Temporally sequenced anticancer drugs overcome adaptive resistance by targeting a vulnerable chemotherapy-induced phenotypic transition

Recent research on the physicochemical properties and biological activities of quinones and their practical applications: a comprehensive review

Quinones represent a class of crude organic compounds ubiquitously distributed in nature. Their distinctive quinone-type structure confers upon them unique properties and applications. Quinones demonstrate significant biological activities, including antioxidant, antimicrobial, and antitumor properties. Additionally, they demonstrate noteworthy physicochemical characteristics, including excellent dyeing properties and stability. Given their diverse qualities, quinones hold significant promise for applications in industrial manufacturing, healthcare, and food production, thus garnering considerable attention in recent years. While there is a growing body of research on quinones, the existing literature falls short of providing a comprehensive review encompassing recent advancements in this field along with established knowledge. This paper offers a comprehensive review of research progress for quinones, encompassing structural classification, source synthesis, extraction methods, properties, functions, and specific applications. It serves as a reference and theoretical foundation for the further development and utilization of quinones.

Recent advances in hematopoietic cell kinase in cancer progression: Mechanisms and inhibitors

Hematopoietic cell kinase (Hck), a non-receptor tyrosine kinase belonging to the Src kinase family, is intricately linked to the pathogenesis of numerous human diseases, with a particularly pronounced association with cancer. Hck not only directly impacts the proliferation, migration, and apoptosis of cancer cells but also interacts with JAK/STAT, MEK/ERK, PI3K/AKT, CXCL12/CXCR4, and other pathways. Hck also influences the tumor microenvironment to facilitate the onset and progression of cancer. This paper delves into the functional role and regulatory mechanisms of Hck in various solid tumors. Additionally, it explores the implications of Hck in hematological malignancies. The review culminates with a summary of the current research status of Hck inhibitors, the majority of which are in the pre-clinical phase of investigation. Notably, these inhibitors are predominantly utilized in the therapeutic management of leukemia, with their combinatorial potential indicating promising avenues for future research. In conclusion, this review underscores the significance of the mechanism of Hck in solid tumors. This insight is crucial for comprehending the current research trends regarding Hck: targeted therapy against Hck shows great promise in both diagnosis and treatment of malignant tumors. Further investigation into the role of Hck in cancer, coupled with the development of specific inhibitors, has the potential to revolutionize approaches to cancer treatment.

Optimizing cancer therapy: a review of the multifaceted effects of metronomic chemotherapy

Metronomic chemotherapy (MCT), characterized by the continuous administration of chemotherapeutics at a lower dose without prolonged drug-free periods, has garnered significant attention over the last 2 decades. Extensive evidence from both pre-clinical and clinical settings indicates that MCT induces distinct biological effects than the standard Maximum Tolerated Dose (MTD) chemotherapy. The low toxicity profile, reduced likelihood of inducing acquired therapeutic resistance, and low cost of MCT render it an attractive chemotherapeutic regimen option. One of the most prominent aspects of MCT is its anti-angiogenesis effects. It has been shown to stimulate the expression of anti-angiogenic molecules, thereby inhibiting angiogenesis. In addition, MCT has been shown to decrease the regulatory T-cell population and promote anti-tumor immune response through inducing dendritic cell maturation and increasing the number of cytotoxic T-cells. Combination therapies utilizing MCT along with oncolytic virotherapy, radiotherapy or other chemotherapeutic regimens have been studied extensively. This review provides an overview of the current status of MCT research and the established mechanisms of action of MCT treatment and also offers insights into potential avenues of development for MCT in the future.

Chemoresistance to additive PARP/PI3K dual inhibition in triple-negative breast cancer cell lines is associated with adaptive stem cell-like prevalence

Cancer stem-like cells (CSCs) are posited to exhibit specialized oncogenic capacity to drive malignancies. CSCs are distinguished by enhanced hallmarks of cancer, including apoptosis avoidance, phenotypic plasticity and aberrant growth pathway signaling. Standard-of-care chemotherapies targeted to rapidly cycling cells routinely fail to eliminate this resistant subpopulation, leading to disease recurrence and metastasis. Triple-negative breast cancer (TNBC), a highly aggressive subtype of breast cancer, is enriched for tumor-propagating CD44+/CD24-/low CSCs, which are poorly ablated by chemotherapeutics and are associated with poor prognosis. CD44 governs sustained PI3K signaling in breast cancer, which is essential for CSC maintenance. PI3K inhibition can elicit DNA damage and down-regulate BRCA1 expression, which in turn enhance the synthetic lethality of PARP inhibitors. Here, we examined a dual chemotherapeutic approach targeting these pathways by combining a pan-PI3K inhibitor (Buparlisib) and a PARP1 inhibitor (Olaparib) on a panel of TNBC cell lines with distinct mutational profiles and proportions of CSCs. We observed differential sensitivity to this dual inhibition strategy and varying cellular stress and resistance responses across eight TNBC lines. The dual chemotherapeutic effect is associated with a reduction in S-phase cells, an increased in apoptotic cells and elevated expression of cleaved PARP, indicating a provoked replicative stress response. We observed that PARP/PI3K inhibition efficacy was potentiated by repeated administration in some TNBC lines and identified critical treatment schedules, which further potentiated the dual chemotherapeutic effect. Dual inhibition induced small but significant increases in CSC relative abundance as marked by CD44+/CD24-/low or ALDH1+ cells and increased stress and survival signaling in multiple TNBC cell lines, suggesting this sub-population contributes to TNBC chemoresistance. These results suggest the additive effects of PARP and PI3K inhibition against CSC phenotypes may be enhanced by temporally-staged administration in TNBC cells.

Modeling tumors as species-rich ecological communities

Many advanced cancers resist therapeutic intervention. This process is fundamentally related to intra-tumor heterogeneity: multiple cell populations, each with different mutational and phenotypic signatures, coexist within a tumor and its metastatic nodes. Like species in an ecosystem, many cancer cell populations are intertwined in a complex network of ecological interactions. Most mathematical models of tumor ecology, however, cannot account for such phenotypic diversity nor are able to predict its consequences. Here we propose that the Generalized Lotka-Volterra model (GLV), a standard tool to describe complex, species-rich ecological communities, provides a suitable framework to describe the ecology of heterogeneous tumors. We develop a GLV model of tumor growth and discuss how its emerging properties, such as outgrowth and multistability, provide a new understanding of the disease. Additionally, we discuss potential extensions of the model and their application to three active areas of cancer research, namely phenotypic plasticity, the cancer-immune interplay and the resistance of metastatic tumors to treatment. Our work outlines a set of questions and a tentative road map for further research in cancer ecology.

Crosstalk between lipid metabolism and EMT: emerging mechanisms and cancer therapy

Lipids are the key component of all membranes composed of a variety of molecules that transduce intracellular signaling and provide energy to the cells in the absence of nutrients. Alteration in lipid metabolism is a major factor for cancer heterogeneity and a newly identified cancer hallmark. Reprogramming of lipid metabolism affects the diverse cancer phenotypes, especially epithelial-mesenchymal transition (EMT). EMT activation is considered to be an essential step for tumor metastasis, which exhibits a crucial role in the biological processes including development, wound healing, and stem cell maintenance, and has been widely reported to contribute pathologically to cancer progression. Altered lipid metabolism triggers EMT and activates multiple EMT-associated oncogenic pathways. Although the role of lipid metabolism-induced EMT in tumorigenesis is an attractive field of research, there are still significant gaps in understanding the underlying mechanisms and the precise contributions of this interplay. Further study is needed to clarify the specific molecular mechanisms driving the crosstalk between lipid metabolism and EMT, as well as to determine the potential therapeutic implications. The increased dependency of tumor cells on lipid metabolism represents a novel therapeutic target, and targeting altered lipid metabolism holds promise as a strategy to suppress EMT and ultimately inhibit metastasis.

