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Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal \"virtual laboratory\" for such multicellular systems simulates both the biochemical microenvironment (the \"stage\") and many mechanically and biochemically interacting cells (the \"players\" upon the stage). PhysiCell-physics-based multicellular simulator-is an open source agent-based simulator that provides both the stage and the players for studying many interacting cells in dynamic tissue microenvironments. It builds upon a multi-substrate biotransport solver to link cell phenotype to multiple diffusing substrates and signaling factors. It includes biologically-driven sub-models for cell cycling, apoptosis, necrosis, solid and fluid volume changes, mechanics, and motility \"out of the box.\" The C++ code has minimal dependencies, making it simple to maintain and deploy across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 105-106 cells are feasible on quad-core desktop workstations; larger simulations are attainable on single HPC compute nodes. We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on the dynamics of hanging drop tumor spheroids and ductal carcinoma in situ (DCIS) of the breast. We demonstrate stochastic motility, chemical and contact-based interaction of multiple cell types, and the extensibility of PhysiCell with examples in synthetic multicellular systems (a \"cellular cargo delivery\" system, with application to anti-cancer treatments), cancer heterogeneity, and cancer immunology. PhysiCell is a powerful multicellular systems simulator that will be continually improved with new capabilities and performance improvements. It also represents a significant independent code base for replicating results from other simulation platforms. The PhysiCell source code, examples, documentation, and support are available under the BSD license at http://PhysiCell.MathCancer.org and http://PhysiCell.sf.net.

Patient-derived colorectal tumor organoids (CTOs) closely recapitulate the complex morphological, phenotypic, and genetic features observed in in vivo tumors. Therefore, evaluation of drug distribution and metabolism in this model system can provide valuable information to predict the clinical outcome of a therapeutic response in individual patients. In this report, we applied matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to examine the spatial distribution of the drug irinotecan and its metabolites in CTOs from two patients. Irinotecan is a prodrug and is often prescribed as part of therapeutic regimes for patients with advanced colorectal cancer. Irinotecan shows a time-dependent and concentration-dependent permeability and metabolism in the CTOs. More interestingly, the active metabolite SN-38 does not co-localize well with the parent drug irinotecan and the inactive metabolite SN-38G. The phenotypic effect of irinotecan metabolism was also confirmed by a viability study showing significantly reduced proliferation in the drug treated CTOs. MALDI-MSI can be used to investigate various pharmaceutical compounds in CTOs derived from different patients. By analyzing multiple CTOs from a patient, this method could be used to predict patient-specific drug responses and help to improve personalized dosing regimens. Graphical Abstract ᅟ

Fluorescence Lifetime Imaging Microscopy (FLIM) quantifies autofluorescence lifetime to assess cellular metabolism, therapeutic efficacy, and disease progression. These dynamic and heterogeneous processes complicate signal analysis. Fit-free analysis methods such as phasor analysis are increasingly used due to limitations of fit-based approaches. However, incorporating photon-counting shot noise often leads to moderate-to-high uncertainty in detecting subtle changes. Common noise-reduction strategies can introduce errors and cause data loss. We developed noise-corrected principal component analysis (NC-PCA), which selectively identifies and removes noise to isolate the signal of interest. We validated NC-PCA by analyzing FLIM images of patient-derived colorectal cancer organoids treated with several therapeutics. First, we show NC-PCA decreases uncertainty by up to 5.5-fold compared to conventional analysis and reduces data loss over 50-fold. Then, using a merged dataset, NC-PCA reveals multiple metabolic states. Overall, NC-PCA offers a powerful, generalizable tool to enhance FLIM analysis and improve detection of biologically relevant metabolic changes.

Nonclinical testing has served as a foundation for evaluating potential risks and effectiveness of investigational new drugs in humans. However, the current two‐dimensional (2D) in vitro cell culture systems cannot accurately depict and simulate the rich environment and complex processes observed in vivo, whereas animal studies present significant drawbacks with inherited species‐specific differences and low throughput for increased demands. To improve the nonclinical prediction of drug safety and efficacy, researchers continue to develop novel models to evaluate and promote the use of improved cell‐ and organ‐based assays for more accurate representation of human susceptibility to drug response. Among others, the three‐dimensional (3D) cell culture models present physiologically relevant cellular microenvironment and offer great promise for assessing drug disposition and pharmacokinetics (PKs) that influence drug safety and efficacy from an early stage of drug development. Currently, there are numerous different types of 3D culture systems, from simple spheroids to more complicated organoids and organs‐on‐chips, and from single‐cell type static 3D models to cell co‐culture 3D models equipped with microfluidic flow control as well as hybrid 3D systems that combine 2D culture with biomedical microelectromechanical systems. This article reviews the current application and challenges of 3D culture systems in drug PKs, safety, and efficacy assessment, and provides a focused discussion and regulatory perspectives on the liver‐, intestine‐, kidney‐, and neuron‐based 3D cellular models.

