RESUMO
(A) Correlation matrix of unsupervised co-regulated genes, based on the 208 genes included in the NanoString platform. Some of the clusters of co-regulated genes corresponded to the following: Inflammatory cells; Epstein-Barr virus; B-cells; Cytotoxic T-cells; T-cells; and Proliferation. (B) Analysis of genomic alterations by targeted sequencing. Distribution of mutations in the 62 analyzed genes. Rows correspond to sequenced genes, columns represent individual patients. Color coding: green, missense; blue, synonymous; pink, frameshift; violet, Indel; red, stop gained; yellow, UTR.
Assuntos
Infecções por Vírus Epstein-Barr , Linfoma Extranodal de Células T-NK , Humanos , Herpesvirus Humano 4/genética , Infecções por Vírus Epstein-Barr/complicações , Infecções por Vírus Epstein-Barr/genética , Infecções por Vírus Epstein-Barr/patologia , Linfoma Extranodal de Células T-NK/terapia , Mutação , Células Matadoras Naturais/patologiaRESUMO
Diffuse large B-cell lymphoma (DLBCL), the most frequent non-Hodgkin's lymphoma subtype, is characterized by strong biological, morphological, and clinical heterogeneity, but patients are treated with immunochemotherapy in a relatively homogeneous way. Here, we have used a customized NanoString platform to analyze a series of 197 homogeneously treated DLBCL cases. The platform includes the most relevant genes or signatures known to be useful for predicting response to R-CHOP (Rituximab, Cyclophosphamide, Doxorubicin, Vincristine, and Prednisone) in DLBCL cases. We generated a risk score that combines the International Prognostic Index with cell of origin and double expression of MYC/BCL2, and stratified the series into three groups, yielding hazard ratios from 0.15 to 5.49 for overall survival, and from 0.17 to 5.04 for progression-free survival. Group differences were highly significant (p < 0.0001), and the scoring system was applicable to younger patients (<60 years of age) and patients with advanced or localized stages of the disease. Results were validated in an independent dataset from 166 DLBCL patients treated in two distinct clinical trials. This risk score combines clinical and biological data in a model that can be used to integrate biological variables into the prognostic models for DLBCL cases.