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Record W2623105223 · doi:10.1002/hon.2437_46

MOLECULAR CLASSIFICATION OF PRIMARY MEDIASTINAL LARGE B CELL LYMPHOMA USING FORMALIN‐FIXED, PARAFFIN‐EMBEDDED TISSUE SPECIMENS – AN LLMPP PROJECT

2017· article· en· W2623105223 on OpenAlexaff
Anja Mottok, George W. Wright, Andreas Rosenwald, German Ott, Colleen Ramsower, Elı́as Campo, Rita M. Braziel, Jan Delabie, Dennis D. Weisenburger, Joo Y. Song, John Chan, James R. Cook, Kai Fu, Timothy C. Greiner, Erlend B. Smeland, Harald Holte, Betty Glinsmann‐Gibson, Randy D. Gascoyne, Louis M. Staudt, Elaine S. Jaffe, Joseph M. Connors, David W. Scott, Christian Steidl, Lisa M. Rimsza

Bibliographic record

VenueHematological Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsUniversity Health NetworkBC Cancer Agency
Fundersnot available
KeywordsLymphomaGene expression profilingMedicineDiffuse large B-cell lymphomaMolecular diagnosticsTissue microarrayCancer researchPathologyComputational biologyGeneGene expressionImmunohistochemistryBioinformaticsBiology

Abstract

fetched live from OpenAlex

Introduction: Primary mediastinal large B cell lymphoma (PMBCL) is recognized as a distinct lymphoma entity in the current World Health Organization classification. However, the diagnosis relies on integration of clinical characteristics and presentation since a reliable distinction from diffuse large B cell lymphoma (DLBCL) solely based on morphological or immunophenotypic features can be difficult. Gene expression profiling studies provided evidence that PMBCL can be distinguished from DLBCL on a molecular level and supported a strong relationship between PMBCL and classical Hodgkin lymphoma. Because these studies were performed using snap-frozen tissue, the molecular classification of PMBCL has not penetrated into clinical practice. We sought to develop a robust and accurate molecular assay for the distinction of PMBCL from DLBCL based on gene expression measurements in routinely available formalin-fixed, paraffin-embedded (FFPE) biopsies. Methods: All cases used in this study were centrally reviewed by a panel of expert hematopathologists in the Lymphoma and Leukemia Molecular Profiling Project consortium. Gene selection was performed using data previously generated on Affymetrix U133 plus 2.0 microarrays and the NanoString platform. The training cohort for the new assay, termed Lymph3Cx, consisted of 68 cases (48 DLBCL and 20 PMBCL); the independent validation cohort comprised 88 PMBCL and 78 DLBCL cases. Cases were required to have a tumor content of ≥60% and nucleic acids were extracted from 10 mm FFPE scrolls. Digital gene expression was performed on 200 ng RNA using NanoString technology. Results: The final Lymph3Cx gene set consisted of 64 probes and included the previously described 20 genes of the Lymph2Cx assay. A probabilistic model accounting for classification error was trained to produce model scores that best distinguished between DLBCL and PMBCL. Cut-points were defined at the 0.1 and 0.9 probability scores. The model, including coefficients and thresholds was then “locked” and applied to the independent validation cohort. The assay yielded gene expression data of sufficient quality in 157/166 cases (94.6%). Among the pathologically-defined PMBCL, 85% were classified as such based on the molecular signature. Ten percent of cases were assigned to the “uncertain” category and 5% showed a molecular signature of DLBCL. Among the pathologically-defined DLBCL cases, 83% were classified as DLBCL by the assay, 14% were “uncertain” and 3% were predicted to be PMBCL. Conclusion: The newly developed and validated Lymph3Cx assay distinguishes PMBCL and DLBCL based on gene expression signatures and shows high concordance with the pathological classification of an expert hematopathologist panel. Future studies will be needed to determine Lymph3Cx's utility for routine diagnostic purposes and therapeutic decision making. Keywords: gene expression profile (GEP); primary mediastinal large B-cell lymphoma (PMLBCL).

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

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

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 head0.094
GPT teacher head0.388
Teacher spread0.294 · 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.

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".

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Citations1
Published2017
Admission routes1
Has abstractyes

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