C03 Predictive mechanical tumor markers: solid stress and multiscale viscoelastic data analysis

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The mechanical traits of cancer include abnormally high solid stress as well as drastic and spatially heterogeneous changes in intrinsic mechanical tissue properties. Whereas solid stress elicits mechanosensory signals promoting tumor progression, mechanical heterogeneity and tissue fluidity are conducive to cell unjamming and metastatic spread. The overall aim of this subproject is to identify predictive fingerprints of mechanical parameter changes that are sensitive to tumor formation and specific to aggressive proliferation and unjamming transitions in tumors. To this end, C03 will first develop the tools to quantify solid stress in vivo, which will ultimately be combined with tissue fluidity (C02) and mechanical heterogeneity (C01) for mechanical profiling of tumors. Solid stress generated by growing tumors forms a biophysical environment in favor of tumor progression that must be analyzed in vivo, as it is not present after resection. Therefore, C03 aims to derive solid stress from in-vivo deformation fields obtained from large stain analysis of high-resolution 3D MRI using trained neuronal networks in combination with in-vivo stiffness maps acquired by tomoelastography. The novel marker of solid stress will support the reconciliation of multiplexed mechanical data, as solid stress is known to alter the apparent stiffness in tumors due to compression stiffening. To address mechanical parameter reconciliation, C03 will compile a library of data from all subprojects that include mechanical parameters from cells, organoids, tissues as well as in-vivo patients. Guided learning approaches that account for solid stress, tissue fluidity and mechanical heterogeneity across length scales will be used to specify best-fitting viscoelastic models and parameter ranges compatible with all mechanical data obtained in this research unit. Modality- and frequency independent mechanical parameters will be combined with clinical, histological and multi-omics data using unsupervised learning in order to identify clusters of high sensitivity, specificity and predictivity with respect to tumor biology of ex vivo and in vivo systems. Overall, our work plan builds on the main hypothesis that the mechanical hallmarks of cancer acquired by a tumor and noninvasively quantified with MRE determine tumor aggressiveness, malignant potential and treatment response. To verify this hypothesis, C03 will focus on (i) solid stress quantification in combination with other cancer-specific mechanical markers such as fluidity and heterogeneity provided by the other subprojects, (ii) learned modality-overarching and tissue-intrinsic constitutive parameters, and (iii) automated assessment of the diagnostic value of multifrequency MRE and quantitative MRI. Collectively, C03 aims to translate the data generated in all A-B-C projects into a roadmap of mechanical profiles which fully reflect the aggressive potential of a tumor and its responsiveness to treatment.