Our recent work has shown that DCAF1 (also known as VprBP) is overexpressed in colon cancer and phosphorylates histone H2AT120 to drive epigenetic gene inactivation and oncogenic transformation. We have extended these observations by investigating whether DCAF1 also phosphorylates non-histone proteins as an additional mechanism linking its kinase activity to colon cancer development. We now demonstrate that DCAF1 phosphorylates EZH2 at T367 to augment its nuclear stabilization and enzymatic activity in colon cancer cells. Consistent with this mechanistic role, DCAF1-mediated EZH2 phosphorylation leads to elevated levels of H3K27me3 and altered expression of growth regulatory genes in cancer cells. Furthermore, our preclinical studies using organoid and xenograft models revealed that EZH2 requires phosphorylation for its oncogenic function, which may have therapeutic implications for gene reactivation in colon cancer cells. Together, our data define a mechanism underlying DCAF1-driven colonic tumorigenesis by linking DCAF1-mediated EZH2 phosphorylation to EZH2 stability that is crucial for establishing H3K27me3 and gene silencing program. DCAF1/VprBP contributes to epigenetic gene inactivation and cancer development. Here the authors show that DCAF1/VprBP phosphorylates and stabilises EZH2 to induce oncogenic gene silencing and colon cancer growth.

Colorectal cancer and other cancers often metastasize to the liver in later stages of the disease, contributing significantly to patient death. While the biomechanical properties of the liver parenchyma (normal liver tissue) are known to affect tumor cell behavior in primary and metastatic tumors, the role of these properties in driving or inhibiting metastatic inception remains poorly understood, as are the longer-term multicellular dynamics. This study adopts a multi-model approach to study the dynamics of tumor-parenchyma biomechanical interactions during metastatic seeding and growth. We employ a detailed poroviscoelastic model of a liver lobule to study how micrometastases disrupt flow and pressure on short time scales. Results from short-time simulations in detailed single hepatic lobules motivate constitutive relations and biological hypotheses for a minimal agent-based model of metastatic growth in centimeter-scale tissue over months-long time scales. After a parameter space investigation, we find that the balance of basic tumor-parenchyma biomechanical interactions on shorter time scales (adhesion, repulsion, and elastic tissue deformation over minutes) and longer time scales (plastic tissue relaxation over hours) can explain a broad range of behaviors of micrometastases, without the need for complex molecular-scale signaling. These interactions may arrest the growth of micrometastases in a dormant state and prevent newly arriving cancer cells from establishing successful metastatic foci. Moreover, the simulations indicate ways in which dormant tumors could “reawaken” after changes in parenchymal tissue mechanical properties, as may arise during aging or following acute liver illness or injury. We conclude that the proposed modeling approach yields insight into the role of tumor-parenchyma biomechanics in promoting liver metastatic growth, and advances the longer term goal of identifying conditions to clinically arrest and reverse the course of late-stage cancer.

Aberrant autophagy is a major risk factor for inflammatory diseases and cancer. However, the genetic basis and underlying mechanisms are less established. UVRAG is a tumor suppressor candidate involved in autophagy, which is truncated in cancers by a frameshift (FS) mutation and expressed as a shortened UVRAG FS . To investigate the role of UVRAG FS in vivo, we generated mutant mice that inducibly express UVRAG FS ( iUVRAG FS ). These mice are normal in basal autophagy but deficient in starvation- and LPS-induced autophagy by disruption of the UVRAG-autophagy complex. iUVRAG FS mice display increased inflammatory response in sepsis, intestinal colitis, and colitis-associated cancer development through NLRP3-inflammasome hyperactivation. Moreover, iUVRAG FS mice show enhanced spontaneous tumorigenesis related to age-related autophagy suppression, resultant β-catenin stabilization, and centrosome amplification. Thus, UVRAG is a crucial autophagy regulator in vivo, and autophagy promotion may help prevent/treat inflammatory disease and cancer in susceptible individuals. UVRAG is involved in autophagy, which loses its tumour suppressor functions when in its truncated form in cancers. Here, the authors use a mouse model that inducibly express this truncated protein and show impaired autophagy, enhanced inflammation and β-catenin stabilisation, which promotes spontaneous tumorigenesis.

