Accurate Classification of Diffuse Large B Cell Lymphoma Into Germinal Center and Activated B Cell Subtypes Using a Nuclease Protection Assay On Formalin Fixed Paraffin Embedded Tissue: A Study From the Lymphoma and Leukemia Molecular Profiling Project.
Notice bibliographique
Résumé
Abstract Abstract 620 Classification of DLBCL into cell-of-origin (COO) subtypes based on gene expression profiles has well-established prognostic value. These subtypes, termed Germinal Center B cell (GCB) and Activated B cell (ABC) also have different genetic alterations and over-expression of different pathways that may serve as therapeutic targets. Thus, accurate classification is essential for analysis of clinical trial results and planning new trials using targeted agents. The gold standard for COO classification uses gene expression profiling (GEP) of snap frozen tissues, and a Bayesian predictor algorithm utilizing the expression levels of 14 key genes (G. Wright et al PNAS 2003). An immunohistochemistry (IHC) classification scheme by C. Hans et al (Blood 2004), based on 3 antibodies, is widely used as a substitute for GEP classification, however does not completely correlate with GEP. We recently described a qNPA assay (ArrayPlateR, High ThroughPut Genomics, Tucson, AZ) with excellent correlation between frozen and formalin fixed paraffin embedded (FFPE) tissues (R. Roberts et al, Lab Invest 2007). In this study, we investigated whether this technique could be used for accurate classification of COO using FFPE tissues. We expanded the previous gene probe repertoire of the DLBCL-ArrayPlateR assay to include the 14 genes (represented by 17 probe sets) most pertinent to COO classification. 52 cases of R-CHOP treated DLBCL that had undergone GEP using the Affymetrix U133 Plus 2.0 microarray and had matching FFPE blocks were analyzed with qNPA in duplicate. The genes included CD10, LRMP, CCND2, ITPKB, PIM1, IL16, IRF4, FUT8, BCL6, PTPN1, LM02, CD39, MYBL1, IGHM. Results were evaluated using the previously published algorithm with a leave-one-out cross validation scheme to classify cases into GCB or ABC subtypes. These results were compared to COO classification based on frozen tissue GEP profiles. All 14 genes in all 52 cases were successfully analyzed with no missing data points. For each case, a probability statistic was generated indicating the likelihood that the classification using qNPA was accurate. Of the 54 cases, 25 were GCB, 27 were ABC and 4 were unclassifiable by GEP. Of the GCB cases, 23/25 (92%) were classified correctly by qNPA with a confidence cut-off of >0.9 and 25/25 (100%) classified correctly with a confidence cut-off of >0.8. Of the ABC cases, 25/27 (93%) were correctly classified as ABC using qNPA with a confidence cut-off of >0.9 and 27/27 (100%) classified correctly with a confidence cut-off of >0.8. In summary, the qNPA technique accurately categorized DLBCL into GCB and ABC subtypes, as defined by GEP. There were no technical difficulties with any of the pathological materials although they were collected retrospectively from a variety of institutions and countries with different fixation methods. This approach represents a substantial improvement over previously published IHC methods and is applicable to FFPE tissues, therefore overcoming the need for snap frozen materials. This technically robust classification method has potential to have a significant impact on future DLBCL research and clinical trial development. Disclosures: Rimsza: High Throughput Genomics: HTG provided the assays at no charge to Dr. Rimsza's lab. Schwartz:High Throughput Genomics: Employment. Gascoyne:Roche Canada: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».