Method to predict cancer development through nanomechanical analysis

Abstract

The present invention relates to methods for staging the metastatic potential of a primary tumor sample or associated lymph nodes by nanomechanical measurements and / or methods for determining the likelihood of recurrence or disease onset.

Description

[0001] Divisional Application Statement

[0002] This application is a divisional application of Chinese patent application No. 201580058782.7.

[0003] background

[0004] Breast cancer is the most frequent malignancy and the second most common cause of cancer death in women in developed countries. In addition, although primary tumors are rarely fatal, metastases are the cause of most cancer-related deaths. Although there are some parameters that serve as prognostic markers for the development of metastases, it is still impossible to accurately predict individual risks despite great efforts. Therefore, adjuvant therapy is often given to patients who may have been cured by surgery and anti-hormonal therapy alone. Current breast cancer classification, including prognostic and predictive markers, is still mainly based on clinical and histopathological criteria, i.e., patient age, tumor size, lymph node involvement, histological type of tumor, expression of estrogen and progesterone receptors, HER2 / neu and Ki-67, and tumor grade.

[0005] However, risk stratification based on clinicopathologic parameters alone can be misleading. Particularly in early-stage HER2 / neu-negative breast cancer (ie, stages I, IIA, IIB, and IIIA), these clinicopathologic factors are insufficient for making clinical decisions, especially regarding adjuvant chemotherapy, because significant over- or undertreatment may occur. Since 2007, international guidelines have recommended the addition of other tests to established risk assessments.

[0006] Therefore, a major goal of research in breast cancer is to develop prognostic markers that are evaluated with high-quality validated tests, can be used routinely and at an acceptable cost. These markers should help optimize cancer diagnosis, direct treatment selection and support patient follow-up.

[0007] However, all currently available tests have certain disadvantages. Often, they are valid only in well-defined patient subsets, for example, in node-negative patients with moderately differentiated tumors. Moreover, these tests are mainly performed at central research institutions, and it takes time to send tumor samples to central laboratories. In addition, there is no local (quality) control over sample processing. In addition to genetic and microenvironmental factors, recent data indicate that the physical interaction of cancer cells with their environment is a key parameter in the metastatic process. Despite this, efforts to understand cancer biomechanics have been strongly biased towards tissue-level and single-cell experiments. Therefore, some findings are controversial due to: 1) the lack of native tissue environment and 2) insufficient measurements / analyses to explain intratumoral heterogeneity. This emphasizes that studying whole tissue fragments with subcellular resolution can provide a more comprehensive understanding of the mechanistic changes associated with carcinogenesis. This prompted us to develop a diagnostic device based on atomic force microscopy (AFM), called ("Automated and Reliable Tissue Diagnostics"; US8,756,711 B2, US 2014338073 A1, WO 2014090971 A1, WO2015001119 A1, WO 2015018865 A1, all of which are incorporated herein by reference), is used to measure the hardness characteristics of unadulterated tissue biopsy samples from human patients under near-natural physiological conditions, using unprecedented hardness sensitivity resolved at nanometer-level spatial resolution. Over a period of about 2 hours, the ARTIDIS test uses a sharp stylus or tip of about 10 nm to make about 10,000 tiny indentations on the surface of the biopsy sample.

[0008] Therefore, based on such background, an object of the present invention is to provide a reliable and simple method for staging cancer and / or providing prognostic and / or predictive information about cancer.

[0009] This object is achieved by the subject matter of the independent claims.

[0010] definition

[0011] In the context of this specification, the term hardness or elasticity refers to the resistance of a tissue sample or tissue to deformation by an applied force. Hardness or elasticity is measured as the elastic modulus of the tissue sample in Pascals (Pa). Soft tissue samples are characterized by low hardness values, and hard tissues are characterized by elevated hardness values.

[0012] Such a deformation force can be applied to a tissue sample or tissue by a stylus that strikes the tissue sample or tissue (e.g. as part of an atomic force microscope), wherein the stylus or tissue sample are moved relative to each other in a vertical direction. In order to measure multiple points on the sample, the stylus or sample can additionally be moved in a lateral direction, wherein lateral in the meaning of the present invention means a direction orthogonal to the vertical direction.

[0013] The stylus can be a cantilever with a sharp end or an attached colloidal particle as a probe. In the meaning of the present invention, a cantilever means a beam or arm that is fixed at only one end. The deflection of the cantilever caused by the repulsion or attraction between the sample surface and the end can be detected optically, for example, by an interferometer or a laser focused on the back of the cantilever and reflected on a slit photodiode, where the photodiode records the deflection of the cantilever as a voltage difference, which can be converted into nanometers. Alternatively, the deflection of the cantilever can be detected by a piezoelectric sensor, where the tension of the cantilever is converted into an electric charge. In addition, optionally, a self-sensing cantilever can be used, such as a Piezo-Resistive Sensing Active (PRSA) probe, for example, a silicon cantilever with an integrated piezoresistor bridge and a heater. Advantageously, when using such a cantilever, no laser adjustment is required.

[0014] In the context of this specification, the term region refers to an area defined by a grid of (measurement) points, each of which corresponds to the indentation trace of the stylus as described above, and each point is no more than 100 μm, preferably 50 μm, 20 μm, 10 μm or 1 μm from the point below it. As a non-limiting example, the size of a region is 25 μm 2 , 50μm 2 , 100μm 2 , 200μm 2 、300μm 2 , 400μm 2 、 500μm 2 、600μm 2 、750μm 2 , 1000μm 2 , 5000μm 2 or 10.000μm 2 , and the geometric center points of the two regions are at least 100 μm, 200 μm, 300 μm, 400 μm, 500 μm or 1 mm apart.

