DE102020113000A1 - Method and system for determining a cancer therapy based on at least one antibody drug - Google Patents

Abstract

The present invention relates to a method for determining a cancer therapy based on at least one antibody drug comprising: a) determining at least one parameter selected from the absolute number of at least one receptor per cell, the mean receptor density of at least one receptor per cell, the cumulative frequency of mean receptor densities of at least one receptor per cell, the mean receptor density of at least one receptor of cells of a subject and cumulative or statistical distributions thereof, b) determining the at least one antibody drug and / or a personalized dose of the at least one antibody drug for the subject based on the parameter (s) determined in a).

Description

TECHNICAL FIELD OF THE INVENTION

The present invention relates to a method for determining a cancer therapy based on at least one antibody drug. The invention further comprises a system for determining a cancer therapy based on at least one antibody drug.

BACKGROUND OF THE INVENTION

According to the WHO, cancer is the number 1 cause of death in industrialized countries and second in all other countries. Many of the modern anti-cancer drugs are biological, i.e. they mainly consist of antibodies that specifically bind to cancer cells and thus influence the disease. In a large majority of patients, however, the cancer cells develop resistance to these drugs, either right from the start (primary) or during the course of therapy (acquired), i.e. they no longer work. After a resistance is detected in the patient, other therapies / drugs are used. Usually, however, these have significantly stronger side effects. The cancer had until then to spread further and the patients are therefore often in an advanced stage at the beginning of the next therapy.

A hitherto unsolved problem in cancer therapy with modern biologics (antibody-based drugs) is therefore drug resistance, which in most of the cancer patients treated either already exists at the start of therapy or occurs as it progresses [1, 2] . In general, the problem of resistance is explained by the heterogeneity of the tumor cells, although the exact reasons are so far only poorly understood [3, 4]. After a resistance has been identified and the patient has switched to other drugs in the subsequent rounds of therapy, the cancer, which has progressed in the meantime, can often no longer be combated effectively enough. Therefore, both the immediate effect at the beginning of the first antibody drug / biological therapy and the maintenance of this effect until the end are to be aimed for and are of decisive importance for the further course of the disease.

It can be assumed that it is not sufficient for the individual patient to achieve a standard, predetermined, minimum blood level concentration of the biological drug in order to achieve the best possible effect, but that there is an optimal blood level to be determined specifically for each patient. Concentration range there. Bell-shaped dose-response curves of biological agents have already been demonstrated for cancer cells kept in culture [5]. In patients, such dose-response curves are also known for a number of drugs [6]. Biological therapy with a suboptimal dose has negative consequences for the patient. Above all, these include an increase in the risk of resistance and a lack of efficiency in the anti-cancer effect. The inventors of the present invention meet the requirement to minimize or remedy these negative consequences.

For decades, patients have been roughly divided into 3 categories based on a visual assessment of the receptor density on immunohistochemical (i.e. with antibody staining) treated biopsy samples, ranging from "very few" to "very much" receptors. Above all, biopsies from patients in the middle category (2+) present a challenge and are not infrequently classified incorrectly [7]. Even more important, however, is the fact that it has so far not been possible to measure the absolute amounts of receptors in the tumor. The present invention also addresses these prior art disadvantages. So far, there has been a great need to remedy this, because estimates of the amount of receptors per cell for the highest category (3+) published in the literature are not based on quantitative measurements that were also carried out on cancer cell lines that were kept under 2-D culture conditions. were carried out. However, it has been proven that these culture conditions lead to significantly lower receptor densities than in cells that grow 3-dimensionally [8].

Up to now it has also not been possible to determine the actual relationship between the density and quantity of receptors present in the patient and the concentration and quantity of the biological agents present. Even if the recommended standard blood level values are adhered to, it can happen that in individual patients, especially those with tumors with a very high receptor density (i.e. category 3+), the total amount of receptors exceeds the amount of available antibodies many times over and these patients are thus underdosed. In addition, the blood level values of biologicals are by no means identical with the concentrations that prevail in the vicinity of the tumor cells, namely, significantly lower values are to be assumed there than in the blood [9]. The present invention also addresses this drawback in the prior art.

An example of a cancer in which drug resistance occurs in the majority of patients is HER2 overexpressing breast cancer, although it should be noted that HER2 overexpression is also found in many other cancers. In this type of cancer is the antibody drug trastuzumab is used to stop cells from growing at the HER2 receptor. Since an exact adjustment of the drug dose to the receptor density has not yet been possible, it can be assumed that in certain patients in whom the antibody dose is too low or too high, there will be a loss of effectiveness, which has already been measured for cancer cells in culture could be [5]. If cell growth is not effectively inhibited in all cancer cells and thus resistant cancer cell subpopulations prevail, the consequences for the patient are extremely harmful. In breast cancer patients with HER2 overexpression, it is known that after combined trastuzumab and chemotherapy, 25% of patients suffer a relapse within 5 years [11]. The additional administration of pertuzumab currently practiced only improves the long-term prognosis of patients statistically if they are already at an advanced stage, ie if metastases are present [12, 13]. More recent studies also indicate patient groups whose long-term prognosis would probably be better with trastuzumab monotherapy [14, 15].

Reliable methods for calculating the receptor densities on patient cells are not yet available or are too imprecise. Methods based on light microscopy, such as the Förster Resonance Energy Transfer (FRET) and the “Proximity ligation assay” (PLA), are unsuitable for this because they both reach a saturation range above certain receptor densities [16, 17]. From these value ranges there is no longer any linear correlation with the calibration values. In addition, inhomogeneous receptor distributions on the cells can falsify the measured values. In general, quantitative receptor density measurements with fluorescence-labeled antibodies are unreliable at high receptor densities [18], since this leads to a mutual weakening of the fluorescence signals (“self quenching”) [19]. In principle, measurement methods with fluorescence-labeled antibodies are therefore not suitable for absolute quantification in tumors overexpressing HER protein [20]. Existing measurement methods for quantifying, for example, HER1-3 in patient tissue samples can therefore only provide semi-quantitative, i.e. relative results [21, 22], but no absolute information can be given here about the density and amount of the HER proteins present.

Despite its importance for the prognosis of the patient, the determination of the heterogeneity in local receptor densities, i.e. different density areas on the individual tumor cells, as well as between different cell subpopulations in the tumor, has so far only been roughly rudimentary, and moreover only on two-dimensional sections, i.e. without quantitative informative value. carried out.

Another very important tumor characteristic, which plays a central role in connection with the problem of heterogeneity, are the so-called cancer stem cells. These have now been recognized as one of the main causes of the development of resistance [23], but have so far hardly been diagnosed (influenced, let alone investigated their receptor density. For this reason, this important subpopulation of cancer cells has not yet been taken into account in diagnosis and treatment selection. Cancer stem cells usually only make one make up a small part of the total cancer cell population, but they have the special ability to self-renew and differentiate. In addition, both their proportion of the total cancer cell population and their special stem cell properties can be changed through contact with the therapeutic agents used [24], which in turn can strongly influence the success of the therapy.

It is therefore the object of the present invention to improve or remedy the above-mentioned disadvantages and existing challenges as well as needs from the prior art. For example, it is an object of the invention to reduce or eliminate the occurrence of drug resistance. To do this, the dose of the drug must be precisely adjusted to the individual patient. The present invention contributes to a greatly increased accuracy of fit of the cancer therapy to be ultimately selected for the individual patient, which increases the chances of an effective fight against cancer.

