New MYC IHC Classifier Integrating Quantitative Architecture Parameters to Predict MYC Gene Translocation in Diffuse Large B-Cell Lymphoma
| Autor: | Iyare Izevbaye, Wei-Feng Dong, Sarah Canil, Gilbert Bigras, Didier Morel, Paul E. Swanson, Raymond Lai |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Pathology medicine.medical_specialty Histology minimum spanning tree graph Genes myc Chromosomal translocation MYC In situ hybridization Biology Stain Translocation Genetic Pathology and Forensic Medicine 03 medical and health sciences 0302 clinical medicine image analysis medicine Humans Diagnosis Computer-Assisted Gene Diagnostic Techniques and Procedures Research Articles MYC Gene Rearrangement Burkitt lymphoma medicine.disease Immunohistochemistry Lymphoma Medical Laboratory Technology 030104 developmental biology DLBCL 030220 oncology & carcinogenesis Cancer research Lymphoma Large B-Cell Diffuse Diffuse large B-cell lymphoma Forecasting IHC |
| Zdroj: | Applied Immunohistochemistry & Molecular Morphology |
| ISSN: | 1541-2016 |
| DOI: | 10.1097/pai.0000000000000367 |
| Popis: | A new automated MYC IHC classifier based on bivariate logistic regression is presented. The predictor relies on image analysis developed with the open-source ImageJ platform. From a histologic section immunostained for MYC protein, 2 dimensionless quantitative variables are extracted: (a) relative distance between nuclei positive for MYC IHC based on euclidean minimum spanning tree graph and (b) coefficient of variation of the MYC IHC stain intensity among MYC IHC-positive nuclei. Distance between positive nuclei is suggested to inversely correlate MYC gene rearrangement status, whereas coefficient of variation is suggested to inversely correlate physiological regulation of MYC protein expression. The bivariate classifier was compared with 2 other MYC IHC classifiers (based on percentage of MYC IHC positive nuclei), all tested on 113 lymphomas including mostly diffuse large B-cell lymphomas with known MYC fluorescent in situ hybridization (FISH) status. The bivariate classifier strongly outperformed the “percentage of MYC IHC-positive nuclei” methods to predict MYC+ FISH status with 100% sensitivity (95% confidence interval, 94-100) associated with 80% specificity. The test is rapidly performed and might at a minimum provide primary IHC screening for MYC gene rearrangement status in diffuse large B-cell lymphomas. Furthermore, as this bivariate classifier actually predicts “permanent overexpressed MYC protein status,” it might identify nontranslocation-related chromosomal anomalies missed by FISH. |
| Databáze: | OpenAIRE |
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