EMP2 Serves as a Functional Biomarker for Chemotherapy-Resistant Triple-Negative Breast Cancer

Breast cancer (BC) remains among the most commonly diagnosed cancers in women worldwide. Triple-negative BC (TNBC) is a subset of BC characterized by aggressive behavior, a high risk of distant recurrence, and poor overall survival rates. Chemotherapy is the backbone for treatment in patients with TNBC, but outcomes remain poor compared to other BC subtypes, in part due to the lack of recognized functional targets. In this study, the expression of the tetraspan protein epithelial membrane protein 2 (EMP2) was explored as a predictor of TNBC response to standard chemotherapy. We demonstrate that EMP2 functions as a prognostic biomarker for patients treated with taxane-based chemotherapy, with high expression at both transcriptomic and protein levels following treatment correlating with poor overall survival. Moreover, we show that targeting EMP2 in combination with docetaxel reduces tumor load in syngeneic and xenograft models of TNBC. These results provide support for the prognostic and therapeutic potential of this tetraspan protein, suggesting that anti-EMP2 therapy may be beneficial for the treatment of select chemotherapy-resistant TNBC tumors.

Optical imaging reveals chemotherapy-induced metabolic reprogramming of residual disease and recurrence

Fewer than 20% of triple-negative breast cancer patients experience long-term responses to mainstay chemotherapy. Resistant tumor subpopulations use alternative metabolic pathways to escape therapy, survive, and eventually recur. Here, we show in vivo, longitudinal metabolic reprogramming in residual disease and recurrence of triple-negative breast cancer xenografts with varying sensitivities to the chemotherapeutic drug paclitaxel. Optical imaging coupled with metabolomics reported an increase in non-glucose-driven mitochondrial metabolism and an increase in intratumoral metabolic heterogeneity during regression and residual disease in resistant MDA-MB-231 tumors. Conversely, sensitive HCC-1806 tumors were primarily reliant on glucose uptake and minimal changes in metabolism or heterogeneity were observed over the tumors' therapeutic life cycles. Further, day-matched resistant HCC-1806 tumors revealed a higher reliance on mitochondrial metabolism and elevated metabolic heterogeneity compared to sensitive HCC-1806 tumors. Together, metabolic flexibility, increased reliance on mitochondrial metabolism, and increased metabolic heterogeneity are defining characteristics of persistent residual disease, features that will inform the appropriate type and timing of therapies.

Stress in the metastatic journey - the role of cell communication and clustering in breast cancer progression and treatment resistance

Breast cancer stands as the most prevalent malignancy afflicting women. Despite significant advancements in its diagnosis and treatment, breast cancer metastasis continues to be a leading cause of mortality among women. To metastasize, cancer cells face numerous challenges: breaking away from the primary tumor, surviving in the circulation, establishing in a distant location, evading immune detection and, finally, thriving to initiate a new tumor. Each of these sequential steps requires cancer cells to adapt to a myriad of stressors and develop survival mechanisms. In addition, most patients with breast cancer undergo surgical removal of their primary tumor and have various therapeutic interventions designed to eradicate cancer cells. Despite this plethora of attacks and stresses, certain cancer cells not only manage to persist but also proliferate robustly, giving rise to substantial tumors that frequently culminate in the patient's demise. To enhance patient outcomes, there is an imperative need for a deeper understanding of the molecular and cellular mechanisms that empower cancer cells to not only survive but also expand. Herein, we delve into the intrinsic stresses that cancer cells encounter throughout the metastatic journey and the additional stresses induced by therapeutic interventions. We focus on elucidating the remarkable strategies adopted by cancer cells, such as cell-cell clustering and intricate cell-cell communication mechanisms, to ensure their survival.

On minimising tumoural growth under treatment resistance

Drug resistance is a major challenge for curative cancer treatment, representing the main reason of death in patients. Evolutionary biology suggests pauses between treatment rounds as a way to delay or even avoid resistance emergence. Indeed, this approach has already shown promising preclinical and early clinical results, and stimulated the development of mathematical models for finding optimal treatment protocols. Due to their complexity, however, these models do not lend themself to a rigorous mathematical analysis, hence so far clinical recommendations generally relied on numerical simulations and ad-hoc heuristics. Here, we derive two mathematical models describing tumour growth under genetic and epigenetic treatment resistance, respectively, which are simple enough for a complete analytical investigation. First, we find key differences in response to treatment protocols between the two modes of resistance. Second, we identify the optimal treatment protocol which leads to the largest possible tumour shrinkage rate. Third, we fit the "epigenetic model" to previously published xenograft experiment data, finding excellent agreement, underscoring the biological validity of our approach. Finally, we use the fitted model to calculate the optimal treatment protocol for this specific experiment, which we demonstrate to cause curative treatment, making it superior to previous approaches which generally aimed at stabilising tumour burden. Overall, our approach underscores the usefulness of simple mathematical models and their analytical examination, and we anticipate our findings to guide future preclinical and, ultimately, clinical research in optimising treatment regimes.

Update on Epithelial-Mesenchymal Plasticity in Cancer Progression

Epithelial-mesenchymal transition (EMT) is a cellular process by which epithelial cells lose their characteristics and acquire mesenchymal traits to promote cell movement. This program is aberrantly activated in human cancers and endows tumor cells with increased abilities in tumor initiation, cell migration, invasion, metastasis, and therapy resistance. The EMT program in tumors is rarely binary and often leads to a series of gradual or intermediate epithelial-mesenchymal states. Functionally, epithelial-mesenchymal plasticity (EMP) improves the fitness of cancer cells during tumor progression and in response to therapies. Here, we discuss the most recent advances in our understanding of the diverse roles of EMP in tumor initiation, progression, metastasis, and therapy resistance and address major clinical challenges due to EMP-driven phenotypic heterogeneity in cancer. Uncovering novel molecular markers and key regulators of EMP in cancer will aid the development of new therapeutic strategies to prevent cancer recurrence and overcome therapy resistance.

