Quantification of effects of cancer on elastic properties of breast tissue by Atomic Force Microscopy

Author’s Accepted Manuscript Quantification of Effects of Cancer on Elastic properties of Breast Tissue by Atomic Force Microscopy Arian Ansardamavandi, Mohammad TafazzoliShadpour, Ramin Omidvar, Iisa Jahanzad www.elsevier.com/locate/jmbbm

PII: DOI: Reference:

S1751-6161(15)00502-0 http://dx.doi.org/10.1016/j.jmbbm.2015.12.028 JMBBM1743

To appear in: Journal of the Mechanical Behavior of Biomedical Materials Received date: 3 June 2015 Revised date: 9 December 2015 Accepted date: 21 December 2015 Cite this article as: Arian Ansardamavandi, Mohammad Tafazzoli-Shadpour, Ramin Omidvar and Iisa Jahanzad, Quantification of Effects of Cancer on Elastic properties of Breast Tissue by Atomic Force Microscopy, Journal of the Mechanical Behavior of Biomedical Materials, http://dx.doi.org/10.1016/j.jmbbm.2015.12.028 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Quantification of Effects of Cancer on Elastic properties of Breast Tissue by Atomic Force Microscopy Arian Ansardamavandia, Mohammad Tafazzoli-Shadpoura, Ramin Omidvara, Iisa Jahanzadb a

Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran

b

Pathology Department, School of Medicine, Tehran University of Medical Science, Tehran, Iran

Corresponding Author: Mohammad Tafazzoli-Shadpour, Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran, phone: 98- 21-6654-2385, fax: 98-21-6646-8186, E-mail: [email protected]

Abstract

Different behaviors of cells such as growth, differentiation and apoptosis widely differ in case of diseases. The mechanical properties of cells and tissues can be used as a clue for diagnosis of pathological conditions. Here, we implemented Atomic Force Microscopy to evaluate the extent of alteration in mechanical stiffness of tissue layers from patients affected by breast cancer and investigated how data can be categorized based on pathological observations. To avoid predefined categories, Fuzzy-logic algorithm as a novel method was used to divide and categorize the derived Young’s modulus coefficients (E). Such algorithm divides data among groups in such way that data of each group are mostly similar while dissimilar with other groups. The algorithm was run for different number of categories. Results showed that three (followed by two with small difference) groups categorized data best. Three categories were defined as (E< 3000 Pa, 30007000 Pa) among which data were allocated. The first cluster was assumed as the cellular region while the last cluster was referred to the fibrous parts of the tissue. The intermediate region was due to other non-cellular parts. Results indicated 50% decline of average Young’s modulus of

cellular region of cancerous

tissues compared to healthy tissues. The average Young’s modulus of non-cellular area of normal tissues was slightly lower than that of cancerous tissues, although the difference was not statistically different. Through clustering, the measured Young’s moduli of different locations of cancerous tissues, a quantified approach was developed to analyze changes in elastic modulus of a spectrum of components of breast tissue which can be applied in diagnostic mechanisms of cancer development, since in cancer progression the softening cell body facilitates the migration of cancerous cells through the original tumor and endothelial junctions. Keywords. Breast tissue; Cancer; Stiffness; Atomic Force Microscopy.

