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

Tie2 activity in cancer associated myofibroblasts serves as novel target against reprogramming of cancer cells to embryonic-like cell state and associated poor prognosis in oral carcinoma patients

BACKGROUND

Myofibroblastic cancer-associated fibroblasts (CAF) in tumor stroma serves as an independent poor prognostic indicator, supporting higher stemness in oral cancer; however, the underlying biology is not fully comprehended. Here, we have explored the crucial role of Tunica Interna Endothelial Cell Kinase (Tie2/TEK) signaling in transition and maintenance of myofibroblastic phenotype of CAFs, and as possible link with the poor prognosis of head and neck squamous cell carcinoma (HNSCC) patients.

METHODS

Bulk and single cell RNA-sequencing (scRNAseq) methods and in-depth bioinformatic analysis were applied for CAF and cancer cells co-culture for studying molecular relationships. In vitro 3D-spheroid-forming ability, expression of stemness markers, in vivo tumor formation ability in zebrafish embryo and syngeneic mouse allografts formation was conducted to test stemness, upon targeting CAF-specific Tie2 activity by gene silencing or with small molecule inhibitor. Immunohistochemistry analysis was performed to locate the distribution of Tie2 and αSMA in primary tumors of oral carcinoma. Prognosis in HNSCC patient cohort from The Cancer Genome Atlas (TCGA) study was analysed based on single sample gene set enrichment score (ssGSEA) and Kaplan-Meier analysis.

RESULTS

Autocrine or exogenous TGFβ-induction in CAF led to the recruitment of histone deacetylase 2 (HDAC2) on the promoter of Tie2-antagonist, Angiopoietin-2 (ANGPT2), resulting in its downregulation, leading to phosphorylation of Tie2 (Y992) and subsequent activation of SRC (Y418). This led to SRC/ROCK mediated αSMA-positive stress-fiber formation with gain of myofibroblast phenotype. The CAF-specific Tie2-signaling was responsible for producing embryonic-like cell state in co-cultured cancer cells; with enhanced tumor initiating ability. Tie2 activity in CAF exerted the dynamic gene expression reprogramming, with the upregulation of 'cell migration' and downregulation of 'protein biosynthesis' related gene-regulatory-network modules in malignant cells. The AUCell scores calculated for gene signatures derived from these modules showed significant concordance in independently reported scRNAseq studies of HNSCC tumors and significant association with poor prognosis in HNSCC patient cohort.

CONCLUSIONS

CAF-specific Tie2 activity may serve as direct stromal-target against cancer cell plasticity leading to poor prognosis of oral cancer patients. Overall, our work has provided wider applicability of Tie2-specific functions in tumor biology, along with its known role in endothelial cell-specific function.

Low dimensionality of phenotypic space as an emergent property of coordinated teams in biological regulatory networks

Cell-fate decisions involve coordinated genome-wide expression changes, typically leading to a limited number of phenotypes. Although often modeled as simple toggle switches, these rather simplistic representations often disregard the complexity of regulatory networks governing these changes. Here, we unravel design principles underlying complex cell decision-making networks in multiple contexts. We show that the emergent dynamics of these networks and corresponding transcriptomic data are consistently low-dimensional, as quantified by the variance explained by principal component 1 (PC1). This low dimensionality in phenotypic space arises from extensive feedback loops in these networks arranged to effectively enable the formation of two teams of mutually inhibiting nodes. We use team strength as a metric to quantify these feedback interactions and show its strong correlation with PC1 variance. Using artificial networks of varied topologies, we also establish the conditions for generating canalized cell-fate landscapes, offering insights into diverse binary cellular decision-making networks.

