Drug screening using a responseomic profile generated from samples of study subjects

Abstract

The present invention relates to a method for identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject using patient-derived samples in response to exposure to drugs or combinations of drugs, comprising the determination of a response profile. The present invention further relates to high-throughput screening in patient-derived samples with minimal material requirements and also includes machine learning-based analysis.

Description

[0001] The present invention relates to a method for identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a patient using patient-derived samples in response to exposure to drugs or combinations of drugs, comprising the determination of a response profile. The present invention further relates to high-throughput screening in patient-derived samples with minimal material requirements and also includes machine learning-based analysis. Background of the invention

[0002] Antiproliferative therapies, such as cancer therapies, have so far shown only limited efficacy and in a small group of patients, despite major efforts to characterize patients biologically and genomically in order to find biomarkers for response.

[0003] Predicting a patient's individual response to a specific treatment is a crucial step in determining the optimal therapy, particularly in personalized medicine. To ensure the most accurate possible prediction of treatment response throughout the entire process of drug or drug combination discovery, preclinical and clinical testing, and use in proliferative diseases, this prediction should ideally be performed in a system that closely mimics the in vivo environment of the disease in a given patient.

[0004] A promising approach to improving this situation is to supplement extensive genomic profiling under normal conditions with measurements taken after the cancer cells have been affected by drugs. However, this approach is limited by the number of assays required for a meaningful analysis, which becomes even more apparent when considering drug combinations due to the sheer number of possible combinations, increasing exponentially with the number of drugs tested.

[0005] Another crucial factor is time, as the success rate in treating a proliferating disease decreases drastically over time. The development of drug resistance can also pose problems for effective treatment.

[0006] Due to limited screening capacities, computer-based approaches have been developed to model drug interactions. While models of drug effects have improved in recent years due to the increase in available data resources, predicting drug response remains challenging and is limited to well-characterized systems such as cell lines, thus restricting their clinical applicability. Among the various data types, the state of cell gene expression has proven to be a strong predictor of drug response.

[0007] 3D microtissues, particularly 3D microtumors, are microphysiological systems that can be used to model normal and pathological human organs in vitro and are of growing interest because they capture important structural and physiological features of the target tissue. Ideally, this technique uses human cells and / or tissues, such as tumor biopsies, along with other cell types, such as immune cells, to form a cell aggregate that grows in an extracellular 3D matrix and depends on nutrient supply from living, vascularized microvessels for its survival.

[0008] Since 3D microtissues can be grown from patient-derived materials, they have the advantage of enabling personalized treatment prediction based on individually curated data.

[0009] Guilliams M et al. (in: Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell. 2022 Jan 20;185(2):379-396.e38. doi: 10.1016 / j.cell.2021.12.018. Epub 2022 Jan 11. PMID: 35021063; PMCID: PMC8809252) reveal a spatial proteogenome atlas of the healthy and obese human and mouse liver, combining single-cell CITE-seq, single-nucleus sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omics datasets, they offer strategies for the reliable differentiation and localization of all liver cells, including a population of lipid-associated macrophages (LAMs) in the bile ducts. They describe the respective spatially resolved cellular niches of the macrophages and the microenvironmental circuits that drive their unique transcriptomic identities.

[0010] WO 2021 / 110799A2 discloses a method and apparatus for analyzing such 3D microtumors for the systematic screening of a potential drug or drug combination. However, the method and apparatus disclosed in this application for determining the efficacy of the tested drugs rely exclusively on parameters relating to the survival or growth of the 3D microtumors, such as size, internal expression of reporter genes, and the release of biomarkers (e.g., LDH release), which may not fully capture the complexity of the response to treatment. Since the outcome of a treatment is undoubtedly the result of a much more complex interplay of many biological factors, a more holistic approach that considers the totality of the test subject's conditions is needed for a faster and more effective analysis of the treatment outcome.

[0011] Especially in the field of cancer treatment, the need for a method that considers the holistic situation of a particular patient is even more urgent due to the multifactorial nature of the disease. Although our understanding of the molecular basis of cancer has significantly contributed to the development of numerous targeted therapies, determining which therapy might be suitable for a specific patient is not trivial, as a single therapy is often only effective in a small subset of patients. In practice, therefore, treatment selection is frequently based on trial and error, guided by the experience of individual physicians.

[0012] One example is the treatment of lung cancer patients with PD-1 / PD-L1 axis blockers, which is effective in only a fraction of patients, without a clear understanding of the specific mechanisms by which lung cancer cells evade treatment in each individual patient. This problem is partly due to the lack of high-throughput, patient-specific therapeutic screening methods, which limits the number of drugs that can be investigated as well as the types of physiological indicators that can be measured in patient samples.

[0013] Mathur et al. (in “Combi-seq for multiplexed transcriptome-based profiling of drug combinations using deterministic barcoding in single-cell droplets”, Nature Communications 13.1 (2022): 4450) propose a solution using a microfluidic-based system capable of detecting transcriptome changes in cancer cells under hundreds of different test conditions. However, this approach is limited to testing a maximum of two drug combinations and is designed solely for transcriptome monitoring. Furthermore, this system cannot directly measure indicators of tumor cell survival, i.e., the efficacy of the drug or drug combination, but can only infer them indirectly from the transcriptome profile.

[0014] The object of the present invention is therefore to provide an improved method for identifying drugs and drug combinations for the rapid and effective treatment of proliferative diseases based on cells and / or tumor tissue obtained from biopsies or resections of subjects, in order to assess a more complete picture of the effect(s) of a drug or drug combination on the cell and / or tumor tissue sample and thus to determine and provide the optimal and fastest therapy for the patient or a group of patients. Other objects and advantages will be readily apparent to the person skilled in the art from the following detailed description and examples.