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1. Introduction

Normal cells in the body have regular growth, proliferation, and prescheduled apoptosis. The uncontrollable growth of cells in a part of the body is mostly related to tumor formation and in many cases cancer. In metastatic stages of the disease, some cells detach from the tumor and move to other tissues through blood circulation or lymphatic vessels, and reproduce new tumors. Secretion of the enzyme protease, transformations in adhesion receptors, and alterations in the structure and skeleton of cells and consequently cell mechanical properties are among metastatic related changes (Hayashi and Iwata, 2015; Ramis-Conde et al., 2008; Yamaguchi and Condeelis, 2007; Zhou et al., 2012). It has been revealed that cancer progression and escalation of invasiveness is accompanied by reorganization of cytoskeleton structure within the cancerous cells (Suresh, 2007a). As a result, the physical and mechanical characteristics of cells change among which cell stiffness has been particularly of scientific interest (Suresh, 2007b). Study of such alterations provides new information on mechanisms of cancer development and possibly new methods in diagnosis and treatment (Bishitz et al., 2014; PalacioTorralba et al., 2015; Remmerbach et al., 2009; Swaminathan et al., 2011; Wang et al., 2012). Various methods such as micropipette aspiration, magnetic bead twisting, optical tweezers, and Atomic Force Microscopy have been used for measuring the stiffness of cancerous cells (Hong-Lian et al., 2004; Lekka et al., 2012b; Li et al., 2008; Mohammadalipour et al., 2012). Atomic Force Microscopy (AFM1) applies forces via interacting an atomic-scaled tip located at the end of cantilever with the sample and records the force-displacement data (Butt et al., 2005). Numerous studies have been conducted using AFM to measure stiffness of biological samples such as biological cells and tissues (Andriotis et al., 2014; Engler et al., 2004; Hayashi and Iwata, 2015; Last et al., 2009; Liu and Tschumperlin, 2011). Many of these studies have been conducted on cell lines (Faria et al., 2008; Lekka et al., 1999; Li et al., 2008; Zhou et al., 2012) and some have been carried out to investigate changes in cell characteristics under pathological conditions including cancer (Canato et al., 2010; Maciaszek et al., 2011; Maciaszek and Lykotrafitis, 2011; Shi et al., 2009). Cancer cell lines have been shown to be softer than normal cells in such way that an increase in the degree of severity of cancer further decreases their stiffness (Suresh, 2007a). Cell softening is one of the requirements of cancer cells at the stage of metastasis (Faria et al., 2008; Li et al., 2008). At this stage, cells with reduction in the expression of adhesion proteins and decreased stiffness detach from the original tumor, drastically change their form, pass through the junction of endothelial cells, and lodge in other tissues. Based on this phenomenon, alterations in the stiffness of cancer cells have been introduced as an important indicator for diagnosing cancers among tissues such as breast and prostate (Faria et al., 2008;

Abbreviations used: 1AFM, Atomic Force Microscopy. 2 H&E, Hematoxylin-Eozin. 3ECM, extra cellular matrix.

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Leporatti et al., 2009; Li et al., 2008). Research has shown that Young’s moduli of three different prostate cell lines were 37% to 85% less than those that of healthy cells (Lekka et al., 2012a). Similarly study of two distinct breast cancer cell lines has demonstrated 53% and 60% decline in Young’s modulus value compared to that of healthy cells (Lekka et al., 2012a). Compared to extensive studies on mechanical analysis of cancer cell lines, fewer studies were carried out on cancer cells or tissues extracted from patients. In one study, Cross et al. extracted lung, pancreas, and breast cancerous cells and analyzed their stiffness using AFM. Their results indicated that the stiffness of metastasized cancer cells was up to 70% less than that of normal cells. They suggested that analysis of the mechanical characteristics of cancer cells can be regarded as a diagnostic method for distinguishing these cells from normal cells even if they are similar in shape (Cross et al., 2008). Cancerous tissues extracted from human bodies have also been characterized by AFM. Such measurements have advantages since mechanical characteristics of cells and their micro-constituents can be simultaneously studied and compared to pathologic conditions Moreover, mechanical behavior of noncellular parts of cancerous tissues including fibrous structure can be investigated. Despite such advantages, few researches have been conducted on measuring mechanical characteristics of cancerous tissues (Lekka et al., 2012a; Plodinec et al., 2012). Recently, Lekka et al. used AFM and showed that Young’s modulus of tissues in endometrioid carcinoma of the uterine, breast cancer and vulvar cancer extracted from patients included an extensive range and the mean value decreased up to 60 percent compared to non-neoplastic regions, although the diversity of Young’s moduli obtained from measuring the tissue elasticity and the different components were not addressed (Lekka et al., 2012a). Another recent study on biopsy samples of breast cancer patients has shown that malignant breast tissues have a broad distribution of elastic modulus with a distinct lower stiffness peak compared to normal tissues (Plodinec et al., 2012). The alterations among stiffness values were observed both at cellular and fibrous levels of the samples as supported by stained pathological images. In this contribution, we measured and compared values of Young’s modulus for various specific parts of breast tissues taken from cancer patients by means of AFM. Using fuzzy clustering as a novel technique in categorizing data, an unbiased method was suggested to present elastic moduli of different parts of cancerous tissue without predefinition of ranges of stiffness values. Fuzzy C-means algorithm was used to obtain the average Young’s modulus of cellular and non-cellular parts of the cancerous and healthy breast tissue samples. Immunohistochemical staining was implemented to compare our numerical calculations with the pathological evaluations to verify results were biologically meaningful.