Cancer Stem Cells in Oral Squamous Cell Carcinoma: A Narrative Review on Experimental Characteristics and Methodological Challenges

Cancer stem cells (CSCs) represent a subpopulation of cancer cells that are believed to initiate and drive cancer progression. In animal models, xenotransplanted CSCs have demonstrated the ability to produce tumors. Since their initial isolation in blood cancers, CSCs have been identified in various solid human cancers, including oral squamous cell carcinoma (OSCC). In addition to their tumorigenic properties, dysregulated stem-cell-related signaling pathways-Wnt family member (Wnt), neurogenic locus notch homolog protein (Notch), and hedgehog-have been shown to endow CSCs with characteristics like self-renewal, phenotypic plasticity, and chemoresistance, contributing to recurrence and treatment failure. Consequently, CSCs have become targets for new therapeutic agents, with some currently in different phases of clinical trials. Notably, small molecule inhibitors of the hedgehog signaling pathway, such as vismodegib and glasdegib, have been approved for the treatment of basal cell carcinoma and acute myeloid leukemia, respectively. Other strategies for eradicating CSCs include natural compounds, nano-drug delivery systems, targeting mitochondria and the CSC microenvironment, autophagy, hyperthermia, and immunotherapy. Despite the extensive documentation of CSCs in OSCC since its first demonstration in head and neck (HN) SCC in 2007, none of these novel pharmacological approaches have yet entered clinical trials for OSCC patients. This narrative review summarizes the in vivo and in vitro evidence of CSCs and CSC-related signaling pathways in OSCC, highlighting their role in promoting chemoresistance and immunotherapy resistance. Additionally, it addresses methodological challenges and discusses future research directions to improve experimental systems and advance CSC studies.

Increased prevalence of hybrid epithelial/mesenchymal state and enhanced phenotypic heterogeneity in basal breast cancer

Intra-tumoral phenotypic heterogeneity promotes tumor relapse and therapeutic resistance and remains an unsolved clinical challenge. Decoding the interconnections among different biological axes of plasticity is crucial to understand the molecular origins of phenotypic heterogeneity. Here, we use multi-modal transcriptomic data-bulk, single-cell, and spatial transcriptomics-from breast cancer cell lines and primary tumor samples, to identify associations between epithelial-mesenchymal transition (EMT) and luminal-basal plasticity-two key processes that enable heterogeneity. We show that luminal breast cancer strongly associates with an epithelial cell state, but basal breast cancer is associated with hybrid epithelial/mesenchymal phenotype(s) and higher phenotypic heterogeneity. Mathematical modeling of core underlying gene regulatory networks representative of the crosstalk between the luminal-basal and epithelial-mesenchymal axes elucidate mechanistic underpinnings of the observed associations from transcriptomic data. Our systems-based approach integrating multi-modal data analysis with mechanism-based modeling offers a predictive framework to characterize intra-tumor heterogeneity and identify interventions to restrict it.

Targeting signaling pathways in cancer stem cells: A potential approach for developing novel anti-cancer therapeutics

Cancer stem cells (CSCs) have emerged as prime players in the intricate landscape of cancer development, progression, and resistance to traditional treatments. These unique cellular subpopulations own the remarkable capability of self-renewal and differentiation, giving rise to the diverse cellular makeup of tumors and fostering their recurrence following conventional therapies. In the quest for developing more effective cancer therapeutics, the focus has now shifted toward targeting the signaling pathways that govern CSCs behavior. This chapter underscores the significance of these signaling pathways in CSC biology and their potential as pivotal targets for the development of novel chemotherapy approaches. We delve into several key signaling pathways essential for maintaining the defining characteristics of CSCs, including the Wnt, Hedgehog, Notch, JAK-STAT, NF-κB pathways, among others, shedding light on their potential crosstalk. Furthermore, we highlight the latest advancements in CSC-targeted therapies, spanning from promising preclinical models to ongoing clinical trials. A comprehensive understanding of the intricate molecular aspects of CSC signaling pathways and their manipulation holds the prospective to revolutionize cancer treatment paradigms. This, in turn, could lead to more efficacious and personalized therapies with the ultimate goal of eradicating CSCs and enhancing overall patient outcomes. The exploration of CSC signaling pathways represents a key step towards a brighter future in the battle against cancer.

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.