Differential Expression of SRY-Related HMG-Box Transcription Factor 2, Oligodendrocyte Lineage Transcription Factor 2, and Zinc Finger E-Box Binding Homeobox 1 in Serum-Derived Extracellular Vesicles: Implications for Mithramycin Sensitivity and Targeted Therapy in High-Grade Glioma

Glioblastoma multiforme (GBM) is the most aggressive type of glioma and is often resistant to traditional therapies. Evidence suggests that glioma stem cells (GSCs) contribute to this resistance. Mithramycin (Mit-A) targets GSCs and exhibits antitumor activity in GBM by affecting transcriptional targets such as SRY-related HMG-box transcription factor 2 (SOX2), oligodendrocyte lineage transcription factor 2 (OLIG2), and zinc finger E-box binding homeobox 1 (ZEB1). However, its clinical use has been limited by toxicity. This study explored the diagnostic potential of serum extracellular vesicles (EVs) to identify Mit-A responders. Serum EVs were isolated from 70 glioma patients, and targeted gene expression was analyzed using qRT-PCR. Using chemosensitivity assay, we identified 8 Mit-A responders and 17 nonresponders among 25 glioma patients. The M-score showed a significant correlation (p = 0.045) with isocitrate dehydrogenase 1 mutation but not other clinical variables. The genes SOX2 (p = 0.005), OLIG2 (p = 0.003), and ZEB1 (p = 0.0281) were found to be upregulated in the responder EVs. SOX2 had the highest diagnostic potential (AUC = 0.875), followed by OLIG2 (AUC = 0.772) and ZEB1 (AUC = 0.632).The combined gene panel showed significant diagnostic efficacy (AUC = 0.956) through logistic regression analysis. The gene panel was further validated in the serum EVs of 45 glioma patients. These findings highlight the potential of Mit-A as a targeted therapy for high-grade glioma based on differential gene expression in serum EVs. The gene panel could serve as a diagnostic tool to predict Mit-A sensitivity, offering a promising approach for personalized treatment strategies and emphasizing the role of GSCs in therapeutic resistance.

Therapeutic Approaches of Dual-targeted Nanomedicines for Tumor Multidrug Resistance

Currently, the main cause of cancer chemotherapy failure is multi-drug resistance (MDR), which involves a variety of complex mechanisms. Compared with traditional small-molecule chemotherapy, targeted nanomedicines offer promising alternative strategies as an emerging form of therapy, especially active targeted nanomedicines. However, although single-targeted nanomedicines have made some progress in tumor therapy, the complexity of tumor microenvironment and tumor heterogeneity limits their efficacy. Dual-targeted nanomedicines can simultaneously target two tumor-specific factors that cause tumor MDR, which have the potential in overcoming tumor MDR superior to single-targeted nanomedicines by further enhancing cell uptake and cytotoxicity in new forms, as well as the effectiveness of tumor-targeted delivery. This review discusses tumor MDR mechanisms and the latest achievements applied to dual-targeted nanomedicines in tumor MDR.

The Potential of Epigallocatechin Gallate in Targeting Cancer Stem Cells: A Comprehensive Review

The dreadful scenario of cancer prevails due to the presence of cancer stem cells (CSCs), which contribute to tumor growth, metastasis, invasion, resistance to chemo- and radiotherapy, and recurrence. CSCs are a small subpopulation of cells within the tumor that are characterized by self-renewal capability and have the potential to manifest heterogeneous lineages of cancer cells that constitute the tumor. The major bioactive green tea polyphenol (-)-epigallocatechin gallate (EGCG) has been fruitful in downgrading cancer stemness signaling and CSC biomarkers in cancer progression. EGCG has been evidenced to maneuver extrinsic and intrinsic apoptotic pathways in order to decrease the viability of CSCs. Cancer stemness is intricately related to epithelial-mesenchymal transition (EMT), metastasis and therapy resistance, and EGCG has been evidenced to regress all these CSC-related effects. By inhibiting CSC characteristics EGCG has also been evidenced to sensitize the tumor cells to radiotherapy and chemotherapy. However, the use of EGCG in in vitro and in vivo cancer models raises concern about its bioavailability, stability and efficacy against spheroids raised from parental cells. Therefore, novel nano formulations of EGCG and adjuvant therapy of EGCG with other phytochemicals or drugs or small molecules may have a better prospect in targeting CSCs. However, extensive clinical research is still awaited to elucidate a full proof impact of EGCG in cancer therapy.

Stochastic epithelial-mesenchymal transitions diversify non-cancerous lung cell behaviours

Epithelial-mesenchymal plasticity (EMP) is a hallmark of cancer. By enabling cells to shift between different morphological and functional states, EMP promotes invasion, metastasis and therapy resistance. We report that near-diploid non-cancerous human epithelial lung cells spontaneously shift along the EMP spectrum without genetic changes. Strikingly, more than half of single cell-derived clones adopt a mesenchymal morphology. We independently characterise epithelial-like and mesenchymal-like clones. Mesenchymal clones lose epithelial markers, display larger cell aspect ratios and lower motility, with mostly unaltered proliferation rates. Stemness marker expression and metabolic rewiring diverge independently of phenotypes. In 3D culture, more epithelial clones become mesenchymal-like. Thus, non-cancerous epithelial cells may acquire cancer metastasis-associated features prior to genetic alterations and cancerous transformation.

HCK Promotes High-Grade Serous Ovarian Cancer Tumorigenesis through CD44 and NOTCH3 Signaling

High-grade serous ovarian cancer (HGSOC) is a highly aggressive and lethal subtype of ovarian cancer. While most patients initially respond to standard-of-care treatment, the majority will eventually relapse and succumb to their disease. Despite significant advances in our understanding of this disease, the mechanisms that govern the distinctions between HGSOC with good and poor prognosis remain unclear. In this study, we implemented a proteogenomic approach to analyze gene expression, proteomic and phosphoproteomic profiles of HGSOC tumor samples to identify molecular pathways that distinguish HGSOC tumors relative to clinical outcome. Our analyses identify significant upregulation of hematopoietic cell kinase (HCK) expression and signaling in poor prognostic HGSOC patient samples. Analyses of independent gene expression datasets and IHC of patient samples confirmed increased HCK signaling in tumors relative to normal fallopian or ovarian samples and demonstrated aberrant expression in tumor epithelial cells. Consistent with the association between HCK expression and tumor aggressiveness in patient samples, in vitro phenotypic studies showed that HCK can, in part, promote cell proliferation, colony formation, and invasive capacity of cell lines. Mechanistically, HCK mediates these phenotypes, partly through CD44 and NOTCH3-dependent signaling, and inhibiting CD44 or NOTCH3 activity, either genetically or through gamma-secretase inhibitors, can revert HCK-driven phenotypes.

IMPLICATIONS

Collectively, these studies establish that HCK acts as an oncogenic driver of HGSOC through aberrant activation of CD44 and NOTCH3 signaling and identifies this network as a potential therapeutic opportunity in a subset of patients with aggressive and recurrent HGSOC.

A Continuation Technique for Maximum Likelihood Estimators in Biological Models

Estimating model parameters is a crucial step in mathematical modelling and typically involves minimizing the disagreement between model predictions and experimental data. This calibration data can change throughout a study, particularly if modelling is performed simultaneously with the calibration experiments, or during an on-going public health crisis as in the case of the COVID-19 pandemic. Consequently, the optimal parameter set, or maximal likelihood estimator (MLE), is a function of the experimental data set. Here, we develop a numerical technique to predict the evolution of the MLE as a function of the experimental data. We show that, when considering perturbations from an initial data set, our approach is significantly more computationally efficient that re-fitting model parameters while producing acceptable model fits to the updated data. We use the continuation technique to develop an explicit functional relationship between fit model parameters and experimental data that can be used to measure the sensitivity of the MLE to experimental data. We then leverage this technique to select between model fits with similar information criteria, a priori determine the experimental measurements to which the MLE is most sensitive, and suggest additional experiment measurements that can resolve parameter uncertainty.

