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Accurate Diagnosis of Aggressive B Cell Non-Hodgkin Lymphomas Using Gene Expression Profiling of Formalin-Fixed, Paraffin-Embedded Tissues

2014· article· en· W2979296208 on OpenAlexaffabout
David W. Scott, George W. Wright, Mickey Williams, Jason Lih, Elaine S. Jaffe, Andreas Rosenwald, Elı́as Campo, Wing C. Chan, Joseph M. Connors, Erlend B. Smeland, Rita M. Braziel, German Ott, Jan Delabie, Dennis D. Weisenburger, James R. Cook, Timothy C. Greiner, Kai Fu, William D. Walsh, Randy D. Gascoyne, Louis M. Staudt, Lisa M. Rimsza

Bibliographic record

VenueBlood · 2014
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsSpinal Cord Injury BCUniversity of British ColumbiaBC Cancer Agency
Fundersnot available
KeywordsGene expression profilingLymphomaPathologyGene expressionBiologyMedicineGeneCancer researchGenetics

Abstract

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Abstract Background: Currently, diagnosis of aggressive B cell non-Hodgkin lymphomas (agg-B-NHL) uses a varying combination of morphology, immunophenotyping, cytogenetics, and/or other molecular techniques resulting in a fragmented, confusing diagnostic system. We sought to develop a multi-analyte gene expression signature assay that could consolidate the diagnostic process into a single platform to improve standardization and accuracy. Methods: We used formalin-fixed, paraffin-embedded tissue biopsies, qualified by an expert Hematopathology review panel, tumor content of ≥60%, and confirmed B cell immunophenotype. Diagnostic categories included diffuse large B cell lymphoma (DLBCL) including the activated B cell-like (ABC), germinal center B cell-like (GCB) subtypes, unclassifiable (UNC) DLBCL, primary mediastinal B cell lymphoma (PMBCL), Burkitt lymphoma (BL), and mantle cell lymphoma (MCL). Using our previous GEP data, diagnostic signatures, nCounter system (Nanostring, Seattle, WA), and employing published procedures (Scott et al, Blood 2014); we designed probes to 800 genes with utility in distinguishing between these pathological entities. The training cohort comprised 107 unique cases, whose FFPET biopsies were independently assayed at the Molecular Characterization Laboratory, Frederick National Laboratory for Cancer Research (Frederick, MD) and the Centre for Lymphoid Cancer, BC Cancer Agency (Vancouver, BC). The resulting algorithm was locked down and applied to an independent cohort of 199 cases. The nucleic acids from FFPET biopsies from these cases were extracted and run across the two independent laboratories with 83 cases run at both laboratories to assess inter-laboratory performance. The “gold standard” by which the Nanostring classification was compared was based on Affymetrix gene expression profiling of matched frozen biopsies in the cases of ABC, GCB, and UNC DLBCL (Lenz et al. NEJM 2008) and on the pathological diagnosis by the Hematopathology review panel in the cases of BL, MCL, and PMBCL. The use of human tissues and clinical data for this study was approved by the University of Arizona Institutional Review Board in accordance with the Declaration of Helsinki. Results: The final locked algorithm consisted of 297 gene probes including 47 housekeeping genes. Thirty-six cases from the training cohort were run again on the new lot of Nanostring code set to allow for cross code set calibration of the assay. The laboratory procedure and algorithm, together termed the "Lymph5Cx" test, consists of a hierarchical series of pair-wise comparisons. In the independent validation set, 257/282 assays (91.1%) yielded gene expression data of sufficient quality (total of 185 of the 199 cases). A classification summary is given in the Table below. In this cohort, 136 cases (82%) were correctly assigned while 11 cases (6.6%) were assigned incorrect diagnoses as follows: 6 BL assigned to GCB, 1 GCB labeled a PMBCL, and 4 PMBCL assigned to DLBCL subtypes. The Lymph5Cx test included categories of indeterminate results between two diagnostic entities and were declared borderline, as indicated in the Table. The agreement between the 2 laboratory sites was 71/72 (99%) of cases that yielded adequate gene expression data at both sites. Conclusions: The Lymph5Cx test was robust and able to discriminate the often clinically difficult diagnostic categories of agg-B-NHL using a single methodology for cases with histologic and immunophenotypic features of an agg-B-NHL. Misclassification errors were low, suggesting that this test would be useful adjunct to current diagnostic methods. In addition, targetable pathways, as well as genes associated with known prognostic signatures in DLBCL (stromal) and MCL (proliferation) were quantified. Investigation into these latter aspects is on-going. Gene expression signature assays have become a useful clinical and research tool in the on-going area of precision therapeutics based on highly-defined molecular entities. Table # cases % accurate % borderline % error ABC 26 76.9% 23.1% 0.0% GCB 27 88.9% 7.4% 3.7% BL 48 68.8% 19.8% 11.5% PMBL 30 80.0% 6.7% 13.3% MCL 34 100.0% 0.0% 0.0% Disclosures Scott: Nanostring: The author is a potential inventor on a patent applicaiton using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties. Wright:Nanostring: The author is a potential inventor on a patent applicaiton using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties. Williams:Nanostring: The author is a potential inventor on a patent applicaiton using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties. Lih:Nanostring: The author is a potential inventor on a patent applicaiton using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties. Jaffe:Nanostring: The author is a potential inventor on a patent applicaiton using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties. Rosenwald:Nanostring: Research Funding, The author is a potential inventor on a patent applicaiton using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties. Campo:Nanostring: Research Funding, The author is a potential inventor on a patent applicaiton using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties. Chan:Nanostring: The author is a potential inventor on a patent applicaiton using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties. Connors:Nanostring: The author is a potential inventor on a patent applicaiton using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties. Smeland:Nanostring: The author is a potential inventor on a patent applicaiton using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties. Braziel:Nanostring: Research Funding, The author is a potential inventor on a patent applicaiton using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties. Ott:Nanostring: The author is a potential inventor on a patent applicaiton using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties. Delabie:Nanostring: Research Funding, The author is a potential inventor on a patent applicaiton using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties. Weisenburger:Nanostring: The author is a potential inventor on a patent applicaiton using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties. Cook:Nanostring: Research Funding, The author is a potential inventor on a patent applicaiton using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties. Greiner:Nanostring: The author is a potential inventor on a patent applicaiton using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties. Fu:Nanostring: Research Funding, The author is a potential inventor on a patent applicaiton using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties. Walsh:Nanostring: The author is a potential inventor on a patent application using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties. Gascoyne:Nanostring: Research Funding, The author is a potential inventor on a patent applicaiton using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties. Staudt:Nanostring: The author is a potential inventor on a patent applicaiton using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties. Rimsza:Nanostring: Research Funding, The author is a potential inventor on a patent applicaiton using Nanostring technology for a different assay, which has been licensed from the NIH by Nanostring Patents & Royalties.

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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.277
Teacher spread0.259 · 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".

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Citations2
Published2014
Admission routes2
Has abstractyes

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