Abstract
Abstract Introduction: Oncology biomarkers have a pivotal role in diagnosis, prognosis, assessment of disease progression and the prediction of treatment efficacy. There are > 5000 known biomarkers; some are well characterised, while others are emerging as biomarkers of potential interest. Biomarker panels, incorporating combinations of biomarkers, are increasingly used to enhance the power and precision of testing. Objective: We sought to identify key emerging biomarkers and biomarker relationships by applying analytical techniques to the corpus of recent scientific publications using an artificial intelligence (AI)-driven platform. Methods: Biomarker targets were obtained from the Early Detection Research Network. Recent (<5 years old) publications mentioning biomarkers in six cancer types (bladder, breast, colorectal, lung, prostate and renal) were identified using Dimensions (a linked research knowledge system); emerging biomarkers were identified by number and growth in publications. For selected biomarkers, full text proximity searching was used to identify biomarker co-occurrence, which was visualized and characterized using network analysis and co-occurrence heatmaps per cancer type. Relationships between biomarkers were also assessed in an indication-agnostic dataset. Key biomarkers were identified by a measure of their network importance. Results: A total of 720 biomarkers were analyzed, and 41 clusters identified across the cancer types. Data visualizations are available at https://reports.dimensions.ai/mined-oncology-biomarkers/ Conclusion: Using large-scale analytics of published literature, biomarkers across six cancer types were successfully characterized in terms of their emergence in the published literature and the context in which they are described. This novel AI-driven approach could help identify biomarkers and biomarker panels for exploration in a clinical setting. Example clusters - growth rate is compound annual growth rate (CAGR)Cancer typeBiomarkers (n)Publications (n)Cluster key biomarkersCluster publication growth rate (%)Bladder49168CAV1/CXCL8/THBS153.1Breast2651593AREG/CXCL10/CXCL832.6Colorectal193921CAV1/CDCP1/MTHFR31.6Lung2231061SFTPC/C9/GPI41.4Prostate166717FABP5/FLNA/CAV131.6Renal44185CXCL10/CXCL8/CSNK2A149.5 Citation Format: Kim Wager, Dheepa Chari, Steffan Ho, Tomas J. Rees, Robert J. Schijvenaars. Navigating networks of oncology biomarkers mined from the scientific literature: A new open research tool [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 252.
Published Version
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