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Kinase-targeted agents demonstrate antitumor activity in advanced metastatic clear cell renal cell carcinoma (ccRCC), which remains largely incurable. Integration of genomic approaches through small-molecules and genetically based high-throughput screening holds the promise of improved discovery of candidate targets for cancer therapy. The 786-O cell line represents a model for most ccRCC that have a loss of functional pVHL (von Hippel-Lindau). A multiplexed assay was used to study the cellular fitness of a panel of engineered ccRCC isogenic 786-O VHL− cell lines in response to a collection of targeted cancer therapeutics including kinase inhibitors, allowing the interrogation of over 2880 drug–gene pairs. Among diverse patterns of drug sensitivities, investigation of the mechanistic effect of one selected drug combination on tumor spheroids and ex vivo renal tumor slice cultures showed that VHL-defective ccRCC cells were more vulnerable to the combined inhibition of the CK2 and ATM kinases than wild-type VHL cells. Importantly, we found that HIF-2α acts as a key mediator that potentiates the response to combined CK2/ATM inhibition by triggering ROS-dependent apoptosis. Importantly, our findings reveal a selective killing of VHL-deficient renal carcinoma cells and provide a rationale for a mechanism-based use of combined CK2/ATM inhibitors for improved patient care in metastatic VHL-ccRCC.

Clear cell renal cell carcinoma (ccRCC) is the third type of urologic cancer. At time of diagnosis, 30% of cases are metastatic with no effect of chemotherapy or radiotherapy. Current targeted therapies lead to a high rate of relapse and resistance after a short-term response. Thus, a major hurdle in the development and use of new treatments for ccRCC is the lack of good pre-clinical models that can accurately predict the efficacy of new drugs and allow the stratification of patients into the correct treatment regime. Here, we describe different 3D cultures models of ccRCC, emphasizing the feasibility and the advantage of ex-vivo treatment of fresh, surgically resected human tumor slice cultures of ccRCC as a robust preclinical model for identifying patient response to specific therapeutics. Moreover, this model based on precision-cut tissue slices enables histopathology measurements as tumor architecture is retained, including the spatial relationship between the tumor and tumor-infiltrating lymphocytes and the stromal components. Our data suggest that acute treatment of tumor tissue slices could represent a benchmark of further exploration as a companion diagnostic tool in ccRCC treatment and a model to develop new therapeutic drugs.

Abstract X-ray Phase Contrast Tomography (XPCT) based on wavefield propagation has been established as a high resolution three-dimensional (3D) imaging modality, suitable to reconstruct the intricate structure of soft tissues, and the corresponding pathological alterations. However, for biomedical research, more is needed than 3D visualisation and rendering of the cytoarchitecture in a few selected cases. First, the throughput needs to be increased to cover a statistically relevant number of samples. Second, the cytoarchitecture has to be quantified in terms of morphometric parameters, independent of visual impression. Third, dimensionality reduction and classification are required for identification of effects and interpretation of results. To address these challenges, we here design and implement a novel integrated and high throughput XPCT imaging and analysis workflow for 3D histology, pathohistology and drug testing. Our approach uses semi-automated data acquisition, reconstruction and statistical quantification. We demonstrate its capability for the example of lung pathohistology in Covid-19. Using a small animal model, different Covid-19 drug candidates are administered after infection and tested in view of restoration of the physiological cytoarchitecture, specifically the alveolar morphology. To this end, we then use morphometric parameter determination followed by a dimensionality reduction and classification based on optimal transport. This approach allows efficient discrimination between physiological and pathological lung structure, thereby providing quantitative insights into the pathological progression and partial recovery due to drug treatment. Finally, we stress that the XPCT image chain implemented here only used synchrotron radiation for validation, while the data used for analysis was recorded with laboratory $$\mu$$ μ CT radiation, more easily accessible for pre-clinical research.