Cancer cell mechanobiology: a new frontier for cancer research

Abstract

The study of physical and mechanical features of cancer cells, or cancer cell mechanobiology, is a new frontier in cancer research. Such studies may enhance our understanding of the disease process, especially mechanisms associated with cancer cell invasion and metastasis, and may help the effort of developing diagnostic biomarkers and therapeutic drug targets. Cancer cell mechanobiological changes are associated with the complex interplay of activation/inactivation of multiple signaling pathways, which can occur at both the genetic and epigenetic levels, and the interactions with the cancer microenvironment. It has been shown that metastatic tumor cells are more compliant than morphologically similar benign cells in actual human samples. Subsequent studies from us and others further demonstrated that cell mechanical properties are strongly associated with cancer cell invasive and metastatic potential, and thus may serve as a diagnostic marker of detecting cancer cells in human body fluid samples. In this review, we provide a brief narrative of the molecular mechanisms underlying cancer cell mechanobiology, the technological platforms utilized to study cancer cell mechanobiology, the status of cancer cell mechanobiological studies in various cancer types, and the potential clinical applications of cancer cell mechanobiological study in cancer early detection, diagnosis, and treatment.

1. Introduction

Cancer is a deadly disease mainly because of its invasive and metastatic ability, which may be associated with a mechanotypical change in individual cancer cells as well as the changes in cancer microenvironment. Often, the ability to invade is the threshold that separates precancerous lesions from cancerous lesions. The underlying molecular mechanisms for regulating cellular mechanotypes are rather elusive, though it is accepted that complex biological processes, such as pathways associated with cell adhesion, cytoskeletal remodeling, epithelial-mesenchymal transition (EMT), and the nucleoskeleton, are involved 1, 2, 3. In recent years, emerging evidence has shown that cell mechanotypical changes, such as alterations in cellular stiffness and deformability, are common phenotypic events in cancer development and progression. Multiple nanotechnology and microfluidic techniques have been applied to quantify cell mechanotypical changes in tumors. The analysis of cell mechanical properties allows us to distinguish metastatic tumor cells from normal epithelial cells in patient body fluid samples ⁴. Additional studies further showed that cell stiffness could be used to predict cell invasion ⁵ or the response of tumor cells to various therapeutic drugs 6, 7, 8, 9. Further, the measurement of cell deformability (i.e., the ability to change shape under load) provides a quantitative analysis for objective algorithmic-based diagnoses of metastatic cancer cells in body fluid, or blood in liquid biopsy settings 10, 11, 12. These works have prompted an emerging new research field – cancer cell mechanobiology.

However, the specific pattern of change in cellular mechanotype during cancer development and progression is not well defined. The molecular network underlying the change of cellular mechanotype is still poorly studied and understood. Studying cellular mechanotypes, as well as the underlying molecular events associated with the mechanotypical changes, paves the foundation of cancer mechanobiology. This review discusses the molecular basis of cancer cell mechanics and summarizes cell mechanobiology study techniques, cancer cell mechanotypes, and the application of mechanobiology approaches in cancer diagnosis and treatment.

2. Molecular components of cancer cell mechanics

There are three main components determining cancer cell mechanics: cytoskeleton, nucleoskeleton, and extracellular matrix (ECM). The ultimate outcome of cancer cell mechanical changes is the altered potential of cancer cell invasion and metastasis.

2.1. Cytoskeleton

The cytoskeleton system is an intracellular network interlinking filaments and tubules that extend throughout the cytoplasm, from the nucleus to the plasma membrane. Microfilament actin, intermediate filaments, and microtubules are three types of cytoskeletal proteins exist in all cell types. These cytoskeletal proteins have different elastic properties and act together to stabilize the cellular morphological and architectural structures. They shape the cell and also actively sense and respond to a cellular environment. These proteins also play a critical role in cancer development and progression. While actin, intermediate filaments, and microtubules have all been implicated in cancer cell mechanobiology, most studies thus far have been focused on the microfilament actin and its associated proteins, which together constitute over 25% of total cellular protein. Actin remodeling is associated with the change of cancer cell phenotypes, including cell mechanical properties. Actin is present in the cell in either a free monomer globular form (G-actin) or a polymerized filamentous form (F-actin). The actin polymerization/depolymerization process is carefully regulated by a series of actin signaling proteins, which are components of important oncogenic signal transduction pathways. Notably, the Ras superfamily GTPase proteins Rac, Rho, and Cdc42 all function as actin signaling proteins ^13,14. These signaling pathways have become the focus for developing small molecules with anticancer properties. On the other hand, many actin-binding proteins help either stabilize the actin filaments or organize the F-actin into larger bundles. Over one hundred actin-binding proteins have been identified to date, many of which are invariably involved in malignant transformation processes ¹⁵.

