Clocking cancer: the circadian clock as a target in cancer therapy.

Disruption of the cellular pathway modulating endogenous 24-h rhythms, referred to as "the circadian clock", has been recently proven to be associated with cancer risk, development, and progression. This pathway operates through a complex network of transcription-translation feedback loops generated by a set of interplaying proteins. The expression of core circadian clock genes is frequently dysregulated in human tumors; however, the specific effects and underlying mechanisms seem to vary depending on the cancer types and are not fully understood. In addition, specific oncogenes may differentially induce the dysregulation of the circadian clock in tumors. Pharmacological modulation of clock components has been shown to result in specific lethality in certain types of cancer cells, and thus holds great promise as a novel anti-cancer therapeutic approach. Here we present an overview of the rationale and current evidence for targeting the clock in cancer treatment.

Imaging-Based Machine Learning Analysis of Patient-Derived Tumor Organoid Drug Response.

Three-quarters of compounds that enter clinical trials fail to make it to market due to safety or efficacy concerns. This statistic strongly suggests a need for better screening methods that result in improved translatability of compounds during the preclinical testing period. Patient-derived organoids have been touted as a promising 3D preclinical model system to impact the drug discovery pipeline, particularly in oncology. However, assessing drug efficacy in such models poses its own set of challenges, and traditional cell viability readouts fail to leverage some of the advantages that the organoid systems provide. Consequently, phenotypically evaluating complex 3D cell culture models remains difficult due to intra- and inter-patient organoid size differences, cellular heterogeneities, and temporal response dynamics. Here, we present an image-based high-content assay that provides object level information on 3D patient-derived tumor organoids without the need for vital dyes. Leveraging computer vision, we segment and define organoids as independent regions of interest and obtain morphometric and textural information per organoid. By acquiring brightfield images at different timepoints in a robust, non-destructive manner, we can track the dynamic response of individual organoids to various drugs. Furthermore, to simplify the analysis of the resulting large, complex data files, we developed a web-based data visualization tool, the Organoizer, that is available for public use. Our work demonstrates the feasibility and utility of using imaging, computer vision and machine learning to determine the vital status of individual patient-derived organoids without relying upon vital dyes, thus taking advantage of the characteristics offered by this preclinical model system.