M Bolis1,2, E Garattini1, G Paroni1, A Zanetti1, M Kurosaki1, T Castrignanò3, S K Garattini1,4, F Biancardi1, M M Barzago1, M Gianni'1, M Terao1, L Pattini2, M Fratelli1. 1. Laboratory of Molecular Biology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milano. 2. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano. 3. Computing Centre, CINECA-Consorzio Interuniversitario per il Calcolo Automatico, Roma. 4. Department of Medical Oncology, University Hospital, Udine, Italy.
Abstract
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.
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.
Authors: G Paroni; M Fratelli; G Gardini; C Bassano; M Flora; A Zanetti; V Guarnaccia; P Ubezio; F Centritto; M Terao; E Garattini Journal: Oncogene Date: 2011-11-07 Impact factor: 9.867
Authors: Chandrani Chattopadhyay; Dae Won Kim; Dan S Gombos; Junna Oba; Yong Qin; Michelle D Williams; Bita Esmaeli; Elizabeth A Grimm; Jennifer A Wargo; Scott E Woodman; Sapna P Patel Journal: Cancer Date: 2016-03-15 Impact factor: 6.860
Authors: Zoltán Nagy; Kornélia Baghy; Éva Hunyadi-Gulyás; Tamás Micsik; Gábor Nyírő; Gergely Rácz; Henriett Butz; Pál Perge; Ilona Kovalszky; Katalin F Medzihradszky; Károly Rácz; Attila Patócs; Peter Igaz Journal: Am J Cancer Res Date: 2015-11-15 Impact factor: 6.166
Authors: Jordi Barretina; Giordano Caponigro; Nicolas Stransky; Kavitha Venkatesan; Adam A Margolin; Sungjoon Kim; Christopher J Wilson; Joseph Lehár; Gregory V Kryukov; Dmitriy Sonkin; Anupama Reddy; Manway Liu; Lauren Murray; Michael F Berger; John E Monahan; Paula Morais; Jodi Meltzer; Adam Korejwa; Judit Jané-Valbuena; Felipa A Mapa; Joseph Thibault; Eva Bric-Furlong; Pichai Raman; Aaron Shipway; Ingo H Engels; Jill Cheng; Guoying K Yu; Jianjun Yu; Peter Aspesi; Melanie de Silva; Kalpana Jagtap; Michael D Jones; Li Wang; Charles Hatton; Emanuele Palescandolo; Supriya Gupta; Scott Mahan; Carrie Sougnez; Robert C Onofrio; Ted Liefeld; Laura MacConaill; Wendy Winckler; Michael Reich; Nanxin Li; Jill P Mesirov; Stacey B Gabriel; Gad Getz; Kristin Ardlie; Vivien Chan; Vic E Myer; Barbara L Weber; Jeff Porter; Markus Warmuth; Peter Finan; Jennifer L Harris; Matthew Meyerson; Todd R Golub; Michael P Morrissey; William R Sellers; Robert Schlegel; Levi A Garraway Journal: Nature Date: 2012-03-28 Impact factor: 49.962
Authors: Floriana Centritto; Gabriela Paroni; Marco Bolis; Silvio Ken Garattini; Mami Kurosaki; Maria Monica Barzago; Adriana Zanetti; James Neil Fisher; Mark Francis Scott; Linda Pattini; Monica Lupi; Paolo Ubezio; Francesca Piccotti; Alberto Zambelli; Paola Rizzo; Maurizio Gianni'; Maddalena Fratelli; Mineko Terao; Enrico Garattini Journal: EMBO Mol Med Date: 2015-07 Impact factor: 12.137
Authors: Kelly E Regan-Fendt; Jielin Xu; Mallory DiVincenzo; Megan C Duggan; Reena Shakya; Ryejung Na; William E Carson; Philip R O Payne; Fuhai Li Journal: NPJ Syst Biol Appl Date: 2019-02-26