The Physics of Cancer

Cell mechanics regulate the migration and invasion of hepatocellular carcinoma cells via JNK signaling

Hepatocellular carcinoma (HCC) cells, especially those with metastatic competence, show reduced stiffness compared to the non-malignant counterparts. However, it is still unclear whether and how the mechanics of HCC cells influence their migration and invasion. This study reports that HCC cells with enhanced motility show reduced mechanical stiffness and cytoskeleton, suggesting the inverse correlation between cellular stiffness and motility. Through pharmacologic and genetic approaches, inhibiting actomyosin activity reduces HCC cellular stiffness but promotes their migration and invasion, while activating it increases cell stiffness but impairs cell motility. Actomyosin regulates cell motility through the influence on cellular stiffness. Mechanistically, weakening/strengthening cells inhibits/promotes c-Jun N terminal kinase (JNK) phosphorylation, activation/inhibition of which rescues the effects of cell mechanics on their migration and invasion. Further, HCC cancer stem cells (CSCs) exhibit higher motility but lower stiffness than control cells. Increasing CSC stiffness weakens migration and invasion through the activation of JNK signaling. In conclusion, our findings unveil a new regulatory role of actomyosin-mediated cellular mechanics in tumor cell motility and present new evidence to support that tumor cell softening may be one driving force for HCC metastasis. STATEMENT OF SIGNIFICANCE: Tumor cells progressively become softened during metastasis and low cell stiffness is associated with high metastatic potential. However, it remains unclear whether tumor cell softening is a by-product of or a driving force for tumor progression. This work reports that the stiffness of hepatocellular carcinoma cells is linked to their migration and invasion. Importantly, tumor cell softening promotes migration and invasion, while cell stiffening impairs the mobility. Weakening/strengthening cells inhibits/promotes JNK phosphorylation, activation/inhibition of which rescues the effects of cell mechanics on their migration and invasion ability. Further, stiffening liver cancer stem cells attenuates their motility through activating JNK signaling. In summary, our study uncovers a previously unappreciated role of tumor cell mechanics in migration and invasion and implicates the therapeutic potential of cell mechanics in the mechanotargeting of metastasis.

Essay: Exploring the Physics of Basic Medical Research

The 20th century witnessed the emergence of many paradigm-shifting technologies from the physics community, which have revolutionized medical diagnostics and patient care. However, fundamental medical research has been mostly guided by methods from areas such as cell biology, biochemistry, and genetics, with fairly small contributions from physicists. In this Essay, I outline some key phenomena in the human body that are based on physical principles and yet govern our health over a vast range of length and time scales. I advocate that research in life sciences can greatly benefit from the methodology, know-how, and mindset of the physics community and that the pursuit of basic research in medicine is compatible with the mission of physics. Part of a series of Essays that concisely present author visions for the future of their field.

Characterization of Cancer Cell Mechanics by Measuring Active Deformation Behavior

Active deformation behavior reflects cell structural dynamics adapting to varying environmental constraints during malignancy progression. In most cases, cell mechanics is characterized by modeling using static equilibrium systems, which fails to comprehend cell deformation behavior leading to inaccuracies in distinguishing cancer cells from normal cells. Here, a method is introduced to measure the active deformation behavior of cancer cells using atomic force microscopy (AFM) and the newly developed deformation behavior cytometry (DBC). During the measurement, cells are deformed and allows a long timescale relaxation (≈5 s). Two parameters are derived to represent deformation behavior: apparent Poisson's ratio for adherent cells, which is measured with AFM and refers to the ratio of the lateral strain to the longitudinal strain of the cell, and shape recovery for suspended cells, which is measured with DBC. Active deformation behavior defines cancer cell mechanics better than traditional mechanical parameters (e.g., stiffness, diffusion, and viscosity). Additionally, aquaporins are essential for promoting the deformation behavior, while the actin cytoskeleton acts as a downstream effector. Therefore, the potential application of the cancer cell active deformation behavior as a biomechanical marker or therapeutic target in cancer treatment should be evaluated.