Utility of CD44/CD24 in the Outcome and Prognosis of Oral Squamous Cell Carcinoma: A Systematic Review

Cancer stem cells (CSCs) are characterized by their capacity for self-renewal and differentiation. CD44 and CD24 are two commonly used markers to identify these CSCs. Despite the enormous amount of data available in the literature, their specificity and coexistence remain elusive in oral squamous cell carcinoma (OSCC). In the present review, we aimed to assess the diagnostic utility of the CD44/CD24 combination in tumor development and metastasis in OSCC. Two investigators independently performed a systematic search to identify all the relevant studies from various electronic databases. Out of 694 articles, 9 were found eligible for further evaluation. Details including the number of patients, gender, site, tobacco and alcohol consumption, histological stage, CD24 expression, CD44 expression, CD44/CD24 expression, nodal status, disease-free survival, and overall survival were extracted. CD44+CD24- expression was noted in 35/207 (16.9%) cases, CD44+CD24+ in 53/207 (25.6%) cases, CD44-CD24- in 49/207 subjects (23.67%), and CD44-/CD24+ in 70/207 (33.81%) cases. CD44 or CD24 or their co-expression did not correlate with the disease-free survival rate, and double negatives (CD44-/CD24-) demonstrated a higher overall survival than other immunotypes. CD44/CD24 profile may be used on small incisional biopsies to predict the outcome and treatment planning. This finding may help in developing new therapeutic targets to suppress cancer metastasis and provide a better long-term prognosis for patients diagnosed with OSCC.

Characteristics and molecular mechanism of drug-tolerant cells in cancer: a review

Drug resistance in tumours has seriously hindered the therapeutic effect. Tumour drug resistance is divided into primary resistance and acquired resistance, and the recent study has found that a significant proportion of cancer cells can acquire stable drug resistance from scratch. This group of cells first enters the drug tolerance state (DT state) under drug pressure, and gradually acquires stable drug resistance through adaptive mutations in this state. Although the specific mechanisms underlying the formation of drug tolerant cells (DTCs) remain unclear, various proteins and signalling pathways have been identified as being involved in the formation of DTCs. In the current review, we summarize the characteristics, molecular mechanisms and therapeutic strategies of DTCs in detail.

Statistical inference of the rates of cell proliferation and phenotypic switching in cancer

Recent evidence suggests that nongenetic (epigenetic) mechanisms play an important role at all stages of cancer evolution. In many cancers, these mechanisms have been observed to induce dynamic switching between two or more cell states, which commonly show differential responses to drug treatments. To understand how these cancers evolve over time, and how they respond to treatment, we need to understand the state-dependent rates of cell proliferation and phenotypic switching. In this work, we propose a rigorous statistical framework for estimating these parameters, using data from commonly performed cell line experiments, where phenotypes are sorted and expanded in culture. The framework explicitly models the stochastic dynamics of cell division, cell death and phenotypic switching, and it provides likelihood-based confidence intervals for the model parameters. The input data can be either the fraction of cells or the number of cells in each state at one or more time points. Through a combination of theoretical analysis and numerical simulations, we show that when cell fraction data is used, the rates of switching may be the only parameters that can be estimated accurately. On the other hand, using cell number data enables accurate estimation of the net division rate for each phenotype, and it can even enable estimation of the state-dependent rates of cell division and cell death. We conclude by applying our framework to a publicly available dataset.

Cancer stem cells: an insight into the development of metastatic tumors and therapy resistance

The term "cancer stem cells" (CSCs) refers to cancer cells that exhibit traits parallel to normal stem cells, namely the potential to give rise to every type of cell identified in a tumor microenvironment. It has been found that CSCs usually develops from other neoplastic cells or non-cancerous somatic cells by acquiring stemness and malignant characteristics through particular genetic modifications. A trivial number of CSCs, identified in solid and liquid cancer, can give rise to an entire tumor population with aggressive anticancer drug resistance, metastasis, and invasiveness. Besides, cancer stem cells manipulate their intrinsic and extrinsic features, regulate the metabolic pattern of the cell, adjust efflux-influx efficiency, modulate different signaling pathways, block apoptotic signals, and cause genetic and epigenetic alterations to retain their pluripotency and ability of self-renewal. Notably, to keep the cancer stem cells' ability to become malignant cells, mesenchymal stem cells, tumor-associated fibroblasts, immune cells, etc., interact with one another. Furthermore, CSCs are characterized by the expression of particular molecular markers that carry significant diagnostic and prognostic significance. Because of this, scientific research on CSCs is becoming increasingly imperative, intending to understand the traits and behavior of cancer stem cells and create more potent anticancer therapeutics to fight cancer at the CSC level. In this review, we aimed to elucidate the critical role of CSCs in the onset and spread of cancer and the characteristics of CSCs that promote severe resistance to targeted therapy.

Identification and panoramic analysis of drug response-related genes in triple negative breast cancer using as an example NVP-BEZ235

Taking NVP-BEZ235 (BEZ235) as an example to screen drug response-related genes (DRRGs) and explore their potential value in triple-negative breast cancer (TNBC). Through high-throughput technique, multidimensional transcriptome expression data (mRNA, miRNA and lncRNA) of BEZ235-treated and -untreated MDA-MB-468 cell lines were obtained. Combined with transcriptome data of the MDA-MB-468 cells and TCGA-TNBC tissues, differential gene expression analysis and WGCNA were performed to identify DRRGs associated with tumor trait by simulating the drug response microenvironment (DRM) of BEZ235-treated patients. Based on DRRGs, we constructed a ceRNA network and verified the expression levels of three key molecules by RT-qPCR, which not only demonstrated the successful construction of a BEZ235-treated cell line model but also explained the antitumor mechanism of BEZ235. Four molecular subtypes related to the DRM with survival difference were proposed using cluster analysis, namely glycolysis subtype, proliferation depression subtype, immune-suppressed subtype, and immune-activated subtype. A novel prognostic signature consisting of four DRRGs was established by Lasso-Cox analysis, which exhibited outstanding performance in predicting overall survival compared with several excellent reported signatures. The high- and low-risk groups were characterized by enrichment of metabolism-related pathways and immune-related pathways, respectively. Of note, the low-risk group had a better response to immune checkpoint blockade. Besides, pRRophetic analysis found that patients in the low-risk group were more sensitive to methotrexate and cisplation, whereas more resistant to BEZ235, docetaxel and paclitaxel. In conclusion, the DRRGs exemplified by BEZ235 are potential biomarkers for TNBC molecular typing, prognosis prediction and targeted therapy. The novel DRRGs-guided strategy for predicting the subtype, survival and therapy efficacy, might be also applied to more cancers and drugs other than TNBC and BEZ235.

Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools

Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.

Insights on hematopoietic cell kinase: An oncogenic player in human cancer

Hematopoietic cell kinase (Hck) is a member of the Src family and is expressed in hematopoietic cells. By regulating multiple signaling pathways, HCK can interact with multiple receptors to regulate signaling events involved in cell adhesion, proliferation, migration, invasion, apoptosis, and angiogenesis. However, aberrant expression of Hck in various hematopoietic cells and solid tumors plays a crucial role in tumor-related properties, including cell proliferation and epithelial-mesenchymal transition. In addition, Hck signaling regulates the function of immune cells such as macrophages, contributing to an immunosuppressive tumor microenvironment. The clinical success of various kinase inhibitors targeting the Src kinase family has validated the efficacy of targeting Src, and therapies with highly selective Hck kinase inhibitors are in clinical trials. This article reviews Hck inhibition as an emerging cancer treatment strategy, focusing on the expressions and functions of Hck in tumors and its impact on the tumor microenvironment. It also explores preclinical and clinical pharmacological strategies for Hck targeting to shed light on Hck-targeted tumor therapy.

