David B. Jackson

A physical and functional map of the human TNF-α/NF-κB signal transduction pathway

Signal transduction pathways are modular composites of functionally interdependent sets of proteins that act in a coordinated fashion to transform environmental information into a phenotypic response. The pro-inflammatory cytokine tumour necrosis factor (TNF)-α triggers a signalling cascade, converging on the activation of the transcription factor NF-κB, which forms the basis for numerous physiological and pathological processes. Here we report the mapping of a protein interaction network around 32 known and candidate TNF-α/NF-κB pathway components by using an integrated approach comprising tandem affinity purification, liquid-chromatography tandem mass spectrometry, network analysis and directed functional perturbation studies using RNA interference. We identified 221 molecular associations and 80 previously unknown interactors, including 10 new functional modulators of the pathway. This systems approach provides significant insight into the logic of the TNF-α/NF-κB pathway and is generally applicable to other pathways relevant to human disease.

Drug profiling: knowing where it hits.

Off-target hits of drugs can lead to serious adverse effects or, conversely, to unforeseen alternative medical utility. Selectivity profiling against large panels of potential targets is essential for the drug discovery process to minimize attrition and maximize therapeutic utility. Lately, it has become apparent that drug promiscuity (polypharmacology) goes well beyond target families; therefore, lowering the profiling costs and expanding the coverage of targets is an industry-wide challenge to improve predictions. Here, we review current and promising drug profiling alternatives and commercial solutions in these exciting emerging fields.

Erythropoietin Stimulates Tumor Growth via EphB4

While recombinant human erythropoietin (rhEpo) has been widely used to treat anemia in cancer patients, concerns about its adverse effects on patient survival have emerged. A lack of correlation between expression of the canonical EpoR and rhEpos effects on cancer cells prompted us to consider the existence of an alternative Epo receptor. Here, we identified EphB4 as an Epo receptor that triggers downstream signaling via STAT3 and promotes rhEpo-induced tumor growth and progression. In human ovarian and breast cancer samples, expression of EphB4 rather than the canonical EpoR correlated with decreased disease-specific survival in rhEpo-treated patients. These results identify EphB4 as a critical mediator of erythropoietin-induced tumor progression and further provide clinically significant dimension to the biology of erythropoietin.

Bioinformatory‐assisted analysis of next‐generation sequencing data for precision medicine in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is a tumor with an extremely poor prognosis, predominantly as a result of chemotherapy resistance and numerous somatic mutations. Consequently, PDAC is a prime candidate for the use of sequencing to identify causative mutations, facilitating subsequent administration of targeted therapy. In a feasibility study, we retrospectively assessed the therapeutic recommendations of a novel, evidence‐based software that analyzes next‐generation sequencing (NGS) data using a large panel of pharmacogenomic biomarkers for efficacy and toxicity. Tissue from 14 patients with PDAC was sequenced using NGS with a 620 gene panel. FASTQ files were fed into treatmentmap. The results were compared with chemotherapy in the patients, including all side effects. No changes in therapy were made. Known driver mutations for PDAC were confirmed (e.g. KRAS, TP53). Software analysis revealed positive biomarkers for predicted effective and ineffective treatments in all patients. At least one biomarker associated with increased toxicity could be detected in all patients. Patients had been receiving one of the currently approved chemotherapy agents. In two patients, toxicity could have been correctly predicted by the software analysis. The results suggest that NGS, in combination with an evidence‐based software, could be conducted within a 2‐week period, thus being feasible for clinical routine. Therapy recommendations were principally off‐label use. Based on the predominant KRAS mutations, other drugs were predicted to be ineffective. The pharmacogenomic biomarkers indicative of increased toxicity could be retrospectively linked to reported negative side effects in the respective patients. Finally, the occurrence of somatic and germline mutations in cancer syndrome‐associated genes is noteworthy, despite a high frequency of these particular variants in the background population. These results suggest software‐analysis of NGS data provides evidence‐based information on effective, ineffective and toxic drugs, potentially forming the basis for precision cancer medicine in PDAC.

Genetic determinants of anticancer drug activity: towards a global approach to personalized cancer medicine.

