Abstract 1044: Development of a miRNA-based signature to predict human cancer metastasis

Abstract

Abstract Metastasis is the primary cause of death for cancer patients, and invasive cancer cell migration is required at multiple steps during metastasis (e.g. intravasation and extravasation). Since microRNAs (miRNAs) have been implicated as key regulators of metastatic spread of cancer cells, we sought to develop a miRNA signature that can be used to predict cancer metastasis. We hypothesized that the miRNAs that are functionally required for invasive cell migration could serve as biomarkers to predict human cancer metastasis. We developed an intravital imaging-based approach combined with NGS to screen for miRNAs that are required for invasive cell migration of human HT1080 fibrosarcoma cells. The screen-identified miRNAs were validated in a panel of in vitro and in vivo assays for invasive migration. Microarray analysis identified genes downregulated in transfected HT1080 cells. Publically available databases were used to correlate the expression of screen-identified miRNAs and the progression of multiple human cancers. We used a qRT-PCR approach combined with machine learning to develop a miRNA signature to predict metastasis in a 66-patient cohort of prostate cancer. We identified over twenty novel miRNAs that regulate directional cancer invasion. Microarray analysis in HT1080 cells revealed that the altered expression of metastasis-regulating miRNAs is associated with 50% reduction in gene expression of migration and adhesion gene network components such as integrin a4, CDC42, and transgelin). We evaluated the potential of screen-identified miRNAs to serve as biomarkers to predict cancer metastasis. Using publically available databases, we found that majority of screen-identified miRNAs are dysregulated in multiple human cancer types (e.g. prostate, breast, ovarian and lung) and the expression of these miRNAs correlate directly with patient disease progression. We analyzed the expression of the top two screen-identified miRNAs in plasma samples from the PCa patient cohort. A signature was generated using a weighted K-nearest neighbor algorithm that provided a ROC area under the curve of 0.79 for predicting metastatic disease. We identified a panel of novel metastasis-regulating miRNAs that is functionally involved in human cancer metastasis. These miRNAs have the potential to serve as both biomarkers to predict metastasis and potentially as therapeutic targets to block metastasis. Citation Format: Lian Willetts, Konstantin Stoletov, Juan Jovel, Emma Woolner, John D. Lewis. Development of a miRNA-based signature to predict human cancer metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1044. doi:10.1158/1538-7445.AM2017-1044