[0015] In a first aspect of the present invention, the problem of the present invention is solved by providing a method for identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a patient, comprising the following steps: a) providing an array of i) primary tissue samples of a proliferative disease and / or ii) 3D microtissues derived from a proliferative disease, in particular 3D microtumors or organoids, from a patient who has or has been diagnosed with a proliferative disease, in a multi-well format in a suitable culture medium, wherein the multi-well format comprises a plurality of wells; b) contacting the 3D microtissues,in particular the 3D microtumors or organoids of the array with a predefined number of drugs and / or combinations of drugs and predefined doses thereof for a predefined period or predefined different periods, c) determining a response profile for the 3D microtissues, in particular for the 3D microtumors or organoids of the array and the tested drugs and / or combinations of drugs and their predefined doses and times, based on i) a first step of a non-disruptive kinetic-based functional analysis of the 3D microtissues, in particular the 3D microtumors or organoids of the array, wherein the analysis includes at least one analysis selected from the group consisting of the absolute number of cells, the proliferation rate, the density or the compactness of the cells,the degree of cell resolution, morphological changes of the cells, cell size, cellular composition of the cells, tissues, tumors or organoids and metabolic rate, and ii) a second step of a molecular-level parameter-based analysis of the 3D microtissues, in particular the 3D microtumors or organoids of the array, wherein the analysis includes at least one analysis selected from the group consisting of transcriptome, proteome, genome, metabolome, gene mutation and selected biomarker-level analyses, wherein i) and ii) are performed on the same 3D microtissue, in particular the 3D microtumor or organoid of the array, and d) identification of a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject based on the responseome profile determined in step c).

[0016] The method according to the present invention is preferred, wherein the determination of the response profile further comprises at least one analysis selected from the group consisting of i) for non-disruptive kinetic-based functional analysis, cell count, 3D microtumor or organoid size determination, 3D microtumor or organoid size determination, 3D microtumor or organoid LDH release determination, 3D microtumor or organoid size determination, intracellular calcium level, physiological parameters directly indicating the absolute cell count, proliferation rate, and metabolic rate, wherein the 3D microtumor or organoid size determination preferably comprises at least one parameter selected from the diameter, circumference, volume, and area of ​​the optical cross-section, and wherein the 3D microtumor or organoid size determination preferably comprises the use of an imaging device.and, where appropriate, further includes the analysis of growth kinetics, and ii) for the analysis of parameters at the molecular level: gene expression level, protein level, metabolite level / amount, histochemistry, preselected biomarker levels, such as PD-L1 expression level, tumor mutational burden (TMB), microsatellite instability (MSI) and mismatch repair deficiency (dMMR).

[0017] The preferred method according to the invention includes the determination of the responseom profile as well as the inclusion of data on the patient's treatment outcome (see also below).

[0018] A further preferred method according to the invention is in which, prior to contacting in step b), the primary proliferative disease samples or the 3D microtissues derived from the proliferative disease, in particular 3D microtumors or organoids, comprise cells that reflect the composition of the main cell types of the native tumor tissue, such as connective tissue cells, CD4+ T lymphocytes, epithelial cancer cells, macrophages, natural killer cells, B cells and CD8+ T lymphocytes.

[0019] A further preferred method is that according to the present invention, which further comprises at least one cellular and / or tissue-specific response analysis based on the molecular-level parameter-based analysis in step c) and the identification of at least one functional relationship between genes and / or proteins of a signaling pathway and the neoplastic disease or tumor and / or the drug or combination of drugs, preferably also the step of identifying genes and / or proteins of the signaling pathway as drug targets. The present method therefore not only serves to identify personalized, effective treatment options; the Responsome is also a tool to gain further insights into why an antiproliferative therapy, which should actually be effective, is ineffective in certain patient populations.The Responsome will help to gain more knowledge about patient- and drug-specific responses in order to make therapies more effective and to treat larger patient populations with the same effective therapy.

[0020] In a second aspect of the present invention, the above problem is solved by providing a method for identifying adverse effects associated with treatment with a patient-specific drug or a combination of drugs in a patient, comprising carrying out the method according to the present invention, which further comprises the step of testing and analyzing the said patient-specific drug or combination of drugs for adverse effects in the said patient.

[0021] In a third aspect of the present invention, the above problem is solved by providing a method for stratifying a patient with respect to treatment with a patient-specific drug or combination of drugs, comprising carrying out the method according to the present invention, which includes identifying at least one subject-specific drug or combination of drugs based on the response profile and optionally further based on the molecular pathway, as determined, and stratifying the subject based on the identified subject-specific drug or combination of drugs.

[0022] In a fourth aspect of the present invention, the above problem is solved by providing a method for monitoring treatment with a subject-specific drug or a combination of drugs, comprising carrying out the method according to the present invention, treating the patient with the at least one patient-specific drug or combination of drugs as identified, and repeating the method according to the present invention on a sample of the subject following treatment and optionally adjusting the treatment based on the result(s) of the repetition.

[0023] In a fifth aspect of the present invention, the above problem is solved by providing a computer program for carrying out the method according to the invention on a suitable computer. Preferably, the computer program according to the present invention comprises a deep learning algorithm, for example, a deep neural network algorithm.

[0024] In a sixth aspect of the present invention, the above problem is solved by providing a drug or a combination of drugs for use in the prevention or treatment of a proliferative disease, wherein the treatment comprises carrying out the method according to the present invention, preferably further comprising the step of providing a subject-specific and / or personalized prevention or treatment.

[0025] In a seventh aspect of the present invention, the above problem is solved by providing a test system comprising means for carrying out the method according to the invention, comprising a) a unit for cultivating an array of 3D microtissues, such as microtumors or organoids, based on dissociated cells of a tissue sample obtained from the provided subject, b) a drug testing unit for contacting the array of 3D microtissues with at least one drug to be tested or a combination of drugs, c) a unit for determining a reaction profile for the 3D microtissues, in particular for the 3D microtumors or organoids of the array and the tested drugs and / or combinations of drugs and their predefined doses and times, comprising i) a first analysis unit for a non-disruptive kinetic-based functional analysis of the 3D microtissues,in particular the 3D microtumors or organoids of the array, ii) a second unit of analysis for determining a molecular parameter-based analysis of the 3D microtissues, in particular the 3D microtumors or organoids of the array, and d) a unit for identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject based on the determined responseomic profile.