2. Materials and Methods

2.1.

Tissue preparation

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Tissue samples were extracted and prepared according to the published protocols (Engler et al., 2004; Puttini et al., 2009). An expert oncologist handled the procedure during surgery on females suspected of breast cancer approved by Ethical Committee, Department of Cancer Research Center, Cancer Institute, Tehran University of Medical Sciences. From samples provided by the pathologist from the removed biopsy specimen, one with the best feature and no physical damage with 0.3 × 1 × 1.5 cm dimensions was chosen for AFM testing, and the rest of blocks were used for routine pathological tests including cancer grading. Care was taken to provide smooth and uniform surfaces when cutting blocks by surgical blade as advised. Samples contained grades two and three breast carcinoma. Besides cancerous samples, healthy tissue samples were obtained from the area around tumors by the primary inspection of the pathologist. Samples for AFM tests were immediately transferred to DMEM solution and kept at 4°C to minimize degradation and tests were performed no later than 6 hours after biopsy.

2.2.

Sample preparation

A graded transparent plate was placed in a 60-milliliter petri dish for positioning the indentation locations during the experiments. The graded transparent determined the initial positioning of the coordinate of measurement on the sample and provided an estimate of the span route of indentation. Samples were then pasted on the plate and immobilized by a thin layer of two component fast drying biocompatible epoxy glue (Devcon). After adding DMEM solution, the petri dish was placed on the AFM stage within the plate with constant temperature of 37°C. Then mechanical characterization tests were performed using AFM (Fig. 1a & b) (Plodinec et al., 2012). Fig. 1.

2.3.

AFM Experiment

Measurements were made using Nanowizard 3 atomic force microscope (JPK Instruments AG, Germany) through the force spectroscopy mode in the liquid environment with a CSC17/noAl cantilever (MikroMasch, USA) with normal spring constant of 0.15 N/m and a conical shaped tip. We performed separate tests to characterize the spring constant of the cantilever as suggested by previous protocols (Butt et al., 2005; Hutter and Bechhoefer, 1993). Utilizing a thermal noise method, first a single test was carried out over a very rigid surface (such as petri dish) compared to the cell body to make sure that only the elasticity of the cantilever is involved. Then, after a thermal fluctuation the spring constant of the cantilever was calculated. The cantilever was lowered in contact with the surface of the sample followed

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by applying a 1.5 nN force for one second. Then the cantilever returned to its initial position at which no force was applied. The force-displacement curves were recorded accordingly. In each sample, at least six locations, and in each location, thirty points in a square of the dimensions of 30

micrometers were

selected and the force-displacement relationships were recorded. The indentation positions were recorded for further immounohistochemical evaluation. All stages of the test, from the preparation of the samples up to the measurement of the mechanical characteristics, were carried out within a few hours to prevent biochemical changes in the tissues.

2.4.