[0015] The force and indentation depth measured for any given sample depend on the cantilever spring constant and tip radius.

[0016] In the context of the present specification, the term spatial resolution refers to the minimum distance between two points on a tissue or tissue sample by which the two points can be distinguished in terms of their hardness. A spatial resolution of at least 1 mm, preferably 100 μm, 10 μm or 1 μm means that the maximum distance at which two points can still be distinguished is 1 mm, preferably 100 μm, 10 μm or 1 μm. A spatial resolution of at least 100 μm, preferably 10 μm or 1 μm also includes higher resolutions. A resolution higher than 1 μm means that two points with a distance of less than 1 μm can still be distinguished. Examples of resolutions higher than 100 μm are 10 μm and 1 μm. Examples of resolutions higher than 1 μm are 0.5 μm, 0.1 μm and 10 nm.

[0017] In the context of this specification, the term tissue sample refers to a tissue sample comprising continuous cells and extracellular matrix. Such a tissue sample can be obtained by biopsy or surgical resection.

[0018] In the context of the present specification, the term surgical resection specimen refers to a sample representing at least a portion of an organ or body that has been removed from said organ or body. A surgical resection specimen may also comprise a whole organ or body part.

[0019] In the context of this specification, the term tissue biopsy refers to a tissue sample obtained by biopsy and comprising continuous cells and extracellular matrix.

[0020] In the context of this specification, the term biopsy refers to a method of removing a tissue portion or tissue for inspection. Such a biopsy can be a needle biopsy, a punch biopsy, a vacuum-assisted core biopsy, a core needle biopsy or a clamp biopsy. Removal can be implemented by means of a suitable tool, such as a hollow needle, a round-pointed knife or a scalpel. A tissue biopsy sample can also be obtained by endoscope or endoscopic method.

[0021] The biopsy procedure can be guided by a suitable method, such as ultrasound or CT (computed tomography), in which a tumor or significant lesion can be detected or localized.

[0022] In the context of this specification, the term normal tissue refers to a continuous collection of cells and extracellular matrix with the same physiological function, characterized by normal, controlled growth and normal cellular and extracellular functions and structures.

[0023] In the context of this specification, the term tumor refers to a growth or lesion formed by the abnormal growth of neoplastic cells. A tumor can be benign, pre-malignant or malignant. The classification of tissue biopsy samples from human breast cancer is preferred. In the context of this specification, the term benign lesions or tumor refers to a tumor that does not have the ability to metastasize.

[0024] In the context of this specification, the term primary tumor refers to a tumor that originates from the same tissue type as the surrounding organs or tissues.

[0025] In the context of this specification, the term metastasis (or metastases) refers to a tumor that has spread from a primary tumor to a distant site (eg, a different organ).

[0026] In the context of this specification, the term malignancy or "malignant tumor" refers to the ability of a tumor to penetrate a basement membrane, invade adjacent tissues, or spread in the body. Malignant tumor is synonymous with malignant neoplasm or cancer, particularly with invasive cancer.

[0027] In the context of this specification, the term tumor border is defined as a rounded, smooth, well-defined (common in benign tumors) or irregular, ill-defined (usually in the case of malignant tumors) border between the tumor and adjacent tissue. Histologically defined as the outermost part of a tumor where tumor cells can be found ( Figure 1 B).

[0028] In the context of this specification, the term adjacent tissue is defined as the part of a tissue or organ other than the tumor ( Figure 1 C). Adjacent tissue usually surrounds the primary tumor but can also be considered to be any part of the tissue of an organ that is not present in the tumor.

[0029] In the context of this specification, the term adjacent lymph nodes or lymph nodes adjacent to a primary tumor refers in particular to lymph nodes leading from the tumor. Such adjacent lymph nodes are also called sentinel lymph nodes.

[0030] In the context of this specification, the term axillary lymph nodes refers to the lymph nodes that drain lymphatic vessels from the outer quadrants of the breast, the superficial lymphatic vessels from the chest wall and abdomen above the level of the umbilicus, and the lymphatic vessels from the upper extremities. Axillary lymph nodes are also known as underarm lymph nodes.

[0031] In the context of this specification, the term hardness distribution refers to the frequency of different hardness values ​​measured from individual tissue biopsy samples. The measured hardness distribution can also be fitted to a Gaussian function. A unimodal hardness distribution is a distribution of discrete hardness values ​​with a single maximum value, which indicates that the sample has a uniform hardness. A bimodal distribution function has two maxima. Such a distribution can be caused by a sample with two different hardness parts, for example, a soft tumor core and a hard edge. In the meaning of the present invention, a trimodal hardness distribution represents a distribution characterized by three local maxima. A trimodal distribution can indicate that normal tissue, a boundary region characterized by a hard matrix, and a soft tumor core have contributed to the values ​​that make up the distribution. At least a sample with a bimodal hardness distribution has a bimodal, trimodal or n-modal (n is an integer > 1) distribution function.

[0032] In the context of this specification, the term inhomogeneous hardness distribution refers to an n-peak distribution function (n being an integer > 1).

[0033] In the context of this specification, a plurality of hardness values ​​refers to at least 100, 200, 300, 400, 500, 900, 1000, 1600, 2500, 3600, 4900, 6400, 8100 or 10000 hardness values.

[0034] Prognostic markers provide the risk of cancer development and / or recurrence, or in other words, give an indication of the likelihood of disease progression.

[0035] Predictive markers provide an indication of a patient's response to a particular treatment.

