Genetics and Prognostication in Splenic Marginal Zone Lymphoma: Revelations from Deep Sequencing

Purpose: Mounting evidence supports the clinical significance of gene mutations and immunogenetic features in common mature B-cell malignancies.

Experimental Design: We undertook a detailed characterization of the genetic background of splenic marginal zone lymphoma (SMZL), using targeted resequencing and explored potential clinical implications in a multinational cohort of 175 patients with SMZL.

Results: We identified recurrent mutations in TP53 (16%), KLF2 (12%), NOTCH2 (10%), TNFAIP3 (7%), MLL2 (11%), MYD88 (7%), and ARID1A (6%), all genes known to be targeted by somatic mutation in SMZL. KLF2 mutations were early, clonal events, enriched in patients with del(7q) and IGHV1-2*04 B-cell receptor immunoglobulins, and were associated with a short median time to first treatment (0.12 vs. 1.11 years; P = 0.01). In multivariate analysis, mutations in NOTCH2 [HR, 2.12; 95% confidence interval (CI), 1.02–4.4; P = 0.044] and 100% germline IGHV gene identity (HR, 2.19; 95% CI, 1.05–4.55; P = 0.036) were independent markers of short time to first treatment, whereas TP53 mutations were an independent marker of short overall survival (HR, 2.36; 95 % CI, 1.08–5.2; P = 0.03).

Conclusions: We identify key associations between gene mutations and clinical outcome, demonstrating for the first time that NOTCH2 and TP53 gene mutations are independent markers of reduced treatment-free and overall survival, respectively.

Proposing the deep dynamic Bayesian network as a future computer based medical system

The development of new learning models has been of great importance throughout recent years, with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.