Improved personalized survival prediction of patients with diffuse large B-cell Lymphoma using gene expression profiling

BACKGROUND: Thirty to forty percent of patients with Diffuse Large B-cell Lymphoma (DLBCL) have an adverse clinical evolution. The increased understanding of DLBCL biology has shed light on the clinical evolution of this pathology, leading to the discovery of prognostic factors based on gene expression data, genomic rearrangements and mutational subgroups. Nevertheless, additional efforts are needed in order to enable survival predictions at the patient level. In this study we investigated new machine learning-based models of survival using transcriptomic and clinical data. METHODS: Gene expression profiling (GEP) of in 2 different publicly available retrospective DLBCL cohorts were analyzed. Cox regression and unsupervised clustering were performed in order to identify probes associated with overall survival on the largest cohort. Random forests were created to model survival using combinations of GEP data, COO classification and clinical information. Cross-validation was used to compare model results in the training set, and Harrel's concordance index (c-index) was used to assess model's predictability. Results were validated in an independent test set. RESULTS: Two hundred thirty-three and sixty-four patients were included in the training and test set, respectively. Initially we derived and validated a 4-gene expression clusterization that was independently associated with lower survival in 20% of patients. This pattern included the following genes: TNFRSF9, BIRC3, BCL2L1 and G3BP2. Thereafter, we applied machine-learning models to predict survival. A set of 102 genes was highly predictive of disease outcome, outperforming available clinical information and COO classification. The final best model integrated clinical information, COO classification, 4-gene-based clusterization and the expression levels of 50 individual genes (training set c-index, 0.8404, test set c-index, 0.7942). CONCLUSION: Our results indicate that DLBCL survival models based on the application of machine learning algorithms to gene expression and clinical data can largely outperform other important prognostic variables such as disease stage and COO. Head-to-head comparisons with other risk stratification models are needed to compare its usefulness

Bendamustine as part of conditioning of autologous stem cell transplantation in patients with aggressive lymphoma: a phase 2 study from the GELTAMO group

We conducted a phase 2 trial to evaluate the safety and efficacy of bendamustine instead of BCNU (carmustine) in the BEAM (BCNU, etoposide, cytarabine and melphalan) regimen (BendaEAM) as conditioning for autologous stem-cell transplantation (ASCT) in patients with aggressive lymphomas. The primary endpoint was 3-year progression-free survival (PFS). Sixty patients (median age 55 [28-71] years) were included. All patients (except one who died early) engrafted after a median of 11 (9-72) and 14 (4-53) days to achieve neutrophil and platelet counts of >0.5 x 10(9) /l and >20 x 10(9) /l, respectively. Non-relapse mortality at 100 days and 1 year were 3.3% and 6.7%, respectively. With a median follow-up of 67 (40-77) months, the estimated 3-year PFS and overall survival (OS) were 58% and 75%, respectively. Patients in partial response at study entry had significantly worse PFS and OS than patients who underwent ASCT in complete metabolic remission, and this was the only prognostic factor associated with both PFS (Relative risk [RR], 0.27 [95% confidence interval {CI} [0.12-0.56]) and OS (RR, 0.40 [95% CI 0.17-0.97]) in the multivariate analysis. BendaEAM conditioning is therefore a feasible and effective regimen in patients with aggressive lymphomas. However, patients not in complete metabolic remission at the time of transplant had poorer survival and so should be considered for alternative treatment strategies