SUMMARY OF THE INVENTION

1. a) Ermitteln mindestens eines Parameters ausgewählt aus der absoluten Anzahl mindestens eines Rezeptors pro Zelle, der mittleren Rezeptordichte mindestens eines Rezeptors pro Zelle, der kumulativen Häufigkeit von mittleren Rezeptordichten mindestens eines Rezeptors pro Zelle, der durchschnittlichen Rezeptordichte mindestens eines Rezeptors von Zellen eines Subjektes und kumulative oder statistische Verteilungen davon,
2. b) Ermitteln des mindestens einen Antikörper-Medikaments und/ oder einer personalisierten Dosis des mindestens einen Antikörper-Medikaments für das Subjekt aufgrund des/ der in a) ermittelten Parameters/ Parameter.

The present invention relates to a method for determining a cancer therapy based on at least one antibody drug comprising:

1. a) determining at least one parameter selected from the absolute number of at least one receptor per cell, the mean receptor density of at least one receptor per cell, the cumulative frequency of mean receptor densities of at least one receptor per cell, the average receptor density of at least one receptor of cells of a subject and cumulative or statistical distributions thereof,
2. b) determining the at least one antibody drug and / or a personalized dose of the at least one antibody drug for the subject based on the parameter (s) determined in a).

Another embodiment relates to the method, wherein in a) the average receptor density of a receptor in a representative number of cancer cells of the subject is determined.

Another embodiment of the invention is the method wherein in a) the statistical distribution of the at least one parameter is determined as a standard deviation or a deviation from a normal distribution.

An additional embodiment further relates to the method, wherein the at least one parameter determined in a) is determined on circulating cancer cells obtained from the subject's blood.

Another embodiment of the invention is the method wherein step b) further includes the weight of the subject and the tumor volume present.

An additional embodiment relates to the method, the mean receptor density per cell and / or the relevant statistical distribution of the HER2 receptor being determined in step a).

Another embodiment of the invention is the method, wherein in step b) the at least one antibody drug is selected from trastuzumab, trastuzumab emtansine, pertuzumab and a combination thereof.

Another embodiment of the invention relates to the method, wherein in step a) the mean receptor density per cell and / or the statistical distribution of the HER2 receptor, the HER1 receptor and / or the HER3 receptor are determined.

One embodiment relates to the method, wherein in step b) the personalized dose of the at least one antibody drug is calculated on the basis of a three-dimensional dose-response curve, preferably the dose-response curve indicating the strength of the effect of the at least one Antibody drug determined as a function of the mean receptor density of the at least one receptor per cell, the relevant statistical distribution and the concentration of the at least one antibody drug.

Another embodiment of the invention relates to the method, wherein in b) a prediction about the occurrence or existence of at least one drug resistance is additionally determined.

Another embodiment of the invention relates to the method, further comprising the detection of cancer stem cells in a sample taken from the subject with fluorescence-labeled antibodies.

1. a) Vorrichtung zum Ermitteln mindestens eines Parameters ausgewählt aus der absoluten Anzahl mindestens eines Rezeptors pro Zelle, der mittleren Rezeptordichte mindestens eines Rezeptors pro Zelle, der kumulativen Häufigkeit von mittleren Rezeptordichten mindestens eines Rezeptors pro Zelle, der durchschnittlichen Rezeptordichte mindestens eines Rezeptors von Zellen eines Subjektes und kumulative oder statistische Verteilungen davon,
2. b) Vorrichtung zum Ermitteln des mindestens einen Antikörper-Medikaments und/ oder einer personalisierten Dosis des mindestens einen Antikörper-Medikaments für das Subjekt aufgrund des/ der in a) ermittelten Parameters/ Parameter.

The invention further comprises a system for determining a cancer therapy based on at least one antibody drug comprising:

1. a) Device for determining at least one parameter selected from the absolute number of at least one receptor per cell, the mean receptor density of at least one receptor per cell, the cumulative frequency of mean receptor densities of at least one receptor per cell, the average receptor density of at least one receptor of cells of a subject and cumulative or statistical distributions thereof,
2. b) Device for determining the at least one antibody drug and / or a personalized dose of the at least one antibody drug for the subject based on the parameter (s) determined in a).

In a preferred embodiment, the system according to the invention comprises a device according to a), which comprises a sample preparation device and a device for fluorescence microscopy.

Figure list

• 1 shows a schematic representation of the step-by-step sample preparation method. I. A section from an FFPE tumor block is dewaxed and dissociated by collagenase and dispase digestion, then heat-induced epitope masking is carried out. II. All non-tumor cells are removed from the resulting cell suspension, for example by a magnetic separation process, as a result of which mainly tumor cells remain in the suspension. III. The tumor cells with HER2 membrane proteins are incubated specifically and monovalently with biotin-conjugated proteins that bind HER2, eg with anti-HER2 affibodies. IV. The tumor cells are then immobilized on a substrate, for example in a well of a microtiter plate, and incubated with streptavidin-conjugated fluorescent markers, for example quantum dots. Optionally (not shown), staining can then be carried out for the parallel identification of cancer stem cells (with fluorescent markers for CD44 ⁺ / CD24 ^- ).
• 2 shows light microscope images (on the left the differential interference contrast images and on the right the associated fluorescence images) of tumor samples which contain the in 1 received labeling of the HER2 receptor. 2A and 2 B show tumor cells which (in B) emit clear fluorescence signals from HER2 labeled with quantum dot. 2C and 2D show an example of a negative control experiment showing the specificity of the HER2 label. Tumor cells, as in 2A and 2 B were treated with the same labeling procedure, but without using the Affibody. There is no noteworthy, unspecific fluorescence in D. 2E and 2F show another negative control experiment for the specificity of the labeling method. The tumor cells shown here were subjected to the same marking procedure as the cells in 2A and 2 B , however, came from a tumor sample that pathologists classified as HER2 negative. Here, too, there are only very weak fluorescence signals (in FIG. 2F), which reflect the low, normal level of the HER2 density in the breast tissue. Scale bar: 50 µm.
• 3 shows light microscope images (fluorescence images) of breast cancer cells in culture (SKBR3 cell line) with double labeling of HER1, in 3A , and HER2, in 3B . HER1 and HER2 were created with the in 1 described method, whereby each receptor was labeled with a different, in each case specific binding protein, and a different type of quantum dot nanoparticle. It can be seen that the ratios of the two receptors to one another are different on the individual cells, since some cells emit more fluorescence from marked HER1 (see cells marked with arrows) than from marked HER2. This indicates different receptor density ratios of HER1 and HER2 in the individual cancer cells. 3C illustrates these different receptor densities through image processing of both recordings. The HER2 intensity was subtracted from the HER1 intensity for each image point (pixel); the light cells (marked with arrows) accordingly have a relatively higher HER1 density than most of the other cells shown. The fluorescence images in 3D , 3E and 3F show another group of breast cancer cells from the same cell line incubated with fluorescent antibodies against the breast cancer stem cell markers CD44 (in Figure 3D) and CD24 (in Figure 3E). Breast cancer stem cells are cells that are CD44 positive and CD24 negative. Two of these cells are marked with arrows. 3F shows the same cells after the described HER2 labeling. You can see that one of these cells has a very high density of HER2. Scale bar: 50 µm.
• 4th shows the representation of a hypothetical 3D dose-effect curve for determining the optimal dose of a cancer therapeutic agent to be selected (e.g. biologics). On the basis of the density of the measured receptors, the heterogeneity of the receptor density within the individual cells and the cancer cell population, previously determined efficacy profiles of the therapeutic agents available for selection, and the patient weight, the individual, personalized dose can be determined for a subject.
• 5 shows the schematic representation of the device for the individual determination of the personalized dose and composition of an antibody-drug based cancer therapy. The biopsy tissue sample (top left) is first processed into a single cell suspension in the device, the cells are immobilized in the wells of a pre-prepared microtiter plate and the receptors are marked with specific markers (as in 1 shown). A connected, fully automatic light microscopy system depicts the cancer cells and determines the values for mean fluorescence intensity and size or surface for all cells depicted, as well as the statistical distribution within the individual cells as well as in the total cell population. Using a calibration curve, fluorescence intensity values are converted into absolute receptor density values and a frequency distribution of the cell population is established. Optionally, additional markers for cancer stem cells can be used after the receptor labeling in the wells. Cancer cell subpopulations can then be analyzed separately and shown separately from the total population (named in the measurement data scheme B). The results of the main analysis (named measurement data A in the scheme) are processed together with information on the total number of tumor cells (calculated from the size of the tumor and the size distribution of the individual cells) and the patient's weight, as well as the 3D dose-effect curve. The information in the measurement data A, with or without measurement data B information, can be converted into a recommendation for the personalized therapy and dose for each individual patient. It is to be expected from this recommendation that it minimizes the risk of developing resistance, or that it can predict an already existing, primary resistance with a high degree of probability, and that it also increases the effectiveness of cancer therapy.

