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Record W4388680196 · doi:10.1016/j.modpat.2023.100384

Canadian Multicentric Pan-TRK (CANTRK) Immunohistochemistry Harmonization Study

2023· article· en· W4388680196 on OpenAlexafffundabout
Martin Hyrcza, Sebastião N. Martins-Filho, Alan Spatz, Hanjun Wang, Bibianna Purgina, Patrice Desmeules, Paul C. Park, Gilbert Bigras, Sungmi Jung, Jean‐Claude Cutz, Zhaolin Xu, David M. Berman, Brandon S. Sheffield, Carol C. Cheung, Charles Leduc, David Hwang, Diana N. Ionescu, Paul Klonowski, Myriam Chévarie‐Davis, Rose Chami, Bernard Lo, Tracy Stockley, Ming‐Sound Tsao, Emina Torlakovic

Bibliographic record

VenueModern Pathology · 2023
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsUniversity of SaskatchewanRoyal University HospitalSickKids FoundationHospital for Sick ChildrenUniversité de MontréalHôpital Maisonneuve-RosemontWilliam Osler Health SystemUniversity of British ColumbiaBC Cancer AgencySunnybrook Health Science CentreQueen's UniversityUniversity of OttawaDalhousie UniversityMcMaster UniversityInstitut universitaire de cardiologie et de pneumologie de QuébecUniversity of ManitobaUniversity of CalgaryHealth Sciences CentreUniversity of AlbertaUniversity Health NetworkOttawa HospitalMcGill UniversityMcGill University Health CentreUniversity of TorontoSaskatchewan Health AuthorityCentre Hospitalier de l’Université de MontréalUniversité Laval
FundersBayer Canada
KeywordsTrk receptorImmunohistochemistryStainingMedicineImmunostainingMonoclonal antibodyPathologyH&E stainAntibodyBiologyInternal medicineImmunologyNeurotrophin

Abstract

fetched live from OpenAlex

Tumor-agnostic testing for NTRK1-3 gene rearrangements is required to identify patients who may benefit from TRK inhibitor therapies. The overarching objective of this study was to establish a high-quality pan-TRK immunohistochemistry (IHC) screening assay among 18 large regional pathology laboratories across Canada using pan-TRK monoclonal antibody clone EPR17341 in a ring study design. TRK-fusion positive and negative tumor samples were collected from participating sites, with fusion status confirmed by panel next-generation sequencing assays. Each laboratory received: (1) unstained sections from 30 cases of TRK-fusion-positive or -negative tumors, (2) 2 types of reference standards: TRK calibrator slides and IHC critical assay performance controls (iCAPCs), (3) EPR17341 antibody, and (4) suggestions for developing IHC protocols. Participants were asked to optimize the IHC protocol for their instruments and detection systems by using iCAPCs, to stain the 30 study cases, and to report the percentage scores for membranous, cytoplasmic, and nuclear staining. TRK calibrators were used to assess the analytical sensitivity of IHC protocols developed by using the 2 reference standards. Fifteen of 18 laboratories achieved diagnostic sensitivity of 100% against next-generation sequencing. The diagnostic specificity ranged from 40% to 90%. The results did not differ significantly between positive scores based on the presence of any type of staining vs the presence of overall staining in ≥1% of cells. The median limit of detection measured by TRK calibrators was 76,000 molecules/cell (range 38,000 to >200,000 molecules/cell). Three different patterns of staining were observed in 19 TRK-positive cases, cytoplasmic-only in 7 samples, nuclear and cytoplasmic in 9 samples, and cytoplasmic and membranous in 3 samples. The Canadian multicentric pan-TRK study illustrates a successful strategy to accelerate the multicenter harmonization and implementation of pan-TRK immunohistochemical screening that achieves high diagnostic sensitivity by using laboratory-developed tests where laboratories used centrally developed reference materials. The measurement of analytical sensitivity by using TRK calibrators provided additional insights into IHC protocol performance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.023
GPT teacher head0.320
Teacher spread0.297 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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

Citations8
Published2023
Admission routes3
Has abstractyes

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