The STAT family: Key transcription factors mediating crosstalk between cancer stem cells and tumor immune microenvironment

Signal transducer and activator of transcription (STAT) proteins compose a family of transcription factors critical for cancer stem cells (CSCs), and they are involved in maintaining stemness properties, enhancing cell proliferation, and promoting metastasis. Recent studies suggest that STAT proteins engage in reciprocal communication between CSCs and infiltrate immune cell populations in the tumor microenvironment (TME). Emerging evidence has substantiated the influence of immune cells, including macrophages, myeloid-derived suppressor cells, and T cells, on CSC survival through the regulation of STAT signaling. Conversely, dysregulation of STATs in CSCs or immune cells contributes to the establishment of an immunosuppressive TME. Thus, STAT proteins are promising therapeutic targets for cancer treatment, especially when used in combination with immunotherapy. From this perspective, we discuss the complex roles of STATs in CSCs and highlight their functions in the crosstalk between CSCs and the immune microenvironment. Finally, cutting-edge clinical trial progress with STAT signaling inhibitors is summarized.

A novel ligand-modified nanocomposite microparticles improved efficiency of quercetin and paclitaxel delivery in the non-small cell lung cancer

Chemotherapy is the first choice for the treatment of cancer but it is still limited by insufficient kill efficiency and drug resistance. These problems urgently need to be overcome in a way that minimizes damage to the body. In this study, we designed the nanocomposite microparticles (NMPs) modified by cetuximab (Cet) and loaded anti-tumor agents- quercetin (QUE) and paclitaxel (PTX)- for eliciting specific drugs homing and enhancing the killing efficiency of chemotherapy drugs (P/Q@CNMPs). Physicochemical characteristics results presented that P/Q@CNMPs have a suitable aerodynamic diameter and uniform morphology that could meet the requirements of particles deposition in the lung. And it also had the characteristics of sustained-release and pH-responsive which could release the agents in the right place and has a continuous effect. In vitro and in vivo analysis results presented that P/Q@CNMPs have the accuracy targeting ability and killing effect on non-small cell lung cancer (NSCLC) which express positive epidermal growth factor receptor (EGFR) on the membrane. Furthermore, this system also has low toxicity and good biocompatibility. These results demonstrated that P/Q@CNMPs could be a potential intelligent targeting strategy used for chemo-resistant NSCLC therapies.

Model-informed experimental design recommendations for distinguishing intrinsic and acquired targeted therapeutic resistance in head and neck cancer

The promise of precision medicine has been limited by the pervasive resistance to many targeted therapies for cancer. Inferring the timing (i.e., pre-existing or acquired) and mechanism (i.e., drug-induced) of such resistance is crucial for designing effective new therapeutics. This paper studies cetuximab resistance in head and neck squamous cell carcinoma (HNSCC) using tumor volume data obtained from patient-derived tumor xenografts. We ask if resistance mechanisms can be determined from this data alone, and if not, what data would be needed to deduce the underlying mode(s) of resistance. To answer these questions, we propose a family of mathematical models, with each member of the family assuming a different timing and mechanism of resistance. We present a method for fitting these models to individual volumetric data, and utilize model selection and parameter sensitivity analyses to ask: which member(s) of the family of models best describes HNSCC response to cetuximab, and what does that tell us about the timing and mechanisms driving resistance? We find that along with time-course volumetric data to a single dose of cetuximab, the initial resistance fraction and, in some instances, dose escalation volumetric data are required to distinguish among the family of models and thereby infer the mechanisms of resistance. These findings can inform future experimental design so that we can best leverage the synergy of wet laboratory experimentation and mathematical modeling in the study of novel targeted cancer therapeutics.

Stochastic population dynamics of cancer stemness and adaptive response to therapies

Intratumoral heterogeneity can exist along multiple axes: Cancer stem cells (CSCs)/non-CSCs, drug-sensitive/drug-tolerant states, and a spectrum of epithelial-hybrid-mesenchymal phenotypes. Further, these diverse cell-states can switch reversibly among one another, thereby posing a major challenge to therapeutic efficacy. Therefore, understanding the origins of phenotypic plasticity and heterogeneity remains an active area of investigation. While genomic components (mutations, chromosomal instability) driving heterogeneity have been well-studied, recent reports highlight the role of non-genetic mechanisms in enabling both phenotypic plasticity and heterogeneity. Here, we discuss various processes underlying phenotypic plasticity such as stochastic gene expression, chromatin reprogramming, asymmetric cell division and the presence of multiple stable gene expression patterns ('attractors'). These processes can facilitate a dynamically evolving cell population such that a subpopulation of (drug-tolerant) cells can survive lethal drug exposure and recapitulate population heterogeneity on drug withdrawal, leading to relapse. These drug-tolerant cells can be both pre-existing and also induced by the drug itself through cell-state reprogramming. The dynamics of cell-state transitions both in absence and presence of the drug can be quantified through mathematical models. Such a dynamical systems approach to elucidating patterns of intratumoral heterogeneity by integrating longitudinal experimental data with mathematical models can help design effective combinatorial and/or sequential therapies for better clinical outcomes.

Mapping phenotypic heterogeneity in melanoma onto the epithelial-hybrid-mesenchymal axis

Epithelial to mesenchymal transition (EMT) is a well-studied hallmark of epithelial-like cancers that is characterized by loss of epithelial markers and gain of mesenchymal markers. Melanoma, which is derived from melanocytes of the skin, also undergo phenotypic plasticity toward mesenchymal-like phenotypes under the influence of various micro-environmental cues. Our study connects EMT to the phenomenon of de-differentiation (i.e., transition from proliferative to more invasive phenotypes) observed in melanoma cells during drug treatment. By analyzing 78 publicly available transcriptomic melanoma datasets, we found that de-differentiation in melanoma is accompanied by upregulation of mesenchymal genes, but not necessarily a concomitant loss of an epithelial program, suggesting a more “one-dimensional” EMT that leads to a hybrid epithelial/mesenchymal phenotype. Samples lying in the hybrid epithelial/mesenchymal phenotype also correspond to the intermediate phenotypes in melanoma along the proliferative-invasive axis - neural crest and transitory ones. As melanoma cells progress along the invasive axis, the mesenchymal signature does not increase monotonically. Instead, we observe a peak in mesenchymal scores followed by a decline, as cells further de-differentiate. This biphasic response recapitulates the dynamics of melanocyte development, suggesting close interactions among genes controlling differentiation and mesenchymal programs in melanocytes. Similar trends were noted for metabolic changes often associated with EMT in carcinomas in which progression along mesenchymal axis correlates with the downregulation of oxidative phosphorylation, while largely maintaining glycolytic capacity. Overall, these results provide an explanation for how EMT and de-differentiation axes overlap with respect to their transcriptional and metabolic programs in melanoma.

Therapeutic inhibition of the SRC-kinase HCK facilitates T cell tumor infiltration and improves response to immunotherapy

Although immunotherapy has revolutionized cancer treatment, many immunogenic tumors remain refractory to treatment. This can be largely attributed to an immunologically "cold" tumor microenvironment characterized by an accumulation of immunosuppressive myeloid cells and exclusion of activated T cells. Here, we demonstrate that genetic ablation or therapeutic inhibition of the myeloid-specific hematopoietic cell kinase (HCK) enables activity of antagonistic anti-programmed cell death protein 1 (anti-PD1), anti-CTLA4, or agonistic anti-CD40 immunotherapies in otherwise refractory tumors and augments response in treatment-susceptible tumors. Mechanistically, HCK ablation reprograms tumor-associated macrophages and dendritic cells toward an inflammatory endotype and enhances CD8⁺ T cell recruitment and activation when combined with immunotherapy in mice. Meanwhile, therapeutic inhibition of HCK in humanized mice engrafted with patient-derived xenografts counteracts tumor immunosuppression, improves T cell recruitment, and impairs tumor growth. Collectively, our results suggest that therapeutic targeting of HCK activity enhances response to immunotherapy by simultaneously stimulating immune cell activation and inhibiting the immunosuppressive tumor microenvironment.

Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy

Oncolytic viruses (OVs) are emerging cancer immunotherapy. Despite notable successes in the treatment of some tumors, OV therapy for central nervous system cancers has failed to show efficacy. We used an ex vivo tumor model developed from human glioblastoma tissue to evaluate the infiltration of herpes simplex OV rQNestin (oHSV-1) into glioblastoma tumors. We next leveraged our data to develop a computational, model of glioblastoma dynamics that accounts for cellular interactions within the tumor. Using our computational model, we found that low stromal density was highly predictive of oHSV-1 therapeutic success, suggesting that the efficacy of oHSV-1 in glioblastoma may be determined by stromal-to-tumor cell regional density. We validated these findings in heterogenous patient samples from brain metastatic adenocarcinoma. Our integrated modeling strategy can be applied to suggest mechanisms of therapeutic responses for central nervous system cancers and to facilitate the successful translation of OVs into the clinic.

Emergence of hybrid states of stem-like cancer cells correlates with poor prognosis in oral cancer

Cancer cell state transitions emerged as powerful mechanisms responsible for drug tolerance and overall poor prognosis; however, evidences were largely missing in oral cancer. Here, by multiplexing phenotypic markers of stem-like cancer cells (SLCCs); CD44, CD24 and aldehyde dehydrogenase (ALDH), we characterized diversity among multiple oral tumor tissues and cell lines. Two distinct patterns of spontaneous transitions with stochastic bidirectional interconversions on ‘ALDH-axis’, and unidirectional non-interconvertible transitions on ‘CD24-axis’ were observed. Interestingly, plastic ‘ALDH-axis’ was harnessed by cells to adapt to a Cisplatin tolerant state. Furthermore, phenotype-specific RNA sequencing suggested the possible maintenance of intermediate hybrid cell states maintaining stemness within the differentiating subpopulations. Importantly, survival analysis with subpopulation-specific gene sets strongly suggested that cell-state transitions may drive non-genetic heterogeneity, resulting in poor prognosis. Therefore, we have described the phenotypic-composition of heterogeneous subpopulations critical for global tumor behavior in oral cancer; which may provide prerequisite knowledge for treatment strategies.

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Demonstrated population trajectory driven non-genetic heterogeneity in oral cancer

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Created transition maps for subpopulations using discrete time Markov chain model

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Demonstrated maintenance of stemness in cells undergoing differentiation

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Uniquely expressed genes of these subpopulations associated with disease prognosis

Functionalized Lineage Tracing Can Enable the Development of Homogenization-Based Therapeutic Strategies in Cancer

The therapeutic landscape across many cancers has dramatically improved since the introduction of potent targeted agents and immunotherapy. Nonetheless, success of these approaches is too often challenged by the emergence of therapeutic resistance, fueled by intratumoral heterogeneity and the immense evolutionary capacity inherent to cancers. To date, therapeutic strategies have attempted to outpace the evolutionary tempo of cancer but frequently fail, resulting in lack of tumor response and/or relapse. This realization motivates the development of novel therapeutic approaches which constrain evolutionary capacity by reducing the degree of intratumoral heterogeneity prior to treatment. Systematic development of such approaches first requires the ability to comprehensively characterize heterogeneous populations over the course of a perturbation, such as cancer treatment. Within this context, recent advances in functionalized lineage tracing approaches now afford the opportunity to efficiently measure multimodal features of clones within a tumor at single cell resolution, enabling the linkage of these features to clonal fitness over the course of tumor progression and treatment. Collectively, these measurements provide insights into the dynamic and heterogeneous nature of tumors and can thus guide the design of homogenization strategies which aim to funnel heterogeneous cancer cells into known, targetable phenotypic states. We anticipate the development of homogenization therapeutic strategies to better allow for cancer eradication and improved clinical outcomes.

Optimizing Dosage-Specific Treatments in a Multi-Scale Model of a Tumor Growth

The emergence of cell resistance in cancer treatment is a complex phenomenon that emerges from the interplay of processes that occur at different scales. For instance, molecular mechanisms and population-level dynamics such as competition and cell–cell variability have been described as playing a key role in the emergence and evolution of cell resistances. Multi-scale models are a useful tool for studying biology at very different times and spatial scales, as they can integrate different processes occurring at the molecular, cellular, and intercellular levels. In the present work, we use an extended hybrid multi-scale model of 3T3 fibroblast spheroid to perform a deep exploration of the parameter space of effective treatment strategies based on TNF pulses. To explore the parameter space of effective treatments in different scenarios and conditions, we have developed an HPC-optimized model exploration workflow based on EMEWS. We first studied the effect of the cells’ spatial distribution in the values of the treatment parameters by optimizing the supply strategies in 2D monolayers and 3D spheroids of different sizes. We later study the robustness of the effective treatments when heterogeneous populations of cells are considered. We found that our model exploration workflow can find effective treatments in all the studied conditions. Our results show that cells’ spatial geometry and population variability should be considered when optimizing treatment strategies in order to find robust parameter sets.

Normalizing tumor microenvironment with nanomedicine and metronomic therapy to improve immunotherapy

Nanomedicine offered hope for improving the treatment of cancer but the survival benefits of the clinically approved nanomedicines are modest in many cases when compared to conventional chemotherapy. Metronomic therapy, defined as the frequent, low dose administration of chemotherapeutics – is being tested in clinical trials as an alternative to the conventional maximum tolerated dose (MTD) chemotherapy schedule. Although metronomic chemotherapy has not been clinically approved yet, it has shown better survival than MTD in many preclinical studies. When beneficial, metronomic therapy seems to be associated with normalization of the tumor microenvironment including improvements in tumor perfusion, tissue oxygenation and drug delivery as well as activation of the immune system. Recent preclinical studies suggest that nanomedicines can cause similar changes in the tumor microenvironment. Here, by employing a mathematical framework, we show that both approaches can serve as normalization strategies to enhance treatment. Furthermore, employing murine breast and fibrosarcoma tumor models as well as ultrasound shear wave elastography and contrast-enhanced ultrasound, we provide evidence that the approved nanomedicine Doxil can induce normalization in a dose-dependent manner by improving tumor perfusion as a result of tissue softening. Finally, we show that pretreatment with a normalizing dose of Doxil can improve the efficacy of immune checkpoint inhibition.

Metabolic Features of Tumor Dormancy: Possible Therapeutic Strategies

Simple Summary

Tumor recurrence still represents a major clinical challenge for cancer patients. Cancer cells may undergo a dormant state for long times before re-emerging. Both intracellular- and extracellular-driven pathways are involved in maintaining the dormant state and the subsequent awakening, with a mechanism that is still mostly unknown. In this scenario, cancer metabolism is emerging as a critical driver of tumor progression and dissemination and have gained increasing attention in cancer research. This review focuses on the metabolic adaptations characterizing the dormant phenotype and supporting tumor re-growth. Deciphering the metabolic adaptation sustaining tumor dormancy may pave the way for novel therapeutic approaches to prevent tumor recurrence based on combined metabolic drugs.