Earlier studies indicated that the polymerization of microfilament actin (from G actin to F action) is closely related to cell differentiation ¹⁶, and that the opposite, i.e., actin depolymerization (from F actin to G actin), is a marker of cancer-associated field change, and thus a biomarker for precancerous lesion ¹⁷. Some chemo-preventive drugs such as green tea extract can inhibit cancer development, at least partly, by inducing actin polymerization by regulating actin-binding or signaling proteins^18,19. However, the alteration of actin remodeling is a dynamic, two-way process ²⁰. Generally, normal epithelial cells have orderly differentiation characterized by actin polymerization. The depolymerization of actin occurs in the early stage of cancer development, especially in the precancerous stage ¹⁷. When the tumor progresses from precancerous in situ lesions to invasive forms, the actin polymerization that occurs is accompanied by altered distribution, resulting in a process called “paradoxical differentiation.” Morphologically, cells show altered polarity, overlapping, and most importantly, increased nuclear-to-cytoplasmic ratio (N:C ratio).

While actin remodeling, especially G and F-actin levels, can be measured using approaches such as quantitative fluorescence image analysis on a single cell level ²¹, the dynamic nature of actin remodeling makes it challenging to utilize actin remodeling per se as a biomarker for cancer ²⁰. Measurements of G or F-actin level do not always correlate with cell behaviors relevant for cancer progression, such as invasion or metastasis. Actin remodeling, or the fluctuations in actin levels (either F or G-actin, or the ratio of the two), may not capture the entire spectrum of biological processes (for example, the distinction between infection-induced cytological atypia versus true malignancy).

2.2. Nuclear envelope

The nucleus is normally the largest organelle of cells and is typically stiffer than the cytoplasm ²². It has only recently been recognized that, as the site of numerous essential functions in eukaryotes, nuclear mechanical properties and interactions with the cytoskeleton play a critical role in cancer development and progression ²³. The nuclear membrane, membrane protein, nuclear lamina, chromatin, and intranuclear proteins are the main components of nucleus mechanics. Among them, the nuclear membrane structure and the protein network (lamin) underlying the inner nuclear membrane, which is known as nuclear envelope, have been extensively studied in cancer cell mechanobiology.

The nuclear membrane consists of two lipid bilayers, the inner nuclear membrane, and the outer nuclear membrane. Lamina are continuous with the inner nuclear membrane and can be divided into type A (lamin A and lamin C) and type B lamina ²⁴. The nuclear lamina provides structural support for the nucleus ²⁵ and can also regulate nuclear shape and size ². Nuclei that contain more significant amounts of lamin A are much stiffer and resist deformation to a high degree than those with lower amounts 26, 27, 28. Studies also demonstrated that chromatin organization and nuclear deformability are primarily affected by lamin deficiency due to gene mutations ^29,30. Lamin interacts with chromatin and many intranuclear proteins, including nuclear actin, spectrin, myosin, and titin ³¹, forming a nucleoskeleton system to resist mechanical stress and serving as scaffolds in transcriptional regulation.

The nucleus is not only a load-bearing organelle, but also actively reacts with the cellular mechanical environment. Through media proteins, the nucleus is physically coupled to the surrounding cytoskeleton. Nesprin, which belongs to the family of spectrin-repeat proteins and is located in the nuclear envelope, can directly bind to Lamin A/C and cytoskeleton proteins in vitro ³². SUN proteins, which are anchored to the nuclear lamina and other components of the nuclear interior, interact with Nesprin (LINC complex), creating a physical link between the cytoskeleton and the nucleus ²³. Studies have indicated several potential mechanisms for the nucleus to transduce mechanical force to biological signals, for example, via the LINC complex and other proteins ³. The interactions between lamina and chromatin result in transcriptional activation or repression.