[0036] In the context of this specification, the term peak refers to a local maximum in a distribution of hardness values ​​and denotes the hardness value with the highest frequency in a sample or among directly adjacent values.

[0037] In the context of this specification, the term maximum frequency, when used in relation to a distribution of hardness values, refers to the hardness value that is characterized by a local or absolute maximum of a graph in which the frequency of hardness values ​​is plotted against the hardness values. Thus, if the hardness value of a first maximum frequency is the same or substantially the same as the hardness value of a second maximum frequency, then the first maximum frequency is the same or substantially the same as the second maximum frequency.

[0038] In the context of this specification, the term physiological conditions refers to the conditions necessary to preserve the structural integrity and mechanical properties of a biopsy tissue sample, which maintains the viability of the tissue by any chemical or physical agent, and specifically includes storing the sample in a physiological buffer, such as phosphate-buffered saline, Ringer's solution or transplantation buffer (such as Custodiol) after collection, and performing the hardness measurement at 20, 25, 30 or 37° C. Ringer's solution can also be supplemented with glucose and a protease mixture. In addition, the hardness measurement of the biopsy tissue sample can be performed within 1 h, 2 h, 6 h, 12 h, 24 h, 48 h or 72 h after collection, which will not change the mechanical properties of the sample. "Physiological conditions" specifically do not include frozen tissue or thawed tissue or paraffin-embedded samples.

[0039] describe

[0040] The present invention is based on the unexpected discovery that metastatic lesions are characterized by a nanomechanical phenotype similar to the primary tumor from which the metastasis originates.

[0041] According to a first aspect of the present invention, a method for classifying a tissue biopsy sample obtained from a tumor is provided. The method comprises measuring hardness values ​​of a plurality of points on the sample with a spatial resolution of at least 100 μm (i.e., each of the plurality of points has a distance of 100 μm or less from an adjacent point), 50 μm, 20 μm, 10 μm or 1 μm, obtaining a hardness distribution, and assigning a malignancy probability to the sample.

[0042] In some embodiments, a high probability of malignancy is assigned to samples that exhibit at least a bimodal hardness distribution, wherein the at least bimodal hardness distribution is characterized by a first peak exhibiting a hardness value at least twice higher than a second peak.

[0043] In some embodiments, a sample exhibiting an inhomogeneous hardness distribution with a maximum frequency below 1 kPa is classified as an aggressive cancer sample, or is assigned a high probability of malignancy. In some embodiments, a sample exhibiting an inhomogeneous hardness distribution with a maximum frequency between 0.3 kPa and 0.8 kPa is classified as an aggressive cancer sample, or is assigned a high probability of malignancy. In some embodiments, a sample exhibiting an inhomogeneous hardness distribution with a maximum frequency between 0.4 kPa and 0.8 kPa is classified as an aggressive cancer sample, or is assigned a high probability of malignancy. In some embodiments, a sample exhibiting an inhomogeneous hardness distribution with a maximum frequency between 0.4 kPa and 0.7 kPa is classified as an aggressive cancer sample, or is assigned a high probability of malignancy. In some embodiments, a sample exhibiting an inhomogeneous hardness distribution with a maximum frequency between 0.3 kPa and 0.6 kPa is classified as an aggressive cancer sample, or is assigned a high probability of malignancy.

[0044] In some embodiments, samples exhibiting exponential decay in the hardness distribution are classified as tumor tissue. In some embodiments, samples exhibiting exponential decay from 0.4 kPa to 10 kPa in the hardness distribution are classified as tumor tissue. In some embodiments, samples exhibiting exponential decay from 0.4 kPa to 15 kPa in the hardness distribution are classified as tumor tissue. In some embodiments, samples exhibiting exponential decay from 0.4 kPa to 20 kPa in the hardness distribution are classified as tumor tissue.

[0045] In some embodiments, the methods of the invention are applied to a first sample and a second sample.

[0046] In some embodiments, the first sample is a primary tumor sample and the second sample is taken from a lymph node adjacent to the sampling site (primary tumor site), and the second sample is classified as a lymph node metastasis if:

[0047] - both the first sample and the second sample exhibit an inhomogeneous hardness distribution having a maximum frequency below 1 kPa, in particular 0.3 kPa to 0.8 kPa, 0.4 kPa to 0.8 kPa, 0.4 kPa to 0.7 kPa or 0.3 kPa to 0.6 kPa, and

[0048] - The maximum frequency of the second sample is the same as the maximum frequency of the first sample.

[0049] In some embodiments,

[0050] a first frequency (or hardness) distribution is obtained from a first location of the sample, a second frequency (or hardness) distribution is obtained from a second location of the sample, and

[0051] - the first site corresponds to a tumor histological classification as part of the tumor tissue, the second site corresponds to a tissue histological classification beyond the tumor border (adjacent tissue), and

[0052] the first frequency distribution is characterized by an inhomogeneous hardness distribution with a maximum frequency below 1 kPa, in particular 0.3 kPa to 0.8 kPa, 0.4 kPa to 0.8 kPa, 0.4 kPa to 0.7 kPa or 0.3 kPa to 0.6 kPa, and

[0053] - If the second frequency distribution is characterized by the absence of a hardness distribution with a maximum frequency below 1 kPa, the tumor sample is classified as having a low probability of having spread to adjacent lymph nodes.

[0054] In some embodiments, if the second frequency distribution maximum is characterized by the presence of a hardness distribution with a maximum frequency below 1 kPa, the tumor sample is classified as having a high probability of having spread to adjacent lymph nodes.

