8047 Background: The current clinical risk stratification of Diffuse Large B-cell Lymphoma (DLBCL) relies on the International Prognostic Index (IPI) comprising a limited number of clinical variables but is imperfect in the identification of high-risk disease. Our study aimed to: (1) develop a risk prediction model based on the genetic and clinical features; and (2) evaluate the model’s biological implications in association with the estimated profiles of immune infiltration. Methods: Gene-expression profiling was performed on 718 patients with DLBCL for which RNA sequencing data and clinical covariates were available by Reddy et al (2017). Unsupervised and supervised machine learning methods were used to discover and identify the best set of survival-associated gene signatures for prediction. A multivariate model of survival from these signatures was constructed in the training set and validated in an independent test set. The compositions of the tumor-infiltrating immune cells were enumerated using CIBERSORT for deconvolution analysis. Results: A four gene-signature-based score was developed that separated patients into high- and low-risk groups with a significant difference in survival in the training, validation and complete cohorts (p < 0.001), independently of the IPI. The combination of the gene-expression-based score with the IPI improved the discrimination on the validation and complete sets. The area-under-the-curve at 2 and 5 years increased from 0.71 and 0.69 to 0.75 and 0.74 in the validation set, respectively. Conclusions: By analyzing the gene-expression data with a systematic approach, we developed and validated a risk prediction model that outperforms existing risk assessment methods. Our study, which integrated the profiles of immune infiltration with prognostic prediction, unraveled important associations that have the potential to identify patients who could benefit from the various therapeutic interventions, as well as highlighting possible targets for new drugs.
Predicting neuroendocrine tumor (carcinoid) neoplasia using gene expression profiling and supervised machine learning