- Hubert, Florence
- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- Mathematical Modelling of Natural Phenomena
- Page Range or eLocation-ID:
- Sponsoring Org:
- National Science Foundation
More Like this
A mathematical model for phenotypic heterogeneity in breast cancer with implications for therapeutic strategiesInevitably, 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.
Phytochemicals inhibit migration of triple negative breast cancer cells by targeting kinase signalingBackground: Cell migration and invasion are essential processes for metastatic dissemination of cancer cells. Significant progress has been made in developing new therapies against oncogenic signaling to eliminate cancer cells and shrink tumors. However, inherent heterogeneity and treatment-induced adaptation to drugs commonly enable subsets of cancer cells to survive therapy. In addition to local recurrence, these cells escape a primary tumor and migrate through the stroma to access the circulation and metastasize to different organs, leading to an incurable disease. As such, therapeutics that block migration and invasion of cancer cells may inhibit or reduce metastasis and significantly improve cancer therapy. This is particularly more important for cancers, such as triple negative breast cancer, that currently lack targeted drugs. Methods: We used cell migration, 3D invasion, zebrafish metastasis model, and phosphorylation analysis of 43 protein kinases in nine triple negative breast cancer (TNBC) cell lines to study effects of fisetin and quercetin on inhibition of TNBC cell migration, invasion, and metastasis. Results: Fisetin and quercetin were highly effective against migration of all nine TNBC cell lines with up to 76 and 74% inhibitory effects, respectively. In addition, treatments significantly reduced 3D invasion of highly motile TNBC cells from spheroids intomore »
Therapeutic Targeting of Stromal-Tumor HGF-MET Signaling in an Organotypic Triple-Negative Breast Tumor ModelAbstract 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 modelmore »
Photothermal-responsive nanosized hybrid polymersome as versatile therapeutics codelivery nanovehicle for effective tumor suppression
Effective cancer therapies often demand delivery of combinations of drugs to inhibit multidrug resistance through synergism, and the development of multifunctional nanovehicles with enhanced drug loading and delivery efficiency for combination therapy is currently a major challenge in nanotechnology. However, such combinations are more challenging to administer than single drugs and can require multipronged approaches to delivery. In addition to being stable and biodegradable, vehicles for such therapies must be compatible with both hydrophobic and hydrophilic drugs, and release drugs at sustained therapeutic levels. Here, we report synthesis of porous silicon nanoparticles conjugated with gold nanorods [composite nanoparticles (cNPs)] and encapsulate them within a hybrid polymersome using double-emulsion templates on a microfluidic chip to create a versatile nanovehicle. This nanovehicle has high loading capacities for both hydrophobic and hydrophilic drugs, and improves drug delivery efficiency by accumulating at the tumor after i.v. injection in mice. Importantly, a triple-drug combination suppresses breast tumors by 94% and 87% at total dosages of 5 and 2.5 mg/kg, respectively, through synergy. Moreover, the cNPs retain their photothermal properties, which can be used to significantly inhibit multidrug resistance upon near-infrared laser irradiation. Overall, this work shows that our nanovehicle has great potential as a drugmore »
Glioblastoma Multiforme, an aggressive primary brain tumor, has a poor prognosis and no effective standard of care treatments. Most patients undergoing radiotherapy, along with Temozolomide chemotherapy, develop resistance to the drug, and recurrence of the tumor is a common issue after the treatment. We propose to model the pathways active in Glioblastoma using Boolean network techniques. The network captures the genetic interactions and possible mutations that are involved in the development of the brain tumor. The model is used to predict the theoretical efficacies of drugs for the treatment of cancer.
We use the Boolean network to rank the critical intervention points in the pathway to predict an effective therapeutic strategy for Glioblastoma. Drug repurposing helps to identify non-cancer drugs that could be effective in cancer treatment. We predict the effectiveness of drug combinations of anti-cancer and non-cancer drugs for Glioblastoma.
Given the genetic profile of a GBM tumor, the Boolean model can predict the most effective targets for treatment. We also identified two-drug combinations that could be more effective in killing GBM cells than conventional chemotherapeutic agents. The non-cancer drug Aspirin could potentially increase the cytotoxicity of TMZ in GBM patients.