[0026] Preferably, the test system according to the invention further comprises at least one database for collecting and / or storing data on the determined response profile and optionally additional data regarding the preselection and / or grouping of the drug combination, physiological parameters, data regarding the effect(s) of the drugs or drug combinations, data relating to the cellular and / or tissue-specific response analysis, data relating to adverse effects, data relating to additional clinical parameters, such as patient-specific data and / or treatment history, data relating to stratification and / or monitoring, and data for the automation of the system, for example, for controlling at least one robot.

[0027] In an eighth aspect of the present invention, the above problem is solved by using the test system or computer program according to the invention to identify at least one subject-specific drug or combination of drugs and / or to perform at least one cellular and / or tissue-specific reaction analysis based on the parameter-based analysis at the molecular level in step c) and to identify at least one functional relationship between genes and / or proteins of a signaling pathway and the neoplastic disease or tumor and / or the drug or combination of drugs according to the method according to the invention.

[0028] As mentioned above, the present invention, in a first aspect, provides a method for identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject, comprising the following steps: a) providing an array of i) primary tissue samples of a proliferative disease and / or ii) 3D microtissues derived from a proliferative disease, in particular 3D microtumors or organoids, from a subject who has or has been diagnosed with a proliferative disease, in a multiwell format in a suitable culture medium, wherein the multiwell format comprises between 5 wells and a number of wells corresponding to the drugs and / or combinations of drugs to be tested and their dosages plus controls; b) contacting the 3D microtissues;in particular, 3D microtumors or organoids of the array are brought into contact with a predefined number of drugs and / or combinations of drugs and predefined doses thereof for a predefined period or predefined different periods of time; c) Determining a reaction profile for the 3D microtissues, in particular for the 3D microtumors or organoids of the array, and the drugs and / or combinations of drugs and predefined doses (e.g., physiologically relevant doses derived from physiological doses). e.g., physiologically relevant doses derived from human C, max-value of a drug (see also below)). and times thereof, as tested, based on i) a first step of a non-disruptive kinetic-based functional analysis of the 3D microtissues, in particular the 3D microtumors or organoids of the array, wherein the analysis includes at least one analysis selected from the group consisting of absolute cell count, proliferation rate, growth kinetics, density or compactness of cells, degree of cell resolution, morphological changes of cells, size of cells, cellular composition of cells, tissues, tumors or organoids and metabolic rate, and ii) a second step of a molecular-level parameter-based analysis of the 3D microtissues, in particular the 3D microtumors or organoids of the array, wherein the analysis includes at least one analysis selected from the group consisting of transcriptome, proteome, genome, metabolome,consists of gene mutation and selected biomarker analyses, wherein i) and ii) are performed on the same 3D microtissue, in particular the 3D microtumor or organoid of the array, and d) identification of a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject based on the responseomic profile determined in step c).

[0029] The present invention relates to a novel approach for identifying a specific and effective, in particular personalized, drug or drug combination against proliferative diseases in a subject. To maximize the information content of subject-specific samples, especially tumor samples, a method has been developed that makes it possible to rapidly obtain detailed information about the effects and efficacy of a drug and / or drug combination on the tested samples and, in a preferred embodiment, about the underlying effects on specific cellular signaling pathways, using one and the same patient sample.The present invention relates to methods and devices for analyzing sets of physiological parameters from samples obtained from test subjects in response to the action of drugs or combinations of drugs, in the form of a response profile, for determining optimal personalized treatment, for identifying cellular signaling pathways involved in drug action, and finally for identifying new and promising drug targets. A particular advantage of the method according to the invention is therefore the ability to identify patient- and drug-specific changes in intercellular signaling pathways depending on the drug action.

[0030] The present invention enables rapid high-throughput screening of the effects of drugs on microtissue or microtumor samples obtained from human subjects with minimal material requirements and therefore ultimately enables rapid analysis based on a more complete and complex drug profile and incorporating additional information, preferably including machine learning-based analysis.

[0031] As an example of a preferred embodiment, lung cancer tissue was resectioned from a patient, dissociated to generate a single-cell solution, and distributed with a defined cell count in a non-adherent U-bottom multiwell plate to enable gravity-enhanced tumor tissue formation. After a 5-day microtumor maturation period, the microtumors were treated either with immune checkpoint inhibitors as monotherapy or in combination with chemotherapy, and the response was observed for 14 days. The drug response was analyzed by image-based (non-disruptive kinetic-based functional analysis) measurement of growth kinetics; subsequently, RNA isolation of the same sample(s) was used to analyze transcriptomic changes (parameter-based analysis at the molecular level).

[0032] While screening platforms already exist in plates for bulk and single-cell transcriptomics, these require a large number of cells per tested condition and have therefore not been used for screening drug combinations. Furthermore, many of these assays rely exclusively on cell lines that no longer resemble the actual tumor- and patient-specific situation and therefore provide only a limited picture of the actual patient and situation.

[0033] Therefore, the integration of, for example, transcriptomic measurements into a miniaturized combinatorial drug screening platform, which has the potential to screen, for example, tumor biopsies, will enable more relevant predictions and improve the understanding of the mechanisms of synergistic and antagonistic drug-drug interactions. Furthermore, the ability to gain an overview of a considerable number of potential therapies when many available or newly identified drugs or combinations thereof are used in the present method according to the invention will lead to a "catalog" of new and more effective therapeutic approaches.

[0034] Within the scope of the present invention, a “responsome profile” is determined for the 3D microtissues, in particular for the 3D microtumors or organoids of the array, and the tested drugs and / or drug combinations, along with their predefined doses and durations. The “profile” refers to the determination of changes in selected response markers and / or parameters in response to the tested drugs or drug combinations.

