Multilayer Model for Personalized Risk Stratification of Follicular Lymphoma Patients
Multilayer Model for Personalized Risk Stratification of Follicular Lymphoma Patients
The study aims at developping and validating an integrated clinico-molecular model for an accurate identification of FL patients who are progression free and progressed, respectively, at 24 months after treatment.
Already existing and coded tumor biological material and health-related personal data will be retrospectively collected. FL diagnosis will be confirmed by central pathology review. Tumor somatic mutations, immunoglobulin gene rearrangement and mutation status will be analyzed by targeted deep next generation sequencing of tumor genomic DNA. Gene expression profiling will be performed by targeted RNA-Seq of biopsy-derived RNA.
An immunohistochemistry panel assessing both tumor phenotype and microenvironment cellular composition will be assessed by Tissue macroarray. FISH will be performed to characterize the most recurrent follicular lymphoma chromosomal translocations.
The adjusted association between exposure variables and progression free survival will be estimated by Cox regression. This approach will provide the covariates independently associated with progression free survival that will be utilized in the development of a hierarchical molecular model to predict progression free survival at 24 months. The hierarchical order of relevance in predicting 24 months progression free survival among covariates will be established by recursive partitioning analysis. Overall, this approach will allow the development of a multilayer dynamic model for anticipating progression within 24 months from treatment.
The model developed in the training set will be tested in the validation sets and the model performance (c-index and net reclassification improvement) in the validation set will be compared with that in the training set. The accuracy of the multilayer model in predicting progression free survival at 24 months will be compared against the FLIPI using c-index and net reclassification improvement.
Inclusion Criteria:
Exclusion Criteria: