0.91. Relative to single-strand Sanger sequencing, the NTRA CDR3 sequence results showed a ROC AUC of 0.83. In a DNA dilution series employing the results of a “clonal” specimen (a Jurkat cell line) spiked into a “polyclonal” specimen (a mononuclear peripheral blood specimen), we could identify “clonal” specimen-specific V-J combinations and error-corrected CDR3 sequences down to less than 10⁁-5. In the final clinical validation, the NTRA performed with a ROC AUC of 0.82 relative to the current TRB & TRG BIOMED-2 clinical assay. Conclusions Our novel assay can overcome the current TCR locus data-yield limitations resulting from primer-based TCAs in a single-tube. The NTRA shows comparable sensitivity and specificity relative to current standard assays and excellent performance relative to current MRD techniques when applied to clinical T-cell lymphoma specimens. Citation Format: Etienne Mahe, David Mulder, Mark Dowar, Mahadeo Sukhai, Linh Nguyen, Pamela Ohashi, Jan Delabie, Tracy L. Stockley, Trevor Pugh, Suzanne Kamel-Reid. Improving T-cell receptor clonotyping of T-cell lymphomas using hybrid-Capture and next-generation sequencing. [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 846.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.170
GPT teacher head0.378
Teacher spread0.208 · 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
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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCancer Research→Same topicCAR-T cell therapy research→French-language works237,207→