Expression of the follicular lymphoma variant translocation 1 gene in diffuse large B-cell lymphoma correlates with subtype and clinical outcome

Sphingolipids and Lymphomas: A Double-Edged Sword

Lymphomas are a highly heterogeneous group of hematological neoplasms. Given their ethiopathogenic complexity, their classification and management can become difficult tasks; therefore, new approaches are continuously being sought. Metabolic reprogramming at the lipid level is a hot topic in cancer research, and sphingolipidomics has gained particular focus in this area due to the bioactive nature of molecules such as sphingoid bases, sphingosine-1-phosphate, ceramides, sphingomyelin, cerebrosides, globosides, and gangliosides. Sphingolipid metabolism has become especially exciting because they are involved in virtually every cellular process through an extremely intricate metabolic web; in fact, no two sphingolipids share the same fate. Unsurprisingly, a disruption at this level is a recurrent mechanism in lymphomagenesis, dissemination, and chemoresistance, which means potential biomarkers and therapeutical targets might be hiding within these pathways. Many comprehensive reviews describing their role in cancer exist, but because most research has been conducted in solid malignancies, evidence in lymphomagenesis is somewhat limited. In this review, we summarize key aspects of sphingolipid biochemistry and discuss their known impact in cancer biology, with a particular focus on lymphomas and possible therapeutical strategies against them.

Artificial neural network inference (ANNI): a study on gene-gene interaction for biomarkers in childhood sarcomas

Objective

To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI).

Method

To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model.

Results

Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing’s sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen.

Conclusions

The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas.

Early lesions of follicular lymphoma: a genetic perspective

The pathogenesis of follicular lymphoma is a multi-hit process progressing over many years through the accumulation of numerous genetic alterations. Besides the hallmark t(14;18), it is still unclear which other oncogenic hits contribute to the early steps of transformation and in which precursor stages these occur. To address this issue, we performed high-resolution comparative genomic hybridization microarrays on laser-capture micro-dissected cases of follicular lymphoma in situ (n=4), partial involvement by follicular lymphoma (n=4), and duodenal follicular lymphoma (n=4), assumed to represent, potentially, the earliest stages in the evolution of follicular lymphoma. Cases of reactive follicular hyperplasia (n=2), uninvolved areas from follicular lymphoma in situ lymph nodes, follicular lymphoma grade 1-2 (n=5) and follicular lymphoma grade 3A (n=5) were used as controls. Surprisingly, alterations involving several relevant (onco)genes were found in all entities, but at significantly lower proportions than in overt follicular lymphoma. While the number of alterations clearly assigns all these entities as precursors, the pattern of partial involvement by follicular lymphoma alterations was quantitatively and qualitatively closer to that of follicular lymphoma, indicating significant selective pressure in line with its faster rate of progression. Among the most notable alterations, we observed and validated deletions of 1p36 and gains of the 7p and 12q chromosomes and related oncogenes, which include some of the most recurrent oncogenic alterations in overt follicular lymphoma (TNFRSF14, EZH2, MLL2). By further delineating distinctive and hierarchical molecular and genetic features of early follicular lymphoma entities, our analysis underlines the importance of applying appropriate criteria for the differential diagnosis. It also provides a first set of candidates likely to be involved in the cascade of hits that pave the path of the various progression phases to follicular lymphoma development.

Sphingolipid signaling and hematopoietic malignancies: to the rheostat and beyond

Sphingosine-1-phosphate (S1P) is a bioactive lipid with diverse functions including the promotion of cell survival, proliferation and migration, as well as the regulation of angiogenesis, inflammation, immunity, vascular permeability and nuclear mechanisms that control gene transcription. S1P is derived from metabolism of ceramide, which itself has diverse and generally growth-inhibitory effects through its impact on downstream targets involved in regulation of apoptosis, senescence and cell cycle progression. Regulation of ceramide, S1P and the biochemical steps that modulate the balance and interconversion of these two lipids are major determinants of cell fate, a concept referred to as the "sphingolipid rheostat." There is abundant evidence that the sphingolipid rheostat plays a role in the origination, progression and drug resistance patterns of hematopoietic malignancies. The pathway has also been exploited to circumvent the problem of chemotherapy resistance in leukemia and lymphoma. Given the broad effects of sphingolipids, targeting multiple steps in the metabolic pathway may provide possible therapeutic avenues. However, new observations have revealed that sphingolipid signaling effects are more complex than previously recognized, requiring a revision of the sphingolipid rheostat model. Here, we summarize recent insights regarding the sphingolipid metabolic pathway and its role in hematopoietic malignancies.

The cross-validated AUC for MCP-logistic regression with high-dimensional data

We propose a cross-validated area under the receiving operator characteristic (ROC) curve (CV-AUC) criterion for tuning parameter selection for penalized methods in sparse, high-dimensional logistic regression models. We use this criterion in combination with the minimax concave penalty (MCP) method for variable selection. The CV-AUC criterion is specifically designed for optimizing the classification performance for binary outcome data. To implement the proposed approach, we derive an efficient coordinate descent algorithm to compute the MCP-logistic regression solution surface. Simulation studies are conducted to evaluate the finite sample performance of the proposed method and its comparison with the existing methods including the Akaike information criterion (AIC), Bayesian information criterion (BIC) or Extended BIC (EBIC). The model selected based on the CV-AUC criterion tends to have a larger predictive AUC and smaller classification error than those with tuning parameters selected using the AIC, BIC or EBIC. We illustrate the application of the MCP-logistic regression with the CV-AUC criterion on three microarray datasets from the studies that attempt to identify genes related to cancers. Our simulation studies and data examples demonstrate that the CV-AUC is an attractive method for tuning parameter selection for penalized methods in high-dimensional logistic regression models.