Background and Significance: We address the problem of metastatic site prediction in prostate adenocarcinoma (PRAD), with a specific focus on identifying molecular pathways that are activated in association with the homing to a particular metastatic site. The approach can reveal the molecular mechanisms in metastatic cancer while also providing clues about potential drug targets. Further experimental validation of our findings may lead to the discovery of novel therapies for patients who are in the advanced stages of disease.

Methods: We downloaded four PRAD datasets that contained met-site information from the Gene Expression Omnibus (GEO) and trained multi-class predictors on this set. The predictors were then evaluated on patient samples collected as part of the Stand Up To Cancer (SU2C) initiative. Standard normalization techniques were used to remove batch effects associated with non-biological factors such as the institution from which the materials were collected and/or assays conducted.

We focused our attention on linear models due to their straightforward interpretation: higher weights indicate stronger association of the corresponding genomic features with a particular metastatic site. To identify pathways implicated by the relevant genomic features, we employed model regularization via group LASSO. This technique groups genes according to their pathway membership using the PathwayCommons database. The regularizer (penalty trading accurate classification with model complexity) sets the weights of an entire group to zero if those groups were uninformative for met-site prediction and non-zero otherwise.

Results: We trained a multi-class linear predictor to recognize lymphatic node, liver and bone metastatic sites from gene expression data. The resulting model gave rise to two linear signatures: one that distinguished liver mets from the rest, and another that distinguished lymph node mets from the rest. The signatures were enriched for pathways commonly associated with liver development and liver progenitor cells, as well as pathways involved in integrin interactions on the cell surface. Based on the latter, we hypothesize that the up-regulation of particular integrin-signaling pathways may be responsible for driving the tendency of metastatic PRAD cells to prefer one site over another. We are currently in the process of investigating whether there is further evidence of this hypothesis in the SU2C data, as well as comparing group LASSO to other regularization techniques that also incorporate prior pathway information.

Conclusion: We used linear methods to identify several pathways that may be responsible for localization of metastatic prostate adenocarcinoma cells to specific tissues. Our empirical results provide evidence that integrin-signaling may play a key role in this process. We are working on robustness evaluation of these findings, as well as experimental validation with our SU2C collaborators.

Citation Format: Adrian Bivol, Kiley Graim, Evan Paull, Dan Carlin, Robert Baertsch, Artem Sokolov, Josh Stuart. Identification of pathways relevant for metastatic site prediction in prostate cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4177. doi:10.1158/1538-7445.AM2014-4177