Abstract 4011: Tumor evolution inferred by patterns of miRNA expression through the course of disease, therapy and recurrence in breast cancer
Authors: Maya Dadiani, Noa Bossel, Shani Paluch-Shimon, Gili Peri, Anya Pavlovski, Smadar Kahana-Edwin, Nora Balint, Adi Yosepovich, Adi Zundelevich, Einav Gal-Yam, Raanan Berger, Iris Barshack, Eytan Domany, Bella Kaufman
Journal: Cancer Research (2015)

Abstract Tumor heterogeneity frequently develops through the course of disease, therapy and metastasis. This molecular evolution is a major challenge in the clinical setting and is the main reason for resistance to therapy. Although metastatic disease is the cause of death in breast cancer, most studies involve primary tumors compared to normal breast. An alternative approach of serial assessments can add an informative value and highlight important molecular players that may be otherwise underestimated. We sought to explore alterations through the course of disease by profiling serial samples from individual breast cancer cases followed from diagnosis to recurrence. We have assembled a unique cohort of patients having matched samples from pre-treatment, post-treatment and recurrent events. We collected paraffin-embedded samples and profiled miRNA expression across all samples for each patient using NanoString analysis. We hypothesized that expression modulations in individual patients through their course of disease can target miRNAs sets that differentially function at the various stages of the disease. We therefore used an innovative approach for analysis, identifying modulation patterns as opposed to differential absolute expression. We defined all the 8 possible patterns of expression modulations in 3 time points across the course of disease. For each pattern, we identified a set of miRNAs that are shared between patients by limiting the search algorithm to find modulations of at least 1.5 fold in at least 3 patients. Each miRNA set was examined for enrichments in known miRNAs datasets (METABRIC), for target predictions and for literature evidences. We found an agreement between the predicted pattern of each assigned miRNAs and the observed trend. For example, miRNAs assigned to a pattern of an increased expression post-treatment and a decrease at recurrence were found to be down-regulated in breast cancer and negatively correlated with the cell cycle pathway. Interestingly, we found that this miRNAs set can differentiate between individual patients, based on their response to therapy. All patients sharing the same pattern for its assigned set of miRNAs had a partial response to therapy. In contrast, patients that showed the opposite pattern demonstrated a minimal response to therapy. Finally, although this miRNA set were identified by the pattern dynamics, we found a significant difference in its average absolute expression level between patients having recurrence and patients that are recurrence free, suggesting that the identified miRNA set may have a prognostic value. In summary, examining serial assessments enabled us to identify significant molecular signatures characterizing breast cancer progression based on expression patterns through the course of disease. This longitudinal approach of profiling individual patients may have important consequences for personalized medicine. Citation Format: Maya Dadiani, Noa Bossel, Shani Paluch-Shimon, Gili Peri, Anya Pavlovski, Smadar Kahana-Edwin, Nora Balint, Adi Yosepovich, Adi Zundelevich, Einav Gal-Yam, Raanan Berger, Iris Barshack, Eytan Domany, Bella Kaufman. Tumor evolution inferred by patterns of miRNA expression through the course of disease, therapy and recurrence in breast cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4011. doi:10.1158/1538-7445.AM2015-4011