[0055] According to one aspect of the present invention, a method for classifying a tissue sample obtained from a patient is provided, wherein the tissue sample is suspected of containing secondary tumor tissue. The method comprises:

[0056] - determining the hardness value of each of the first plurality of points on the primary tumor sample to obtain a first hardness distribution,

[0057] - measuring the hardness value of each of the second plurality of points on the tissue sample to obtain a second hardness distribution,

[0058] in

[0059] - classifying the tissue sample as a metastasis if both the first hardness distribution and the second hardness distribution exhibit an inhomogeneous hardness distribution having a maximum frequency at substantially the same hardness values ​​below 1 kPa.

[0060] In some embodiments, the tissue sample is a tissue biopsy sample or a surgical resection specimen.

[0061] In some embodiments, the first and second plurality of points are determined with a spatial resolution of at least 100 μm, 50 μm, 20 μm, 10 μm, or 1 μm.

[0062] In some embodiments, the tissue sample is taken or obtained from a lymph node, particularly a lymph node adjacent to the site of sampling of the tumor sample or an axillary lymph node.

[0063] In some embodiments, primary tumor samples that exhibit a maximum frequency below 0.5 kPa are classified as metastatic tumors.

[0064] According to one aspect of the present invention, a method for classifying a tissue sample obtained from a tumor is provided. The method comprises:

[0065] - measuring the hardness value of each of a plurality of points on the sample with a spatial resolution of at least 100 μm, 50 μm, 20 μm, 10 μm or 1 μm to obtain a hardness distribution,

[0066] - assigning a probability of malignancy to said sample,

[0067] in

[0068] - the method is applied to a first sample and a second sample, the first sample being a primary tumor sample and the second sample being a sample taken or obtained from a lymph node, in particular a lymph node adjacent to a sampling site of the first sample or an axillary lymph node, and

[0069] If both the first sample and the second sample exhibit an inhomogeneous stiffness distribution with a maximum frequency below 1 kPa, and the maximum frequency of the second sample is the same as the maximum frequency of the first sample, the second sample is classified as a lymph node metastasis.

[0070] In some embodiments, the tissue sample is a tissue biopsy sample or a surgical resection specimen.

[0071] In some embodiments, primary tumor samples that exhibit a maximum frequency below 0.5 kPa are classified as metastatic tumors or as having a high probability of having spread to adjacent tissues, particularly adjacent lymph nodes or axillary lymph nodes.

[0072] In some embodiments, a primary tumor sample, particularly a primary tumor biopsy sample, comprises at least a portion of a tumor core and at least a portion of a tumor margin.

[0073] In some embodiments, the primary tumor sample, in particular the primary tumor biopsy sample, represents at least half of the tumor cross section described above and exhibits a distinct orientation from the tumor core to the edge. In some embodiments, the primary tumor biopsy sample is a cylindrical or prismatic biopsy sample.

[0074] According to one aspect of the present invention, a method for classifying a tissue sample obtained from a tumor is provided. The method comprises

[0075] - measuring the hardness values ​​of a plurality of points on the sample with a spatial resolution of at least 100 μm, 50 μm, 20 μm, 10 μm or 1 μm to obtain a hardness distribution,

[0076] - assigning a probability of malignancy to said sample,

[0077] in

[0078] a first frequency or hardness distribution is obtained from a first location of a sample, a second frequency or hardness distribution is obtained from a second location of said sample, and

[0079] - a first site corresponds to a part of the tumor tissue that is histologically classified as tumor, a second site corresponds to adjacent tissue, in particular tissue that is histologically classified as beyond the tumor border, and

[0080] - if the first frequency or hardness distribution is characterized by an inhomogeneous hardness distribution with a maximum frequency below 1 kPa and the second frequency or hardness distribution is characterized by the absence of a hardness distribution with a maximum frequency below 1 kPa, the tumor sample is classified as having a low probability of having spread to adjacent tissues, in particular adjacent lymph nodes or axillary lymph nodes, and / or

[0081] If the second frequency or hardness distribution is characterized by the presence of a hardness distribution with a maximum frequency below 1 kPa, the tumor sample is classified as having a high probability of having spread to adjacent tissues, in particular adjacent lymph nodes or axillary lymph nodes.

[0082] In some embodiments, the tissue sample is a tissue biopsy sample or a surgical resection specimen.

[0083] In some embodiments, a tumor sample exhibiting a maximum frequency below 0.5 kPa, particularly at the first frequency or a stiffness distribution exhibiting a maximum frequency below 0.5 kPa is classified as having a high probability of having spread to adjacent tissues, particularly adjacent lymph nodes.

[0084] In some embodiments, the tumor sample includes the core and margin or border of the primary tumor and tissue adjacent to the primary tumor, and optionally includes one or more adjacent lymph nodes or axillary lymph nodes, particularly one or more lymph nodes.

[0085] In some embodiments, the plurality of points are arranged as a grid of n1×n2 points, the grid defining a region, wherein n1 and n2 are independently integers >1.

[0086] In some embodiments, for one region, a grid of 5×5 points (yielding 25 points), 7×7 points, 10×10 points, 15×15 points, 20×20 points, 50×50 points, or 100×100 points is measured. In some embodiments, a region is defined as a grid of 24×24 points with a size of 400 μm. 2 .

[0087] In some embodiments, the hardness values ​​of at least two different regions of the same sample are determined, and the distance between the geometric centers of the regions is a multiple of the spatial resolution, and the multiple is at least 10 times the spatial resolution. In some embodiments, the multiple is 20, 30, or 50.