2.3. Interactions between cancer cells and ECM

The ECM exists in varying degrees of stiffness, which is primarily dependent on collagen and elastin concentration. Physical property changes in the ECM may occur early during malignant transformation process. It is reported that high matrix stiffness resulted in an increased formation of activated 3D-matrix adhesions, chronically elevated FAK-Rho signaling system and MAPK pathway, promoted growth of mammary epithelial cells, and maintained the invasive phenotype of cancer cells ³³. ECM properties and crosslinking architecture also directly regulate cell migration and invasion ³⁴. Different collagen alignments determine the efficiency of migration through increasing directional persistence and restricting protrusions along aligned fibers ³⁵. Likewise, cancer cells can regulate the surrounding ECM and facilitate their migration ability by increasing fibrillar collagen, adjusting collagen crosslinking by lysyl oxidases, and creating parallel bundles by metalloproteinase (MMP)-mediated proteolytic degradation 36, 37, 38, 39, 40.

These interactions can be summarized as bidirectional mechanical feedback or dynamic reciprocity between cells and matrix determine remodeling. In this process, integrins, known as the transmembrane cell adhesion receptors, play a pivotal role in sensing mechanical signals and transduce the information into cells. Typically, integrin clusters recruit cytoplasmic focal adhesion proteins and connect to the cytoskeleton and ECM. This structure is called focal adhesion, and from here, an outside signal activates intracellular signaling cascades ⁴¹. Recent studies also showed that actin-binding proteins are involved in regulating this signal transduction. Vinculin, a well-recognized actin-binding protein, connects F-actin and focal adhesion proteins. Through phosphorylation on Y100 and Y1065 residues, vinculin is activated and primed to bind to F-actin and talin, which then enables integrin-mediated mechanical signal transmission ⁴².

2.4. Mechanical changes of cancer microenvironment

Cancer cells inhabit a complex microenvironment containing surrounding blood vessels, immune cells, fibroblasts, bone marrow-derived inflammatory cells, lymphocytes, signaling molecules, and ECM ⁴³. The mechanical environment is composed of endogenous forces generated by the cells themselves, as well as exogenous forces that are applied to cells by the surrounding microenvironment ⁴⁴. While cancer cells are generally softer than their normal counterparts, tumors tend to be stiffer than surrounding tissues. The possible reason lies in tumor expansion, vasculogenesis, and matrix stiffening linked to fibrosis ⁴⁵. Paszek et al. examined the relationship between tissue rigidity and tumor biological behavior and found that the stiff microenvironment actively promotes tumor progression and malignancy through increased integrin signaling ⁴⁶.

Therefore, mimics of the in-vivo environment have been widely used in the study of cancer cell mechanobiology. With the development of tissue engineering, researchers can design different three-dimensional (3D) cell culture matrices with different structures and physical properties ⁴⁷. In a 3D setting, cell mechanical properties are modulated in a manner distinct from cells in a planar substrate. Some focal adhesion proteins which can regulate cell motility in a 2D condition are not predictive of regulation of 3D cell motility in a matrix ⁴⁸. Another significant improvement is the development of intravital microscopes, which have given us the ability to visualize cell interaction and movement in real time. Although multiple technological challenges still exist when targeting a micron-size object (e.g., single cells or organelles) in a complex living organism, this state-of-the-art platform offers full ultrastructural details of dynamic or transient events in vivo and is promising for future cell biological and mechanical studies ⁴⁹.

