Mathematical characterization of population dynamics in breast cancer cells treated with doxorubicin

Understanding therapeutic tolerance through a mathematical model of drug-induced resistance

There is growing recognition that phenotypic plasticity enables cancer cells to adapt to various environmental conditions. An example of this adaptability is the ability of an initially sensitive population of cancer cells to acquire resistance and persist in the presence of therapeutic agents. Understanding the implications of this drug-induced resistance is essential for predicting transient and long-term tumor dynamics subject to treatment. This paper introduces a mathematical model of drug-induced resistance which provides excellent fits to time-resolved in vitro experimental data. From observational data of total numbers of cells, the model unravels the relative proportions of sensitive and resistance subpopulations and quantifies their dynamics as a function of drug dose. The predictions are then validated using data on drug doses that were not used when fitting parameters. Optimal control techniques are then utilized to discover dosing strategies that could lead to better outcomes as quantified by lower total cell volume.

Validating the predictions of mathematical models describing tumor growth and treatment response

Despite advances in methods to interrogate tumor biology, the observational and population-based approach of classical cancer research and clinical oncology does not enable anticipation of tumor outcomes to hasten the discovery of cancer mechanisms and personalize disease management. To address these limitations, individualized cancer forecasts have been shown to predict tumor growth and therapeutic response, inform treatment optimization, and guide experimental efforts. These predictions are obtained via computer simulations of mathematical models that are constrained with data from a patient's cancer and experiments. This book chapter addresses the validation of these mathematical models to forecast tumor growth and treatment response. We start with an overview of mathematical modeling frameworks, model selection techniques, and fundamental metrics. We then describe the usual strategies employed to validate cancer forecasts in preclinical and clinical scenarios. Finally, we discuss existing barriers in validating these predictions along with potential strategies to address them.

A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin

Tumor heterogeneity contributes significantly to chemoresistance, a leading cause of treatment failure. To better personalize therapies, it is essential to develop tools capable of identifying and predicting intra- and inter-tumor heterogeneities. Biology-inspired mathematical models are capable of attacking this problem, but tumor heterogeneity is often overlooked in in-vivo modeling studies, while phenotypic considerations capturing spatial dynamics are not typically included in in-vitro modeling studies. We present a data assimilation-prediction pipeline with a two-phenotype model that includes a spatiotemporal component to characterize and predict the evolution of in-vitro breast cancer cells and their heterogeneous response to chemotherapy. Our model assumes that the cells can be divided into two subpopulations: surviving cells unaffected by the treatment, and irreversibly damaged cells undergoing treatment-induced death. MCF7 breast cancer cells were previously cultivated in wells for up to 1000 hours, treated with various concentrations of doxorubicin and imaged with time-resolved microscopy to record spatiotemporally-resolved cell count data. Images were used to generate cell density maps. Treatment response predictions were initialized by a training set and updated by weekly measurements. Our mathematical model successfully calibrated the spatiotemporal cell growth dynamics, achieving median [range] concordance correlation coefficients of > .99 [.88, >.99] and .73 [.58, .85] across the whole well and individual pixels, respectively. Our proposed data assimilation-prediction approach achieved values of .97 [.44, >.99] and .69 [.35, .79] for the whole well and individual pixels, respectively. Thus, our model can capture and predict the spatiotemporal dynamics of MCF7 cells treated with doxorubicin in an in-vitro setting.

Drug tolerance and persistence in bacteria, fungi and cancer cells: Role of non-genetic heterogeneity

A common feature of bacterial, fungal and cancer cell populations upon treatment is the presence of tolerant and persistent cells able to survive, and sometimes grow, even in the presence of usually inhibitory or lethal drug concentrations, driven by non-genetic differences among individual cells in a population. Here we review and compare data obtained on drug survival in bacteria, fungi and cancer cells to unravel common characteristics and cellular pathways, and to point their singularities. This comparative work also allows to cross-fertilize ideas across fields. We particularly focus on the role of gene expression variability in the emergence of cell-cell non-genetic heterogeneity because it represents a possible common basic molecular process at the origin of most persistence phenomena and could be monitored and tuned to help improve therapeutic interventions.

Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations

Intratumor heterogeneity reduces treatment efficacy and complicates our understanding of tumor progression and there is a pressing need to understand the functions of heterogeneous tumor cell subpopulations within a tumor, yet systems to study these processes in vitro are limited. Single-cell RNA sequencing (scRNA-seq) has revealed that some cancer cell lines include distinct subpopulations. Here, we present clusterCleaver, a computational package that uses metrics of statistical distance to identify candidate surface markers maximally unique to transcriptomic subpopulations in scRNA-seq which may be used for FACS isolation. With clusterCleaver, ESAM and BST2/tetherin were experimentally validated as surface markers which identify and separate major transcriptomic subpopulations within MDA-MB-231 and MDA-MB-436 cells, respectively. clusterCleaver is a computationally efficient and experimentally validated workflow for identification of surface markers for tracking and isolating transcriptomically distinct subpopulations within cell lines. This tool paves the way for studies on coexisting cancer cell subpopulations in well-defined in vitro systems.

A novel combination therapy for ER+ breast cancer suppresses drug resistance via an evolutionary double-bind

Chemotherapy remains a commonly used and important treatment option for metastatic breast cancer. A majority of ER+ metastatic breast cancer patients ultimately develop resistance to chemotherapy, resulting in disease progression. We hypothesized that an "evolutionary double-bind", where treatment with one drug improves the response to a different agent, would improve the effectiveness and durability of responses to chemotherapy. This approach exploits vulnerabilities in acquired resistance mechanisms. Evolutionary models can be used in refractory cancer to identify alternative treatment strategies that capitalize on acquired vulnerabilities and resistance traits for improved outcomes. To develop and test these models, ER+ breast cancer cell lineages sensitive and resistant to chemotherapy are grown in spheroids with varied initial population frequencies to measure cross-sensitivity and efficacy of chemotherapy and add-on treatments such as disulfiram combination treatment. Different treatment schedules then assessed the best strategy for reducing the selection of resistant populations. We developed and parameterized a game-theoretic mathematical model from this in vitro experimental data, and used it to predict the existence of a double-bind where selection for resistance to chemotherapy induces sensitivity to disulfiram. The model predicts a dose-dependent re-sensitization (a double-bind) to chemotherapy for monotherapy disulfiram.

Patient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data

Despite the remarkable advances in cancer diagnosis, treatment, and management over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires the integration of patient-specific information with an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous yet practical mathematical theory of tumor initiation, development, invasion, and response to therapy. We begin this review with an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on big data and artificial intelligence. We then present illustrative examples of mathematical models manifesting their utility and discuss the limitations of stand-alone mechanistic and data-driven models. We then discuss the potential of mechanistic models for not only predicting but also optimizing response to therapy on a patient-specific basis. We describe current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models.

Conjugate of Natural Bacteriochlorin with Doxorubicin for Combined Photodynamic and Chemotherapy

Chemotherapy is among the main classical approaches to the treatment of oncologic diseases. Its efficiency has been comprehensively proven by clinical examinations; however, the low selectivity of chemotherapeutic agents limits the possibilities of this method, making it necessary to search for new approaches to the therapy of oncologic diseases. Photodynamic therapy is the least invasive method and a very efficient alternative for the treatment of malignant tumors; however, its efficiency depends on the depth of light penetration into the tissue and on the degree of oxygenation of the treatment zone. In this work, a hitherto unknown conjugate of a natural bacteriochlorin derivative and doxorubicin was obtained. In vitro and in vivo studies showed a more pronounced activity of the conjugate against MCF-7 and 4T1 cells and its higher tumorotropicity in animal tumor-bearing animals compared to free anthracycline antibiotic. The suggested conjugate implements the advantages of photodynamic therapy and chemotherapy and has great potential in cancer treatment.

Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations

Intratumor heterogeneity reduces treatment efficacy and complicates our understanding of tumor progression. There is a pressing need to understand the functions of heterogeneous tumor cell subpopulations within a tumor, yet biological systems to study these processes in vitro are limited. With the advent of single-cell RNA sequencing (scRNA-seq), it has become clear that some cancer cell line models include distinct subpopulations. Heterogeneous cell lines offer a unique opportunity to study the dynamics and evolution of genetically similar cancer cell subpopulations in controlled experimental settings. Here, we present clusterCleaver, a computational package that uses metrics of statistical distance to identify candidate surface markers maximally unique to transcriptomic subpopulations in scRNA-seq which may be used for FACS isolation. clusterCleaver was experimentally validated using the MDA-MB-231 and MDA-MB-436 breast cancer cell lines. ESAM and BST2/tetherin were experimentally confirmed as surface markers which identify and separate major transcriptomic subpopulations within MDA-MB-231 and MDA-MB-436 cells, respectively. clusterCleaver is a computationally efficient and experimentally validated workflow for identification and enrichment of distinct subpopulations within cell lines which paves the way for studies on the coexistence of cancer cell subpopulations in well-defined in vitro systems.

Hypophyllanthin and Phyllanthin from Phyllanthus niruri Synergize Doxorubicin Anticancer Properties against Resistant Breast Cancer Cells

Doxorubicin (DOX) is a cornerstone chemotherapeutic agent for the treatment of several malignancies such as breast cancer; however, its activity is ameliorated by the development of a resistant phenotype. Phyllanthus species have been studied previously for their potential anticancer properties. The current work is aimed to study the potential cytotoxicity and chemomodulatory effects of hypophyllanthin (PN4) and phyllanthin (PN5) isolated from Phyllanthus niruri to DOX against the adriamycin multidrug-resistant breast cancer cells (MCF-7ADR) and elucidate their mechanism of action. The major compounds of the active methylene chloride fraction were isolated and assessed for their potential cytotoxicity and chemomodulatory effects on DOX against naïve (MCF-7) and resistant breast (MCF-7ADR) cancer cells. The mechanism of action of both compounds in terms of their impacts on programmed/non-programmed cell death (apoptosis and autophagy/necrosis), cell cycle progression/arrest, and tumor cell migration/invasion was investigated. Both compounds PN4 and PN5 showed a moderate but similar potency against MCF-7 as well as MCF-7ADR and significantly synergized DOX-induced anticancer properties against MCF-7ADR. The chemomodulatory effect of both compounds to DOX was found to be via potentiating DOX-induced cell cycle interference and apoptosis induction. It was found that PN4 and PN5 blocked the apoptosis-escape autophagy pathway in MCF-7ADR. On the molecular level, both compounds interfered with SIRT1 expression and consequently suppressed Akt phosphorylation, and PN5 blocked apoptosis escape. Furthermore, PN4 and PN5 showed promising antimigratory and anti-invasive effects against MCF-7ADR, as confirmed by suppression of N-cadherin/β-catenin expression. In conclusion, for the first time, hypophyllanthin and phyllanthin isolated from P. niruri showed promising chemomodulatory effects to the DOX-induced chemotherapeutic activity against MCF-7ADR. Both compounds significantly synergized DOX-induced anticancer properties against MCF-7ADR. This enhanced activity was explained by further promoting DOX-induced apoptosis and suppressing the apoptosis-escape autophagy feature of the resistant breast cancer cells. Both compounds (hypophyllanthin and phyllanthin) interfered with the SIRT1/Akt pathway and suppressed the N-cadherin/β-catenin axis, confirming the observed antiproliferative, cytotoxic, and anti-invasive effects of hypophyllanthin and phyllanthin.