DETAILED DESCRIPTION OF THE INVENTION

1. a) Ermitteln mindestens eines Parameters ausgewählt aus der absoluten Anzahl mindestens eines Rezeptors pro Zelle, der mittleren Rezeptordichte mindestens eines Rezeptors pro Zelle, der kumulativen Häufigkeit von mittleren Rezeptordichten mindestens eines Rezeptors pro Zelle, der durchschnittlichen Rezeptordichte mindestens eines Rezeptors von Zellen eines Subjektes und kumulative oder statistische Verteilungen davon,
2. b) Ermitteln des mindestens einen Antikörper-Medikaments und/oder einer personalisierten Dosis des mindestens einen Antikörper-Medikaments für das Subjekt aufgrund des/ der in a) ermittelten Parameters/ Parameter.

The present invention relates to a method for determining a cancer therapy based on at least one antibody drug comprising:

1. a) determining at least one parameter selected from the absolute number of at least one receptor per cell, the mean receptor density of at least one receptor per cell, the cumulative frequency of mean receptor densities of at least one receptor per cell, the average receptor density of at least one receptor of cells of a subject and cumulative or statistical distributions thereof,
2. b) determining the at least one antibody drug and / or a personalized dose of the at least one antibody drug for the subject based on the parameter (s) determined in a).

The term “determination of a cancer therapy based on at least one antibody drug” can include the determination of a personalized dose or a personalized dose range of at least one antibody drug. This is a dose or dose range tailored to the particular needs of the subject for the particular subject. The term “determination of a cancer therapy based on at least one antibody drug” can also include the determination of the at least one antibody drug or the determination of a combination of antibody drugs. It is particularly preferred that the term “determination of a cancer therapy based on at least one antibody drug” includes both the determination of the at least one antibody drug or the determination of a combination of antibody drugs and the determination of a personalized dose or a personalized dose range the at least one antibody drug. The term “antibody drug”, as used in the present application, includes any form of drug that can be used, for example, in cancer therapy, which can be antibodies for this purpose, but also biologicals. Biologics or biopharmaceuticals are pharmaceutical drugs that are manufactured using biotechnology. Biotechnological processes for the production of biologics usually make use of living cells, which have mostly been genetically modified in order to obtain biomolecules from them. Administered as a drug, these active ingredients can correct derailed pathological processes in the human body or activate the immune system against a certain disease.

The absolute number of at least one receptor per cell means the absolute value of how often a certain receptor or receptor type is present on a cell. This value can be determined by quantitative measurements as described herein.

The term “receptor density” according to the present application can be used synonymously with the term “receptor surface density”. According to the present application, the receptor density is preferably given in the unit “number of receptors / µm ^2”.

The term “average receptor density”, as used in the present application, can be used synonymously with the term “absolute receptor density”. This receptor density means the absolute value (ie an exact, specific number) which specifies / indicates how many receptors per unit area (eg per square micrometer) are on average on a measured cell. So it also indicates the “mean receptor density” as used herein. The unit of the receptor density is “number of receptors / µm ² ”. This mentioned value thus specifies both at the same time, an absolute receptor density and an average receptor density, the latter being to be understood as indicating an average value for each individual cell. This value can be determined from the fluorescence measurement of a cell and then consists of the “sum of all fluorescence intensity values of a cell” divided by the “surface of the measured cell [in µm ² ]”. Using a calibration curve, this value can then be converted into a density value, namely in “number of receptors / µm ² ”. A single cell usually always has different receptor densities locally on its surface, ie the receptors are mostly inhomogeneously distributed. These inhomogeneities of the receptor density existing within the individual cell can also be taken into account for the determination of the at least one antibody drug and / or the personalized dose of the at least one antibody drug for the subject. If a statistical distribution profile thereof, based on a total cell population, a cell subpopulation or cell subpopulation, is established, this is to be understood as the cumulative frequency thereof.

The term “average receptor density of a receptor of cells” refers to the average value of the absolute / mean receptor densities related to a very specific receptor or receptor type for several cells, i.e. a specific, limited amount of cells, a cell population or a cell subpopulation. These cells are preferably cancer cells that have been removed from the subject, i.e. a specific cancer cell population. It can particularly preferably also be a cancer stem cell population Act. The invention therefore also relates in a special way to taking into account the inhomogeneities of the receptor density at the level of the cell population, that is to say the (absolute / mean) receptor densities of different cells among one another, indicated in the form of the average receptor density.

The term “personalized dose”, as used in the present invention, means that a dose is determined for the respective subject based on the parameter / parameters determined in step a) and forms the basis of cancer therapy. Such a dose, which can also be a dose range, takes into account the individual and special needs of the respective subject. The dose is given, for example, in mg antibody drug / kg body weight (of the subject).

The subject, as mentioned herein, is preferably a mammal and particularly preferably a human.

1. a) Ermitteln der absoluten Anzahl mindestens eines Rezeptors pro Zelle eines Subjektes, und
2. b) Ermitteln des mindestens einen Antikörper-Medikaments und/oder einer personalisierten Dosis des mindestens einen Antikörper-Medikaments für das Subjekt aufgrund des in a) ermittelten Parameters.

Another embodiment relates to a method for determining a cancer therapy based on at least one antibody drug comprising:

1. a) determining the absolute number of at least one receptor per cell of a subject, and
2. b) Determining the at least one antibody drug and / or a personalized dose of the at least one antibody drug for the subject based on the parameter determined in a).

1. a) Ermitteln der absoluten Rezeptordichte mindestens eines Rezeptors pro Zelle eines Subjektes, und
2. b) Ermitteln des mindestens einen Antikörper-Medikaments und/oder einer personalisierten Dosis des mindestens einen Antikörper-Medikaments für das Subjekt aufgrund des in a) ermittelten Parameters.

Another embodiment relates to a method for determining a cancer therapy based on at least one antibody drug comprising:

1. a) determining the absolute receptor density of at least one receptor per cell of a subject, and
2. b) Determining the at least one antibody drug and / or a personalized dose of the at least one antibody drug for the subject based on the parameter determined in a).

1. a) Ermitteln der mittleren Rezeptordichte mindestens eines Rezeptors pro Zelle eines Subjektes, und
2. b) Ermitteln des mindestens einen Antikörper-Medikaments und/oder einer personalisierten Dosis des mindestens einen Antikörper-Medikaments für das Subjekt aufgrund des in a) ermittelten Parameters.

