Abstract 2101: A gene-expression fingerprint predicting sensitivity to all-trans-retinoic acid in breast cancer cells is tumor-context independent
Authors: Garattini Enrico , Gianni’ Maurizio , Bolis Marco , Fratelli Maddalena , Paroni Gabriela , Terao Mineko 
Published: July 2016

Abstract
All-trans retinoic acid (ATRA) and derived natural as well as synthetic retinoids are promising agents in the treatment and chemoprevention of various types of neoplasia, including mammary tumors. ATRA is an important component of the therapeutic schemes used for the treatment of a rare form of Acute Myelogenous Leukemia known as Acute Promyelocytic Leukemia (APL). A rational use of the paradigmatic retinoid, ATRA, in a heterogeneous disease, like breast cancer, requires the definition of the cellular and molecular determinants of sensitivity to the agent. The major aim of the study was the definition of a predictive gene expression fingerprint that can be used for the selection of patients who may benefit from treatment protocols containing ATRA. To this purpose, we selected 53 breast cancer cell lines characterized for the constitutive whole genome gene expression profiles. The sensitivity of 30 cell lines (training set) to the anti-proliferative action of ATRA was defined after challenge with increasing concentrations of the retinoid for 3, 6 and 9 days. This analysis established that Luminal and ER+ cell lines are enriched within the ATRA sensitive group. In contrast, cell lines characterized by a Basal-like phenotype, according to the PAM50 gene expression signature, are generally refractory to the growth inhibitory action of ATRA. The sensitivity of Luminal-A and Luminal-B and the general refractoriness of Basal-like tumors to ATRA was validated in short-term tissue slice cultures of surgical breast cancer specimens. The training set was used to define a gene-expression fingerprint consisting of morethan 100 genes significantly associated with ATRA sensitivity. The fingerprint was generated by reprocessing the RNA sequencing data contained in the CCLE (Cancer Cell line Encyclopedia) of the Broad Institute and it was built from approximately 60,000 coding and non-coding loci. The approach involved the use of general linear models (machine learning algorithm). The identified gene-expression fingerprint was subsequently used to successfully predict ATRA sensitivity in a test set consisting of the remaining 23 cell lines. As a first step towards the use of the fingerprint for the stratification of patients, we evaluated the proportion of predicted ATRA sensitive breast tumors in the TCGA dataset. In accordance with the cell line and primary tumor data, approximately 30% of the Luminal tumors present with a high similarity score to the identified gene expression fingerprint associated with ATRA sensitivity. In contrast, only 5% of the Basal-like or Triple-negative mammary tumors are characterized by the same high similarity score. Interestingly, the ATRA sensitivity signature seems to be tumor context independent, as it correctly identifies the Acute Promyelocytic Leukemia patients present in the Acute Myelogenous Leukemia patients present in two publicly available datasets.
Citation Format: Enrico Garattini, Maurizio Gianni’, Marco Bolis, Maddalena Fratelli, Gabriela Paroni, Mineko Terao. A gene-expression fingerprint predicting sensitivity to all-trans-retinoic acid in breast cancer cells is tumor-context independent. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2101.