Can't handle the stress? Mechanobiology and disease

Mechanobiology is a rapidly growing research area focused on how mechanical forces and properties influence biological systems at the cell, molecular, and tissue level, and how those biological systems, in turn, control mechanical parameters. Recently, it has become apparent that disrupted mechanobiology has a significant role in many diseases, from cardiovascular disease to muscular dystrophy and cancer. An improved understanding of this intricate process could be harnessed toward developing alternative and more targeted treatment strategies, and to advance the fields of regenerative and personalized medicine. Modulating the mechanical properties of the cellular microenvironment has already been used successfully to boost antitumor immune responses and to induce cardiac and spinal regeneration, providing inspiration for further research in this area.

The Unfolded Protein Response Sensor PERK Mediates Stiffness-Dependent Adaptation in Glioblastoma Cells

Glioblastoma multiforme (GBM) is the most aggressive brain tumor in adults. In addition to genetic causes, the tumor microenvironment (TME), including stiffening of the extracellular matrix (ECM), is a main driver of GBM progression. Mechano-transduction and the unfolded protein response (UPR) are essential for tumor-cell adaptation to harsh TME conditions. Here, we studied the effect of a variable stiff ECM on the morphology and malignant properties of GBM stem cells (GSCs) and, moreover, examined the possible involvement of the UPR sensor PERK herein. For this, stiffness-tunable human blood plasma (HBP)/alginate hydrogels were generated to mimic ECM stiffening. GSCs showed stiffness-dependent adaptation characterized by elongated morphology, increased proliferation, and motility which was accompanied by F-Actin cytoskeletal remodeling. Interestingly, in PERK-deficient GSCs, stiffness adaptation was severely impaired, which was evidenced by low F-Actin levels, the absence of F-Actin remodeling, and decreased cell proliferation and migration. This impairment could be linked with Filamin-A (FLN-A) expression, a known interactor of PERK, which was strongly reduced in PERK-deficient GSCs. In conclusion, we identified a novel PERK/FLNA/F-Actin mechano-adaptive mechanism and found a new function for PERK in the cellular adaptation to ECM stiffening.

The biophysics of cancer: emerging insights from micro- and nanoscale tools

Cancer is a complex and dynamic disease that is aberrant both biologically and physically. There is growing appreciation that physical abnormalities with both cancer cells and their microenvironment that span multiple length scales are important drivers for cancer growth and metastasis. The scope of this review is to highlight the key advancements in micro- and nano-scale tools for delineating the cause and consequences of the aberrant physical properties of tumors. We focus our review on three important physical aspects of cancer: 1) solid mechanical properties, 2) fluid mechanical properties, and 3) mechanical alterations to cancer cells. Beyond posing physical barriers to the delivery of cancer therapeutics, these properties are also known to influence numerous biological processes, including cancer cell invasion and migration leading to metastasis, and response and resistance to therapy. We comment on how micro- and nanoscale tools have transformed our fundamental understanding of the physical dynamics of cancer progression and their potential for bridging towards future applications at the interface of oncology and physical sciences.

Data-Driven Discovery of Mathematical and Physical Relations in Oncology Data Using Human-Understandable Machine Learning

