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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. Methylation of CpG Sites as Biomarkers Predictive of Drug-Specific Patient Survival in Cancer

   https://doi.org/10.1177/11769351221131124  

   Neary, Bridget; Lin, Shuting; Qiu, Peng (January 2022, Cancer Informatics)

   Background: Though the development of targeted cancer drugs continues to accelerate, doctors still lack reliable methods for predicting patient response to standard-of-care therapies for most cancers. DNA methylation has been implicated in tumor drug response and is a promising source of predictive biomarkers of drug efficacy, yet the relationship between drug efficacy and DNA methylation remains largely unexplored. Method: In this analysis, we performed log-rank survival analyses on patients grouped by cancer and drug exposure to find CpG sites where binary methylation status is associated with differential survival in patients treated with a specific drug but not in patients with the same cancer who were not exposed to that drug. We also clustered these drug-specific CpG sites based on co-methylation among patients to identify broader methylation patterns that may be related to drug efficacy, which we investigated for transcription factor binding site enrichment using gene set enrichment analysis. Results: We identified CpG sites that were drug-specific predictors of survival in 38 cancer-drug patient groups across 15 cancers and 20 drugs. These included 11 CpG sites with similar drug-specific survival effects in multiple cancers. We also identified 76 clusters of CpG sites with stronger associations with patient drug response, many of which contained CpG sites in gene promoters containing transcription factor binding sites. Conclusion: These findings are promising biomarkers of drug response for a variety of drugs and contribute to our understanding of drug-methylation interactions in cancer. Investigation and validation of these results could lead to the development of targeted co-therapies aimed at manipulating methylation in order to improve efficacy of commonly used therapies and could improve patient survival and quality of life by furthering the effort toward drug response prediction. 

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3. DRMref: comprehensive reference map of drug resistance mechanisms in human cancer

   https://doi.org/10.1093/nar/gkad1087  

   Liu, Xiaona; Yi, Jiahao; Li, Tina; Wen, Jianguo; Huang, Kexin; Liu, Jiajia; Wang, Grant; Kim, Pora; Song, Qianqian; Zhou, Xiaobo (November 2023, Nucleic Acids Research)

   Abstract Drug resistance poses a significant challenge in cancer treatment. Despite the initial effectiveness of therapies such as chemotherapy, targeted therapy and immunotherapy, many patients eventually develop resistance. To gain deep insights into the underlying mechanisms, single-cell profiling has been performed to interrogate drug resistance at cell level. Herein, we have built the DRMref database (https://ccsm.uth.edu/DRMref/) to provide comprehensive characterization of drug resistance using single-cell data from drug treatment settings. The current version of DRMref includes 42 single-cell datasets from 30 studies, covering 382 samples, 13 major cancer types, 26 cancer subtypes, 35 treatment regimens and 42 drugs. All datasets in DRMref are browsable and searchable, with detailed annotations provided. Meanwhile, DRMref includes analyses of cellular composition, intratumoral heterogeneity, epithelial–mesenchymal transition, cell–cell interaction and differentially expressed genes in resistant cells. Notably, DRMref investigates the drug resistance mechanisms (e.g. Aberration of Drug’s Therapeutic Target, Drug Inactivation by Structure Modification, etc.) in resistant cells. Additional enrichment analysis of hallmark/KEGG (Kyoto Encyclopedia of Genes and Genomes)/GO (Gene Ontology) pathways, as well as the identification of microRNA, motif and transcription factors involved in resistant cells, is provided in DRMref for user’s exploration. Overall, DRMref serves as a unique single-cell-based resource for studying drug resistance, drug combination therapy and discovering novel drug targets. 