Evaluation of Young’s modulus

The depth of indentation (  ) in the sample was calculated based on the difference between the cantilever’s tip deflection ( d ) and position of piezoelectric crystals ( Z ) according to Eq. 1 in which d 0 and Z 0 parameters represent cantilever deflection and the piezoelectric position on the contact point of the tip and tissue surface:

  d  d 0 )  (Z  Z 0 )

(1)

The vertical deflection of the cantilever which is proportional to the applied force through predefined physical properties of cantilever based on the Hooke’s law (Butt et al., 2005) was continuously recorded. The resultant force-displacement curves were used to evaluate Young’s modulus of samples by application of the Hertz model (Faria et al., 2008; Lekka et al., 2012a), considering modification by geometrical shape of the tip of cantilever. Hence the Hertzian force-indentation depth equation can be written as Eq. 2.

F

2 tan(  E sample  2     sample )

(2)

Where α represents half of the angle of the tip’s cone that was 20 degrees for the specified cantilever. Parameter  sample indicates the Poisson ratio of the sample supposed to be 0.5 due to the assumption of incompressibility of soft biological tissues (Li et al., 2014). The  parameter shows the indentation depth of the tip in the sample and F stands for the force applied on the sample. Similar to the reported algorithm (Nikkhah et al., 2011), substituting Eq. 1 in Eq. 2, a linear version of Eq. 2 is derived (Eq. 3) in which the Young’s modulus of cells can be derived from the slope of this line.

F 1/ 2  [

2 E sample tan 

 (1  

2 sample

)

]1 / 2 ( Z  d )  [

2 E sample tan 

 (1  

2 sample

)

]1 / 2 ( Z 0  d 0 )

5

(3)

The calculated values of Young’s modulus can be shown in a histogram. The horizontal axis of histogram represents the calculated young’s modulus and the vertical axis indicates the frequency of occurrence.

2.5.

Data clustering

During the experiment, different locations of tissues, including cellular, fibrous, and other parts were examined. Since the elastic characteristics of different parts substantially vary, a wide spectrum of values of Young’s modulus is obtained which can be observed in the histogram. The fuzzy C-means clustering algorithm, as one of the most widely used fuzzy clustering methods was employed for classifying experimental data to distinguish the elastic properties of different parts of tissue (Höppner, 1999). Clustering involves the task of dividing data points into homogeneous clusters such that the data within each cluster are as similar as possible and the data in different clusters are as dissimilar as possible. Avoiding predefined categories for elastic modulus of different parts of tissues, we investigated whether data can be “naturally” divided in different categories with biological interpretation. Such algorithm is among the most commonly used methods to divide data between defined groups in such way that data of each group are mostly interrelated while least correlated with other groups. Different numbers of clusters were used (c=2, 3, 4, 5, 20) and the clustering algorithm was carried out accordingly. The optimized number of clusters was then chosen as the case with highest correlation within the clusters and least correlation among clusters. The optimized number of clusters was found to be three in this study. Using Matlab Fuzzy Logic Toolbox, the cumulative center of the data in each cluster was determined and the Young’s moduli of cancerous and healthy samples were sorted to three category. Statistical t-test analysis was performed to compare Young’s modulus values of the healthy and cancerous samples among three distinct regions with the statistical significance set at p < 0.05.

2.6.

Immounohistochemical (IHC) Staining

After completion of mechanical characterization, samples were treated by 10% formalin solution for 24 hours for fixation. Then they were placed in a tissue processor to eliminate water, were clean-cut by Xylenol (Sigma Aldrich product) followed by Parraffinization to make them ready for sectioning by microtome. The sectioned samples were conditioned by alcohol (20% concentration) and placed in a tissue floating bath with controllable temperature to eliminate paraffin and wrinkles. The samples underwent Hematoxylin-Eozin (H&E2) staining through different steps. First, samples were treated by Xylenol for 5 minutes to eliminate the remaining paraffin, then by alcohol and next washed by water. Then they were treated by Hematoxylin for 10 minutes and further washed by water. Then they were placed in acidalcohol solution (99 percent alcohol with 1 percent HCL acid) to remove excessive stains. Next steps