Another embodiment relates to a method for determining a cancer therapy based on at least one antibody drug comprising:

1. a) determining the mean receptor density of at least one receptor per cell of a subject, and
2. b) Determining the at least one antibody drug and / or a personalized dose of the at least one antibody drug for the subject based on the parameter determined in a).

1. a) Ermitteln der kumulativen Häufigkeit von mittleren Rezeptordichten mindestens eines Rezeptors pro Zelle eines Subjektes, und
2. b) Ermitteln des mindestens einen Antikörper-Medikaments und/oder einer personalisierten Dosis des mindestens einen Antikörper-Medikaments für das Subjekt aufgrund des in a) ermittelten Parameters.

Another embodiment relates to a method for determining a cancer therapy based on at least one antibody drug comprising:

1. a) determining the cumulative frequency of mean receptor densities of at least one receptor per cell of a subject, and
2. b) Determining the at least one antibody drug and / or a personalized dose of the at least one antibody drug for the subject based on the parameter determined in a).

1. a) Ermitteln der durchschnittlichen Rezeptordichte eines Rezeptors von Zellen eines Subjektes,
2. b) Ermitteln des mindestens einen Antikörper-Medikaments und/oder einer personalisierten Dosis des mindestens einen Antikörper-Medikaments für das Subjekt aufgrund des in a) ermittelten Parameters.

The present invention relates to a method for determining a cancer therapy based on at least one antibody drug comprising:

1. a) determining the average receptor density of a receptor from cells of a subject,
2. b) Determining the at least one antibody drug and / or a personalized dose of the at least one antibody drug for the subject based on the parameter determined in a).

Another embodiment relates to the method, wherein in a) the average receptor density of a receptor in a representative number of cancer cells of the subject is determined. A representative number of cancer cells, i.e. the required sample size, depends on the total amount of available, intact cancer cells that were obtained, for example, from at least 2 biopsy samples of the subject. The numerical value of the sample size can be calculated individually, e.g. For example, with a total of ~ 2000 available, intact cancer cells, a confidence level of 99% and a margin of error of 5%, a sample size of at least 500 subject cells to be measured results.

Another embodiment of the invention is the method wherein in a) the statistical distribution of the at least one parameter is determined as a standard deviation or a deviation from a normal distribution. The statistical distribution can serve as a measure of the cell heterogeneity. The invention therefore comprises in one embodiment that Method whereby in a) the heterogeneity of the mean receptor density of at least one receptor per cell is determined. “Heterogeneity” here means looking at the distribution of receptor densities, i.e. the different density areas on a cell or individual cells or between different cell populations or cell subpopulations.

An additional embodiment further relates to the method, wherein in a) the proportion of cancer stem cells in a sample taken from the subject is additionally determined.

An additional embodiment further relates to the method, wherein in a) the size of the cell or the size of the cells in a sample taken from the subject is additionally determined. These data can also be used for step b).

An additional embodiment further relates to the method, wherein the at least one parameter determined in a) is determined on circulating cancer cells obtained from the blood of the subject. Circulating cancer cells are usually present when cancer cells have detached themselves from the primary tumor cell cluster or metastases and can spread systemically via the blood or lymphatic system in the body of a subject, preferably a patient. An alternative term in cancer research is circulating tumor cells (CTCs).

Another embodiment of the invention is a method, wherein step b) further includes the weight of the subject and the tumor volume present in the subject. The weight of the subject is usually the body weight given in kg. The existing tumor volume of the subject is determined using established tumor volumetry methods [25].

An additional embodiment relates to the method, the mean receptor density per cell and / or the relevant statistical distribution of the HER2 receptor being determined in step a). The personalized dose is calculated from these two specific parameters and optionally with the help of other parameters such as the weight of the subject and the tumor volume. The HER2 receptor (also called: "human epidermal growth factor receptor 2", official name: ERBB2, "erb-b2 receptor tyrosine kinase 2") belongs to the family of epidermal growth factor receptors (EGF receptors). It stimulates cell proliferation via the RAS-MAP kinase pathway and inhibits programmed cell death (apoptosis) via the mTOR signal path.

Another embodiment of the invention is the method, wherein in step b) the at least one antibody drug is selected from trastuzumab, trastuzumab emtansine, pertuzumab and a combination thereof. Trastuzumab is a humanized monoclonal antibody that is used as a drug in certain forms of breast and stomach cancer. Trastuzumab binds to the epidermal growth factor receptor HER2 on the cell surface of cancer cells, thereby inhibiting their growth. Trastuzumab emtansine is used to treat HER2-positive, inoperable locally advanced or metastatic breast cancer after previous therapy with trastuzumab and a taxane. Pertuzumab (also known as 2C4 or Omnitarg) is also a monoclonal antibody that is used with trastuzumab and docetaxel in the treatment of HER2-positive breast cancer. When selecting the antibody drug, the following criteria can also have an influence: Pertuzumab, which is more effective against heterodimers, is recommended if the receptor density of HER1 is the same as that of HER2, or trastuzumab, which is only more effective against HER2, if the receptor density of HER2 exceeds that of HER1 many times over. The drug trastuzumab emtansine can be recommended, for example, if the cancer stem cell population has exceeded a certain minimum percentage, whereby this minimum percentage can be determined taking into account other parameters such as tumor size, lymph node involvement or the presence of metastases.

Another embodiment of the invention relates to the method, wherein in step a) the mean receptor density per cell and / or the statistical distribution of the HER2 receptor, the HER1 receptor and / or the HER3 receptor are determined. The HER1 receptor, also called EGF receptor, (abbreviation for "Epidermal Growth Factor Receptor", EGFR) is a protein found in vertebrate cell membranes. It is the receptor for the epidermal growth factor (EGF) and is a member of the ErbB family, a subfamily of four closely related receptor tyrosine kinases: EGFR1 / HER1 (ErbB-1), HER2 / c-neu (ErbB- 2), HER3 (ErbB-3) and HER4 (ErbB-4). The HER3 receptor (abbreviated for “human epidermal growth factor receptor 3”) is also called “Receptor tyrosineprotein kinase erbB-3” and is a membrane-bound protein which is encoded by the ERBB3 gene. With regard to the HER2 receptor, we refer to the statements above.

So far, the estimation of the receptor density on the cancer cells was only possible very roughly and the dose of the medication was only adjusted with the help of the weight of the subject. Even the parallel detection and density determination of other receptors interacting with HER2 have hardly been carried out so far. This could lead to an inadequate elimination of important bypass routes of the tumor cells against the drug, which ultimately results in the resistance of the tumor cells. The present invention, however, involves the precise quantification of receptor density on a number of representative cells of a subject. The values for the absolute receptor densities, ie how many receptors there are per cell membrane surface on each examined cell, are preferably determined by means of light microscopy. The mean receptor densities of at least one receptor per cell can then be determined from this, as defined above. From the mean receptor density of the at least one receptor per cell, for example for the examined cells, and the associated standard deviation, which can be determined for each measured receptor, in combination with an estimate of the tumor cell size and total number, the relative density ratios of the measured receptors, given in the form of heterogeneity, which also allow information about homo- and heterodimerization, as well as the weight of the subject, the optimized and personalized dose, which can also be a dose range of the at least one antibody drug, and / or the most promising antibody -Medication or antibody-drug combination identified for each subject.