3. Technology platforms for cancer cell mechanobiology studies

There has been a growing body of work linking cellular biomechanics with a variety of platforms, which have been engineered to perform mechanical measurements and have generated robust datasets in different cell types (Table 1). The most common explored method is mechanical probes, represented by Atomic Force Microscopy (AFM) and Magnetic Tweezer (MT) ⁵⁰. In cell biology, typical force ranges from 1-10 pN in the case of kinesin and myosin, ∼1-200 pN for protein-protein interactions, ∼100 pN for partial protein unfolding, and ∼1 nN to 10 pN for migrating or contracting cells ⁵¹. Mechanical properties of living cells can be probed and characterized by recording force-displacement curves under 1 pN-1 µN force on a part of a cell surface. AFM is a powerful tool for imaging biological samples with sub-nanometer resolution, providing tremendous insight into cell surface features and cell mechanical properties, as well as cellular processes that affect the mechanical properties of living cells ⁵². The main parts of the AFM are the cantilever, the tip that is mounted to a soft cantilever spring, the sample stage, and the optical deflection system, which consists of a laser diode and a photodetector. Force spectroscopy relies on measuring the force as the tip is pushed toward the cell, indented into the sample, and subsequently retracted. A force-displacement curve can be obtained by monitoring the deflection of the cantilever; this data enables extraction of a value for the Young's modulus, E, of living cells.

Table 1.

Cellular biomechanical studies with different technologies.

Tissue type	Cell line / Clinical sample	Technique
Breast	Benign (MCF-10A) vs. cancerous (MCF-7) human breast epithelial cells	AFM55
	Benign (MCF-10A), non-invasive malignant (MCF-7), and highly-invasive malignant (MDA-MB-231) breast cancer cells	AFM 56
	Benign (MCF-10A) vs. non-metastatic tumor breast cells (MCF-7)	Microfluidics 57
	Higher deformability in invasive cells (transformed MCF7) than non-metastatic MCF10 and non-transformed MCF7 breast cell	Optical deformability 58
	Suspected metastatic breast cancer cells	AFM 4
	Normal breast and breast cancer tissues	Spectrum reponse 59
	T47D and MCF7 breast cancer cells, fresh tissue samples	AFM 60,61
	Breast tissue samples	Indenter 62
Bladder Urothelial	Normal (Hu609, HCV29) and bladder cancer cells (Hu456, T24, BC3726)	AFM 63
	Normal human urothelial (SV-HUC-1) and bladder cancer cells (MGH-U1)	AFM 64
	Non-malignant urothelial cell HCV29 and transitional bladder cancer cell T24	AFM 65
Cervical Ovarian	Metastatic potential of ovarian cancer cells and non-malignant immortalized ovarian surface epithelial cells (IOSE)	AFM 66
	Ovarian cancer stem-like/tumor initiating cells and their aggressive late stage, intermediate, and non-malignant early stage cancer cells.	AFM 67
	Metastatic potential of OVCA429, IGROV, SKOV3, HEY, DOV13, OV2008, and Ovca420 show inverse power-law relationship with cell stiffness	Magnetic tweezers 68
	Exfoliated cells collected from patients with chronic cervicitis or CIN1, patients with CIN2-3, and patients with cervical cancer	AFM60,69
Colorectal	SW480 and SW620 human colon carcinoma-derived cell lines	AFM 70
	HCT-8 colon cancer cells	AFM 71
Kidney	Carcinoma (A-498), adenocarcinoma (ACHN)) and non-tumorigenic cells (RC-124)	AFM 72
Lung	Human non-small lung cancer cells (H1299 and Lu99)	AFM 73
Melanoma	Metastatic B16 melanoma variants (B16-F10, B16-BL6, and B16-F1 cells)	AFM 74
Prostate	LNCaP, PC-3 and BPH cells	AFM 75
	Circulating tumor cells from peripheral blood of prostate cancer patients	Microfiltration 76
	Clinical specimens taken during radical prostatectomy	Indenter 77,78
Esophagus	Normal Squamous cells (EPC2), metastatic (CP-A) and dysplastic esophageal cells	AFM 79
Thyroid	Primary thyroid cells and S748/carcinoma cell S277	AFM 80
Blood	Red blood cells under different osmotic conditions	Optical tweezers 81
	AML CD33+CD34− cells and CD33+CD34+ cells	Optical Tweezers 82
	Myeloid (HL60) cells, lymphoid (Jurkat) cells and human neutrophils	AFM 83
	Hematopoietic cells were obtained from bone marrow (BM) of patients with AML and umbilical cord blood (UCB)	Optical tweezers 84
	Lymphocytes in chronic lymphocytic leukemia (CLL) patients	Microfluidics and AFM 85
	The viscosity of human promyelocytic leukemia cells HL-60	Micropipette aspiration 86
Pleural effusion	Leukocytes and malignant cells in pleural effusions	Microfluidics 10
	Malignant pleural effusions	Microfluidics 11
	Malignant pleural effusions	AFM 7

