Theodoros Soldatos

Src activation by adrenoreceptors is a key switch for tumour metastasis

Norepinephrine (NE) can modulate multiple cellular functions important for cancer progression; however, how this single extracellular signal regulates such a broad array of cellular processes is unknown. Here, we identify Src as a key regulator of phosphoproteomic signaling networks activated in response to beta-adrenergic signaling in cancer cells. These results also identify a new mechanism of Src phosphorylation that mediates beta-adrenergic/PKA regulation of downstream networks, thereby enhancing tumor cell migration, invasion and growth. In human ovarian cancer samples, high tumoral NE levels were correlated with high pSrcY419 levels. Moreover, among cancer patients, the use of beta blockers was significantly associated with reduced cancer-related mortality. Collectively, these data provide a pivotal molecular target for disrupting neural signaling in the tumor microenvironment.

Association Between Serotonin Syndrome and Second‐Generation Antipsychotics via Pharmacological Target‐Adverse Event Analysis

Case reports suggest an association between second‐generation antipsychotics (SGAs) and serotonin syndrome (SS). The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) was analyzed to generate hypotheses about how SGAs may interact with pharmacological targets associated with SS. FAERS was integrated with additional sources to link information about adverse events with drugs and targets. Using Proportional Reporting Ratios, we identified signals that were further investigated with the literature to evaluate mechanistic hypotheses formed from the integrated FAERS data. Analysis revealed common pharmacological targets perturbed in both SGA and SS cases, indicating that SGAs may induce SS. The literature also supported 5‐HT2A antagonism and 5‐HT1A agonism as common mechanisms that may explain the SGA‐SS association. Additionally, integrated FAERS data mining and case studies suggest that interactions between SGAs and other serotonergic agents may increase the risk for SS. Computational analysis can provide additional insights into the mechanisms underlying the relationship between SGAs and SS.

Abstract A41: Automated retrieval and assessment of biomarker-related evidence for cancer treatment decision support

Introduction: Molecular characterization is rapidly becoming a standard of care in clinical oncology practice. Simultaneously, growing volumes of peer-reviewed literature reporting the relationships between genomic aberrations, tumor responsiveness to treatments, and associated patient outcomes has significantly increased the challenge facing clinicians to comprehensively identify and prioritize personalized treatments. This challenge is further compounded by a) the disease-specific nature of most predictive biomarkers, b) discordant published observations, c) the varying levels of clinical validity associated with most biomarker:drug response relationships, and d) the time constraints that all treating oncologists face. To overcome such challenges, we developed an automated computational system to assess the predicted effects of published genomic aberrations on drug responsiveness in a patient/disease-specific manner. Here we describe how computational selection and assessment of peer-reviewed published evidence relevant to a given cancer patient compares to that of a human expert with access to the same body of data. Methods: We developed a computational treatment decision support system (TDSS) for the analysis of cancer patients with a set of known mutations. The system holds a drug-response database (DRDB) containing expert curated information on three levels of clinical validity: 1) clinically endorsed (>100 FDA-approved facts), 2) clinically observed (>5,000 patients), and 3) translationally observed (∼4,000 model systems). Given a patient mutation profile, the TDSS retrieves related DRDB entries and weighs them according to validity level, relationship to patient disease (using the Medical Subject Headings ontology, MeSH), and RECIST response criteria. The TDSS then summarizes whether a given genotype is likely to confer response or resistance to available cancer drugs. To compare the performance of this system to that of a human molecular diagnostics expert, we created 48 virtual patients, each from one of five highly frequent cancer indications (lung, CRC, GIST, melanoma, breast) and with one or two randomly drawn predictive mutations. They were both processed by the TDSS and surveyed by an expert with electronically access to all DRDB data. The task was to recommend up to three treatments with highest likelihood of response, and identify up to three with highest risk of resistance for each case. Results: The TDSS required a few seconds of computation, compared to more than five hours of work by the expert. Both obtained a similar number of actionable predictions, for essentially the same 75% of the cases. For each case, the three treatments selected for response / resistance were compared and critically re-assessed. We found the error rates to be higher for the molecular diagnostics expert (9.6% of treatments for response, 2.1% for resistance) than for the TDSS (3.0% response, 1.4% resistance). Frequent causes of human error include a) overlooking evidence and b) failure to consider the numbers of patient cases observed. Conclusions: Computational aggregation and rules-based analysis of the clinical effects of tumor mutations on cancer drug responsiveness can greatly aid the evidence-based prioritization of treatment options for cancer patients with superior accuracy and turn-around-times. While a large-scale study is planned to compare TDSS performance against multiple experts in a real-life clinical setting, our preliminary insights suggest that the necessity for such computational systems will parallel both the emergence of clinico-molecular knowledge in this domain and the adoption rates for clinical use of genomic technologies. Citation Format: Alexander Zien, Francesca Diella, Anja Doerks, Theodoros Soldatos, Markus Hartenfeller, David B. Jackson. Automated retrieval and assessment of biomarker-related evidence for cancer treatment decision support. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Drug Sensitivity and Resistance: Improving Cancer Therapy; Jun 18-21, 2014; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(4 Suppl): Abstract nr A41.