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included treatment by different solutions, first by lithium carbonate solution for 10 seconds, then by eosin solution for one minutes; after further washing by water they were treated by different concentrations of alcohol (70, 80, 90, and 96%). Finally samples were put in Xylenol for 5 minutes and then mounted on a lam for further examinations (Bancroft and Stevens, 1975; Fischer et al., 2008). Through pathological examination of stained tissues we located the fibrous or cellular parts of the tissues within the indentation sites. Although pathological staining caused some degree of shrinkage, the stained pathological images remained comparable to indentation locations in AFM tests, considering the usage of graded plate which facilitated positioning of the indentation sites and helped to compare between pathological images and data obtained from AFM.

3. Results

Results of elastic moduli of cancerous tissue samples indicated no statistically significant difference between the values of Young’s modulus of cancerous tissues with two grades of invasiveness (P> 0.05). Hence all cancerous samples were considered as one group. Fig. 2 displays two typical force-displacement curves for healthy and cancerous samples, describing lower value of Young’s modulus for cancerous samples. Considering all samples of two groups, statistical analysis revealed strong difference between Young’s moduli of healthy and cancerous samples (P<0.05) indicating breast cancer tissues significantly softer than healthy samples. The histogram of Young’s moduli for cancerous and healthy tissues is presented in Fig. 3. Generally, in lower ranges of Young’s modulus, higher number of occurrence was recorded for cancerous samples, while in higher ranges no noticeable differences were observed. Since cellular parts of tissues are markedly softer than the fibrous parts of tissues (ECM 3), it is highly likely that the lower range represents the cellular part and therefore cancerous tissues are softer than healthy tissues in cellular region. To analyze such phenomenon in details the C-means clustering algorithm was conducted to define distinct ranges for values of Young’s modulus without predefined categorization. The algorithm was tested for different number of clusters. Results indicated the highest correlation among data in each cluster and least correlation between centers of clusters when three distinct clusters were allocated to data. We assumed that three clusters correspond to samples from cellular region, fibrous region (ECM), and intermediate region containing ducts and lumens, fatty tissues and fine and less dense fibers. These are correspondingly similar to reported three parts of cancerous breast tissues as dense cellular-low stroma, dense stroma-low cellular, and finally medium cellular-medium stroma (Plodinec et al., 2012); moreover our pathological observation of stained tissues confirmed bold zones as mentioned. Fig. 4 represents the values of Young’s modulus for the centers of three clusters among healthy and cancerous tissues. Comparison of results of the centers for both cancerous and healthy samples for the least values showed significant difference, with healthy samples almost twice stiffer than cancerous

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samples (fig. 4a). Results were close to those reported by other researchers for healthy and breast cancer cells (Cross et al., 2007; Lekka et al., 2012a; Lekka et al., 2012b), justifying that these centers were assumed to be related to the cellular zone of breast tissue. Statistical tests revealed that there are significant differences (P<0.05) between data of cancerous and healthy tissues with Young’s modulus values less than 3000 Pa representing the cellular region. However, in the other two ranges corresponding to those of intermediate and fibrous regions of the samples, there were no significant differences among cancerous and healthy tissues (P>0.05). It has been suggested that while cancer cells are generally softer than corresponding healthy cells, the ECM synthesized by cancer cells are composed more stromal collagen (Boyd et al., 2001; Guo et al., 2001). The collagen-rich ECM is mechanically stiffer in comparison with collagen-poor ECM (PaszeK et al., 2005; Provenzano et al., 2009). Increasing the stiffness of ECM has been suggested as a risk factor for metastasis (PaszeK et al., 2005; Provenzano et al., 2009; Schedin and Keely, 2011). Here, results indicated that for the fibrous region, assumed to represent ECM, the average Young’s modulus of cancerous tissue was 11% higher than that of healthy tissues, although this alteration was not statistically significant (f 4b). The stiffness values of the intermediate region corresponding to the second cluster of data were relatively the same for all samples. Following AFM tests the indentation sites were examined for histopathology observations. Results confirmed that areas with lower values of Young’s modulus (cluster one with maximum value of 3000 Pa) mostly contained highly cellular regions (Samples are shown in Fig. 5). Fig. 6 compares the detailed histogram of cellular region with maximum stiffness modulus value of 3000 Pa among healthy and cancerous samples. Marked variation among ranges of Young’s modulus indicates the leading effect of cancer on cell mechanical properties most probably through altered internal structure. Fig. 2. Fig. 3. Fig. 4. Fig. 5. Fig. 6.