Microfluidics studies the behavior of fluids through micro-channels. With the development of technology in manufacturing microminiaturized devices, microfluidic approaches have been used to exploit the physical properties of single cells. Deformability cytometry (DC) is a high-throughput (>2000 cells/sec) and label-free technology based on microfluidic inertial focusing, hydrodynamic stretching, and automated high-speed image analysis. This approach integrates breakthroughs in microfluidic automation to yield a transformational instrument that provides a label-free, inexpensive single-cell analytic approach to medical diagnostics. A continuous stream of single cells is created in a high-speed microfluidic flow. These cells then enter an extensional flow, where each cell's deformation is measured with high-speed imaging. Automated image analysis software is used to extract a host of independent physical parameters from these images. The software also stores and graphs properties of thousands of cells in an easily interpreted density scatter plot format similar to that used in flow cytometry. Thus, biologists and clinicians accustomed to flow cytometry studies can adapt to interpreting and using the cell deformability data created by the instrument.

Quantitative deformability cytometry (Q-DC) is another microfluidic-based device that quantifies cell transit time ⁵³ and extracts calibrated measurements of cellular mechanical properties ⁵⁴. The displacements of single cells through a narrow, micron-scale gap in a microfluidic channel simulate physiological deformations during cancer cell extravasation, intravasation, and circulation. By tracking shape changes in single cells through the deformation process using high-speed image analysis, quantitative metrics can be extracted, including transit time – which is associated with cell stiffness – as well as elastic modulus and fluidity. In conjunction with machine learning algorithms, the rich data sets obtained using q-DC show potential to predict cancer cell invasion ⁵.

4. Cell mechanotype studies in various cancer types

As invasion and metastasis phenotype is a common denominator for all cancer types, we expect that changes of cancer cell mechanobiology are also a shared common feature for various cancers. Indeed, using various approaches, mostly mechanical probes and microfluidic devices, this hypothesis has been supported by observations from various groups. In 2007, our group first reported that metastatic pleural fluid cancer cells are softer than benign cells, starting a global interest in exploring the relationship between cell stiffness and malignancy ⁴. Indeed, cell stiffness is one of the most important cell mechanical property indicators, which can be described as Young's modulus or the elastic modulus ⁸⁷. Researchers have compared cell stiffness in different cancer in vitro models and most evidence demonstrated that lower stiffness is correlated with increased cell invasiveness. In breast cancer, highly invasive MDA-MB-231 cells demonstrated the lowest Young's modulus in comparison to invasive T47D cells and MCF7 cells ⁸⁸. In human cervix cancer, cancerous Hela cells are much softer than normal End1/E6E7 cells, though no significant locational differences in the stiffness of cancer cells were observed between the central and the peripheral regions ⁸⁹. Charlotte et al. reviewed various experimental studies that compared the mechanics of individual normal and cancer cells and argued that cancer cells could indeed be considered softer than many normal cells ⁹⁰. However, there are also reports that invasive cells are stiffer than their counterparts. Thus, the relationship between cell stiffness and motility is still a matter of debate ⁸⁷. In the same breast cancer MCF7 cell line, genetically modified MCF7 cells with increased motility demonstrated higher stiffness than untreated cells ⁹¹. Another study showed that activation of β-adrenergic signaling by βAR agonists reduces the deformability of highly metastatic human breast cancer cells, measured by parallel microfiltration, a microfluidic q-DC device, and AFM. This reduced deformability related to βAR activation was also observed in ovarian, prostate, melanoma, and leukemia cells. The fact that βAR activation resulted in stiffer breast cancer cells that were actually more invasive in vitro suggests that the relationship between cell mechanical properties and invasion may be dependent on context ⁹².