For decades, researchers have used the concepts of rate of change and differential equations to model and forecast neoplastic processes. This expressive mathematical apparatus brought significant insights in oncology by describing the unregulated proliferation and host interactions of cancer cells, as well as their response to treatments. Now, these theories have been given a new life and found new applications. With the advent of routine cancer genome sequencing and the resulting abundance of data, oncology now builds an "arsenal" of new modeling and analysis tools. Models describing the governing physical laws of tumor-host-drug interactions can be now challenged with biological data to make predictions about cancer progression. Our study joins the efforts of the mathematical and computational oncology community by introducing a novel machine learning system for data-driven discovery of mathematical and physical relations in oncology. The system utilizes computational mechanisms such as competition, cooperation, and adaptation in neural networks to simultaneously learn the statistics and the governing relations between multiple clinical data covariates. Targeting an easy adoption in clinical oncology, the solutions of our system reveal human-understandable properties and features hidden in the data. As our experiments demonstrate, our system can describe nonlinear conservation laws in cancer kinetics and growth curves, symmetries in tumor's phenotypic staging transitions, the preoperative spatial tumor distribution, and up to the nonlinear intracellular and extracellular pharmacokinetics of neoadjuvant therapies. The primary goal of our work is to enhance or improve the mechanistic understanding of cancer dynamics by exploiting heterogeneous clinical data. We demonstrate through multiple instantiations that our system is extracting an accurate human-understandable representation of the underlying dynamics of physical interactions central to typical oncology problems. Our results and evaluation demonstrate that, using simple-yet powerful-computational mechanisms, such a machine learning system can support clinical decision-making. To this end, our system is a representative tool of the field of mathematical and computational oncology and offers a bridge between the data, the modeler, the data scientist, and the practicing clinician.

DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification

BACKGROUND

Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data.

RESULTS

We present DECONbench, a standardized unbiased benchmarking resource, applied to the evaluation of computational methods quantifying cell-type heterogeneity in cancer. DECONbench includes gold standard simulated benchmark datasets, consisting of transcriptome and methylome profiles mimicking pancreatic adenocarcinoma molecular heterogeneity, and a set of baseline deconvolution methods (reference-free algorithms inferring cell-type proportions). DECONbench performs a systematic performance evaluation of each new methodological contribution and provides the possibility to publicly share source code and scoring.

CONCLUSION

DECONbench allows continuous submission of new methods in a user-friendly fashion, each novel contribution being automatically compared to the reference baseline methods, which enables crowdsourced benchmarking. DECONbench is designed to serve as a reference platform for the benchmarking of deconvolution methods in the evaluation of cancer heterogeneity. We believe it will contribute to leverage the benchmarking practices in the biomedical and life science communities. DECONbench is hosted on the open source Codalab competition platform. It is freely available at: https://competitions.codalab.org/competitions/27453 .

Interdisciplinary research: shaping the healthcare of the future

The hospitals of the future will be shaped by scientific and technical advances made across a wide range of disciplines because complex problems in healthcare cannot be addressed successfully by a single discipline. This paper considers how interdisciplinary research is being promoted and the prospects for developing stronger and deeper collaborations between medicine, health and other disciplines, drawing on case studies from mathematics, physics and engineering. The anticipated impact of greater interdisciplinarity on clinical training and the provision of care is also reviewed. While the role and training of clinicians in the provision of care will continue to evolve, they will remain leading members of a much broader and more diverse interdisciplinary team, alert to the value of deep and sustained interdisciplinary research.

Cell Cytoskeleton and Stiffness Are Mechanical Indicators of Organotropism in Breast Cancer

Simple Summary

Cancer cell dissemination exhibits organ preference or organotropism. Although the influence of intrinsic biochemical factors on organotropism has been intensely studied, little is known about the roles of mechanical properties of metastatic cancer cells. Our study suggests that there may be a correlation between cell cytoskeleton/stiffness and organotropism. We find that the cytoskeleton and stiffness of breast cancer cell subpopulations with different metastatic preference match the mechanics of the metastasized organs. The modification of cell cytoskeleton significantly influences the organotropism-related gene expression pattern and mechanoresponses on soft substrates which mimic brain tissue stiffness. These findings highlight the key role of cell cytoskeleton in specific organ metastasis, which may not only reflect but also impact the metastatic organ preference.

