RESUMEN
Background: The histopathological classification of T-cell lymphoma (TCL) in humans has distinctive mutational genotyping that suggests different lymphomagenesis. A similar concept is assumed to be observed in dogs with different TCL phenotypes. Objective: This study aimed to identify the previously reported single-nucleotide polymorphisms (SNPs) in both human beings and dogs in canine TCLs and null-cell lymphomas (NCLs) and to design compatible oligonucleotides from each variant based on the multiplex polymerase chain reaction. Methods: Genomic DNA was extracted from 68 tumor specimens (62 TCLs and 6 NCLs) and 5 buffy coat samples from dogs with TCL. Four TCL subtypes and NCL were analyzed in 44 SNPs from 21 genes using the MassARRAY. Results: The greatest incidences of SNPs observed in all TCL subtypes and NCL ware SATB1 c.1259A > C, KIT c.1275A > G, SEL1L c.2040 + 200C > G, and TP53 c.1024C > T, respectively. Some SNP locations were statistically significant associated with NCL, including MYC p.S75F (p = 0.0003), TP53 p.I149N (p = 0.030), PDCD1 p.F37LX (p = 0.012), and POT1 p.R583* (p = 0.012). Conclusion: Each TCL histological subtype and NCL are likely to contain distinctive mutational genetic profiles, which might play a role in lymphoma gene-risk factors and might be useful for selecting therapeutic target drugs for each canine patient.
RESUMEN
BACKGROUND: The diagnosis of T-cell lymphomas is typically established through a multiparameter approach that combines clinical, morphologic, immunophenotypic, and genetic features, utilizing a variety of histopathologic and molecular techniques. However, accurate diagnosis of such lymphomas and distinguishing them from reactive lymph nodes remains challenging due to their low prevalence and heterogeneous features, hence limiting the confidence of pathologists. We investigated the use of microRNA (miRNA) expression signatures as an adjunctive tool in the diagnosis and classification of T-cell lymphomas that involve lymph nodes. This study seeks to distinguish reactive lymph nodes (RLN) from two types of frequently occurring nodal T-cell lymphomas: nodal T-follicular helper (TFH) cell lymphomas (nTFHL) and peripheral T-cell lymphomas, not otherwise specified (nPTCL). METHODS: From the formalin-fixed paraffin-embedded (FFPE) samples from a cohort of 88 subjects, 246 miRNAs were quantified and analyzed by differential expression. Two-class logistic regression and random forest plot models were built to distinguish RLN from the nodal T-cell lymphomas. Gene set enrichment analysis was performed on the target genes of the miRNA to identify pathways and transcription factors that may be regulated by the differentially expressed miRNAs in each subtype. RESULTS: Using logistic regression analysis, we identified miRNA signatures that can distinguish RLN from nodal T-cell lymphomas (AUC of 0.92 ± 0.05), from nTFHL (AUC of 0.94 ± 0.05) and from nPTCL (AUC of 0.94 ± 0.08). Random forest plot modelling was also capable of distinguishing between RLN and nodal T-cell lymphomas, but performed worse than logistic regression. However, the miRNA signatures are not able to discriminate between nTFHL and nPTCL, owing to large similarity in miRNA expression patterns. Bioinformatic analysis of the gene targets of unique miRNA expression revealed the enrichment of both known and potentially understudied signalling pathways and genes in such lymphomas. CONCLUSION: This study suggests that miRNA biomarkers may serve as a promising, cost-effective tool to aid the diagnosis of nodal T-cell lymphomas, which can be challenging. Bioinformatic analysis of differentially expressed miRNAs revealed both relevant or understudied signalling pathways that may contribute to the progression and development of each T-cell lymphoma entity. This may help us gain further insight into the biology of T-cell lymphomagenesis.