[0035] In the context of the present invention, the term "responsome" comprises a set or collection of selected biological, morphological, and / or physiological markers and / or parameters, as described herein, which are tested for changes in response to contact with the drug substance or combination of drugs. This set comprises at least one (first) step of a non-disruptive kinetic-based functional analysis of the 3D microtissues, in particular the 3D microtumors or organoids of a provided array, and (ii) a (second) step of a parameter-based analysis of the same 3D microtissues, in particular the 3D microtumors or organoids of a provided array.The analysis of responsome may, for example, include at least one analysis selected from the group consisting of the absolute number of cells, the proliferation rate, the growth kinetics, the density or compactness of the cells, the degree of cell resolution, the morphological changes of the cells, the size of the cells, the cellular composition of the cells, tissues, tumors or organoids, and the metabolic rate as non-disruptive (i.e., essentially cellular, i.e., essentially preserving cellular integrity), the metabolome, the proteome, genetic mutations and selected biomarker and / or expression analyses, the transcriptome and / or the genome (i.e., parameters at the molecular level) in the 3D microtissues, especially the 3D microtumors or organoids, or a combination thereof.

[0036] Kovács et al. (in: Transcriptomic datasets of cancer patients treated with immune-checkpoint inhibitors: a systematic review. Journal of Translational Medicine 20.1 (2022): 1-15) describe a transcriptomic analysis, a sub-aspect of the present responseomic analysis, of cancer patients treated with immune checkpoint inhibitors to develop guidelines for the use of this type of treatment. Furthermore, Baia et al. (in: Mining the cancer immuno-responsome: The identification of functional antitumor antibodies from patients receiving checkpoint inhibitors." Cancer Research 78.13 Supplement (2018): 3966-3966) identified an antibody isolated from a patient with non-progressing metastatic cancer treated with checkpoint immunotherapy as a potential therapeutic agent by analyzing a responsome profile of syngeneic mouse tumor models. The described responsome analysis is performed either directly in vivo, e.g.,in human patients or in animal models, or in vitro, e.g., in artificial cell lines, and therefore does not offer a possibility for high-throughput drug screening to make personalized predictions about treatment success. Furthermore, the 3D microtissues, in particular the 3D microtumors or organoids of the array, as used in the invention, resemble in vivo conditions, which cannot be reproduced with cell lines alone.

[0037] In a preferred embodiment of the method according to the invention, the response profile data to be acquired are defined as the collective cohort of all or all essential measurable physiological responses that can be influenced by a particular therapeutic treatment with one or more drugs or combinations thereof.

[0038] According to the present invention, as described above, the response profile to be acquired within the scope of the present invention must comprise two categories of parameters as follows. The first category of non-disruptive kinetic-based functional analysis may include indicators of the survival status of the patient-derived tissue sample; these are typically physical parameters such as cellular composition, tumor and / or cell size determination, growth kinetics, and certain biomarker values ​​such as LDH release, which at least substantially directly reflect the efficacy of the administered drugs or drug combinations.

[0039] The second category of analysis based on parameters at the molecular level can include indicators of molecular-level features that reveal the underlying biological state of the cell and / or tissue sample in question. These include, for example, the transcriptome, the proteome, and the metabolome. Another preferred example is a corresponding assay that incorporates a spatial biology test, as described below.

[0040] The preferred method is according to the present invention, wherein the determination of the response profile further comprises at least one analysis selected from the group consisting of i) for non-disruptive kinetic-based functional analysis: cell count, size determination of the 3D microtumor or organoid, determination of released biomarkers such as lactate dehydrogenase (LDH) in the 3D microtumor or organoid culture and determination of the level and / or expression of preselected biomarkers in the 3D microtumor or organoid, determination of the intracellular calcium level, determination of physiological parameters that correlate with the absolute number of cells, the cellular proliferation rate and the metabolic rate of the cells.wherein preferably the size determination of the 3D microtumor or organoid comprises at least one parameter selected from the diameter, circumference, volume, and area of ​​the optical cross-section, and wherein preferably the size determination of the 3D microtumor or organoid comprises the use of an imaging device and optionally further comprises the analysis of growth kinetics, and ii) for parameter analysis at the molecular level; gene expression level, protein level, metabolite level / amount, histochemistry, preselected biomarker levels, such as PD-L1 expression level, tumor mutational burden (TMB), microsatellite instability (MSI), and mismatch repair deficiency (dMMR).

[0041] Another crucial advantage of the method according to the invention is the rapid acquisition and subsequent analysis of a comprehensive response profile that includes the two aforementioned categories of physiological indicators, in order to ensure that the effect / efficacy of the treatment with the drug or drug combination can be correlated with the corresponding changes in the patient-derived cell and / or tissue sample.

[0042] In a preferred embodiment of the method according to the invention, at least one direct indicator for the survival status of the patient-derived cell and / or tissue sample, in particular the microtumor sample, is determined. Within the scope of the present invention, the survival status preferably refers to the absolute number of cells present in the patient-derived cell and / or tissue sample, the proliferation rate of the patient-derived cells, or the metabolic rate in the patient-derived sample.

[0043] In a preferred embodiment of the method according to the invention, the size determination of the patient-derived samples comprises at least one parameter selected from the diameter, circumference, volume and area of ​​the optical cross-section, and wherein the size determination of the patient-derived samples preferably comprises the use of an imaging device, and optionally further comprises the analysis of the growth kinetics.

[0044] In a preferred embodiment of the method according to the invention, the detection of preselected biomarker levels as indicator(s) of the survival status in the patient-derived tissue and / or cell sample is preferred, wherein at least one of the biomarkers indicating the survival status of the patient-derived sample can be released biomarker levels such as LDH. Various methods for determining these biomarker levels are known to those skilled in the art. For example, the LDH level can be determined using optical methods, such as the luciferase assay, or spectroscopic methods, such as nuclear magnetic resonance (NMR), and the intracellular calcium level can be determined using a fluorescence assay, such as the use of chemical fluorescent probes.

