Tumor Evolution Inferred by Patterns of microRNA Expression through the Course of Disease, Therapy and Recurrence in Breast Cancer
A partir d'échantillons tumoraux prélevés à plusieurs étapes du traitement sur des patientes atteintes d'un cancer du sein, cette étude identifie des profils d'expression de micro-ARNs dont l'analyse de l'évolution permet de prédire la réponse thérapeutique et le risque de récidive
Purpose: Molecular evolution of tumors during progression, therapy and metastasis is a major clinical challenge and the main reason for resistance to therapy. We hypothesized that miRNAs that exhibit similar variation of expression through the course of disease in several patients have a significant function in the tumorigenic process.
Experimental design: Exploration of evolving disease by profiling 800 microRNAs (miRNAs) expression from serial samples of individual breast cancer patients at several time points: pre-treatment, post-treatment, lymph nodes and recurrence sites when available (58 unique samples from 19 patients). Using a dynamic approach for analysis, we identified expression modulation patterns and classified varying miRNAs into one of the 8 possible temporal expression patterns.
Results: The various patterns were found to be associated with different tumorigenic pathways. The dominant pattern identified a miRNA set that significantly differentiated between disease stages and its pattern in each patient was also associated with response to therapy. These miRNAs were related to tumor proliferation and to the cell-cycle pathway and their mRNA targets showed anti-correlated expression. Interestingly, the level of these miRNAs was lowest in matched recurrent samples from distant metastasis, indicating a gradual increase in proliferative potential through the course of disease. Finally, the average expression level of these miRNAs in the pre-treatment biopsy was significantly different comparing patients experiencing recurrence to recurrence free patients.
Conclusion: Serial tumor sampling combined with analysis of temporal expression patterns enabled to pinpoint significant signatures characterizing breast cancer progression, associated with response to therapy and with risk of recurrence.
Clinical Cancer Research , résumé, 2016