The studies discussed above compared either the intrinsic differences between cancerous cell lines and their normal counterparts or the mechanical difference between cell lines and their counterparts with genetic modifications. It should be noted that cell line studies may not represent what happens in vivo, especially in the human body. Furthermore, genetic modifications may not lead to an intrinsic change in cells, but rather only a series of alternations in gene expression ⁸⁷. We recently examined cell mechanotypical changes in early stages of cancer transformation and progression using an established in vitro multi-step human urothelial cell carcinogenic model. Young's modulus, deformability, and transit time were measured respectively by single-cell atomic force microscopy, microfluidic-based deformability cytometry, and qDC. We demonstrated that as cells progressed from normal to preinvasive to invasive cells, the stiffness decreased and deformability gradually increased ⁹³. Urothelial carcinoma of the bladder has a well-defined, multi-step nature of development. Notably, urinary exfoliated cells, derived from primary urothelial tumors, provide a unique living model for the study of urothelial tumors. In our study, the mechanotypical changes that were observed in the cell model also occurred in urine exfoliated cells directly from patients. However, it is still challenging to characterize specific cell mechanotypes in a clinical setting due to technical complexity and the lack of standard sample processing approaches. Novel analyzing models have also been investigated to address clinical needs. A study using AFM as a tool reported that cancer cells can be classified by surface parameters, which are typically used in engineering to describe surfaces. In combination with machine learning, this method evaluated cells collected from urine, and showed 94% diagnostic accuracy when examining five cells per patient's urine sample ⁹⁴.

5. Cell mechanobiology in cancer diagnosis and treatment

As discussed above, the dynamic nature of the cell mechanotype provides important information that may be used for cancer early detection and prognostic analysis. Analyzing the cell mechanotype can generate an objective and unbiased output for the characterization of cell behavior. Further, a mechanobiological study generally uses a label-free method, whereas the cells can still be utilized for the downstream morphological and molecular analysis. There are many potential applications. In this review, we will focus our discussion on three different settings. The first is the application of cancer cell mechanobiology in liquid biopsy setting to detect and analyze circulating tumor cells (CTC). The second is the application of cell mechanobiology in determining the invasion and metastatic potential of tumor cells in the setting of urine cytological diagnosis of urothelial carcinoma. The third is the effort of developing smart touch fine needle, or fine needle electrograph (FNE), to diagnose malignant thyroid tumor.

Liquid biopsy, including CTC analysis, has generated tremendous enthusiasm in the cancer diagnosis field as it provides a non-invasive diagnostic platform that can potentially be used in many different diagnostic settings. CTC-based liquid biopsy has several limitations, including the lack of cancer cell-specific biomarkers to detect /enrich tumor cells and the inability to determine whether the detected cells, even though they may be from the cancer, can form metastatic cancerous lesions. In other words, the functionality of the CTC cannot be measured or determined by the current morphological or even molecular analysis. Previous studies have shown that procedures such as biopsy or excisional surgery often release tumor cells into the blood, yet the consequences of such tumor cells in the blood are unclear ⁹⁵. Thus far, most of the efforts have been focused on utilizing cell mechanobiological properties to identify or enrich CTC. A representative microfluidic technique uses deterministic lateral displacement, inertial focusing and magnetophoresis to sort abnormal CTCs from blood samples. According to the reported protocol, a microfluidic chip equipped with two-stage magnetophoresis and leukocytes antibody depletion can achieve 3.8-log depletion of white blood cells and a 97% yield of rare cells. The processing time for 8 ml of blood sample is about two hours ⁹⁶. We have established a workflow by combining cell mechanobiology for detecting and analyzing CTC and downstream molecular analysis (Fig. 1). We are in the process of testing this approach.

Fig. 1.

Integration of cell mechanobiology approach into urine cytology (A) and liquid biopsy analysis (B). A. In urine cytology, urothelial cells from exfoliated urine samples can be harvested using a cytospin method onto a microscopic slide, whereas measurements of cell mechanical properties can be performed directly with AFM on cells in a label-free manner. B. In liquid biopsy, circulating tumor cells (CTC) can be enriched first based on cell mechanophenotype (e.g., deformability) through microfluidic chip (negative selection), followed by tumor specific antibody (e.g., SP70) for positive selection. The selected tumor cells can then be first analyzed by mechanotype analysis (again in label free manner), and further by cytomorphology, cytogenetic and molecular analysis (NGS).