Histone modification is aberrantly regulated in cancer and generates an unbalanced state of gene transcription. VprBP, a recently identified kinase, phosphorylates histone H2A on threonine 120 (T120) and is involved in oncogenic transcriptional dysregulation; however, its specific role in colon cancer is undefined. Here, we show that VprBP is overexpressed in colon cancer and directly contributes to epigenetic gene silencing and cancer pathogenesis. Mechanistically, the observed function of VprBP is mediated through H2AT120 phosphorylation (H2AT120p)‐driven transcriptional repression of growth regulatory genes, resulting in a significantly higher proliferative capacity of colon cancer cells. Our preclinical studies using organoid and xenograft models demonstrate that treatment with the VprBP inhibitor B32B3 impairs colonic tumor growth by blocking H2AT120p and reactivating a transcriptional program resembling that of normal cells. Collectively, our work describes VprBP as a master kinase contributing to the development and progression of colon cancer, making it a new molecular target for novel therapeutic strategies. VprBP is a recently identified kinase that induces transcriptional silencing of growth suppressive genes to cause cancer. In this study, we demonstrate that VprBP is overexpressed in colon cancer and drives tumorigenic transcription program through H2AT120 phosphorylation. VprBP‐mediated H2AT120 phosphorylation is functionally important, because blocking VprBP kinase activity impedes colonic tumor growth.

Tumor progression results from a complex interplay between cellular heterogeneity, treatment response, microenvironment and heterocellular interactions. Existing approaches to characterize this interplay suffer from an inability to distinguish between multiple cell types, often lack environmental context and are unable to perform multiplex phenotypic profiling of cell populations. Here we present a high-throughput platform for characterizing, with single-cell resolution, the dynamic phenotypic responses (i.e. morphology changes, proliferation, apoptosis) of heterogeneous cell populations both during standard growth and in response to multiple, co-occurring selective pressures. The speed of this platform enables a thorough investigation of the impacts of diverse selective pressures including genetic alterations, therapeutic interventions, heterocellular components and microenvironmental factors. The platform has been applied to both 2D and 3D culture systems and readily distinguishes between (1) cytotoxic versus cytostatic cellular responses; and (2) changes in morphological features over time and in response to perturbation. These important features can directly influence tumor evolution and clinical outcome. Our image-based approach provides a deeper insight into the cellular dynamics and heterogeneity of tumors (or other complex systems), with reduced reagents and time, offering advantages over traditional biological assays.

Experimental studies have shown that one key factor in driving the emergence of drug resistance in solid tumors is tumor hypoxia, which leads to the formation of localized environmental niches where drug-resistant cell populations can evolve and survive. Hypoxia-activated prodrugs (HAPs) are compounds designed to penetrate to hypoxic regions of a tumor and release cytotoxic or cytostatic agents; several of these HAPs are currently in clinical trial. However, preliminary results have not shown a survival benefit in several of these trials. We hypothesize that the efficacy of treatments involving these prodrugs depends heavily on identifying the correct treatment schedule, and that mathematical modeling can be used to help design potential therapeutic strategies combining HAPs with standard therapies to achieve long-term tumor control or eradication. We develop this framework in the specific context of EGFR-driven non-small cell lung cancer, which is commonly treated with the tyrosine kinase inhibitor erlotinib. We develop a stochastic mathematical model, parametrized using clinical and experimental data, to explore a spectrum of treatment regimens combining a HAP, evofosfamide, with erlotinib. We design combination toxicity constraint models and optimize treatment strategies over the space of tolerated schedules to identify specific combination schedules that lead to optimal tumor control. We find that (i) combining these therapies delays resistance longer than any monotherapy schedule with either evofosfamide or erlotinib alone, (ii) sequentially alternating single doses of each drug leads to minimal tumor burden and maximal reduction in probability of developing resistance, and (iii) strategies minimizing the length of time after an evofosfamide dose and before erlotinib confer further benefits in reduction of tumor burden. These results provide insights into how hypoxia-activated prodrugs may be used to enhance therapeutic effectiveness in the clinic.