Abstract

Tumor relapse represents one of the main obstacles to cancer treatment. Many patients experience cancer relapse even decades from the primary tumor eradication, developing more aggressive and metastatic disease. This phenomenon is associated with the emergence of dormant cancer cells, characterized by cell cycle arrest and largely insensitive to conventional anti-cancer therapies. These rare and elusive cells may regain proliferative abilities upon the induction of cell-intrinsic and extrinsic factors, thus fueling tumor re-growth and metastasis formation. The molecular mechanisms underlying the maintenance of resistant dormant cells and their awakening are intriguing but, currently, still largely unknown. However, increasing evidence recently underlined a strong dependency of cell cycle progression to metabolic adaptations of cancer cells. Even if dormant cells are frequently characterized by a general metabolic slowdown and an increased ability to cope with oxidative stress, different factors, such as extracellular matrix composition, stromal cells influence, and nutrient availability, may dictate specific changes in dormant cells, finally resulting in tumor relapse. The main topic of this review is deciphering the role of the metabolic pathways involved in tumor cells dormancy to provide new strategies for selectively targeting these cells to prevent fatal recurrence and maximize therapeutic benefit.

Interconnected high-dimensional landscapes of epithelial-mesenchymal plasticity and stemness in cancer

Establishing macrometastases at distant organs is a highly challenging process for cancer cells, with extremely high attrition rates. A very small percentage of disseminated cells have the ability to dynamically adapt to their changing micro-environments through reversibly switching to another phenotype, aiding metastasis. Such plasticity can be exhibited along one or more axes-epithelial-mesenchymal plasticity (EMP) and cancer stem cells (CSCs) being the two most studied, and often tacitly assumed to be synonymous. Here, we review the emerging concepts related to EMP and CSCs across multiple cancers. Both processes are multi-dimensional in nature; for instance, EMP can be defined on morphological, molecular and functional changes, which may or may not be synchronized. Similarly, self-renewal, multi-lineage potential, and resistance to anoikis and/or therapy may not all occur simultaneously in CSCs. Thus, understanding the complexity in defining EMP and CSCs is essential if we are to understand their contribution to cancer metastasis. This will require a more comprehensive understanding of the non-linearity of these processes. These processes are dynamic, reversible, and semi-independent in nature; cells traverse the inter-connected high-dimensional EMP and CSC landscapes in diverse paths, each of which may exhibit a distinct EMP-CSC coupling. Our proposed model offers a potential unifying framework for elucidating the coupled decision-making along these dimensions and highlights a key set of open questions to be answered.

Transition Therapy: Tackling the Ecology of Tumor Phenotypic Plasticity

Phenotypic switching in cancer cells has been found to be present across tumor types. Recent studies on Glioblastoma report a remarkably common architecture of four well-defined phenotypes coexisting within high levels of intra-tumor genetic heterogeneity. Similar dynamics have been shown to occur in breast cancer and melanoma and are likely to be found across cancer types. Given the adaptive potential of phenotypic switching (PHS) strategies, understanding how it drives tumor evolution and therapy resistance is a major priority. Here we present a mathematical framework uncovering the ecological dynamics behind PHS. The model is able to reproduce experimental results, and mathematical conditions for cancer progression reveal PHS-specific features of tumors with direct consequences on therapy resistance. In particular, our model reveals a threshold for the resistant-to-sensitive phenotype transition rate, below which any cytotoxic or switch-inhibition therapy is likely to fail. The model is able to capture therapeutic success thresholds for cancers where nonlinear growth dynamics or larger PHS architectures are in place, such as glioblastoma or melanoma. By doing so, the model presents a novel set of conditions for the success of combination therapies able to target replication and phenotypic transitions at once. Following our results, we discuss transition therapy as a novel scheme to target not only combined cytotoxicity but also the rates of phenotypic switching.

Dynamic Phenotypic Switching and Group Behavior Help Non-Small Cell Lung Cancer Cells Evade Chemotherapy

Drug resistance, a major challenge in cancer therapy, is typically attributed to mutations and genetic heterogeneity. Emerging evidence suggests that dynamic cellular interactions and group behavior also contribute to drug resistance. However, the underlying mechanisms remain poorly understood. Here, we present a new mathematical approach with game theoretical underpinnings that we developed to model real-time growth data of non-small cell lung cancer (NSCLC) cells and discern patterns in response to treatment with cisplatin. We show that the cisplatin-sensitive and cisplatin-tolerant NSCLC cells, when co-cultured in the absence or presence of the drug, display dynamic group behavior strategies. Tolerant cells exhibit a 'persister-like' behavior and are attenuated by sensitive cells; they also appear to 'educate' sensitive cells to evade chemotherapy. Further, tolerant cells can switch phenotypes to become sensitive, especially at low cisplatin concentrations. Finally, switching treatment from continuous to an intermittent regimen can attenuate the emergence of tolerant cells, suggesting that intermittent chemotherapy may improve outcomes in lung cancer.

The IL-6R and Bmi-1 axis controls self-renewal and chemoresistance of head and neck cancer stem cells

Despite major progress in elucidating the pathobiology of head and neck squamous cell carcinoma (HNSCC), the high frequency of disease relapse correlates with unacceptably deficient patient survival. We previously showed that cancer stem-like cells (CSCs) drive tumorigenesis and progression of HNSCC. Although CSCs constitute only 2–5% of total tumor cells, CSCs contribute to tumor progression by virtue of their high tumorigenic potential and their resistance to chemo-, radio-, and immunotherapy. Not only are CSCs resistant to therapy, but cytotoxic agents actually enhance cancer stemness by activating transcription of pluripotency factors and by inducing expression of Bmi-1, a master regulator of stem cell self-renewal. We hypothesized therapeutic inhibition of interleukin-6 receptor (IL-6R) suppresses Bmi-1 to overcome intrinsic chemoresistance of CSCs. We observed that high Bmi-1 expression correlates with decreased (p = 0.04) recurrence-free survival time in HNSCC patients (n = 216). Blockade of IL-6R by lentiviral knockdown or pharmacologic inhibition with a humanized monoclonal antibody (Tocilizumab) is sufficient to inhibit Bmi-1 expression, secondary sphere formation, and to decrease the CSC fraction even in Cisplatin-resistant HNSCC cells. IL-6R inhibition with Tocilizumab abrogates Cisplatin-mediated increase in CSC fraction and induction of Bmi-1 in patient-derived xenograft (PDX) models of HNSCC. Notably, Tocilizumab inhibits Bmi-1 and suppresses growth of xenograft tumors generated with Cisplatin-resistant HNSCC cells. Altogether, these studies demonstrate that therapeutic blockade of IL-6R suppresses Bmi-1 function and inhibits cancer stemness. These results suggest therapeutic inhibition of IL-6R might be a viable strategy to overcome the CSC-mediated chemoresistance typically observed in HNSCC patients.

