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Network-guided modeling allows tumor-type independent prediction of sensitivity to all-trans-retinoic acid
M Bolis1,2, E Garattini1, G Paroni1
1Laboratory of Molecular Biology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milano.
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.
Insights
A new predictor, ATRA-21, identifies cancer types and patients likely to respond to all-trans-retinoic acid (ATRA) therapy. This tool aids in personalized cancer treatment strategies, expanding ATRA
Area of Science:
- Oncology
- Pharmacogenomics
- Computational Biology
Background:
- All-trans-retinoic acid (ATRA) is an effective differentiating agent for acute promyelocytic leukemia (APL).
- ATRA's potential in other malignancies is under-explored due to a lack of predictive tools for patient and tumor sensitivity.
- Personalized medicine requires identifying specific cancer types and individuals who will benefit from ATRA treatment.
Purpose of the Study:
- To develop a predictive model for identifying cancer types and individuals sensitive to all-trans-retinoic acid (ATRA).
- To create a generalized, tumor-type independent predictor for ATRA sensitivity.
- To facilitate the rational and personalized application of ATRA in cancer therapy.
Main Methods:
- Utilized RNA-sequencing data from 10,080 patients across 33 tumor types (TCGA and Leucegene datasets).
- Employed machine learning and network analysis to build and validate predictive models.
- Developed the ATRA-21 predictor, initially based on breast cancer cell lines and generalized to other tumor types.
Main Results:
- The ATRA-21 model accurately predicts in vitro ATRA sensitivity across diverse cancer cell lines.
- ATRA-21 identifies acute promyelocytic leukemia (APL) as the most sensitive acute myeloid leukemia subtype.
- Uveal melanoma and low-grade glioma show high predicted responsiveness; increased predictions correlate with better patient outcomes in certain tumors.
Conclusions:
- Generated a validated, tumor-type independent predictor (ATRA-21) for ATRA sensitivity, comprising a limited gene set.
- ATRA-21 has potential clinical applications for identifying sensitive tumor types and individual patients for ATRA-based therapies.
- This predictor can guide clinical study design and aid in selecting patients for ATRA treatment, even in less sensitive tumors.
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