While current trials of anticancer agents serve to provide a population-based validation of therapeutic activity, clinical success is typically restricted to tumors of select molecular subtype. Recent insights have yielded a growing catalogue of germline and tumor-based aberrations that can predetermine whether a patient will achieve clinical benefit from a drug or not. Thus, in order to realize the true potential of anticancer agents, we need to define the molecular contexts under which they will prove both efficacious and safe. In this article, we provide an overview of such molecular determinants and introduce the concept of ‘cancer patient profiling’ – the process and science of defining the optimal therapy for a given patient through the generation and analysis of system-wide molecular information.

Microarrays meet the Voltaire challenge: Drug discovery on a chip?

The co-emergence of microarray technologies with systems oriented approaches to discovery is testament to the technological and conceptual advancements of recent years. By providing a platform for massively parallelized reductionism, microarrays are enabling us to examine the functional features of diverse classes of bio-system components in a contextually meaningful manner. Yet, to provide economic impact, future development of these technologies demands intimate alignment with the goal of producing safer and more efficacious drugs.:

Molecular perspectives on the non-responder phenomenon.

With the advent of targeted therapies promising to revolutionise the nature and success of patient care, the field of clinical oncology is facing a highly exciting future. While much of this enthusiasm comes from the hope for improved patient outcomes, a review of clinical response/relapse rates for current therapies provides a more sobering perspective. Given that the majority of patients are intrinsically resistant to the therapeutic potential of these molecules, efforts are now directed at characterising such non-responsive system behaviour and causative molecular insults. Testament to this is an expanding catalogue of target and system-based aberrations, often defined through retrospective analyses of clinical tissue and associated outcome data. What has emerged is a complex picture, where numerous potential sources of cancer-specific aberration can contribute to refractory tumour behaviour. Clinicians, regulators and sponsors must now collaborate to determine how such knowledge should be used to enhance the clinical decision process and associated regulatory guidance.

Brief communication: Prediction of small molecule binding property of protein domains with Bayesian classifiers based on Markov chains

Accurate computational methods that can help to predict biological function of a protein from its sequence are of great interest to research biologists and pharmaceutical companies. One approach to assume the function of proteins is to predict the interactions between proteins and other molecules. In this work, we propose a machine learning method that uses a primary sequence of a domain to predict its propensity for interaction with small molecules. By curating the Pfam database with respect to the small molecule binding ability of its component domains, we have constructed a dataset of small molecule binding and non-binding domains. This dataset was then used as training set to learn a Bayesian classifier, which should distinguish members of each class. The domain sequences of both classes are modelled with Markov chains. In a Jack-knife test, our classification procedure achieved the predictive accuracies of 77.2% and 66.7% for binding and non-binding classes respectively. We demonstrate the applicability of our classifier by using it to identify previously unknown small molecule binding domains. Our predictions are available as supplementary material and can provide very useful information to drug discovery specialists. Given the ubiquitous and essential role small molecules play in biological processes, our method is important for identifying pharmaceutically relevant components of complete proteomes. The software is available from the author upon request.

Abstract A34: Conquering resistance to targeted therapies through system‐based analysis of clinico‐molecular information

While the advent of targeted therapies has promised to revolutionize the success of cancer treatment, a critical review of clinical response rates provides a sobering perspective on the challenge at hand. New levels of discovery innovation are urgently required to redress this enormous medical need. Focusing on primary imatinib resistance in chronic myeloid leukemia, we demonstrate that the systems‐based modeling and analysis of clinico‐molecular information from responder and non‐responder patients can provide unique molecular insights into the sources of therapy resistance. By then utilizing these models to prioritize therapeutic drug space, the strategy elaborates rational multi‐component therapies to potentially redress the issue. Moreover, by hypothesizing that co‐medications may perturb the activity of resistance model components, we utilize prescription information to “probe” the functional network in search of key resistance associated targets. Not only does this method provide a novel approach to the analysis of patient information, it also permits the repositioning of drugs from independent therapeutic areas within predicted combinatorial regimens. Taken together, our results provide an exciting new approach to the computational analysis of clinico‐molecular data, and suggest a future where rationally designed theranostic‐linked combination therapies could significantly address the non‐responder problem. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):A34.

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.