[0088] In some embodiments, the area of ​​the biopsy sample is located on the surface of the sample and spans a distance of 1 mm, 2 mm, 3 mm, 4 mm, 5 mm, 6 mm, 7 mm, 8 mm, 9 mm, 10 mm, 15 mm, or 20 mm along the longitudinal axis of the sample.

[0089] In some embodiments, the plurality of points includes 100, 400, 900, 1000, 1600, 2500, 3600, 4900, 6400, 8100, 10000, or 20000 hardness values.

[0090] In some embodiments, samples that exhibit a unimodal hardness distribution are assigned a high probability of being non-malignant.

[0091] In some embodiments, the tissue biopsy sample is a cylindrical or prismatic biopsy sample having a diameter of at least 7 μm. In some embodiments, the biopsy tissue sample is a cylindrical or prismatic biopsy sample having a diameter of at least 0.5 mm.

[0092] In some embodiments, the tumor is human breast cancer or a lymph node, lung, bone, liver, or brain metastasis.

[0093] In some embodiments, the firmness value is determined under physiological conditions.

[0094] In some embodiments, the firmness of a breast biopsy sample is determined, and

[0095] - samples exhibiting a hardness distribution characterized by a peak between 1.1 kPa and 1.5 kPa are assigned with a high probability to be normal breast tissue,

[0096] - samples exhibiting a hardness profile characterized by a peak between 1.9 kPa and 3.7 kPa were assigned a high probability of being benign lesions, and

[0097] - Samples exhibiting a hardness distribution characterized by a range between 0.31 kPa and 0.75 kPa and a peak located at a value greater than 1.2 kPa were assigned a high probability of being malignant.

[0098] In some embodiments, samples exhibiting a hardness distribution characterized by peaks between 0.31 kPa and 0.75 kPa and between 1.2 kPa and 2.0 kPa are assigned a high probability of being malignant.

[0099] According to another aspect of the present invention, a method for analyzing cancer or prognosing cancer development is provided. The method comprises

[0100] - obtaining a first tissue sample and a second tissue sample from the primary tumor, in particular from tissue adjacent to the primary tumor, in particular within 5 mm to 10 mm or no more than 5 mm to 10 mm from the histological tumor border, from adjacent lymph nodes or axillary lymph nodes,

[0101] - determining hardness values ​​at a plurality of points on the first biopsy sample and hardness values ​​at a plurality of points on the second biopsy sample, each of the plurality of points being characterized by a spatial resolution of at least 100 μm, 50 μm, 20 μm, 10 μm or 1 μm, to obtain a hardness profile for each of the first and second samples,

[0102] - Assigning a probability of malignancy to the first sample and assigning a probability of being invaded by a primary tumor to the second sample.

[0103] In some embodiments, a first sample obtained from a primary tumor is provided. In some embodiments, a second sample is provided, which is obtained from tissue adjacent to the primary tumor, particularly within 5 mm to 10 mm of the histological tumor border or no greater than 5 mm from the histological tumor border, from an adjacent lymph node or an axillary lymph node.

[0104] In some embodiments, the first sample represents at least half of a cross-section of the primary tumor described above and exhibits a distinct orientation from the core to the edge of the primary tumor.

[0105] In some embodiments, the first tissue sample and / or the second tissue biopsy sample is a tissue biopsy sample or a surgical resection specimen.

[0106] In some embodiments,

[0107] - assigning said first sample a high probability of malignancy if said first sample is characterized by at least a bimodal hardness distribution having a first peak exhibiting at least two times higher hardness values ​​than a second peak, and / or

[0108] - assigning said second sample as being invaded by said primary tumor with high probability if said second sample is characterized by at least a bimodal hardness distribution characterized by a first peak exhibiting hardness values ​​at least two times higher than a second peak (or an inhomogeneous hardness spectrum).

[0109] In some embodiments, tissue adjacent to the primary tumor is contained in lymph nodes, particularly adjacent lymph nodes (adjacent to the primary tumor) or axillary lymph nodes.

[0110] In some embodiments, a first sample exhibiting a second ΔΨ below 0.5 kPa is classified as a metastatic tumor.

[0111] According to another aspect of the present invention, a method for staging cancer and / or determining the onset of cancer is provided. The method comprises:

[0112] - Obtain tissue samples from tissue adjacent to the primary tumor,

[0113] - measuring the hardness values ​​of a plurality of points on the sample with a spatial resolution of at least 100 μm, 50 μm, 20 μm, 10 μm or 1 μm to obtain a hardness distribution,

[0114] - assigning to said sample a probability of being invaded by said primary tumor.

[0115] In some embodiments, a tissue sample from tissue adjacent to a primary tumor is provided.

[0116] In some embodiments, if the hardness distribution is characterized by a maximum value of 0.2 kPa-1 kPa, in particular 0.3 kPa-0.8 kPa, the sample is assigned a high probability of being invaded by the primary tumor.

[0117] In some embodiments, a sample exhibiting at least a bimodal hardness distribution characterized by a first peak exhibiting a hardness value at least two times higher than a second peak is assigned a high probability of being invaded by the primary tumor.

[0118] In some embodiments, the tissue sample is a tissue biopsy sample or a surgical resection specimen.

[0119] According to another aspect of the present invention, there is provided a method for classifying a tissue sample obtained from a tumor, the method comprising:

[0120] - measuring the hardness values ​​of a plurality of points on the sample with a spatial resolution of at least 100 μm, 50 μm, 20 μm, 10 μm or 1 μm to obtain a hardness distribution,

[0121] - assigning a probability of malignancy to said sample,

[0122] in

[0123] - Samples exhibiting at least a bimodal hardness distribution with a peak below 0.5 kPa were classified as metastatic tumors.