[0045] In a preferred embodiment of the method according to the invention, at least one indicator is determined at the molecular level, reflecting the cellular or molecular state of the cells and / or tissues of the patient-derived sample. For example, the cellular or molecular state can be defined as the cellular and / or molecular composition of a cell and / or tissue of a patient-derived sample at a specific time point. Specific examples include determining the transcriptome, proteome, metabolome, and cellular composition of the patient-derived sample(s), which may involve, for example, the use of various imaging, spectroscopy, staining, and sequencing methods known to those skilled in the art.

[0046] A preferred method according to the invention is one in which the transcriptome in the patient-derived sample(s) can be determined at the molecular level by sequencing and, if preferred, quantifying the total mRNA composition in the cell(s) of the patient-derived sample as an indicator. Examples of sequencing may include the following: 1. Production of an mRNA library, wherein all or a selected number of all mRNAs can be extracted or isolated from the patient-derived sample and subsequently subjected to reverse transcription to generate a corresponding cDNA library. 2. Sequencing of the corresponding cDNA library using a method such as next-generation sequencing, and 3. Processing of the sequencing data. The mRNA sequences in the library can be generated from the cDNA sequences by biochemical principles of reverse transcription, and additional quality control parameters such as the number of reads, the GC content, and the number of read duplicates can be checked to filter out poor-quality data.

[0047] In a further preferred embodiment of the method according to the invention, mRNA sequencing can be used to quantify the amount of a specific mRNA, for example on a single-cell basis, so that mRNAs in each cell or cell type of the 3D microtissue can be sequenced individually, particularly in combination with cell sorting techniques such as fluorescence-activated cell sorting (FACS).

[0048] In a preferred embodiment of the method according to the invention, the proteome in patient-derived samples can be detected by analyzing the protein composition. This analysis can also provide information about the amino acid sequence and, in some cases, about the nature of the post-translational modifications of each protein present in the sample(s). An example of a method for determining the proteome, in particular an example in which mass spectrometry is used for proteome determination, comprises the following steps: extraction of the total protein from the patient-derived samples, cleavage of the protein into peptides using sequence-specific protease, optional enrichment of the peptides containing post-translational modifications with suitable surface chemistry, peptide separation by high-performance liquid chromatography (HPLC), and analysis by a time-of-flight mass spectrometer (TOF).

[0049] In a preferred embodiment of the method according to the invention, the metabolome in patient-derived samples can be determined by analyzing a series of low-molecular-weight chemical substances and / or other organic compounds that are the product(s) of metabolism in the cell(s) and / or the sample. Metabolome analysis can be performed using techniques such as mass spectrometry (MS) and / or nuclear magnetic resonance (NMR) spectroscopy.

[0050] In a preferred embodiment of the method according to the invention, the cellular composition in the patient-derived tissue samples can be determined by analyzing the cell types in the patient-derived tissue sample(s). Exemplary methods for such an analysis include histological staining and cytometry.

[0051] In a preferred embodiment of the method according to the invention, preselected biomarkers can be determined in the patient-derived sample, e.g. as additional indicators at the molecular level, such as PD-L1 expression level, tumor mutational burden (TMB), microsatellite instability (MSI) or mismatch repair deficiency (dMMR).

[0052] Specific examples of responseome analysis according to the present invention include size determination as an indicator of survival status in combination with transcriptome measurement as a molecular-level indicator; size determination as an indicator of survival status in combination with transcriptome and / or genome measurement as a molecular-level indicator; or size determination and LDH release as an indicator of survival status in combination with transcriptome and cell composition measurement as molecular-level indicator(s).

[0053] Within the scope of the present invention, the term “specific” refers to a drug or drug combination that is associated – at least substantially – with a specific response-some profile and / or an effect on at least one proliferative disease.

[0054] Within the scope of the present invention, the term "effective" refers to a drug or drug combination that has a beneficial effect on at least one proliferative disease, i.e., mediates and / or causes an improvement in the clinical phenotype of the disease.

[0055] Within the scope of the present invention, the term "contacting" refers to providing an interaction between a tumor tissue sample, such as a microtumor or organoid, and a drug or drug combination, as tested according to the present invention. The contacting can be performed sequentially or simultaneously, as described herein, and for preselected times, as specified. In some cases, the contacting as described herein is referred to as "treatment" or "therapy."

[0056] In the context of the present invention, the term "medicinal product" may also include a substance that supports, enhances or enables the pharmaceutical effect of one or more other medicinal products and that may itself not have a direct pharmaceutical effect.

[0057] Within the scope of the present invention, the term “personalized” refers to a drug or drug combination that is associated – at least substantially – with a response profile and / or effect on at least one proliferative disease of an individual subject or patient.

[0058] In the context of the present invention, the term "suitable culture medium" refers to any cell culture medium that can be used to cultivate tissue samples of proliferative diseases in order to generate microtissues and microtumors or organoids, as also described below.

[0059] As already mentioned, several physiological parameters can be determined during the generation of the Responsome according to the invention in order to identify improved antiproliferative and preferably patient-specific drugs and / or drug combinations. In the method according to the invention, in which the identification in step d) is based on the Responsome profile determined in step c), a preferred drug or drug combination can be identified if: i) according to the non-disruptive kinetic-based functional analysis, the growth of the 3D microtissue, in particular the 3D microtumor or organoid of the array, is substantially inhibited, as determined by a reduction in tissue size, slower growth kinetics, and / or LDH release; and ii) according to the analysis of the parameters at the molecular level, the tissue does not exhibit resistance to the drug or drug combination.This can be determined, for example, by a mutation in specific genes targeted by the drug or drug combination, a lower expression level of the products of the genes targeted by the drug or drug combination, and / or the absence of drug-resistance-inducing mutations.

[0060] In a preferred aspect, the method according to the present invention comprises at least one spatial biology assay to evaluate the spatial context of properties of cells within the tissue(s), wherein the identification is based at least partially on the data generated in the at least one spatial biology assay.