Abstract A1-33: Knowledge prioritization of cancer genomes using oncoscores and functional impact scores to support biomarker discovery and clinical decision making

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 patients9 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 patient9s 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.

Abstract 56: MASE: A system for the molecular analysis of side effect information and its application to marketed cancer therapeutics

Proceedings: AACR 102nd Annual Meeting 2011‐‐ Apr 2‐6, 2011; Orlando, FL Adverse events (AEs) are a common and unavoidable consequence of therapeutic intervention. Nevertheless, available tomes of such data now provide us with an invaluable opportunity to study the relationship between human phenotype and drug-induced protein perturbations within a patient system. Deciphering the molecular basis of such adverse responses is not only paramount to the development of safer drugs, but it also presents a unique opportunity to dissect disease systems in search of novel response biomarkers, drug targets and efficacious combination therapies. Inspired by the potential application of this approach in clinical oncology, we have developed an in silico platform dedicated to the Molecular Analysis of Side Effect information (MASE). Combining data from the FDAs Adverse Event Reporting System (AERS) with highly curated knowledge about drug, target and pathway associations, MASE provides a biosystem level view on adverse event reports. In terms of data integration process, the free-text drug names provided by the AERS system are first mapped to standard drug synonyms. By then associating these small-molecule/biological drugs with known and predicted protein partners, we transform adverse events information from a purely drug-centric resource, to one that emphasizes the functional mediators of drug activity within the patient system. Employing this strategy, AE cases associated with a total of 97 marketed cancer drugs were curated and contextualized. A total of 208,364 adverse event cases (i.e. 14% of all) were reported as either involving a cancer drug or cancer indication, with 1243 other drugs (79%) reported as co-medicated throughout these cases. Using this cancer-focused subset, case-specific molecular views were generated highlighting all elements of the proteome perturbed through multi-component therapies – totaling 1663 direct targets, 49 metabolizing enzymes and 407 pathways. The system may be queried to analyze side effects and outcomes associated with specific combinations of drugs, targets, metabolizing enzymes, pathways or cancer indications. Importantly, we have developed a set of analytical approaches that mine MASE for evidence of 1) efficacious target combinations 2) target combinations with increased side effects, 3) target combinations that appear to attenuate certain drug side effects. In addition, we also report a strategy to predict novel targets of established drugs, based on side effect dissimilarities between otherwise structurally comparable agents. In summary, by permitting direct assessment relationships between the human proteome and drug-induced phenotypes, MASE provides a novel approach to the analysis and molecular dissection of AE information in oncology. Current developments are focused on the integration of patient specific clinico-molecular data and the combined application to treatment decision support. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 56. doi:10.1158/1538-7445.AM2011-56