Combination of a proteomics approach and reengineering of meso scale network models for prediction of mode-of-action for tyrosine kinase inhibitors

From hype to reality: data science enabling personalized medicine

BACKGROUND

Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of 'big data' and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future.

CONCLUSIONS

There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.

Systems Medicine in Pharmaceutical Research and Development

The development of new drug therapies requires substantial and ever increasing investments from the pharmaceutical company. Ten years ago, the average time from early target identification and optimization until initial market authorization of a new drug compound took more than 10 years and involved costs in the order of one billion US dollars. Recent studies indicate even a significant growth of costs in the meanwhile, mainly driven by the increasing complexity of diseases addressed by pharmaceutical research.Modeling and simulation are proven approaches to handle highly complex systems; hence, systems medicine is expected to control the spiral of complexity of diseases and increasing costs. Today, the main focus of systems medicine applications in industry is on mechanistic modeling. Biological mechanisms are represented by explicit equations enabling insight into the cooperation of all relevant mechanisms. Mechanistic modeling is widely accepted in pharmacokinetics, but prediction from cell behavior to patients is rarely possible due to lacks in our understanding of the controlling mechanisms. Data-driven modeling aims to compensate these lacks by the use of advanced statistical and machine learning methods. Future progress in pharmaceutical research and development will require integrated hybrid modeling technologies allowing realization of the benefits of both mechanistic and data-driven modeling. In this chapter, we sketch typical industrial application areas for both modeling techniques and derive the requirements for future technology development.

Current aspects in resistance against tyrosine kinase inhibitors in chronic myelogenous leukemia

Resistance against tyrosine kinase inhibitors (TKIs) represents a relevant clinical problem in treatment of chronic myelogenous leukemia (CML). On the basis of their activity against the spectrum of BCR-ABL mutations that have shown to be the most prominent mechanism of resistance to imatinib, new TKIs have been classified as second generation (such as nilotinib, dasatinib and bosutinib) or third generation (also cover- ing T315I such as ponatinib) TKIs. However, mutations in BCR-ABL only account for about half of the cases of treatment failure under TKI and other mechanisms either rendering the leukemic cells still dependent of BCR-ABL activity or supporting oncogenic properties of the leukemic cells independent of BCR-ABL signaling have been identified. A detailed understanding of the different underlying resistance mechanisms will be the prerequisite to eventually overcome clinical resistance and for the successful use of tailored combinations of targeted inhibitors in the future.

Proteome Changes Induced by Imatinib and Novel Imatinib Derivatives in K562 Human Chronic Myeloid Leukemia Cells

Imatinib mesylate is the leading compound to treat chronic myeloid leukemia (CML) and other cancers, through its inhibition of Bcr-Abl tyrosine kinases. However, resistance to imatinib develops frequently, particularly in late-stage disease and has necessitated the development of new Bcr-Abl inhibitors. The synthesis of a new series of phenylaminopyrimidines, structurally related to imatinib, showed large interest since the introduction of nilotinib. Here, we compare the protein levels in K562 cells treated with either imatinib or with novel imatinib derivates. Our results revealed that among the 986 quantified proteins, 35 had significantly altered levels of expression by imatinib or its derivates. In a second series of experiments, we directly compared the proteomes of imatinib treated K562 cells with those K562 cells treated with any of the four imatinib derivates. More than 1029 protein were quantified, 80 of which had altered levels of expression. Both experiments pointed to changes in the expression of the ATP-dependent RNA helicase DDX3X and of two mitochondrial coiled-coil-helix-coiled-coil-helix domain-containing proteins.

Towards defining biomarkers indicating resistances to targeted therapies

An impressive, but often short objective response was obtained in many tumor patients treated with different targeted therapies, but most of the patients develop resistances against these drugs. So far, a number of distinct mechanisms leading to intrinsic as well as acquired resistances have been identified in tumors of distinct origin. These can arise from genetic alterations, like mutations, truncations, and amplifications or due to deregulated expression of various proteins and signal transduction pathways, but also from cellular heterogeneity within tumors after an initial response. Therefore, biomarkers are urgently needed for cancer prognosis and personalized cancer medicine. The application of "ome"-based technologies including cancer (epi)genomics, next generation sequencing, cDNA microarrays and proteomics might led to the predictive or prognostic stratification of patients to categorize resistance mechanisms and to postulate combinations of treatment strategies. This review discusses the implementation of proteome-based analysis to identify markers of pathway (in)activation in tumors and the resistance mechanisms, which represent major clinical problems as a tool to optimize individually tailored therapies based on targeted drugs. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.