[0124] In some embodiments, the tissue sample is obtained from a breast cancer, particularly a human breast cancer.

[0125] In some embodiments, the tissue sample is a tissue biopsy sample or a surgical resection specimen.

[0126] In some embodiments, the tumor described above or the primary tumor described above is a breast cancer, a kidney tumor, a prostate tumor, a brain tumor, a lung tumor, an ovarian tumor, a pancreatic tumor, or a stomach tumor, a liver tumor, a skin tumor, or a gastric tumor.

[0127] According to another aspect of the present invention, a tumor sample diagnosis device is provided, wherein the device comprises an atomic force microscope and a computer connected thereto, wherein the computer is configured to run a program for implementing any of the methods of the present invention described above.

[0128] In some embodiments of any of the methods described previously, the spatial resolution is 20 μm, 10 μm, 5 μm, or 1 μm.

[0129] Regardless of the fact that any single discrete feature alternatives are stated herein as "embodiments," it should be understood that such alternatives can be freely combined to form independent embodiments of the invention disclosed herein.

[0130] The invention is further described by the following examples and the accompanying drawings, from which further embodiments and advantages can be derived. These examples are intended only to illustrate the invention, not to limit its scope. BRIEF DESCRIPTION OF THE DRAWINGS

[0131] Figure 1 Shows the tumor A, tumor boundary B, and adjacent tissue C.

[0132] Figure 2Soft nanomechanical features of primary tumor and adjacent tissue are shown, which demonstrate the usual phenotype detected in lymph node metastasis. (A) The nanomechanical features of two cancer patients show bimodal distribution, which is exponential decay in patient 1 case, with a peak value of 0.39+ / -0.21kPa for specific cancer cell colonies, and 3.66+ / -2.14kPa decaying to 10kPa for the matrix components of surrounding cells and tissues. The adjacent tissue with the pathological histological features of normal breast tissue shows a uniform hardness peak of 1.9+ / -0.7kPa (above). Patient 2 is measured for sharp distribution and overall softer phenotype, with a peak value of 0.45+ / -0.18kPa and 1.05+ / -0.55kPa (below) for cells. The adjacent tissue of the patient shows a bimodal distribution, with a very prominent soft peak of 0.4+ / -0.2 kPa and a harder peak of 1.05+ / -0.5kPa. In pathological histology molecules, this tissue is labeled as non-malignant. Therefore, in this article, we use the term "damaged" health because the tissue stiffness values ​​and bimodal distribution do not correspond to the values ​​measured for healthy breast samples from control patients. (B) Patient 1 has a cancer-negative lymph node, which exhibits a stiffness value of 3.02+ / -2.10kPa (above). The lymph node containing breast cancer metastasis from patient 2 exhibits a bimodal stiffness distribution, with a peak of 0.49+ / -0.23kPa in the cancerous area, which directly corresponds to the "soft" cancer cell phenotype found in the primary tumor and adjacent ("damaged" healthy tissue), while the value measured for the rest of the lymph node is 2.11+ / -0.78kPa (below).

[0133] Figure 3 Showing post-AFM histological summary of surgically resected specimens from two breast cancer patients. H&E staining of the two samples showed similar features; that is, connective tissue and normal epithelium were not significantly different from normal breast (above). The primary tumors in both cases showed invasive breast cancer cells, with nests of cells infiltrating that had triggered a dense fibrous tissue reaction (middle). The nests of breast cancer cells displayed by H&E staining of the corresponding lymph nodes from patient 2 were similar to the primary tumor (lower right), while the lymph nodes from patient 1 were free of cancer cells (lower left).

[0134] Figure 4Shows a standardized graph of the nanomechanical features of primary tumors (biopsy and surgical resection samples) of patients with no involved lymph nodes N0 (n=10) and with involved lymph nodes N+ (n=11). N+ patients clearly show a shift to softer values ​​(below 1 kPa) in the cancerous region. Their peak is at 0.325 kPa, while the peak of N0 patients is at 0.625 kPa. Both distributions remain very broad, and the portion above 2 kPa in the graph looks very similar in both cases.

[0135] Figure 5 Display representative corresponds to Figure 4 Normalized plots of nanomechanical signatures of uninvolved and involved lymph nodes of the primary tumors shown. Cancer cell positive (N+) lymph nodes have a strong soft peak around 0.4 kPa, while cancer negative (N-) lymph nodes lack the soft peak. Example

[0136] In order to transfer the preliminary results to the clinical setting, the ARTIDIS technology was optimized for the analysis of non-fixed (measured in physiological aqueous environment or frozen tissue) human breast cancer samples obtained by tumor surgical resection. For this purpose, 152 tissue samples were collected from surgical resection specimens of 56 patients undergoing breast lumpectomy or mastectomy operations, including primary breast cancers of different stages and grades, lymph node metastases and non-tumor human breast tissues.

[0137] The nanomechanical measurements of the samples were carried out as disclosed in US 8,756,711 B2. Briefly, each sample was examined in a systematic manner by uniformly distributing the FV map over the entire sample surface to account for possible inhomogeneities. A micrometer screw or an automatic positioning system was used to maintain a prescribed distance of about 500 μm between scans. Thus, about 10 to 15 FV maps were obtained for each sample, depending on the size of the entire biopsy sample.

