LT and SOX9 expression are associated with gene sets that distinguish Merkel cell polyomavirus (MCPyV)-positive and MCPyV-negative Merkel cell carcinoma.

BACKGROUND: Merkel cell carcinoma (MCC) is an aggressive malignant neuroendocrine tumour. There are two subsets of MCC, one related to Merkel cell polyomavirus (MCPyV) and the other to ultraviolet radiation (UVR). MCPyV-positive and MCPyV-negative MCCs have been considered to be different tumours, as the former harbour few DNA mutations and are not related to UVR, and the latter usually arise in sun-exposed areas and may be found in conjunction with other keratinocytic tumours, mostly squamous cell carcinomas. Two viral oncoproteins, large T antigen (LT; coded by MCPyV_gp3) and small T antigen (sT; coded by MCPyV_gp4), promote different carcinogenic pathways. OBJECTIVES: To determine which genes are differentially expressed in MCPyV-positive and MCPyV-negative MCC; to describe the mutational burden and the most frequently mutated genes in both MCC subtypes; and to identify the clinical and molecular factors that may be related to patient survival. METHODS: Ninety-two patients with a diagnosis of MCC were identified from the medical databases of participating centres. To study gene expression, a customized panel of 172 genes was developed. Gene expression profiling was performed with nCounter technology. For mutational studies, a customized panel of 26 genes was designed. Somatic single nucleotide variants (SNVs) were identified following the GATK Best Practices workflow for somatic mutations. RESULTS: The expression of LT enabled the series to be divided into two groups (LT positive, n = 55; LT negative, n = 37). Genes differentially expressed in LT-negative patients were related to epithelial differentiation, especially SOX9, or proliferation and the cell cycle (MYC, CDK6), among others. Congruently, LT displayed lower expression in SOX9-positive patients, and differentially expressed genes in SOX9-positive patients were related to epithelial/squamous differentiation. In LT-positive patients, the mean SNV frequency was 4.3; in LT-negative patients it was 10 (P = 0.03). On multivariate survival analysis, the expression of SNAI1 [hazard ratio (HR) 1.046, 95% confidence interval (CI) 1.007-1.086; P = 0.02] and CDK6 (HR 1.049, 95% CI 1.020-1.080; P = 0.001) were identified as risk factors. CONCLUSIONS: Tumours with weak LT expression tend to co-express genes related to squamous differentiation and the cell cycle, and to have a higher mutational burden. These findings are congruent with those of earlier studies.

Merkel cell carcinoma (MCC) is an aggressive form of skin tumour. There are two subtypes of MCC: one of them is related to a virus called Merkel cell polyomavirus (MCPyV); the other one is related to persistent exposure to sunlight. The aim of this research was to find differences between these subtypes in their molecular behaviour (the genes that are expressed and the mutations that may be found). To do this, we carried out two studies, one to investigate gene expression (the process cells use to convert the instructions in our DNA into a functional product such as a protein) and one to look at gene mutations (changes in the DNA sequence). We found that the tumours that were not related to MCPyV expressed genes related to epithelial differentiation (the process by which unspecialized cells gain features characteristics of epithelial cells, which, among other things, make up the outer surface of the body), which means that the origin of both MCC subtypes may be different. We also found that MCPyV-related tumours had fewer mutations. Our findings are important because they help us to understand the biology of the MCC subtypes and could help with the development of new treatments for people diagnosed with skin tumours.

An integrated prognostic model for diffuse large B-cell lymphoma treated with immunochemotherapy.

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.

Peripheral T-cell lymphoma: molecular profiling recognizes subclasses and identifies prognostic markers.

Peripheral T-cell lymphoma (PTCL) is a clinically aggressive disease, with a poor response to therapy and a low overall survival rate of approximately 30% after 5 years. We have analyzed a series of 105 cases with a diagnosis of PTCL using a customized NanoString platform (NanoString Technologies, Seattle, WA) that includes 208 genes associated with T-cell differentiation, oncogenes and tumor suppressor genes, deregulated pathways, and stromal cell subpopulations. A comparative analysis of the various histological types of PTCL (angioimmunoblastic T-cell lymphoma [AITL]; PTCL with T follicular helper [TFH] phenotype; PTCL not otherwise specified [NOS]) showed that specific sets of genes were associated with each of the diagnoses. These included TFH markers, cytotoxic markers, and genes whose expression was a surrogate for specific cellular subpopulations, including follicular dendritic cells, mast cells, and genes belonging to precise survival (NF-κB) and other pathways. Furthermore, the mutational profile was analyzed using a custom panel that targeted 62 genes in 76 cases distributed in AITL, PTCL-TFH, and PTCL-NOS. The main differences among the 3 nodal PTCL classes involved the RHOAG17V mutations (P < .0001), which were approximately twice as frequent in AITL (34.09%) as in PTCL-TFH (16.66%) cases but were not detected in PTCL-NOS. A multivariate analysis identified gene sets that allowed the series of cases to be stratified into different risk groups. This study supports and validates the current division of PTCL into these 3 categories, identifies sets of markers that can be used for a more precise diagnosis, and recognizes the expression of B-cell genes as an IPI-independent prognostic factor for AITL.

Subcutaneous panniculitis-like T-cell lymphoma, lupus erythematosus profundus, and overlapping cases: molecular characterization through the study of 208 genes.

Subcutaneous panniculitis-like T-cell lymphoma (SPTCL) is a rare cytotoxic cutaneous lymphoma. Differential diagnosis with lupus erythematosus panniculitis (LEP) can be challenging and overlapping cases have been described. In this study, we investigate whether gene expression profiling may or not identify markers that can be used to improve our understanding of the disease and to make a precise differential diagnosis. SPTCL, LEP, and overlapping cases were analyzed using a customized NanoString platform including 208 genes related to T-cell differentiation, stromal signatures, oncogenes, and tumor suppressor genes. Gene expression unsupervised analysis of the samples differentiated SPTCL from LEP samples. Most overlapping cases were clustered with LEP cases. Differentially expressed genes were observed when comparing SPTCL with LEP cases; and overlapping with LEP cases. Gene set enrichment analysis recognized gene sets defining each group. In conclusion, SPTCL and LEP have distinctive molecular profiles and the molecular background of overlapping cases more closely resembles LEP.