Another example of using cancer cell mechanobiology as a marker for cancer diagnosis is in urine cytology setting, especially to help the cytologist to determine whether detected cancer cells are invasive or not. Urine cytology is a quite effective non-invasive method for diagnosing high-grade urothelial carcinoma, with an overall sensitivity of 70% and specificity of over 95% ⁹⁷. However, traditional urine cytomorphology cannot distinguish non-invasive cancer cells (carcinoma in situ) from invasive cancer cells. The latter requires invasive procedures, including biopsy or transurethral resection (TUR), in order to determine whether cancer is invasive, especially muscle-invasive, whereas muscle invasion is the threshold for cystectomy, which is a major surgical procedure that carries a high risk for developing mobility. Recently, a multifaceted AFM-based analysis method was developed. The analysis included three properties: the cytoskeleton modulus E_C, the apparent viscosity of cytoplasm η, and the poroelastic diffusion coefficient D_p as plotted along three axes. We tested this method using a multi-step in vitro human urothelial cell carcinogenic model (HUC) and urine cytology samples from the patients with various stages of urothelial carcinoma to recapitulate the multistep nature of cancer progression, focusing on the changes of cellular mechanical properties during the conversion from non-invasive to invasive cancer cells. Overall, our data showed that mechanobiological-marker panel not only diagnoses urothelial cancer with high accuracy but also allows distinguishing non-invasive from invasive cancer cells, with very high sensitivity and specificity (unpublished data).

The analysis of cancer cell mechanobiology may also aid in distinguishing small nodular lesions into benign or malignant categories. In the past, ultrasound elastography measuring tissue stiffness was widely utilized to evaluate thyroid nodules for malignancy non-invasively. However, it is generally not very accurate due to the low resolution of distinguishing physical differences at the tissue level (on a scale of millimeters). FNE, which generates a force measurement during needle insertion using a piezoelectric transducer axially coupled with a biopsy needle, can reach a 10 µm-50cm in vivo measurement range ⁹⁸. We have tested the FNE for thyroid and breast tissues. In our preliminary studies, freshly excised patient thyroids with varying fibrotic and malignant potential were utilized for the testing. The results revealed discrete variations in tissue stiffness and stiffness heterogeneity, which was well correlated with the final histopathology ⁹⁹. Although these studies are mostly in pre-clinical stages, they will boost the efforts for the use of mechanobiology approaches in cancer diagnosis by developing rapid point-of-care diagnostic technology platforms. Further, as cancer cell mechanobiology changes often reflects aggressive cancer cell phonotype, it is plausible that such analysis allows the separation of benign or indolent small tumor nodule from malignant aggressive cancerous nodule, which is urgently needed for the screening and early detection of many different cancer types.

However, utilizing mechanotype analysis in cancer diagnosis still has a number of limitations. The procedure and data processing are often complicated and time-consuming. The lack of standard methodology, protocol, and reference dataset represents the main hurdles for applying cell mechanotype analysis in clinical settings. Recently, the concept of standardized protocols of elasticity measurements and data analysis, including standardized nanomechanical atomic force microscopy procedure (SNAP), was introduced ^100,101. Some researchers investigated the usage of a modulus ratio to define the relation of Young's modulus of the tumor to the reference normal tissues ^102,103. Machine learning technology was reported to detect subtle changes in nuclear morphometrics at single-cell resolution ¹⁰⁴. Researchers established a convolutional neural net pipeline to discriminate human breast cancer cells from normal cells in tissue slices and demonstrated high accuracy compared to pathological diagnoses.