One embodiment forms the method, wherein in step b) the personalized dose of the at least one antibody drug is calculated on the basis of a three-dimensional dose-response curve, preferably wherein the dose-response curve is the strength of the effect of the at least one Antibody drug determined as a function of the mean receptor density of the at least one receptor per cell, the relevant statistical distribution and the concentration of the at least one antibody drug. The dose-response curve is determined, for example, first in cell cultures, then in tumor organoids and then in animal models. For a specific subject, the mean receptor density of at least one receptor per cell and its relevant statistical distribution are preferably determined. Then, using these values and other optional parameters, such as the weight of the subject, the personalized dose can be determined by means of the 3D dose-effect curve, as it is 4th can be found.

Another embodiment of the invention relates to the method, wherein in b) a prediction of the occurrence or existence of at least one drug resistance is additionally determined. The prediction of the risk of the occurrence or occurrence of one or more drug resistance (s) can, for example, be based on the respective receptor density of one or more receptors, their statistical distribution and their relative density ratios or heterogeneity, within individual cells or within a cell population can be calculated. These values can be determined, for example, from the subject's cancer cells that have been removed from the subject. Other combinations and drugs are also possible and by no means excluded.

Another embodiment of the invention relates to the method, further comprising the detection of cancer stem cells in a sample taken from the subject with fluorescence-labeled antibodies. With the help of specific, fluorescence-marked antibodies against cancer stem cell markers (CD44 + / CD24-, CD133, or ALDH1) [26, 27], cancer cells in a sample taken from the subject can be examined for any cancer stem cells that may be present . Detected cancer stem cells can then be examined separately from the remaining tumor cells for their mean receptor density of at least one receptor per cell and their statistical distributions. This embodiment is also preferred for circulating cancer cells. From the parameters that are determined in this embodiment, additional parameters, such as the weight of the subject and the tumor volume, can then optionally be used to determine the at least one antibody drug and / or the personalized dose of the at least one antibody drug for the subject be included.

The invention further comprises a system for determining a cancer therapy based on at least one antibody drug comprising: a) device for determining at least one parameter selected from the absolute number of at least one receptor per cell, the mean receptor density of at least one receptor per cell, the cumulative frequency of mean receptor densities of at least one receptor per cell, the average receptor density of at least one receptor of cells of a subject and cumulative or statistical distributions thereof, b) device for determining the at least one antibody drug and / or a personalized dose of the at least one antibody Medicament for the subject based on the parameter (s) determined in a).

Another embodiment relates to the system, wherein the device according to a) serves to determine the average receptor density of a receptor in a representative number of cancer cells of the subject.

A further embodiment of the invention is the system in which the device according to a) serves to determine the statistical distribution of the at least one parameter as a standard deviation or a deviation from a normal distribution.

In a further embodiment, the invention comprises the system, wherein the device according to a) serves to determine the heterogeneity of the mean receptor density of at least one receptor per cell.

An additional embodiment further relates to the system, wherein the device according to a) serves to determine the at least one parameter on circulating cancer cells obtained from the blood of the subject.

A further embodiment of the invention is the system, wherein the device according to b) serves to also include the weight of the subject and the existing tumor volume.

Another embodiment of the invention further relates to the system, the device according to a) additionally serving to determine the size of the cell or the size of the cells in a sample which was taken from the subject.

An additional embodiment relates to the system, the device according to a) serving to determine the mean receptor density per cell and / or the relevant statistical distribution of the HER2 receptor.

Another embodiment of the invention relates to the system, wherein the device according to b) serves to select the at least one antibody drug from trastuzumab, trastuzumab emtansine, pertuzumab and a combination thereof.

Another embodiment of the invention relates to the system, the device according to a) serving to determine the mean receptor density per cell and / or the statistical distribution of the HER2 receptor, the HER1 receptor and / or the HER3 receptor.

One embodiment relates to the system, wherein the device according to b) is used to calculate the personalized dose of the at least one antibody drug on the basis of a three-dimensional dose-effect curve, preferably used for the dose-effect curve to determine the strength of the effect of the at least one antibody drug as a function of the mean receptor density of the at least one receptor per cell, the relevant statistical distribution and the concentration of the at least one antibody drug.

A further embodiment of the invention relates to the system, the device according to b) additionally serving to determine a prediction about the occurrence or existence of at least one drug resistance.

Another embodiment of the invention relates to the system, the system additionally comprising a device for detecting cancer stem cells in a sample taken from the subject with fluorescence-labeled antibodies.

In one embodiment, the system according to the invention comprises as a device for determining at least one parameter selected from the absolute number of at least one receptor per cell, the mean receptor density of at least one receptor per cell, the cumulative frequency of mean receptor densities of at least one receptor per cell, the average receptor density at least a receptor of cells of a subject and cumulative or statistical distributions thereof, a sample preparation device and a device for fluorescence microscopy. The latter can also include automatic sample loading. The device for fluorescence microscopy can have automatic imaging and image processing modules. For example, depending on the type of sample, either fresh or fixed tissue biopsies are first broken into individual cells, i.e. the cells are separated, tumor cells are selected, chemically processed (in the case of fixed tissues), immobilized in wells of a microtiter plate with a glass bottom for microscopy and existing receptors in labeled with the fluorescent samples in a defined (maximum 1: 1) ratio. A further marking step with markers for cancer stem cells can optionally follow. The entire preparation process takes a few hours and can be carried out for many samples at the same time. 3 - 5 samples can be processed in parallel per subject.

The marked and immobilized cells can then be automatically imaged in the device for fluorescence microscopy. For example, the absolute or mean receptor densities of at least one receptor per cell for the individual cells can first be determined by means of a computer and from this a statistical distribution profile can be determined for a total cell population (cumulative frequency). This provides information about the heterogeneity within the sample. The heterogeneity of the receptor distribution (i.e. how is the absolute number of at least one receptor per cell distributed over a cell) or receptor density distribution (i.e. how is the mean receptor density of at least one receptor per cell distributed over a cell) within the individual cells can be measured and in the Identifying Cancer Therapy must be taken into account. In an embodiment in which the cancer stem cells are marked in a), each cell subpopulation can be analyzed separately and the data can accordingly be displayed separately. The receptor densities of the measured receptors on the cancer cells of the subject can also be used to calculate the HER2 homo- and hetero-dimer densities and the corresponding total amounts, which are determined using standard data from the pathology, including a 3D dose-effect curve ( please refer 4th ), as well as the body weight of the subject and the tumor volume, can be converted into an optimal dose value for the optimal antibody drug, or a combination of several antibody drugs. The antibody drug in the context of the present invention can also be a biological.

This description of the devices contained in the system according to the invention is an example of a possible embodiment. However, the devices of the system can also be built differently, for example tissue sections can also be marked and analyzed. For this purpose, the results can be converted to the extrapolated 3-dimensionality in the tissue in an additional step.

The system according to the invention can furthermore, for example, also enable the cancer therapy based on at least one antibody drug to be determined in the following way: Cancer cells from biopsy samples are first made available. If cancer cells are present, the receptors on the cancer cells can be specifically marked using a pipetting device and prepared for light microscopy. The system may further include a device that takes light microscopic images thereof. Such a device can be, for example, a light microscopy device. This device can then use the appropriate software to determine, for example, the mean receptor densities of at least one receptor per cell and their statistical distribution within the analyzed cells, cell population or cell subpopulation, preferably cancer cells or a cancer cell population, from the subject using calibration curves previously created. The device then calculates the personalized dose of the at least one antibody drug using a three-dimensional dose-effect curve previously determined for the respective drug.

All embodiments for the method are disclosed herein in the same way for the system. All of the definitions given above for the method according to the invention apply in the same way to the system according to the invention described herein.

