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1. The impact of competition between cancer cells and healthy cells on optimal drug delivery

   https://doi.org/10.1051/mmnp/2019043  

   Cho, Heyrim; Levy, Doron (January 2020, Mathematical Modelling of Natural Phenomena)

   Hubert, Florence (Ed.)

   Cell competition is recognized to be instrumental to the dynamics and structure of the tumor-host interface in invasive cancers. In mild competition scenarios, the healthy tissue and cancer cells can coexist. When the competition is aggressive, competitive cells, the so called super-competitors, expand by killing other cells. Novel chemotherapy drugs and molecularly targeted drugs are commonly administered as part of cancer therapy. Both types of drugs are susceptible to various mechanisms of drug resistance, obstructing or preventing a successful outcome. In this paper, we develop a cancer growth model that accounts for the competition between cancer cells and healthy cells. The model incorporates resistance to both chemotherapy and targeted drugs. In both cases, the level of drug resistance is assumed to be a continuous variable ranging from fully-sensitive to fully-resistant. Using our model we demonstrate that when the competition is moderate, therapies using both drugs are more effective compared with single drug therapies. However, when cancer cells are highly competitive, targeted drugs become more effective. The results of the study stress the importance of adjusting the therapy to the pre-treatment resistance levels. We conclude with a study of the spatiotemporal propagation of drug resistance in a competitive setting, verifying that the same conclusions hold in the spatially heterogeneous case. 

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2. Cell cycle plasticity underlies fractional resistance to palbociclib in ER+/HER2− breast tumor cells

   https://doi.org/10.1073/pnas.2309261121  

   Zikry, Tarek M.; Wolff, Samuel C.; Ranek, Jolene S.; Davis, Harris M.; Naugle, Ander; Luthra, Namit; Whitman, Austin A.; Kedziora, Katarzyna M.; Stallaert, Wayne; Kosorok, Michael R.; et al (February 2024, Proceedings of the National Academy of Sciences)

   The CDK4/6 inhibitor palbociclib blocks cell cycle progression in Estrogen receptor–positive, human epidermal growth factor 2 receptor–negative (ER+/HER2−) breast tumor cells. Despite the drug’s success in improving patient outcomes, a small percentage of tumor cells continues to divide in the presence of palbociclib—a phenomenon we refer to as fractional resistance. It is critical to understand the cellular mechanisms underlying fractional resistance because the precise percentage of resistant cells in patient tissue is a strong predictor of clinical outcomes. Here, we hypothesize that fractional resistance arises from cell-to-cell differences in core cell cycle regulators that allow a subset of cells to escape CDK4/6 inhibitor therapy. We used multiplex, single-cell imaging to identify fractionally resistant cells in both cultured and primary breast tumor samples resected from patients. Resistant cells showed premature accumulation of multiple G1 regulators including E2F1, retinoblastoma protein, and CDK2, as well as enhanced sensitivity to pharmacological inhibition of CDK2 activity. Using trajectory inference approaches, we show how plasticity among cell cycle regulators gives rise to alternate cell cycle “paths” that allow individual tumor cells to escape palbociclib treatment. Understanding drivers of cell cycle plasticity, and how to eliminate resistant cell cycle paths, could lead to improved cancer therapies targeting fractionally resistant cells to improve patient outcomes. 

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3. Metabolomics studies of cell–cell interactions using single cell mass spectrometry combined with fluorescence microscopy

   https://doi.org/10.1039/d2sc02298b  

   Chen, Xingxiu; Peng, Zongkai; Yang, Zhibo (June 2022, Chemical Science)