4. Discussion

Rapid diagnosis of cancer, as one of leading causes of human mortality, is highly in demand for an effective therapy plan. Various studies are being conducted to achieve new diagnostic approaches, however they have not yet succeeded to diagnose cancer quickly (Kumar et al., 2012; Stewart et al., 2003). An understanding of specific physical and chemical characteristics of cancer cells and their alterations during metastatic phases is essential in terms of diagnosis and treatment, among which change

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in cell deformability through altered structure has been recently emphasized (Suresh, 2007a). The cancer treatment strategies may further consider the combined physical, chemical and biological cues by which cells metastasize, specifically the behavior of mechanotransductive pathways that define mechanical and adhesive properties of cells. In recent years, due to progress in mechanobiology studies, it has been suggested that elastic properties of cells can be regarded as a biomarker of cell behavior and a potential diagnostic tool and treatment plan especially among diseases which affect cell physical behavior (Lim et al., 2006; Suresh, 2007a). The interaction between elastic properties of cells and their biological function has been vastly studied through cell cytoskeleton, morphology, gene expression, protein synthesis, adhesion properties, proliferation and differentiation. Due to the fact that cancer invasion is accompanied by softening of cancerous cells, such idea has been particularly presented in cancer studies (Lekka et al., 2012b; Suresh, 2007a). Despite the complexity of cancerous tissues, the average changes in mechanical properties of cells within the malignant tissues can give clue in using Young’s modulus as an indicator of cancer progression. This might lead to a more accurate identification of cancer (Lekka et al., 2012a). Here, the indentation by the probe of AFM spanned the whole surface of the sectioned tissue and data of Young’s moduli represented a wide range of cellular and non-cellular regions. The Fuzzy clustering assisted to categorize the acquired data. By implementing fuzzy C-means algorithm with stepwise increase of number of groups from two to 20, it was found that three (followed by two with small difference) groups can categorize data best such data within each group were mostly similar while dissimilar with other groups through distinct mechanical properties. The analysis was biologically meaningful by pathological evaluation as shown by three bold zones of dense cellular-low stroma, medium cellularmedium stroma, and dense stroma-low cellular similar to previously reported data (Plodinec et al., 2012). The accurate biological interpretation of data by fuzzy C-means algorithm proposes the usage of fuzzy clustering in pathological studies in line with statistical analyses. The highest values representing the highest stiffness were attributed to ECM and the lowest values to cellular regions with elastic modulus of less than 3000 Pa. For cancerous samples the ECM was 22.6 stiffer than cellular region, while for healthy samples the ECM was 10.1 stiffer. Such marked difference in ECM to cell stiffness ratio determines the cell-ECM interaction in cancer progression, and considering lower cell-cell adhesion among cancer cells (Omidvar et al., 2014), they tend to detach from original tumor and migrate more easily and finally can pass through endothelial junctions with less difficulty. While cell softening has been vastly reported among cancer cell lines compared to healthy cell lines, here the effects of cancer development on elasticity of components of breast tissue is analyzed. Results of this study confirmed almost 50% decrease in stiffness of cancer cell regions of breast cancer tissues (Fig. 4a). Due to changes in cell structure and function of cancer cells, the synthesis of ECM and its mechanical properties might also be affected during cancer progression. This study has targeted evaluation of