[0061] Within the scope of the present invention, spatial biological tests for evaluating the spatial context of cells within a specific tissue are an extremely valuable tool for further understanding tumor biology. The value of the data obtained within the framework of the inventive method is greatly enhanced by the addition of spatial-functional information, which provides information about the effects of specific drugs and drug combinations and their impact on spatial cell rearrangements, as well as which cell groups are affected.

[0062] Spatial profiling assays generally characterize tissue organization and three-dimensional architecture. For the methods according to the present invention, preferred examples of spatial biological assays for cells and / or the 3D microtissues, in particular the 3D microtumors or organoids of the array according to the present invention, include two well-established molecular biology techniques, namely immunofluorescence and next-generation sequencing. By combining both technologies, the present method can determine how transcriptional dynamics vary in a spatial context. Spatial information can be obtained at various levels, including the tissue, single-cell, and subcellular levels (see also Method of the Year 2020: spatially resolved transcriptomics. Nat Methods 18, 1 (2021). https: / / doi.org / 10.1038 / s41592-020-01042-x). Other preferred methods can be found, for example, in Guilliams et al.(in: Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell. 2022 Jan 20;185(2):379-396.e38. doi: 10.1016 / j.cell.2021.12.018. Epub 2022 Jan 11. PMID: 35021063; PMCID: PMC8809252). Die räumliche Analyse kann auch computergestützte Methoden umfassen (siehe z. B. Fleck JS, et al. Resolving organoid brain region identities by mapping single-cell genomic data to reference atlases. Cell Stem Cell. 2021 Jun 3;28(6):1148-1159.e8. doi: 10.1016 / j.stem.2021.02.015. Epub 2021 Mar 11, Wahle, P., Brancati, G., Harmel, C. et al. Multimodal spatiotemporal phenotyping of human retinal organoid development. Nat Biotechnol (2023). https: / / doi.org / 10.1038 / s41587-023-01747-2).

[0063] In a further preferred embodiment of the method according to the invention, steps a) to d) can be repeated at least once (or 2, 3, 4 or more times). In this aspect, the method includes combining the drugs as identified in the first round of the method. Through these selection rounds, the most effective drug combinations are identified and can be selected for the selective treatment of the patient. It is expected that the method will make it possible to identify new, improved, and even synergistic drug combinations that, for example, allow for a reduction in drug dosage and the avoidance of resistance development.

[0064] Once a drug or combination of drugs has been identified as specific and effective, the method according to the invention may further comprise step e) of selecting a drug or combination of drugs as specific and effective and, in particular, personalized against a proliferative disease in a patient based on the identification in step d), whether in a single-cycle procedure or a repeated procedure as described above.

[0065] In a preferred aspect, the method according to the present invention comprises a selection of the drug or combination of drugs based on the identification as described herein, wherein the selection is based on a combination of response analysis as described above and additionally on the at least one functional relationship between genes and / or proteins of a signaling pathway and the neoplastic disease or tumor and / or the drug or combination of drugs as identified and / or provided by a database.For example, if several drugs and / or drug combinations have been identified as potentially effective, incorporating information about genes and / or proteins of a signaling pathway involved in the drug and / or drug combination's action can be used to select a specific and more effective drug or drug combination for treatment. This can increase efficacy, decrease the likelihood of resistance, and reduce side effects for the patient.

[0066] In principle, any known antiproliferative drug or combination of drugs can be used within the scope of the present invention; however, the method can also be used to identify new therapeutics. The preferred method according to the invention is one in which the active ingredient is selected from the group of cancer drugs, such as alkylating agents, antimetabolites, natural products, hormones, tyrosine inhibitors, chemotherapeutic compounds, anticancer antibodies, in particular gemcitabine, abraxame, trametinib, olaparib, oxaliplatin, erlotinib, 5-FU, docetaxel, and pemetrexed, small molecule drugs, chemotherapeutic cancer drugs, protein-containing drugs, antibodies and genetically modified variants thereof, nucleic acid drugs, antibody-drug conjugates, and genetically modified cells, such as CAR-T cells.

[0067] In another aspect of the inventive method, the drug or combination of drugs with which the 3D microtissues are brought into contact in step b) is selected from the group consisting of cytotoxic, cytostatic and / or chemotherapeutic agents, targeted drugs, immunotherapeutic agents, small molecules from screening libraries and / or combinations thereof.Examples of cytotoxic, cytostatic, and / or chemotherapeutic agents include anastrozole, azathioprine, Bcg, bicalutamide, chloramphenicol, cyclosporine, cidofovir, coal tar products, colchicine, danazol, diethylstilbestrol, dinoprostone, ditoprostone and others, dithranol-containing products, dutasteride, estradiol, exemestane, finasteride, flutamide, ganciclovir, gonadotropin, chorionic acid, goserelin, interferon-containing products (including PEG-interferon), leflunomide, letrozole, leuprorelin acetate, medroxyprogesterone, megestrol, menotropins, mifepristone, mycophenolate mofetil, nafarelin, estrogen-containing products, oxytocin (including syntocinon and syntometrin), podophyllyn, and progesterone-containing products. Raloxifene, ribavirin, sirolimus, streptozocin, tacrolimus, tamoxifen, testosterone, thalidomide, toremifene, trifluridine, triptorelin, valganciclovir and zidovudine.Targeted drugs are medications whose concentration is higher in certain parts of the body than in others, such as antibodies. Examples include bevacizumab and everolimus for brain tumors; bevacizumab, everolimus, lapatinib, pertuzumab, trastuzumab, and its antibody-drug conjugates for breast cancer; aflibercept, bevacizumab, cetuximab, panitumumab, and regorafenib for colorectal cancer; and imatinib for dermatofibrosarcoma protuberans. Immunotherapeutic agents are used in immunotherapy, a form of cancer treatment that harnesses the power of the body's own immune system to prevent, control, and eliminate cancer. Examples include monoclonal antibodies for cancer treatment, CAR T-cell therapy, immune checkpoint inhibitors for cancer treatment, cancer vaccines, immunomodulatory drugs (IMiDs), and cytokines.Immune checkpoint inhibitors (ICIs) are inhibitors of so-called immune checkpoints, which in physiological situations play a role in preventing an excessive immune response. However, cancer cells could also use these checkpoints to suppress immune responses that might otherwise trigger cancer cell death. Examples include anti-CTLA-4 antibodies, which inhibit the cytotoxic T-lymphocyte-associated protein (CTLA-4), such as ipilimumab; anti-PD-1 antibodies, which inhibit the programmed cell death receptor 1 (PD-1), such as pembrolizumab, nivolumab, or sintilimab; and anti-PD-L1 antibodies, which inhibit programmed cell death ligand 1 (PD-L1), such as atezolizumab. Other examples include natural product libraries, which consist of compounds from natural sources; libraries of synthetic compounds, which are synthesized in the laboratory; and fragment libraries, which consist of small, low-molecular-weight compounds.Examples of antibody-drug conjugates include belantamab mafodotin, brentuximab vedotin, enfortumab vedotin, gemtuzumab ozogamicin, inotuzumab ozogamicin, loncastuximab tesirin, mirvetuximab soravtansine, polatuzumab vedotin, sacituzumab govitecan, tisotumab vedotin, trastuzumab deruxtecan, trastuzumab emtansine, and valemetostat tosilate. Examples of nucleic acid conjugates include custirsen, aprinocarsen, imetelstat, and oblimersen.