In summary, the present invention provides a system and a method that determines the optimized and personalized dose, as well as the type of antibody or biological drug to be used, or a combination of several antibody drugs or biological drugs or other drugs, with the The aim is to prevent the development of resistance or, if primary resistance is to be expected, to provide a therapy recommendation for an alternative drug. The ratio of the absolute number per cell, the mean receptor density per cell, the cumulative frequency of the mean receptor density per cell, the average receptor density of cells of a subject or cumulative or statistical distributions thereof of HER2 with the corresponding value of another interacting and thus activating receptor, for example HER1 or HER3, determined and taken into account accordingly when determining the cancer therapy based on at least one antibody drug. The proportion of cancer stem cells and their receptor densities can be determined as further parameters. However, this invention is not limited to receptors of the HER family, which are given here as an example, but can also be used with other receptors.

The system and method according to the invention can also be used to determine the receptor densities and the heterogeneity in the respective cancer cell populations of a subject. Optionally, the absolute total amount of receptors to be attacked can also be determined, taking into account the estimated total number of tumor cells. On the basis of these values, optionally including additional measurements on cancer stem cells and the body weight of the subject, the optimized and personalized dose of the at least one antibody drug is determined for the subject. Furthermore, the present invention results in fewer side effects by avoiding unnecessarily excessive drug concentrations.

EXAMPLES OF THE INVENTION

• Histochoice Clearing Reagenz, Epitopdemaskierungsreagenz (pH 6), Kollagenase, Dispase, biotin-konjugierter anti-HER2 Affibody, Ziegenserum, streptavidin-konjugierte Quantum Dots (Qdot® 655 oder 565 nm), ultra-reines, deionisiertes Wasser, Aceton und Ethanol, Phosphor-gepufferte Kochsalzlösung („PBS“), Formaldehyd, Kochsalz, Glyzin, biotinfreies Rinderserumalbumin Fraction V, Histochoice Reinigungsagenz, Kollagenase IA, Tween, poly-L-Lysin (mol wt 70,000-150,000), MAPTrix™ Reagenz (high MW) Natriumbicarbonat, Natriumhydroxid, Natriumtetraborat und Borsäure.

Material and methods:

• Histochoice clearing reagent, epitope masking reagent (pH 6), collagenase, dispase, biotin-conjugated anti-HER2 affibody, goat serum, streptavidin-conjugated quantum dots (Qdot® 655 or 565 nm), ultra-pure, deionized water, acetone and ethanol, phosphorus -Buffered saline solution ("PBS"), formaldehyde, saline, glycine, biotin-free bovine serum albumin Fraction V, Histochoice Cleansing agent, collagenase IA, Tween, poly-L-lysine (mol wt 70,000-150,000), MAPTrix ™ reagent (high MW) sodium bicarbonate, sodium hydroxide, sodium tetraborate and boric acid.

Example: The method and system according to the invention for determining a cancer therapy based on at least one antibody drug can be carried out as follows:

Standard biopsy tissue samples are first processed into a single cell suspension. For this purpose, the formalin-fixed, paraffin-embedded tissue sections are subjected to integrated dewaxing and rehydration, an enzymatic digestion, followed by heat-induced epitope unmasking with a unmasking reagent.

The cancer cells isolated in this way are immobilized on the glass bottom of wells of a pre-prepared microtiter plate and the receptors are marked with specific markers and the fluorescent nanoparticles (Qdots).

A connected, fully automatic light microscopy system maps the cells, recognizes the individual spatial areas that are occupied by cells, and determines the values for mean fluorescence intensity, standard deviation and surface size, as well as the statistical distribution within the total cell population, for these areas.

Using a calibration curve, fluorescence intensity values are converted into mean receptor density values and, using the surface area, into absolute amounts of receptors per cell, and a frequency distribution for this is created in the cell population. Optionally, additional markers for cancer stem cells can be used after the receptor labeling in the wells. Cancer cell subpopulations can then be analyzed separately and displayed separately from the total population (hereinafter referred to as measurement data B).

The results of the main analysis (hereinafter referred to as measurement data A) are processed together with information on the total number of tumor cells (calculated from the size of the tumor and the size frequency distribution of the individual cells) and the patient's weight, as well as the 3D dose-effect curve.

The information in the measurement data A, with or without measurement data B information, can be converted into a recommendation for the personalized therapy of each individual patient. It is to be expected from this recommendation that it minimizes the risk of developing resistance, or that it can predict an already existing, primary resistance with a high degree of probability, and that it also increases the effectiveness of cancer therapy.

The present invention also enables the use of other receptor binding proteins, for example Fab's (antibody fragments), or Darpins (Designed Ankyrin Repeat Proteins, artificial proteins that are capable of recognizing and binding antigens and are structurally derived from ankyrin proteins), or nanobodies (Single domain antibody). Instead of QDs, it is also possible to use other fluorescent dyes or nanoparticles.

