Modeling the Role of Proteostasis Network Shifts in Cancer Progression

The Proteostasis Network (PN) is an intricately regulated network of conserved biological processes that cooperate to maintain proteome balance, or proteostasis. Proteostasis imbalances are implicated in diseases and the correction of the underlying PN alterations could readjust these defects. Therefore, the PN represents an important therapeutic target space for proteostasis regulators. However, the relevance of PN state changes in critical disease transition stages has not been addressed from a systems angle.

Our emerging understanding of proteostasis signaling has already revealed links to a variety of diseases, from aging and neurodegeneration to metabolic, cardiovascular and immune disease to cancer (Brehme et al. 2014). Chaperones and heat shock proteins are frequently found upregulated in cancers. The ensemble of chaperones and their co-chaperones, the ‘chaperome’, represents a central functional arm of the PN. However, PN and chaperome alterations and their dynamics during tumorigenesis and cancer disease progression have not been studied at a systems level. Recently, the proteostasis factor AIRAPL has been functionally implicated in myeloproliferative neoplasms (MPNs), underlining the importance of proteostasis also in blood cancer. In this project we will systematically chart PN states in progression stages of MPNs and assess the role of PN alterations in disease progression.

In light of the fundamental role of the PN in maintaining cellular health, various questions arise with respect to cancer: • To what extent does the PN control cellular resilience to carcinogenesis and how is it altered in different cancers? • Which nodes and functional arms in the PN are crucially involved in tumorigenesis? • Are certain PN state changes associated with drug response, disease progression and outcome?

Modelling PN shifts during MPN progression will improve our understanding of the relationship between proteostasis signaling and carcinogenic transformation, towards improved predictive models of disease progression of clinical value for prognosis and patient risk assessment (Brehme et al. 2016).

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