[0068] The preferred method according to the invention comprises at least two drugs that are administered together or sequentially. While the combination of two drugs is preferred, combinations of three or more, four or more, or five or more drugs are also covered by the present invention.

[0069] The drugs or drug combinations used in the tests according to the present invention can be tested at any suitable concentration, preferably at concentrations commonly used in the treatment of proliferative diseases. A further preferred method according to the invention is in which the 3D microtissues, in particular 3D microtumors or organoids of the array, are treated with the drug or drug combination in the C max -Concentration(s) brought into contact. Other concentrations may be within a physiological range, such as C. max up to C trough , i.e., the lowest concentration a drug reaches before the next dose is administered, if known.

[0070] The drugs or drug combinations used in the tests according to the present invention can be brought into contact with the drugs or drug combinations used in the tests according to the present invention for any desired period of time, preferably for periods commonly used in the treatment of proliferative diseases. A further preferred method according to the invention is in which the drugs or drug combinations are brought into contact with the 3D microtissues, in particular 3D microtumors or organoids of the array, for a period of time that depends at least partially on their t½ value(s).

[0071] Another aspect of the method according to the invention relates to the acquisition of data for response analysis at multiple time points. Sampling is preferably performed using primary volunteer or patient biopsy materials both before and after treatment with a drug or drug combination, and / or using the patient-derived array of 3D microtissues both before and after treatment with a drug or drug combination. Depending on the parameters to be determined as data for response analysis, sampling can be performed at least once, but preferably at all of the aforementioned sampling points.

[0072] A particular advantage of the present invention lies in the fact that proliferative, disease-derived 3D microtissues, in particular 3D microtumors or organoids, are produced from the subject sample and then used for the investigation according to the invention. This leads to a largely complete preservation of the in vivo situation, in particular the cellular composition and diversity, as it exists in the subject during testing of the drug or combination of drugs. A preferred method is therefore one in which, prior to contacting in step b), the primary proliferative disease samples or the 3D microtissues derived from the proliferative disease, in particular 3D microtumors or organoids, comprise cells that reflect the composition of the main cell types of native tumor tissue, such as connective tissue cells, CD4+ T lymphocytes, epithelial cancer cells, macrophages, natural killer cells, B cells, and CD8+ T lymphocytes.

[0073] The sample obtained from the volunteer can also be a sample of secondarily proliferative disease tissue (e.g., metastatic tissue), volunteer xenografts (PDX tissue), or patient-derived cell lines. "Classical" cell lines can also be used for comparison purposes.

[0074] Since the in vivo situation, in particular the cellular composition and diversity as it is present in the subject, is essentially preserved or maintained, the relevance of the data obtained and thus the significance is better than, for example, in a procedure that uses cell lines as a source for testing drugs or drug combinations.

[0075] The method according to the invention is preferably used in a mammal, such as a human patient, and in which the patient preferably suffers from or is diagnosed with a neoplastic disease or tumor. Examples include a proliferative disease selected from neoplastic diseases, benign proliferative diseases, malignant proliferative diseases, psoriasis, cancer such as lung cancer, cervical cancer, brain cancer, stomach cancer, liver cancer, bone cancer, head and neck cancer, benign tumors, adenomas, endometriosis, hyperplasia (e.g., prostate), thyroid nodules, hemangioma, lymphangioma, keloids, warts, and colon cancer.

[0076] Within the scope of the present invention, any suitable sample from a patient with a proliferative disease or suspected proliferative disease can be used. A preferred method according to the present invention is one in which the patient-derived tissue sample is selected from a subsample derived from a primary tissue sample, a primary tumor sample, and a metastasis sample, and in which the tissue sample is preferably obtained by a method comprising nuclear biopsy, tumor resection, liquid biopsy, and / or needle aspiration, and / or in which the tissue sample and / or the dissociated cells are frozen and thawed prior to the production of the 3D microtissues.Preferably, prior to the generation of the microtumor or organoid and the testing of the drug or combination of drugs, a sample is obtained that reflects or substantially reflects the cellular composition and diversity as found or present in the diseased tissue.

[0077] The present invention is generally based on responsive data obtained from tissue samples derived from volunteers or patients, in particular patient-derived 3D microtissues. In the present invention, the term "volunteer-derived" materials preferably refers to all cell and / or tissue samples from patient biopsies or to all ex vivo generated cell and / or tissue samples containing cells and / or tissues obtained from volunteer or patient biopsies.