Targeting cancer stem cells as a strategy for reducing chemotherapy resistance in head and neck cancers

PURPOSE

Resistance to chemotherapy and radiotherapy is the primary cause of a poor prognosis in oncological patients. Researchers identified many possible mechanisms involved in gaining a therapy-resistant phenotype by cancer cells, including alterations in intracellular drug accumulation, detoxification, and enhanced DNA damage repair. All these features are characteristic of stem cells, making them the major culprit of chemoresistance. This paper reviews the most recent evidence regarding the association between the stemness phenotype and chemoresistance in head and neck cancers. It also investigates the impact of pharmacologically targeting cancer stem cell populations in this subset of malignancies.

METHODS

This narrative review was prepared based on the search of the PubMed database for relevant papers.

RESULTS

Head and neck cancer cells belonging to the stem cell population are distinguished by the high expression of certain surface proteins (e.g., CD10, CD44, CD133), pluripotency-related transcription factors (SOX2, OCT4, NANOG), and increased activity of aldehyde dehydrogenase (ALDH). Chemotherapy itself increases the percentage of stem-like cells. Importantly, the intratumor heterogeneity of stem cell subpopulations reflects cell plasticity which has great importance for chemoresistance induction.

CONCLUSIONS

Evidence points to the advantage of combining classical chemotherapeutics with stemness modulators thanks to the joint targeting of the bulk of proliferating tumor cells and chemoresistant cancer stem cells, which could cause recurrence.

Dynamical hallmarks of cancer: Phenotypic switching in melanoma and epithelial-mesenchymal plasticity

Phenotypic plasticity was recently incorporated as a hallmark of cancer. This plasticity can manifest along many interconnected axes, such as stemness and differentiation, drug-sensitive and drug-resistant states, and between epithelial and mesenchymal cell-states. Despite growing acceptance for phenotypic plasticity as a hallmark of cancer, the dynamics of this process remains poorly understood. In particular, the knowledge necessary for a predictive understanding of how individual cancer cells and populations of cells dynamically switch their phenotypes in response to the intensity and/or duration of their current and past environmental stimuli remains far from complete. Here, we present recent investigations of phenotypic plasticity from a systems-level perspective using two exemplars: epithelial-mesenchymal plasticity in carcinomas and phenotypic switching in melanoma. We highlight how an integrated computational-experimental approach has helped unravel insights into specific dynamical hallmarks of phenotypic plasticity in different cancers to address the following questions: a) how many distinct cell-states or phenotypes exist?; b) how reversible are transitions among these cell-states, and what factors control the extent of reversibility?; and c) how might cell-cell communication be able to alter rates of cell-state switching and enable diverse patterns of phenotypic heterogeneity? Understanding these dynamic features of phenotypic plasticity may be a key component in shifting the paradigm of cancer treatment from reactionary to a more predictive, proactive approach.

Cytotoxic Imidazolyl-Mesalazine Ester-Based Ru(II) Complexes Reduce Expression of Stemness Genes and Induce Differentiation of Oral Squamous Cell Carcinoma

The aggressiveness and recurrence of cancer is linked to cancer stem cells (CSCs), but drugs targeting CSCs may not succeed in the clinic due to the lack of a distinct CSC subpopulation. Clinical Pt(II) drugs can increase stemness. We screened 15 RuII or IrIII complexes with mesalazine or 3-aminobenzoate Schiff bases of the general formulas [Ru(p-cym)L]+, [Ru(p-cym)L], and [Ir(Cp*)L]+ (L = L1-L9) and found three complexes (2, 12, and 13) that are active against oral squamous cell carcinoma (OSCC) CSCs. There is a putative oncogenic role of transcription factors (viz. NOTCH1, SOX2, c-MYC) to enhance the stemness. Our work shows that imidazolyl-mesalazine ester-based RuII complexes inhibit growth of CSC-enriched OSCC 3D spheroids at low micromolar doses (2 μM). Complexes 2, 12, and 13 reduce stemness gene expression and induce differentiation markers (Involucrin, CK10) in OSCC 3D cultures. The imidazolyl-mesalazine ester-based RuII complex 13 shows the strongest effect. Downregulating c-MYC suggests that RuII complexes may target c-MYC-driven cancers.