CREDENTIALS

1. 1. Lavaud P, Andre F: Strategies to overcome trastuzumab resistance in HER2-overexpressing breast cancers: focus on new data from clinical trials. BMC Medicine 2014, 12 (1): 132 .
2. 2. Lubner SJ, Uboha NV, Deming DA: Primary and acquired resistance to biological therapies in gastrointestinal cancers. Journal of Gastrointestinal Oncology 2017, 8 (3): 499-512 .
3. 3. Janiszewska M, Liu L, Almendro V, Kuang Y, Paweletz C, Sakr RA, Weigelt B, Hanker AB, Chandarlapaty S, King TA et al: In situ single-cell analysis identifies heterogeneity for PIK3CA mutation and HER2 amplification in HER2-positive breast cancer. Nature Genet 2015, 47 (10): 1212- + .
4. 4th Wu DJ, Wang DC, Cheng YF, Qian MJ, Zhang MM, Shen Q, Wang XD: Roles of tumor heterogeneity in the development of drug resistance: A call for precision therapy. Semin Cancer Biol 2017, 42: 13-19 .
5. 5. Riedl T, Boxtel Ev, Bosch M, Parren PWHI, Gerritsen AF: High-Throughput Screening for Internalizing Antibodies by Homogeneous Fluorescence Imaging of a pH-Activated Probe. Journal of Biomolecular Screening 2016, 21 (1): 12-23 .
6. 6th Reynolds AR: Potential relevance of bell-shaped and u-shaped dose-responses for the therapeutic targeting of angiogenesis in cancer. Dose Response 2010, 8 (3): 253-284 .
7. 7th Qaiser T, Mukherjee A, Reddy PBC, Munugoti SD, Tallam V, Pitkaaho T, Lehtimaki T, Naughton T, Berseth M, Pedraza A et al: HER2 challenge contest: a detailed assessment of automated HER2 scoring algorithms in whole slide images of breast cancer tissues. Histopathology 2018, 72 (2): 227-238 .
8. 8th. Breslin S, O'Driscoll L: The relevance of using 3D cell cultures, in addition to 2D monolayer cultures, when evaluating breast cancer drug sensitivity and resistance. Oncotarget 2016, 7 (29): 45745-45756 .
9. 9. Thurber GM, Schmidt MM, Wittrup KD: Antibody tumor penetration: transport opposed by systemic and antigen-mediated clearance. Adv Drug Deliv Rev 2008, 60 (12): 1421-1434 .
10. 10. Romond EH, Perez EA, Bryant J, Suman VJ, Geyer CE, Davidson NE, Tan-Chiu E, Martino S, Paik S, Kaufman PA et al: Trastuzumab plus Adjuvant Chemotherapy for Operable HER2-Positive Breast Cancer. New England Journal of Medicine 2005, 353 (16): 1673-1684 .
11. 11th Cameron D, Piccart-Gebhart MJ, Gelber RD, Procter M, Goldhirsch A, de Azambuja E, Castro G, Untch M, Smith I, Gianni L et al: 11 years' follow-up of trastuzumab after adjuvant chemotherapy in HER2-positive early breast cancer: final analysis of the HERceptin Adjuvant (HERA) trial. The Lancet 2017, 389 (10075): 1195-1205 .
12. 12th (IQWiG) IfQuWiG: [A18-41] Pertuzumab (breast cancer) - Benefit assessment according to §35a Social Code Book V. In. https://www.iqwig.de/en/projects-results/projects/drug-assessment/a18-41-pertuzumab-breast-cancer-benefit-assessment-according-to-35a-social-code-book-v. 10102.html: Institute for Quality and Efficiency in Health Care (IQWiG) 2018 .
13. 13th Von Minckwitz G, Procter M, De Azambuja E, Zardavas D, Benyunes M, Viale G, Suter T, Arahmani A, Rouchet N, Clark E: Adjuvant pertuzumab and trastuzumab in early HER2-positive breast cancer. New England Journal of Medicine 2017, 377 (2): 122-131 .
14. 14th Schmid S, Klingbiel D, Aebi S, Goldhirsch A, Mamot C, Munzone E, Nole F, Oehlschlegel C, Pagani O, Pestalozzi B et al: Long-term responders to trastuzumab monotherapy in first-line HER-2 + advanced breast cancer : characteristics and survival data. BMC Cancer 2019, 19 (1): 902-902 .
15. 15th Dall P, Koch T, Göhler T, Selbach J, Ammon A, Eggert J, Gazawi N, Rezek D, Wischnik A, Hielscher C et al: Trastuzumab without chemotherapy in the adjuvant treatment of breast cancer: subgroup results from a large observational study . BMC Cancer 2018, 18 (1): 51 .
16. 16. Mocanu MM, Váradi T, Szöllösi J, Nagy P: Comparative analysis of fluorescence resonance energy transfer (FRET) and proximity ligation assay (PLA). PROTEOMICS 2011, 11 (10): 2063-2070 .
17. 17th Szendi-Szatmári T, Szabo A, Szollosi J, Nagy P: Reducing the Detrimental Effects of Saturation Phenomena in FRET Microscopy. Analytical Chemistry 2019, 91 .
18. 18th Bondza S, Björkelund H, Nestor M, Andersson K, Buijs J: Novel Real-Time Proximity Assay for Characterizing Multiple Receptor Interactions on Living Cells. Analytical Chemistry 2017, 89 (24): 13212-13218 .
19. 19th Swiecicki JM, Thiebaut F, Di Pisa M, Gourdin -Bertin S, Tailhades J, Mansuy C, Burlina F, Chwetzoff S, Trugnan G, Chassaing G et al: How to unveil self-quenched fluorophores and subsequently map the subcellular distribution of exogenous peptides. Scientific Reports 2016, 6: 20237 .
20. 20th Sapino A, Goia M, Recupero D, Marchio C: Current Challenges for HER2 Testing in Diagnostic Pathology: State of the Art and Controversial Issues. Frontiers in Oncology 2013, 3 (129) .
21. 21. McCabe A, Dolled-Filhart M, Camp RL, Rimm DL: Automated Quantitative Analysis (AQUA) of In Situ Protein Expression, Antibody Concentration, and Prognosis. JNCI: Journal of the National Cancer Institute 2005, 97 (24): 1808-1815 .
22. 22nd Kashyap A, Khartchenko AF, Pati P, Gabrani M, Schraml P, Kaigala GV: Quantitative microimmunohistochemistry for the grading of immunostains on tumor tissues. Nature biomedical engineering 2019, 3 (6): 478-490 .
23. 23 Korkaya H, Paulson A, lovino F, Wicha MS: HER2 regulates the mammary stem / progenitor cell population driving tumorigenesis and invasion. Oncogene 2008, 27 (47): 6120-6130 .
24. 24 Rodriguez CE, Berardi DE, Abrigo M, Todaro LB, Joffe E, Fiszman GL: Breast cancer stem cells are involved in trastuzumab resistance through the HER2 modulation in 3D culture. J Cell Biochem 2018, 119 (2): 1381-1391 .
25. 25th Thick V: tumor volumetry. In: RöFo advances in the field of X-rays and imaging methods: 2006. RK_208_202 .
26. 26th Brugnoli F, Grassilli S, Al-Qassab Y, Capitani S, Bertagnolo V: CD133 in Breast Cancer Cells: More than a Stem Cell Marker. Journal of oncology 2019, 2019 .
27. 27 Ricardo S, Vieira AF, Gerhard R, Leitão D, Pinto R, Cameselle-Teijeiro JF, Milanezi F, Schmitt F, Paredes J: Breast cancer stem cell markers CD44, CD24 and ALDH1: expression distribution within intrinsic molecular subtype. Journal of clinical pathology 2011, 64 (11): 937-946 .

QUOTES INCLUDED IN THE DESCRIPTION

This list of the documents listed by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Non-patent literature cited