Understanding cancer cell mechanobiology also gives us new insights into the development of novel therapeutic methods. The first concept is the usage of stress alleviation strategies to improve the efficacy of cancer treatment. Within a tumor mass, solid and fluid stresses create an interstitial hypertension status. The pressure gradient potentially transfers growth factors and tumor cells to surrounding tissues ^105,106. More importantly, interstitial hypertension causes hypoperfusion and hypoxia in tumors, which induces chronic inflammation and promotes EMT ^107,108. In our previous urothelial cell mechanobiology study, 3D-cultured urothelial carcinoma mass with central hypoxia demonstrated a significantly activated EMT pathway compared to the 2D-cultured cells ⁹³. Thus, alleviation of solid stresses may restore tumor oxygenation, improve growth pressure, and directly eliminate drug delivery barrier through increased profusion. Theoretically, the goal of stress alleviation can be achieved by depleting specific components in the tumor microenvironment ^106,109. This is also called the ECM altering strategy. Researchers have explored reducing the production of collagen and hyaluronan using an angiotensin receptor blocker ¹¹⁰, sonic hedgehog pathway inhibitor ¹¹¹, and lysyl oxidase inhibitor ³⁹. A few clinical trials were conducted to investigate these strategies in cancer combination treatment. However, there is still no substantial evidence to support their clinical benefits. Another concept is to directly target the existing biophysical cues of the ECM without depleting or modulating matrix components ¹¹². To reach this goal, a mechanoresponsive cell system was developed. In this system, when a YAP/TAZ stiffness-sensing promoter is activated, YAP/TAZ can drive the expression of downstream reporters such as enhanced green fluorescent protein for in vitro imaging. Using mechanosensitive promoter-driven mesenchymal stem cell (MSC)-based vectors, this system can be activated by the specific cancer-associated mechano-cues and deliver therapeutics to effectively kill cancer cells ¹¹³. This novel approach demonstrated efficacy in reducing the growth of metastatic breast cancer in a mouse model and will help us to further understand the basis of cell mechanobiology.

Potential areas of application of cancer mechanobiology for cancer diagnosis and treatment are shown in Fig. 2. The mechanotypical measurements including cell or tissue stiffness, deformability, permeability, and motility can be performed on either single cell or small tissue cores from liquid biopsy, fine needle aspiration, and core biopsy samples. Such measurements may provide objective assessment of cancer cell invasive and metastatic capabilities, which are not otherwise attainable by usual morphological or even molecular analysis. Thus, such measurements may complement traditional diagnostic analysis by adding the objective values to guide the practice of cancer diagnosis and management. Further, identifying specific molecular targets that are key to regulate these mechnotypic phenotypes may in turn help us develop novel therapeutic drug targets.

Fig. 2.

Mechanobiology approaches for cancer diagnosis (A) and treatment (B): In diagnostic setting (A), cancer mechanobiology can be applied in both cell-based (liquid biopsy and fine needle aspiration) and tissue based (biopsy) testings. The outputs of mechanotype analysis include measurements of cell stiffness, deformability, permeability, motility, among others. The corresponding malignant phenotypes are tumor invasion, metastasis, and recurrence. For treatment setting (B), strategies include modulating ECM components, restore perfusion and reduce hypoxia, and targeting mechanoresponsive pathways, that may ultimately result in reducing cancer cell viability, invasiveness, and metastasis.

6. Conclusion

The development of cancer cell mechanobiology depends on the deep understanding of cell molecular changes in malignant transformation and progression, and cutting-edge technology platforms for robust cellular mechanotype characterization. Cancer cell mechanobiology studies represent a new frontier in cancer research. From the physical environment, ECM, focal adhesion proteins, and cytoskeleton, to the nucleoskeletal framework, cellular mechanical regulation can on the one hand regulate cancer cell behavior phenotypes (e.g., invasion and metastasis), and on the other regulate and control cancer-associated gene expression. In recent years, many mechanobiology analysis approaches have been developed. Together with the advances in biomechanical probes and microfluidic techniques, this allows us to address more outstanding questions in cancer development and progression. Cancer cell mechanobiology studies generate novel concepts for the improvement of current diagnostic and treatment methods. While we expect mechanotyping analysis of cancer cells may serve as a novel diagnostic marker for cancer, the exact clinical value and utility remain to be determined in future clinical and pathological studies. With more studies getting involved, overcoming existing limitations and improving the efficacy of mechanobiology strategies can be expected in the future.

Declaration of competing interest

The authors declare that they have no conflict of interest.