[0138] When analyzing samples by AFM, biopsy samples were glued to culture dishes using a 2-component, 5-minute fast-drying epoxy glue. After a 2-minute pre-drying step (to avoid mixing of epoxy and sample buffer), the samples were laid flat on the glue to optimize the indentation angle and avoid influence from external components (e.g., cantilever holder). The pipette tip, as a "ramp," was placed directly under the uneven portion of each sample to maintain high consistency. At any time when handling samples, the use of excess force (e.g., tearing or stretching) was minimized. All preparation steps were performed in a sterile buffer environment supplemented with protease inhibitors or transplantation buffer to avoid contamination and ensure that the samples remained in a state close to that in vivo. The fixed samples were kept in ice-cold Ringer's solution or Custodiol until nanomechanical testing, which was performed at room temperature or 37°C.

[0139] For a sharp conical tip (205-μm long silicon nitride cantilever, the cantilever spring constant k = 0.06 N m -1 , resonance frequency [air] = 18 kHz). Before each experiment, the exact spring constant k of the cantilever was determined using a thermal tuning method, while the deflection sensitivity was measured in a fluid, using a solid glass base as an infinite hardness reference material.

[0140] Contact hardness (elastic modulus, E) measurements of biopsy samples were derived as follows; recorded at a given site, in a directed manner, during loading and unloading. Load-displacement curves, also designated as force-indentation curves. A defined distance of about 500 μm was maintained between scans using a micrometer screw or an automatic positioning system. At an indentation speed of 16 μm / s, individual data sets consisted of 1024 load-displacement curves. Approximately 15 to 20 force volume maps were thus obtained for each sample. Force volume maps (FV) were prepared, if possible, at a speed of about 1 loading and unloading cycle per second, on a grid of 32x32 points, with a scan size of 20x20 μm. Each load-displacement curve consisted of at least 512 data points, while the Z length was set to 5 μm to 8 μm, depending on the nature of the analyzed area. Each FV map was set to 20x20 μm 2 , with the goal of (i) optimizing the experimental time and (ii) providing a sufficiently large area to incorporate all components of the tissue (e.g., cells and extracellular matrix). The maximum applied load was set to 1.8 nN, with indentation depths ranging from approximately 150 to 3000 nm. In addition, 72x72 FV images (5184 force-displacement curves per image, pixel size 277 nm) were acquired to increase the spatial resolution of key areas of interest.

[0141] The force indentation curves were analyzed using a previously described method (Loparic, et al., Biophysical Journal, 98(11): p.2731-40, 2010, Plodinec, et al., Journal of Structural Biology, 174(3): p.476-484, 2011). Briefly, LabVIEW (National Instrument, US) software was developed for automated analysis of FV data. The contact point was determined. The force curve was obtained by converting the piezoelectric displacement to a tip-sample distance indicating the bending of the cantilever, and the load F was obtained by multiplying the cantilever deflection d by the spring constant k. The unloading force curve was analyzed by performing a linear fit to the upper 50% of the force curve, which defines the stiffness between the maximum load F=1.8 nN and the load 0.9 nN. External influences on the force curve (such as adhesion) can be avoided by this operation. The Poisson's ratio was set to 0.5. Young's modulus was determined according to the method of Oliver and Pharr (Oliver et al., Journal of Materials Research, 7(6), 1564-1583, 1992). The slope values ​​were plotted in space, analyzed and displayed in ARTIDIS offline software.

[0142] After AFM, tissue samples were fixed and paraffin-embedded in an oriented manner. ARTIDIS data have confirmed initial findings: all cancer samples exhibited a heterogeneous hardness phenotype, with a characteristic 2-fold softer phenotype compared to surrounding non-tumor and morphologically normal breast tissue. Healthy breast tissue from patients without breast cancer exhibited an average hardness value of approximately 1.6 kPa.

[0143] Most importantly, Figure 2 and Figure 3 The data shown indicate that:

[0144] 1) Tumor tissue from breast cancer patients showed an uneven distribution from 0.4 kPa and an exponential decay with amplitude up to 20 kPa. We identified invasive breast cancer samples by a characteristic soft peak from 0.4 to 0.8 kPa. The corresponding lymph node metastasis hardness distribution from the same patient was characterized by an uneven hardness spectrum with a characteristic soft peak from 0.4 to 0.8 kPa, similar to primary breast cancer tissue.

[0145] 2) Adjacent tissues that were histologically identified as "non-malignant" in these patients showed a bimodal distribution, with a prominent soft and hardness peak from 0.4 to 0.8 kPa. The presence of these tissues was determined to be cancerous according to nanomechanical analysis, but histologically identified as non-malignant, which is an indication of a poor prognosis. In addition, hardness values ​​of 1.2 to 1.9 kPa corresponding to healthy breast tissue also existed.

[0146] 3) For patient cases where soft peaks were detected only in the primary tumor but not in the adjacent tissues, lymph node metastases were not present.

[0147] 4) When measuring adipose tissue, there is a specific hardness peak of 0.2 kPa.

[0148] 5) Since the fat component in breast tissue generally increases with age, a fat-specific hardness peak of 0.2 kPa is often measured.

[0149] also, Figure 4 and 5 The data shown in for a tumor that has spread to nearby lymph nodes shows a shift toward softer values ​​in the cancer region.

[0150] Therefore, to assess cancer aggressiveness and prognosis in breast cancer patients, it is important to consider not only the primary tumor but also the nanomechanical response of adjacent tissues.

[0151] The data presented here demonstrate the clinical applicability of nanomechanical analysis using ARTIDIS for:

[0152] 1) Prognosis of cancer occurrence, development and recurrence.

[0153] 2) Predicting treatment response.