• Lavaud P, Andre F: Strategies to overcome trastuzumab resistance in HER2-overexpressing breast cancers: focus on new data from clinical trials. BMC Medicine 2014, 12 (1): 132 [0079]
• Lubner SJ, Uboha NV, Deming DA: Primary and acquired resistance to biological therapies in gastrointestinal cancers. Journal of Gastrointestinal Oncology 2017, 8 (3): 499-512 [0079]
• Janiszewska M, Liu L, Almendro V, Kuang Y, Paweletz C, Sakr RA, Weigelt B, Hanker AB, Chandarlapaty S, King TA et al: In situ single-cell analysis identifies heterogeneity for PIK3CA mutation and HER2 amplification in HER2-positive breast cancer. Nature Genet 2015, 47 (10): 1212- + [0079]
• Wu DJ, Wang DC, Cheng YF, Qian MJ, Zhang MM, Shen Q, Wang XD: Roles of tumor heterogeneity in the development of drug resistance: A call for precision therapy. Semin Cancer Biol 2017, 42: 13-19 [0079]
• Riedl T, Boxtel Ev, Bosch M, Parren PWHI, Gerritsen AF: High-Throughput Screening for Internalizing Antibodies by Homogeneous Fluorescence Imaging of a pH-Activated Probe. Journal of Biomolecular Screening 2016, 21 (1): 12-23 [0079]
• Reynolds AR: Potential relevance of bell-shaped and u-shaped dose-responses for the therapeutic targeting of angiogenesis in cancer. Dose Response 2010, 8 (3): 253-284 [0079]
• Qaiser T, Mukherjee A, Reddy PBC, Munugoti SD, Tallam V, Pitkaaho T, Lehtimaki T, Naughton T, Berseth M, Pedraza A et al: HER2 challenge contest: a detailed assessment of automated HER2 scoring algorithms in whole slide images of breast cancer tissues. Histopathology 2018, 72 (2): 227-238 [0079]
• Breslin S, O'Driscoll L: The relevance of using 3D cell cultures, in addition to 2D monolayer cultures, when evaluating breast cancer drug sensitivity and resistance. Oncotarget 2016, 7 (29): 45745-45756 [0079]
• Thurber GM, Schmidt MM, Wittrup KD: Antibody tumor penetration: transport opposed by systemic and antigen-mediated clearance. Adv Drug Deliv Rev 2008, 60 (12): 1421-1434 [0079]
• Romond EH, Perez EA, Bryant J, Suman VJ, Geyer CE, Davidson NE, Tan-Chiu E, Martino S, Paik S, Kaufman PA et al: Trastuzumab plus Adjuvant Chemotherapy for Operable HER2-Positive Breast Cancer. New England Journal of Medicine 2005, 353 (16): 1673-1684 [0079]
• Cameron D, Piccart-Gebhart MJ, Gelber RD, Procter M, Goldhirsch A, de Azambuja E, Castro G, Untch M, Smith I, Gianni L et al: 11 years' follow-up of trastuzumab after adjuvant chemotherapy in HER2-positive early breast cancer: final analysis of the HERceptin Adjuvant (HERA) trial. The Lancet 2017, 389 (10075): 1195-1205 [0079]
• (IQWiG) IfQuWiG: [A18-41] Pertuzumab (breast cancer) - Benefit assessment according to §35a Social Code Book V. In. https://www.iqwig.de/en/projects-results/projects/drug-assessment/a18-41-pertuzumab-breast-cancer-benefit-assessment-according-to-35a-social-code-book-v. 10102.html: Institute for Quality and Efficiency in Health Care (IQWiG) 2018 [0079]
• Von Minckwitz G, Procter M, De Azambuja E, Zardavas D, Benyunes M, Viale G, Suter T, Arahmani A, Rouchet N, Clark E: Adjuvant pertuzumab and trastuzumab in early HER2-positive breast cancer. New England Journal of Medicine 2017, 377 (2): 122-131 [0079]
• Schmid S, Klingbiel D, Aebi S, Goldhirsch A, Mamot C, Munzone E, Nole F, Oehlschlegel C, Pagani O, Pestalozzi B et al: Long-term responders to trastuzumab monotherapy in first-line HER-2 + advanced breast cancer : characteristics and survival data. BMC Cancer 2019, 19 (1): 902-902 [0079]
• Dall P, Koch T, Göhler T, Selbach J, Ammon A, Eggert J, Gazawi N, Rezek D, Wischnik A, Hielscher C et al: Trastuzumab without chemotherapy in the adjuvant treatment of breast cancer: subgroup results from a large observational study . BMC Cancer 2018, 18 (1): 51 [0079]
• Mocanu M-M, Váradi T, Szöllösi J, Nagy P: Comparative analysis of fluorescence resonance energy transfer (FRET) and proximity ligation assay (PLA). PROTEOMICS 2011, 11 (10): 2063-2070 [0079]
• Szendi-Szatmári T, Szabo A, Szollosi J, Nagy P: Reducing the Detrimental Effects of Saturation Phenomena in FRET Microscopy. Analytical Chemistry 2019, 91 [0079]
• Bondza S, Björkelund H, Nestor M, Andersson K, Buijs J: Novel Real-Time Proximity Assay for Characterizing Multiple Receptor Interactions on Living Cells. Analytical Chemistry 2017, 89 (24): 13212-13218 [0079]
• Swiecicki JM, Thiebaut F, Di Pisa M, Gourdin -Bertin S, Tailhades J, Mansuy C, Burlina F, Chwetzoff S, Trugnan G, Chassaing G et al: How to unveil self-quenched fluorophores and subsequently map the subcellular distribution of exogenous peptides. Scientific Reports 2016, 6: 20237 [0079]
• Sapino A, Goia M, Recupero D, Marchio C: Current Challenges for HER2 Testing in Diagnostic Pathology: State of the Art and Controversial Issues. Frontiers in Oncology 2013, 3 (129) [0079]
• McCabe A, Dolled-Filhart M, Camp RL, Rimm DL: Automated Quantitative Analysis (AQUA) of In Situ Protein Expression, Antibody Concentration, and Prognosis. JNCI: Journal of the National Cancer Institute 2005, 97 (24): 1808-1815 [0079]
• Kashyap A, Khartchenko AF, Pati P, Gabrani M, Schraml P, Kaigala GV: Quantitative microimmunohistochemistry for the grading of immunostains on tumor tissues. Nature biomedical engineering 2019, 3 (6): 478-490 [0079]
• Korkaya H, Paulson A, lovino F, Wicha MS: HER2 regulates the mammary stem / progenitor cell population driving tumorigenesis and invasion. Oncogene 2008, 27 (47): 6120-6130 [0079]
• Rodriguez CE, Berardi DE, Abrigo M, Todaro LB, Joffe E, Fiszman GL: Breast cancer stem cells are involved in trastuzumab resistance through the HER2 modulation in 3D culture. J Cell Biochem 2018, 119 (2): 1381-1391 [0079]
• Thick V: tumor volumetry. In: RöFo advances in the field of X-rays and imaging methods: 2006. RK_208_202 [0079]
• Brugnoli F, Grassilli S, Al-Qassab Y, Capitani S, Bertagnolo V: CD133 in Breast Cancer Cells: More than a Stem Cell Marker. Journal of oncology 2019, 2019 [0079]
• Ricardo S, Vieira AF, Gerhard R, Leitão D, Pinto R, Cameselle-Teijeiro JF, Milanezi F, Schmitt F, Paredes J: Breast cancer stem cell markers CD44, CD24 and ALDH1: expression distribution within intrinsic molecular subtype. Journal of clinical pathology 2011, 64 (11): 937-946 [0079]

Claims (10)

A method for determining a cancer therapy based on at least one antibody drug comprising: a) determining at least one parameter selected from the absolute number of at least one receptor per cell, the mean receptor density of at least one receptor per cell, the cumulative frequency of mean receptor densities of at least one receptor per cell, the average receptor density of at least one receptor of cells of a subject and cumulative or statistical distributions thereof, b) determining the at least one antibody drug and / or a personalized dose of the at least one antibody drug for the subject based on the parameter (s) determined in a). Procedure according to Claim 1 , wherein in a) the average receptor density of at least one receptor in a representative number of cancer cells of the subject is determined. Procedure according to Claim 1 or 2 , wherein in a) the statistical distribution of the at least one parameter is determined as a standard deviation or a deviation from a normal distribution. Method according to any one of the preceding claims, wherein in step a) the mean receptor density per cell and / or the relevant statistical distribution of the HER2 receptor is determined. The method according to any one of the preceding claims, wherein in step b) the at least one antibody drug is selected from trastuzumab, trastuzumab emtansine, pertuzumab and a combination thereof. Method according to any one of the preceding claims, wherein in step a) the mean receptor density per cell and / or the relevant statistical distribution of the HER2 receptor, the HER1 receptor and / or the HER3 receptor are determined. The method according to any one of the preceding claims, wherein in step b) the personalized dose of the at least one antibody drug is calculated on the basis of a three-dimensional dose-response curve, preferably wherein the dose-response curve the strength of the effect of the at least an antibody drug is determined as a function of the mean receptor density of the at least one receptor per cell, the relevant statistical distribution and the concentration of the at least one antibody drug. Method according to any one of the preceding claims, wherein in b) a prediction of the occurrence or existence of at least one drug resistance is additionally determined. System for determining a cancer therapy based on at least one antibody drug comprising: a) Device for determining at least one parameter selected from the absolute number of at least one receptor per cell, the mean receptor density of at least one receptor per cell, the cumulative frequency of mean receptor densities of at least one receptor per cell, the average receptor density of at least one receptor of cells of a subject and cumulative or statistical distributions thereof, b) Device for determining the at least one antibody drug and / or a personalized dose of the at least one antibody drug for the subject based on the parameter (s) determined in a). System according to Claim 9 , wherein the device according to a) comprises a sample preparation device and a device for fluorescence microscopy.

Images