Multi-modal transcriptomic analysis unravels enrichment of hybrid epithelial/mesenchymal state and enhanced phenotypic heterogeneity in basal breast cancer

Intra-tumoral phenotypic heterogeneity promotes tumor relapse and therapeutic resistance and remains an unsolved clinical challenge. It manifests along multiple phenotypic axes and decoding the interconnections among these different axes is crucial to understand its molecular origins and to develop novel therapeutic strategies to control it. Here, we use multi-modal transcriptomic data analysis - bulk, single-cell and spatial transcriptomics - from breast cancer cell lines and primary tumor samples, to identify associations between epithelial-mesenchymal transition (EMT) and luminal-basal plasticity - two key processes that enable heterogeneity. We show that luminal breast cancer strongly associates with an epithelial cell state, but basal breast cancer is associated with hybrid epithelial/mesenchymal phenotype(s) and higher phenotypic heterogeneity. These patterns were inherent in methylation profiles, suggesting an epigenetic crosstalk between EMT and lineage plasticity in breast cancer. Mathematical modelling of core underlying gene regulatory networks representative of the crosstalk between the luminal-basal and epithelial-mesenchymal axes recapitulate and thus elucidate mechanistic underpinnings of the observed associations from transcriptomic data. Our systems-based approach integrating multi-modal data analysis with mechanism-based modeling offers a predictive framework to characterize intra-tumor heterogeneity and to identify possible interventions to restrict it.

Research Landscape of Lymphovascular Invasion in Oral Squamous Cell Carcinoma: A Bibliometric Analysis From 1994 to 2023

The primary factor affecting tumor biology is neo-lymphangiogenesis in solid epithelial malignancies like oral squamous cell carcinoma (OSCC). Determining the impact of lymphovascular invasion is critical in order to determine OSCC's locoregional and global dissemination. Bibliometric landscapes are vital to learning about the most recent advancements in the aforementioned topic because the ongoing research in OSCC is multifaceted. This analysis can reveal the progressions that might modernize OSCC diagnosis and treatment. The present analysis has been therefore undertaken to study the relevance and effects of lymphovascular invasion in OSCC utilizing co-occurrence of keywords analysis and co-authorship analysis in the PubMed database. The keywords included "lymphovascular invasion in oral squamous cell carcinoma" using the Boolean operator (AND). A cross-sectional bibliometric analysis of full-text articles from 1994 to 2023 using VOSviewer (Version 1.6.19; Centre for Science and Technology Studies, Leiden University, The Netherlands) was performed. The data obtained was analyzed for co-occurrence and co-authorship analysis using the VOSviewer standard protocol. The query revealed 296 searches in the PubMed database. Seven clusters were found with default colors in the representation of the entire term co-occurrence network, which also displayed a total link strength of 22,262. The items were categorized into clusters based on their commonalities. The labels' weights, as determined by links and occurrences, did not depend on one another, and the co-occurrence of keywords does not imply a causal association. In the item density visualization, item labels represented individual things. The number of items from a cluster that was close to the point was represented by the weight given to its color, which was formed by combining the colors of other clusters. A network of 57 authors who matched the search parameters was discovered by the co-authorship analysis. The network visualization map displayed three clusters with a total link strength of 184. The quantity of co-authorship relationships and the number of publications did not appear to be significantly correlated. In conclusion, this investigation uncovered a sizable body of bibliometric data that emphasizes key trends and advancements in the aforementioned theme. The observed variances may be a result of the various objectives of the researchers and journals, who collaborate to provide the best possible literature dissemination.