[0154] 3) Determine appropriate treatment and follow-up plans based on a combination of nanomechanical features of the primary tumor and adjacent tissues.

[0155] 4) Screening for soft and hard peaks in histologically "non-malignant" tissue (i.e., non-malignant breast tissue with no obvious pathological changes observed by standard screening methods (such as ultrasound, mammography, or H&E staining after local biopsy)) with the aim of identifying the presence and degree of tumor aggressiveness of locally invasive cancer cells long before solid tumors grow and symptoms appear.

[0156] 5) Screening using ARTIDIS nanomechanical analysis is particularly suitable for patients carrying genetic mutations such as BRCA 1 and BRCA2 or any other who are at high risk of developing cancer.

[0157] The nanomechanical analysis method of the present invention is very suitable for routine practice because it allows rapid on-site assessment of samples and does not produce inter-observer variability, for example, when using other markers (such as Ki-67).

[0158] The method of the present invention is based on the following principles:

[0159] 1. Before a tumor (cancer) begins to develop, tissues (cells and extracellular matrix) undergo mechanical / structural changes at the nanoscale.

[0160] 2. The changes in point 1 are present in the organ or adjacent tissues (e.g., diffuse / multifocal presentation).

[0161] 3. The exact location where cancer first develops depends on the specific local microenvironmental conditions (eg, changes and interactions between cells and the surrounding extracellular matrix, as well as changes and interactions within themselves).

[0162] 4. Measurement of the nanomechanical characteristics of organs / adjacent tissues can detect specific changes in point 1.

[0163] 5. Measurement of nanomechanical features of organs / adjacent tissues can distinguish age-related mechanical changes from pre-neoplastic, neoplastic, and inflammation-related changes.

[0164] 6. Measurement of nanomechanical features of organs / adjacent tissues can correlate different types and / or degrees of mechanical changes with the ability of tumors to develop and / or spread metastases.

[0165] 7. The results of point 6 can be used as prognostic and / or predictive markers for tumor development and progression. In practice, this can be used to better treat patients for specific diseases.

[0166] 8. The methodology of the present invention is generally applied to the detection of breast tumors (e.g., screening), and as a prognostic marker for existing tumors or tumor-related metastases (but not limited to breast tissue). It can also be used for other organ-specific tumors, inflammatory or medically related diseases, such as prostate cancer, colorectal cancer, lung cancer, malignant melanoma, liver cancer, dermatitis, gastritis, etc.

[0167] 9. Using the proposed method, specific states such as dysplasia, metaplasia, hyperplasia (pre-neoplastic changes) and age-related changes can be specifically detected and distinguished from tumor-related changes.

Claims

1. A tumor sample diagnostic device, comprising an atomic force microscope and a computer connected thereto, wherein the computer is configured to run a program, wherein the program is configured to implement the following steps: - measuring the hardness values ​​of multiple points on the tissue sample with a spatial resolution of at least 100 μm to obtain a hardness distribution, and - assigning a malignancy probability to the tissue samples, wherein a sample exhibiting an inhomogeneous hardness distribution having a maximum frequency below 1 kPa is assigned a high probability of malignancy, wherein the inhomogeneous hardness distribution is an n-peaked distribution having n maxima, wherein n is an integer greater than 1, in - the tissue sample includes the core and margin or border of the primary tumor and adjacent tissues of the primary tumor, and a first hardness profile is obtained from a first site of the tissue sample, a second hardness profile is obtained from a second site of the tissue sample, and - the first site corresponds to a portion of the primary tumor that is histologically classified as tumor tissue, the second site corresponds to the adjacent tissue that is histologically classified as tissue beyond the boundary of the primary tumor, and - classifying the primary tumor as having a low probability of having spread to the adjacent tissue if the first hardness distribution is an inhomogeneous hardness distribution having a maximum frequency below 1 kPa and the second hardness distribution is characterized by the absence of a hardness distribution maximum frequency below 1 kPa, and / or - if said second hardness distribution is characterized by the presence of a hardness distribution maximum frequency below 1 kPa, classifying said primary tumor as having a high probability of having spread to said adjacent tissue. 2 . The tumor sample diagnostic device according to claim 1 , wherein the tissue sample is a tissue biopsy sample or a surgical resection specimen. 3 . The tumor sample diagnostic device according to claim 1 , wherein the plurality of points are arranged as a grid of n1×n2 points, the grid defining an area.

4. The tumor sample diagnostic device according to claim 1, wherein the hardness values ​​of at least two different regions of the tissue sample are measured, and the distance between the geometric centers of the regions is at least 10 times the spatial resolution.

5. The tumor sample diagnostic device of claim 1, wherein the plurality of points comprises 100, 400, 900, 1000, 1600, 2500, 3600, 4900, 6400, 8100 or 10000 hardness values. 6 . The tumor sample diagnostic device according to claim 1 , wherein the tissue sample is a cylindrical or prismatic biopsy sample with a diameter of at least 7 μm.

7. The tumor sample diagnostic device according to claim 1, wherein the tumor is human breast cancer or lymph node, lung, bone, liver or brain metastasis.

8. The tumor sample diagnostic device according to claim 1, wherein the hardness value is measured under physiological conditions.

9. The tumor sample diagnostic device of claim 1, wherein a primary tumor sample showing a maximum frequency below 0.5 kPa is classified as a metastatic tumor or is classified as having a high probability of having spread to the adjacent tissue.

10. The tumor sample diagnostic device according to claim 1, wherein the adjacent tissue is an adjacent lymph node or an axillary lymph node.

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