Abstract 839: Network-driven analytics of published tissue-based biomarkers to predict response to neoadjuvant therapy in rectal cancer

Abstract

Abstract Introduction Neoadjuvant chemoradiotherapy (nCRT) plays a central role in the management of locally advanced rectal cancer. For many, nCRT leads to clinically meaningful tumour regression. However, up to 20% exhibit little to no response and, in this group, nCRT results in unnecessary delays to definitive treatment. There is a critical need for development of robust molecular methods to predict response to nCRT, to allow for more precise treatment stratification. Although numerous molecular pathways and biomarkers have been implicated in radiosensitivity, the lack of a unifying interpretation of these findings has restricted translational deployment. The aim of the current study was to develop a ‘knowledge network’ with which to visualise and interpret published, quantitative, biomarker data relating to radiosensitivity in rectal cancer, beyond the conventional format of a systematic review. Methods Existing data on predictive biomarkers were retrieved by way of a systematic review of electronic bibliographic databases. Biomarkers were classified according to biological function and built into a hierarchical Gene Ontology tree. Significance was binarized based on p-values or multivariate statistics. An interactive, direct acyclic graph was developed using the Dagre-D3 JavaScript library. Nodes were sized by number of studied biomarkers and color-coded according to their significance scores. The scores reflect the ratio of significant versus non-significant evidence across studied biomarkers. A negative score range indicates more non-significant biomarker findings for that ontological term (node). Weightings were applied to reflect those biomarkers confirmed as significant across two or more studies. p-values of 0.05 or less (adjusted for multiple comparative analysis where appropriate) were considered to be statistically significant. Results 72 individual biomarkers were identified through review. On highest order classification, the domains of response to stress and factors inhibiting apoptosis were found to be most significant (aggregate significance scores across identified biomarkers, 0.75 and 0.714 respectively). A predictive power was not reached for the majority of prognostic biomarkers; rather, the levels of their statistical significance were assessed. Conclusions Building a knowledge-based network analysis of published data identifies promising areas for further research into cellular mechanisms, which may aid in biomarker discovery. Regarding significant node clusters within a network of published data on predictive biomarkers, modifications in cellular metabolic responses to the insult posed by nCRT appear to hold promise in developing a panel of biomarkers with a predictive capacity for response. Network-based analytics takes into account the complex nature of response to therapy, and is a novel way of presenting results obtained from a systematic review. Citation Format: Liam R. Poynter, Kirill Veselkov, Dieter Galea, James Kinross, Alexander Mirnezami, Jeremy Nicholson, Zoltan Takats, Reza Mirnezami, Ara Darzi. Network-driven analytics of published tissue-based biomarkers to predict response to neoadjuvant therapy in rectal cancer [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 839. doi:10.1158/1538-7445.AM2017-839