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4. Generation and Evaluation of Hydrogel-Facilitated 3D Tumor Microenvironments of Breast Cancer

   https://doi.org/10.1142/S1793984422500118  

   Goklany, Sheba; Brown, Earl; De La Torre, Lauryn; Rege, Kaushal (December 2022, Nano LIFE)

   Engineered three-dimensional (3D) cell culture models can accelerate drug discovery, and lead to new fundamental insights in cell–cell, cell–extracellular matrix (ECM), and cell–biomolecule interactions. Existing hydrogel or scaffold-based approaches for generating 3D tumor models do not possess significant tunability and possess limited scalability for high throughput drug screening. We have developed a new library of hydrogels, called Amikagels, which are derived from the crosslinking of amikacin hydrate (AH) and poly(ethylene glycol) diglycidyl ether (PEGDE). Here we describe the use of Amikagels for generating 3D tumor microenvironments (3DTMs) of breast cancer cells. Biological characteristics of these breast cancer 3DTMs, such as drug resistance and hypoxia were evaluated and compared to those of two-dimensional (2D) monolayer cultures. Estrogen receptor (ER) positive breast cancer 3DTMs formed on Amikagels were more dormant compared to their respective 2D monolayer cultures. Relative to their respective 2D cultures, breast cancer 3DTMs were resistant to cell death induced by mitoxantrone and doxorubicin, which are commonly used chemotherapeutic drugs in cancer, including breast cancer. The drug resistance seen in 3DTMs was correlated with hypoxia seen in these cultures but not in 2D monolayer cultures. Inhibition of Mucin 1 (MUC1), which is overexpressed in response to hypoxia, resulted in nearly complete cell death of 2D monolayer and 3DTMs of breast cancer. Combination of an ER stress inducer and MUC1 inhibition further enhanced cell death in 2D monolayer and 3DTMs. Taken together, this study shows that the Amikagel platform represents a novel technology for the generation of physiologically relevant 3DTMs in vitro and can serve as a platform to discover novel treatments for drug-resistant breast cancer. 

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5. Therapeutic Targeting of Stromal-Tumor HGF-MET Signaling in an Organotypic Triple-Negative Breast Tumor Model

   https://doi.org/10.1158/1541-7786.MCR-21-0317  

   Singh, Sunil; Lamichhane, Astha; Rafsanjani Nejad, Pouria; Heiss, Jacob; Baumann, Hannah; Gudneppanavar, Ravindra; Leipzig, Nic D.; Konopka, Michael; Luker, Gary D.; Tavana, Hossein (March 2022, Molecular Cancer Research)

   Abstract The tumor microenvironment (TME) promotes proliferation, drug resistance, and invasiveness of cancer cells. Therapeutic targeting of the TME is an attractive strategy to improve outcomes for patients, particularly in aggressive cancers such as triple-negative breast cancer (TNBC) that have a rich stroma and limited targeted therapies. However, lack of preclinical human tumor models for mechanistic understanding of tumor–stromal interactions has been an impediment to identify effective treatments against the TME. To address this need, we developed a three-dimensional organotypic tumor model to study interactions of patient-derived cancer-associated fibroblasts (CAF) with TNBC cells and explore potential therapy targets. We found that CAFs predominantly secreted hepatocyte growth factor (HGF) and activated MET receptor tyrosine kinase in TNBC cells. This tumor–stromal interaction promoted invasiveness, epithelial-to-mesenchymal transition, and activities of multiple oncogenic pathways in TNBC cells. Importantly, we established that TNBC cells become resistant to monotherapy and demonstrated a design-driven approach to select drug combinations that effectively inhibit prometastatic functions of TNBC cells. Our study also showed that HGF from lung fibroblasts promotes colony formation by TNBC cells, suggesting that blocking HGF-MET signaling potentially could target both primary TNBC tumorigenesis and lung metastasis. Overall, we established the utility of our organotypic tumor model to identify and therapeutically target specific mechanisms of tumor–stromal interactions in TNBC toward the goal of developing targeted therapies against the TME. Implications: Leveraging a state-of-the-art organotypic tumor model, we demonstrated that CAFs-mediated HGF-MET signaling drive tumorigenic activities in TNBC and presents a therapeutic target. 

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