Background: All-trans-retinoic acid (ATRA) is a differentiating agent used in the treatment of acute-promyelocytic-leukemia (APL) and it is under-exploited in other malignancies despite its low systemic toxicity. A rational/personalized use of ATRA requires the development of predictive tools allowing identification of sensitive cancer types and responsive individuals. Materials and methods: RNA-sequencing data for 10 080 patients and 33 different tumor types were derived from the TCGA and Leucegene datasets and completely re-processed. The study was carried out using machine learning methods and network analysis. Results: We profiled a large panel of breast-cancer cell-lines for in vitro sensitivity to ATRA and exploited the associated basal gene-expression data to initially generate a model predicting ATRA-sensitivity in this disease. Starting from these results and using a network-guided approach, we developed a generalized model (ATRA-21) whose validity extends to tumor types other than breast cancer. ATRA-21 predictions correlate with experimentally determined sensitivity in a large panel of cell-lines representative of numerous tumor types. In patients, ATRA-21 correctly identifies APL as the most sensitive acute-myelogenous-leukemia subtype and indicates that uveal-melanoma and low-grade glioma are top-ranking diseases as for average predicted responsiveness to ATRA. There is a consistent number of tumor types for which higher ATRA-21 predictions are associated with better outcomes. Conclusions: In summary, we generated a tumor-type independent ATRA-sensitivity predictor which consists of a restricted number of genes and has the potential to be applied in the clinics. Identification of the tumor types that are likely to be generally sensitive to the action of ATRA paves the way to the design of clinical studies in the context of these diseases. In addition, ATRA-21 may represent an important diagnostic tool for the selection of individual patients who may benefit from ATRA-based therapeutic strategies also in tumors characterized by lower average sensitivity.
|Titolo:||Network-guided modelling allows tumor-type independent prediction of sensitivity to all-trans retinoic acid|
|Data di pubblicazione:||2017|
|Appare nelle tipologie:||01.1 Articolo in Rivista|
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