Abstract A1-33: Knowledge prioritization of cancer genomes using oncoscores and functional impact scores to support biomarker discovery and clinical decision making
Authors: Soldatos Theodoros G. , Badbanchi Sasha , Vivas Sonia , Zien Alexander , Diella Francesca , Hartenfeller Markus , Picker Alexander , Stein Martin A. , Jackson David B. 
Published: November 2015

Abstract
Introduction: To accelerate the translation of genome information in important biological and clinical insights requires systematic integration and analysis of functional genomic and proteomic information. In this way molecular landscapes of cancer patients can be characterized efficiently to impact their care. To fully rationalize the potential treatment options for patients based on mechanistic considerations, we must understand two key parameters: (a) how important is each protein/gene to the known mechanisms underlying specific cancer types and (b) what are the functional implications of variants at the level of protein and system function. Of the many modules in our pipeline, here we present two of the components we have developed to address these questions.
Methods: Based on expert-curated data, combined with bioinformatics and text-data mining methods we have modeled (a) the relative biological importance of each gene/protein to specific cancer types, with a measure we call ‘oncoscore’ (b) the functional effects of a variant with a process we call ‘functional impact score’. First, the oncoscore method relies on multidimensional data types that summarize real-time evidence regarding clinical and molecular importance with respect to specific cancer indications. Such features include, among others, gene/protein pathway inclusion facts, drug-targetness, disease association, interaction neighborhood, as well as indication-specific protein and druggability attributes. Applying these parameters across individual cancer indications provides us with a prioritization of the functionally most important genes associated with each cancer type. Secondly, to understand the impact of aberrations we contextualized structural, functional, drug response and safety information to provide a novel approach for the prediction of functionally important aberrations. Combining the two scores permits prioritization of the functionally most important genes and aberrations in any patient tumor. Interestingly, the strategy can also be applied in absence of DNA sequence information, where the oncoscore method alone can be used to prioritize potential drug targets, again based on levels of real-time evidence.
Results: We present a subset of our results in the context of 25 different cancer conditions and demonstrate how the two scores can help prioritize the most important clinico-molecular players of a disease, decipher the most important aberrations found in patients' molecular profiles, and respectively combine this information to prioritize treatments for each indication/patient.
Conclusion: While our database contains curated information about the relationship between a gene/protein mutation and drug response within specific cancer types, we have devised two additional mechanisms to expand the clinical actionability of this information. The oncoscore and functional-impact scores provide an additional modus to decipher clinically actionable information from a patient tumor, especially when no known biomarkers are detected in the patient's profile. These methods are also particularly applicable to the identification of novel treatment biomarkers. Another advantage is that they can be used to prioritize patient treatments in the absence of sequence information, a feature that can be helpful when it comes to non-resectable disease in rare cancers. In summary, our cancer-specific integration of biological and clinical knowledge allows us to predict potentially actionable mutations in patient tumors. This is an important extension to the identification of previously known predictive biomarkers and lends itself to translational level clinical applications including biomarker discovery, drug repositioning and clinical trial prioritization for ex-guideline patients.
Citation Format: Theodoros G. Soldatos, Sasha Badbanchi, Sonia Vivas, Alexander Zien, Francesca Diella, Markus Hartenfeller, Alexander Picker, Martin A. Stein, David B. Jackson. Knowledge prioritization of cancer genomes using oncoscores and functional impact scores to support biomarker discovery and clinical decision making. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-33.