Population dynamics of EMT elucidates the timing and distribution of phenotypic intra-tumoral heterogeneity

The Epithelial-to-Mesenchymal Transition (EMT) is a hallmark of cancer metastasis and morbidity. EMT is a non-binary process, and cells can be stably arrested en route to EMT in an intermediate hybrid state associated with enhanced tumor aggressiveness and worse patient outcomes. Understanding EMT progression in detail will provide fundamental insights into the mechanisms underlying metastasis. Despite increasingly available single-cell RNA sequencing (scRNA-seq) data that enable in-depth analyses of EMT at the single-cell resolution, current inferential approaches are limited to bulk microarray data. There is thus a great need for computational frameworks to systematically infer and predict the timing and distribution of EMT-related states at single-cell resolution. Here, we develop a computational framework for reliable inference and prediction of EMT-related trajectories from scRNA-seq data. Our model can be utilized across a variety of applications to predict the timing and distribution of EMT from single-cell sequencing data.

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.

Interdisciplinary research in cancer and immunity employing biophysical approaches

Three leading scientists Fabrizio Mattei, Kandice Tanner, and Mohit Kumar Jolly working in different continents and in different areas of cancer and immunology came together for an iScience Special Issue focused on the biophysical aspect of the tumor-immune dynamics. In this backstory, the iScience editor discusses with Mattei and Jolly their thoughts about this topic, the current state of the field, the collection of articles in this Special Issue, and the future of the research in this area in the coming years, and personal advice to aspiring young minds.

NOTCH pathway inactivation reprograms stem-like oral cancer cells to JAK-STAT dependent state and provides the opportunity of synthetic lethality

BACKGROUND

We have recently provided the evidence of interconvertible cellular states, driving non-genetic heterogeneity among stem-like oral cancer cells (oral-SLCCs). Here, NOTCH pathway-activity status is explored as one of the possible mechanisms behind this stochastic plasticity.

METHODS

Oral-SLCCs were enriched in 3D-spheroids. Constitutively-active and inactive status of NOTCH pathway was achieved by genetic or pharmacological approaches. RNA sequencing and real-time PCR was performed for gene expression studies. in vitro cytotoxicity assessments were performed by AlamarBlue assay and in vivo effects were studied by xenograft growth in zebrafish embryo.

RESULTS

We have observed stochastic plasticity in oral-SLCCs, spontaneously maintaining both NOTCH-active and inactive states. While cisplatin refraction was associated with post-treatment adaptation to the active-state of NOTCH pathway, oral-SLCCs with inactive NOTCH pathway status showed aggressive tumor growth and poor prognosis. RNAseq analysis clearly suggested the upregulation of JAK-STAT pathway in NOTCH pathway-inactive subset. The 3D-spheroids with lower NOTCH-activity status displayed significantly higher sensitivity to JAK-selective drugs, Ruxolitinib or Tofacitinib or siRNA mediated downregulation of tested partners STAT3/4. Oral-SLCCs were programmed to adapt the inactive status of NOTCH pathway by exposing to γ-secretase inhibitors, LY411575 or RO4929097, followed by targeting with JAK-inhibitors, Ruxolitinib or Tofacitinib. This approach resulted in a very significant inhibition in viability of 3D-spheroids as well as xenograft initiation in Zebrafish embryos.

CONCLUSION

Study revealed for the first time that NOTCH pathway-inactive state exhibit activation of JAK-STAT pathways, as synthetic lethal pair. Therefore, co-inhibition of these pathway may serve as novel therapeutic strategy against aggressive oral cancer.

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.

Characterizing diversity among human oral stem-like cancer cells using flow cytometry

We have developed the protocol of flow cytometry for characterizing diversity among oral stem-like cancer cells (SLCCs) using CD44, CD24, and aldehyde dehydrogenase (ALDH) in oral tumors. We are also reporting the protocol for tumor-derived explant cultures to develop oral cancer cell lines and enriching these diverse hybrid states of cancer cells in 3D spheroids from established cell lines. For complete details on the use and execution of this protocol, please refer to Vipparthi et al. (2022) and Vipparthi et al. (2021).

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.