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mechanical properties of both cellular and fibrous parts of cancerous tissues. The ECM of cancerous tissue showed a rise compared to normal tissue although the increase was not statistically significant (Fig. 4c). Since the ECM of cancerous tissue is made up of more collagen compared to normal tissue (ShermanBaust et al., 2003), the increase of Young’s modulus in ECM is expectable. It has been indicated that the proliferation and migration rate of cells are higher in stiffer microenvironment in comparison with soft ECM (Wells, 2008), a property which partly describes the invasive and malignant nature of cancerous cells (Liotta, 1986). AFM is a potent tool to survey a wide range of material properties through interaction with the material surface at high resolutions and measuring force-displacement curves. The obtained data can be used in evaluation of mechanical properties such as elasticity, hardness, and adhesion. The possibility to be operated in liquids, especially physiological environments, has increased declared biological applications of AFM among in situ researches on cell elasticity and adhesiveness (Butt et al., 2005; Radmacher, 2002). Despite the quantification of data and precise probing on detailed part of cells and tissues, the differences among protocols including base motion velocity, penetration depth, and contact duration has limited the usage of such method in diagnostic tools (Butt et al., 2005; Domke and Radmacher, 1998). Hence, the development of a unique protocol is recommended to enable the possibility of quantitative comparisons and presenting diagnostic means.

5. Conclusion

Our study confirmed the general alteration in stiffness of cellular and non-cellular parts among breast tissues affected by cancer. It is concluded that breast cancer development results in softening of cellular region of the tissue while the fibrous region of cancerous tissue slightly hardens. The ability of fuzzy clustering to quantitatively distinguish among major parts of breast tissue was promising to further use such method in study of mechanical properties of biological tissues in cancer progression.

The

quantification of alterations in mechanical properties of cancerous tissues among different types of cancer and different stages of invasiveness provides a better understanding on how cancer cells metastasize to other tissues. Such characteristics may assist to develop new diagnostic tools. Currently cancer diagnosis and grading based on tissue biopsy are achieved by qualitative observations by pathologists and such quantification may assist in more precise diagnosis. Moreover results may further raise a promising topic to therapeutic means by targeting the cytoskeleton to adjust its structural, mechanical and biochemical functions to avoid metastasis.

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Figure captions

Fig. 1. Sample preparation before the test. (A) Schematic of AFM measurement, the tip of AFM pushes down the cells or ECM and record the indentation depth versus force that is generated by pushing down the sample. (B) The image of a slice of breast tissue with a specific dimension (0.3 × 1 × 1.5 cm) submerged in DMEM solution. Fig. 2. Typical diagrams of force-depth of penetration. The curve which is specified by young’s modulus of 1410, a fitting curve passed over it, is related to the healthy breast tissues and the curve which is specified by young’s modulus of 730 ,a fitting curve passed over it, is related to the cancerous breast tissues. The values of Young’s modulus are in Pa. Fig. 3. Histograms of the elasticity of cancerous (solid black) and healthy samples (hatched bars). The horizontal axis represents the range of values of Young’s modulus and the vertical axis is the frequency of data for each range in percentages. Fig. 4. The centers of Young’s modulus that was obtained by C-means clustering after examination of elasticity by AFM versus types of breast tissue samples (healthy or cancerous). (A): the cellular region, (B): the intermediate region, and (C): the Fibrous region of the breast tissues. Fig. 5. Stained slices of tissues that were tested with AFM. (A-B) Two slices of cancerous tissue that conain mostly breast cancer cells grade 2 and 3, and are branded as the cellular region. (C-D) Two slices of healthy tissues that contain mainly fibrous parts (fibrous region) and some diffuse cells and other parts (intermediate region). Fig. 6. Histograms of the elasticity of cancerous and healthy breast cells. The solid black diagram shows the variations of stiffness modulus of cancerous breast tissues and the hatched diagram shows the changes in the stiffness modulus of healthy breast tissues in cellular region.

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