Abstract

Tumor metastasis involves the dissemination of tumor cells from the primary lesion to other organs and the subsequent formation of secondary tumors, which leads to the majority of cancer-related deaths. Clinical findings show that cancer cell dissemination is not random but exhibits organ preference or organotropism. While intrinsic biochemical factors of cancer cells have been extensively studied in organotropism, much less is known about the role of cell cytoskeleton and mechanics. Herein, we demonstrate that cell cytoskeleton and mechanics are correlated with organotropism. The result of cell stiffness measurements shows that breast cancer cells with bone tropism are much stiffer with enhanced F-actin, while tumor cells with brain tropism are softer with lower F-actin than their parental cells. The difference in cellular stiffness matches the difference in the rigidity of their metastasized organs. Further, disrupting the cytoskeleton of breast cancer cells with bone tropism not only elevates the expressions of brain metastasis-related genes but also increases cell spreading and proliferation on soft substrates mimicking the stiffness of brain tissue. Stabilizing the cytoskeleton of cancer cells with brain tropism upregulates bone metastasis-related genes while reduces the mechanoadaptation ability on soft substrates. Taken together, these findings demonstrate that cell cytoskeleton and biophysical properties of breast cancer subpopulations correlate with their metastatic preference in terms of gene expression pattern and mechanoadaptation ability, implying the potential role of cell cytoskeleton in organotropism.

The biology and engineered modeling strategies of cancer-nerve crosstalk

A recent finding critical to cancer aggravation is the interaction between cancer cells and nerves. There exist two main modes of cancer-nerve interaction: perineural invasion (PNI) and tumor innervation. PNI occurs when cancer cells infiltrate the adjacent nerves, and its relative opposite, tumor innervation, occurs when axons extend into tumor bodies. Like most cancer studies, these crosstalk interactions have mostly been observed in patient samples and animal models at this point, making it difficult to understand the mechanisms in a controlled manner. As such, in recent years in vitro studies have emerged that have helped identify various microenvironmental factors responsible for cancer-nerve crosstalk, including but not limited to neurotrophic factors, neurotransmitters, chemokines, cancer-derived exosomes, and Schwann cells. The versatility of in vitro systems warrants continuous development to increase physiological relevance to study PNI and tumor innervation, for example by utilizing biomimetic three-dimensional (3D) culture systems. Despite the wealth of 3D in vitro cancer models, comparatively there exists a lack of 3D in vitro models of nerve, PNI, and tumor innervation. Native-like 3D in vitro models of cancer-nerve interactions may further help develop therapeutic strategies to curb nerve-mediated cancer aggravation. As such, we provide an overview of the key players of cancer-nerve crosstalk and current in vitro models of the crosstalk, as well as cancer and nerve models. We also discuss a few future directions in cancer-nerve crosstalk research.

Mechanical tumor microenvironment and transduction: cytoskeleton mediates cancer cell invasion and metastasis

Metastasis is a complicated, multistep process that is responsible for over 90% of cancer-related death. Metastatic disease or the movement of cancer cells from one site to another requires dramatic remodeling of the cytoskeleton. The regulation of cancer cell migration is determined not only by biochemical factors in the microenvironment but also by the biomechanical contextual information provided by the extracellular matrix (ECM). The responses of the cytoskeleton to chemical signals are well characterized and understood. However, the mechanisms of response to mechanical signals in the form of externally applied force and forces generated by the ECM are still poorly understood. Furthermore, understanding the way cellular mechanosensors interact with the physical properties of the microenvironment and transmit the signals to activate the cytoskeletal movements may help identify an effective strategy for the treatment of cancer. Here, we will discuss the role of tumor microenvironment during cancer metastasis and how physical forces remodel the cytoskeleton through mechanosensing and transduction.

Connecting Students with Patients and Survivors to Enhance Cancer Research Training

The professional training of cancer researchers in the basic sciences rarely involves interactions with patients. To provide nascent cancer scientists with an appreciation for and experience in interacting with the people most vested in their work, we created a program at Cornell University in which cancer researchers in training engage with the local patient community. Through this program, trainees gain a broader understanding of cancer, beyond the fundamental biology, and learn to effectively communicate scientific information to the public. We find that trainees and community members both benefit from interacting with one another and learning together about cancer using a common language.