   Cell–cell interactions are critical for transmitting signals among cells and maintaining their normal functions from the single-cell level to tissues. In cancer studies, interactions between drug-resistant and drug-sensitive cells play an important role in the development of chemotherapy resistance of tumors. As metabolites directly reflect the cell status, metabolomics studies provide insight into cell–cell communication. Mass spectrometry (MS) is a powerful tool for metabolomics studies, and single cell MS (SCMS) analysis can provide unique information for understanding interactions among heterogeneous cells. In the current study, we utilized a direct co-culture system (with cell–cell contact) to study metabolomics of single cells affected by cell–cell interactions in their living status. A fluorescence microscope was utilized to distinguish these two types of cells for SCMS metabolomics studies using the Single-probe SCMS technique under ambient conditions. Our results show that through interactions with drug-resistant cells, drug-sensitive cancer cells acquired significantly increased drug resistance and exhibited drastically altered metabolites. Further investigation found that the increased drug resistance was associated with multiple metabolism regulations in drug-sensitive cells through co-culture such as the upregulation of sphingomyelins lipids and lactic acid and the downregulation of TCA cycle intermediates. The method allows for direct MS metabolomics studies of individual cells labeled with fluorescent proteins or dyes among heterogeneous populations. 

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4. A mathematical model for phenotypic heterogeneity in breast cancer with implications for therapeutic strategies

   https://doi.org/10.1098/rsif.2021.0803  

   Li, Xin; Thirumalai, D. (January 2022, Journal of The Royal Society Interface)

   Inevitably, almost all cancer patients develop resistance to targeted therapy. Intratumour heterogeneity is a major cause of drug resistance. Mathematical models that explain experiments quantitatively are useful in understanding the origin of intratumour heterogeneity, which then could be used to explore scenarios for efficacious therapy. Here, we develop a mathematical model to investigate intratumour heterogeneity in breast cancer by exploiting the observation that HER2+ and HER2− cells could divide symmetrically or asymmetrically. Our predictions for the evolution of cell fractions are in quantitative agreement with single-cell experiments. Remarkably, the colony size of HER2+ cells emerging from a single HER2− cell (or vice versa), which occurs in about four cell doublings, also agrees with experimental results, without tweaking any parameter in the model. The theory explains experimental data on the responses of breast tumours under different treatment protocols. We then used the model to predict that, not only the order of two drugs, but also the treatment period for each drug and the tumour cell plasticity could be manipulated to improve the treatment efficacy. Mathematical models, when integrated with data on patients, make possible exploration of a broad range of parameters readily, which might provide insights in devising effective therapies. 

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5. Trellis tree-based analysis reveals stromal regulation of patient-derived organoid drug responses

   https://doi.org/10.1016/j.cell.2023.11.005  

   Ramos_Zapatero, Maria; Tong, Alexander; Opzoomer, James W; O’Sullivan, Rhianna; Cardoso_Rodriguez, Ferran; Sufi, Jahangir; Vlckova, Petra; Nattress, Callum; Qin, Xiao; Claus, Jeroen; et al (December 2023, Cell)

   Patient-derived organoids (PDOs) can model personalized therapy responses; however, current screening technologies cannot reveal drug response mechanisms or how tumor microenvironment cells alter therapeutic performance. To address this, we developed a highly multiplexed mass cytometry platform to measure post- translational modification (PTM) signaling, DNA damage, cell-cycle activity, and apoptosis in >2,500 colorectal cancer (CRC) PDOs and cancer-associated fibroblasts (CAFs) in response to clinical therapies at single-cell resolution. To compare patient- and microenvironment-specific drug responses in thousands of single-cell da- tasets, we developed ‘‘Trellis’’—a highly scalable, tree-based treatment effect analysis method. Trellis single- cell screening revealed that on-target cell-cycle blockage and DNA-damage drug effects are common, even in chemorefractory PDOs. However, drug-induced apoptosis is rarer, patient-specific, and aligns with cancer cell PTM signaling. We find that CAFs can regulate PDO plasticity—shifting proliferative colonic stem cells (proCSCs) to slow-cycling revival colonic stem cells (revCSCs) to protect cancer cells from chemotherapy. 

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