Modulation of the cytoskeleton for cancer therapy

Vollständiger Abstract

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Introduction All degenerative diseases are associated with impairment in intracellular trafficking. The highly dynamic organization and remodeling of the cellular cytoskeleton are dysfunctional in pathology and often lead to drug resistance. Successful analyses of the mechanisms of drug action require statistical analysis of large-scale readouts of molecular interactions at nanometer-scale resolution. The focus of our work is the automated extraction of unbiased information from time-lapse microscopy image series of the response of cytoskeletal meshworks, intracytoplasmic membranous networks, and vesicle trafficking to ex vivo drug treatment in cancer. We present the computer vision algorithms we have developed in this regard. Methods We have performed cell biological profiling of cellular interactions and molecular mechanisms of pathogenesis and drug resistance. We are developing a platform for multifaceted analyses of intracellular and intercellular dynamics in patient-derived cultures. We aim to correlate our analyses with patterns of genetic and epigenetic variations in order to anticipate drug resistance and unfavorable treatment outcome. While we have established organoid cultures from solid tumor samples, we will perform preclinical and clinical analyses in a clinically-relevant model system in kidney organoids obtained from patient urine samples for which we will perform sequencing of long and small RNAs and will measure the expression levels of microtubule (MT) regulators and other cytoskeletal modulators. We label MT ends and will label other cellular components, such as actin, E-cadherin, cytoplasmic dynein, mitochondria, lysosomes, or exosomes, depending on the tumor type and disease stage. Our technology allows us to build a medical digital twin. Results We report new function of the MT-stabilizing drug paclitaxel and the MT-destabilizing drug vinorelbine, which elucidate mechanisms beyond the canonical function of these tubulin inhibitors. We present new analysis results on patient stratification using non-invasive biomarkers, such as urinary small RNA. We identified dysregulated MT-regulating genes in colorectal cancer organoids that can be linked to spindle rotation during mitosis and resistance to MT-stabilizing drugs. The paper provides new results from our work with patient-derived cells cultured as organoids and the analysis of cancer vulnerabilities in the context of precision medicine. We report real-time computer vision analysis and lattice light-sheet live-cell organoid imaging before drug treatment and will extend this work after treatment with low drug doses of MT, GSK3β, or/and tropomyosin inhibitors, and small molecules that induce ferroptosis or inhibit Rho GTPase activity. Conclusion Our biomedical computer vision approach allows us to elucidate mechanisms of drug action and uncover molecular interactions inaccessible by sequencing methods alone. After clinical validation, it may contribute to anticipating drug resistance and identifying sensitizing regimens that lead to complete response with minimal toxicity and the elimination of residual disease.