Systems-level network modeling deciphers the master regulators of phenotypic plasticity and heterogeneity in melanoma

Phenotypic (i.e. non-genetic) heterogeneity in melanoma drives dedifferentiation, recalcitrance to targeted therapy and immunotherapy, and consequent tumor relapse and metastasis. Various markers or regulators associated with distinct phenotypes in melanoma have been identified, but, how does a network of interactions among these regulators give rise to multiple "attractor" states and phenotypic switching remains elusive. Here, we inferred a network of transcription factors (TFs) that act as master regulators for gene signatures of diverse cell-states in melanoma. Dynamical simulations of this network predicted how this network can settle into different "attractors" (TF expression patterns), suggesting that TF network dynamics drives the emergence of phenotypic heterogeneity. These simulations can recapitulate major phenotypes observed in melanoma and explain de-differentiation trajectory observed upon BRAF inhibition. Our systems-level modeling framework offers a platform to understand trajectories of phenotypic transitions in the landscape of a regulatory TF network and identify novel therapeutic strategies targeting melanoma plasticity.

Advances in understanding the role of P-gp in doxorubicin resistance: Molecular pathways, therapeutic strategies, and prospects

P-glycoprotein (P-gp) is a drug efflux transporter that triggers doxorubicin (DOX) resistance. In this review, we highlight the molecular avenues regulating P-gp, such as Nrf2, HIF-1α, miRNAs, and long noncoding (lnc)RNAs, to reveal their participation in DOX resistance. These antitumor compounds and genetic tools synergistically reduce P-gp expression. Furthermore, ATP depletion impairs P-gp activity to enhance the antitumor activity of DOX. Nanoarchitectures, including liposomes, micelles, polymeric nanoparticles (NPs), and solid lipid nanocarriers, have been developed for the co-delivery of DOX with anticancer compounds and genes enhancing DOX cytotoxicity. Surface modification of nanocarriers, for instance with hyaluronic acid (HA), can promote selectivity toward cancer cells. We discuss these aspects with a focus on P-gp expression and activity.

Devices for drug delivery in the gastrointestinal tract: A review of systems physically interacting with the mucosa for enhanced delivery

The delivery of macromolecules via the gastrointestinal (GI) tract remains a significant challenge. A variety of technologies using physical modes of drug delivery have been developed and investigated to overcome the epithelial cell layer of the GI tract for local and systemic delivery. These technologies include direct injection, jetting, ultrasound, and iontophoresis, which have been largely adapted from transdermal drug delivery. Direct injection of agents using needles through endoscopy has been used clinically for over a century. Jetting, a needle-less method of drug delivery where a high-speed stream of fluid medication penetrates tissue, has been evaluated pre-clinically for delivery of agents into the buccal mucosa. Ultrasound has been shown to be beneficial in enhancing delivery of macromolecules, including nucleic acids, in pre-clinical animal models. The application of an electric field gradient to drive drugs into tissues through the technique of iontophoresis has been shown to deliver highly toxic chemotherapies into GI tissues. Here in, we provide an in-depth overview of these physical modes of drug delivery in the GI tract and their clinical and preclinical uses.

Nanotherapeutic approaches to overcome distinct drug resistance barriers in models of breast cancer

Targeted delivery of drugs to tumor cells, which circumvent resistance mechanisms and induce cell killing, is a lingering challenge that requires innovative solutions. Here, we provide two bioengineered strategies in which nanotechnology is blended with cancer medicine to preferentially target distinct mechanisms of drug resistance. In the first 'case study', we demonstrate the use of lipid-drug conjugates that target molecular signaling pathways, which result from taxane-induced drug tolerance via cell surface lipid raft accumulations. Through a small molecule drug screen, we identify a kinase inhibitor that optimally destroys drug tolerant cancer cells and conjugate it to a rationally-chosen lipid scaffold, which enhances anticancer efficacy in vitro and in vivo. In the second 'case study', we address resistance mechanisms that can occur through exocytosis of nanomedicines. Using adenocarcinoma HeLa and MCF-7 cells, we describe the use of gold nanorod and nanoporous vehicles integrated with an optical antenna for on-demand, photoactivation at ~650 nm enabling release of payloads into cells including cytotoxic anthracyclines. Together, these provide two approaches, which exploit engineering strategies capable of circumventing distinct resistance barriers and induce killing by multimodal, including nanophotonic mechanisms.

Translocation of intracellular CD24 constitutes a triggering event for drug resistance in breast cancer

The capacity of tumor cells to shift dynamically between different states could be responsible for chemoresistance and has been commonly linked to the acquisition of stem cell properties. Here, we have evaluated the phenotype switching associated with drug resistance in breast cancer cell lines and cell lineage obtained from Brazilian patients. We have highlighted the role of the cancer stem cell marker CD24 in the dynamics of cell plasticity and the acquirement of drug resistance. We showed that the translocation of CD24 from cytosol to cell membrane is a triggering event for the phenotype change of breast tumor cells exposed to drug stress. Here, we provide evidence that the phenotype switching is due to the presence of a cytosolic pool of CD24. Importantly, the cellular localization of CD24 was correlated with the changes in the dynamics of p38 MAPK activation. A strong and continuous phosphorylation of the p38 MAPK led to the overexpression of Bcl-2 after treatment in persistent cells presenting high density of CD24 on cell membrane. This phenotype enabled the cells to enter in slow-down of cell cycle, after which several weeks later, the dormant cells proliferated again. Importantly, the use of a p38 activity inhibitor sensitized cells to drug treatment and avoided chemoresistance.

Cell-intrinsic mechanisms of drug tolerance to systemic therapies in cancer

In cancer patients with metastatic disease, the rate of complete tumor response to systemic therapies is low, and residual lesions persist in the majority of patients due to early molecular adaptation in cancer cells. A growing body of evidence suggests that a subpopulation of drug-tolerant « persister » cells - a reversible phenotype characterized by reduced drug sensitivity and decreased cell proliferation - maintains residual disease and may serve as a reservoir for resistant phenotypes. The survival of these residual tumor cells can be caused by reactivation of specific signaling pathways, phenotypic plasticity (i.e., transdifferentiation), epigenetic or metabolic reprogramming, downregulation of apoptosis as well as transcriptional remodeling. In this review, we discuss the molecular mechanisms that enable adaptive survival in drug-tolerant cells. We describe the main characteristics and dynamic nature of this persistent state, and highlight the current therapeutic strategies that may be used to interfere with the establishment of drug-tolerant cells, as an alternative to improve objective response to systemic therapies and delay the emergence of resistance to improve long-term survival.

The role of memory in non-genetic inheritance and its impact on cancer treatment resistance

Intra-tumour heterogeneity is a leading cause of treatment failure and disease progression in cancer. While genetic mutations have long been accepted as a primary mechanism of generating this heterogeneity, the role of phenotypic plasticity is becoming increasingly apparent as a driver of intra-tumour heterogeneity. Consequently, understanding the role of this plasticity in treatment resistance and failure is a key component of improving cancer therapy. We develop a mathematical model of stochastic phenotype switching that tracks the evolution of drug-sensitive and drug-tolerant subpopulations to clarify the role of phenotype switching on population growth rates and tumour persistence. By including cytotoxic therapy in the model, we show that, depending on the strategy of the drug-tolerant subpopulation, stochastic phenotype switching can lead to either transient or permanent drug resistance. We study the role of phenotypic heterogeneity in a drug-resistant, genetically homogeneous population of non-small cell lung cancer cells to derive a rational treatment schedule that drives population extinction and avoids competitive release of the drug-tolerant sub-population. This model-informed therapeutic schedule results in increased treatment efficacy when compared against periodic therapy, and, most importantly, sustained tumour decay without the development of resistance.