Unravelling the hidden heterogeneities of diffuse large B-cell lymphoma based on coupled two-way clustering

We applied the proposed method to two publicly available microarray datasets of diffuse large B-cell lymphoma (DLBCL), a notoriously heterogeneous phenotype. A feature subset of 30 genes (38 probes) derived from analysis of the first dataset consisting of 4026 genes and 42 DLBCL samples identified three categories of patients with very different five-year overall survival rates (70.59%, 44.44% and 14.29% respectively; p = 0.0017). Analysis of the second dataset consisting of 7129 genes and 58 DLBCL samples revealed a feature subset of 13 genes (16 probes) that not only replicated the findings of the important DLBCL genes (e.g. JAW1 and BCL7A), but also identified three clinically similar subtypes (with 5-year overall survival rates of 63.13%, 34.92% and 15.38% respectively; p = 0.0009) to those identified in the first dataset. Finally, we built a multivariate Cox proportional-hazards prediction model for each feature subset and defined JAW1 as one of the most significant predictor (p = 0.005 and 0.014; hazard ratios = 0.02 and 0.03, respectively for two datasets) for both DLBCL cohorts under study.Our results showed that the proposed algorithm is a promising computational strategy for peeling off the hidden genetic heterogeneity based on transcriptionally profiling disease samples, which may lead to an improved diagnosis and treatment of cancers.When a patient is diagnosed with cancer, various clinical parameters are used to assess the patient's risk profile. However, the patients with a similar prognosis frequently respond very differently to the same treatment. This may occur because two apparently similar tumours are actually completely different diseases at the molecular level, often called genetic heterogeneity. It describes the biological complexity whereby apparently similar inheritable characters result from different genes or different genetic mechanisms. The presence of such heterogeneity has a significant impact on both the efficiency of modern clinical p