[0078] In this context, a 3D microtissue refers to an in vitro generated cell aggregate comprising any cell types, and a patient-derived 3D microtissue or organoid refers to a 3D microtissue that contains at least some cells and / or tissue from patient biopsies. Consequently, a patient-derived 3D microtumor, which is itself a type of 3D microtissue, is a cell aggregate that contains at least some cancer cells from volunteer or patient biopsies. In a preferred aspect of the invention, the addition of other cell types during the generation of the 3D microtissue is possible, in particular immune cells that could potentially invade and interact with the cancer cells, or other stromal cells such as angiogenic endothelial cells.Methods for generating these 3D microtissues from patient-derived biopsy samples are known to those skilled in the art, and WO 2021 / 110799A2, for example, teaches protocols for generating 3D microtissues, in particular 3D microtumors and organoids, from materials derived from the subject or patient.

[0079] In general, the method according to the invention is carried out in culture vessels that allow the formation of 3D microtissues from biopsy samples obtained from volunteers or patients. Given the need to generate a response profile when testing the drug or drug combination, multi-well plates are preferably used, each plate comprising at least 5, at least 16, or at least 32 wells. Of course, several plates can also be used in parallel. In the context of the present invention, an "array" refers to a series of separate tissue samples from volunteers or patients that are analyzed in parallel. An array of tissue samples from volunteers or patients can be provided in the form of separate tissue samples cultured in multi-well plates.The array can then comprise any number of separate tissue samples, as described above and herein, but preferably comprises at least as many as, or more than, the number of drugs and / or drug combinations to be tested. Preferably, the number is equal to or greater than the product of the number of drugs and / or drug combinations to be tested and the number of parameters to be measured as a response profile. The array can comprise at least 5 or more, or 6, 7, 8, 9, 10 or more, 12, 48, 96, 128, 384 or more, or even 200, 300, 400 or more tissue samples obtained from volunteers or patients.

[0080] Within the framework of the method according to the invention, microtumors or microtissues can be produced from primary tissue. A preferred method according to the present invention comprises a maturation period of approximately 6 hours to 7 days, preferably approximately 1 to 6 days, and particularly preferably approximately 2 to 5 days, for the provision of the 3D microtissues, in particular 3D microtumors or organoids. This allows the 3D microtissues to grow to an acceptable size for carrying out the method according to the invention, such as 3D microtumors produced with a size of 350 µm ± 100 µm, and in particular a uniform size.

[0081] A further particular advantage of the present invention is that the method is rapid in obtaining a meaningful response analysis from a sample taken from the subject. As already mentioned, time is of the essence when efficiently treating a patient suffering from a proliferative disease, particularly cancer. In a preferred method according to the present invention, steps a) to d) and optionally a) to e) are carried out within about 1 to 7 days, preferably within about 3 to 6 days.

[0082] In another important aspect of the method according to the invention, the method further comprises the step of identifying genes and / or proteins of a cellular drug-specific signaling pathway as novel drug targets. This aspect of the invention benefits from the analysis of the response profile according to the present invention, which makes it possible to identify novel cellular drug-specific signaling pathways that are altered or modified during the analysis of the effects of the drug or drug combination within the scope of the present invention.

[0083] The preferred method is therefore the one according to the invention which further comprises at least one cellular and / or tissue-specific reaction analysis based on the molecular-level parameter-based analysis in step c) and the identification of at least one functional relationship between genes and / or proteins of a pathway and the neoplastic disease or tumor and / or the drug or drug combination.

[0084] Another important aspect of the method according to the invention is the use of computer programs and databases in the creation and analysis of the reaction profile(s) according to the invention. Therefore, the method according to the invention is preferred in which the identified response profile is stored on a central server and / or the identification comprises at least one of the following steps: i) comparing the response profile with a response profile generated from tissue samples of a healthy subject, ii) comparing the response profile with a response profile generated from tissue samples of other subjects diagnosed with a treated or untreated proliferative disorder.

[0085] Therefore, in a further preferred embodiment of the method according to the invention, improved or even optimal therapy is predicted not only based on the data of the response profile and / or the analysis data obtained from a patient sample, but also by the temporal accumulation of the response profile data of a number of individuals for groups of patients who exhibit the same or similar proliferative disease feature(s). This number of individuals can be 50, 100, 200, 500, or 1000 or more subjects with known proliferative diseases or conditions.

[0086] In a further preferred embodiment of the method according to the invention, the changes identified in a specific (e.g., personalized) responsome profile can be compared or otherwise integrated with responsome data from various sources. Such a comparison can, in particular, be made between one patient and another or a group of healthy individuals, or between one patient and another or a group of patients suffering from the same type of disease or diagnosed with the same type of disease, especially the same type of proliferative disease, tumor, or neoplastic disease.

[0087] Comparing the response profile between a subject or patient and a group of healthy individuals can be used both as a strategy to identify possible side effects of a drug or combination of drugs and to determine an underlying molecular mechanism and / or cellular pathway of the proliferative disease.Similarly, comparing the response profile between one patient and another patient or group of patients suffering from, or diagnosed with, the same type of proliferative disorder can be used both as a way to identify a drug or combination of drugs that is / are effective for all the individuals studied and their proliferative disorder, and as a method to understand why a particular antiproliferative treatment is not effective in certain subjects or patients, even though they have – supposedly – ​​been diagnosed with the same type of proliferative disorder (e.g., an underlying cellular mechanism that causes resistance in certain scenarios).

[0088] A further advantage of the method according to the invention is the possibility of using machine learning algorithms. A method according to the present invention is therefore preferred, wherein the identification comprises a method using a deep learning algorithm. Preferably, the algorithm is trained on the generated and / or stored response profiles.

[0089] Machine learning algorithms are helpful in finding the optimal drug or drug combination, predicting effective treatments, and / or identifying novel therapeutic concepts, particularly for cancer patients. The use of machine learning algorithms has significantly increased the accuracy of cancer therapy predictions in recent years. Despite a growing number of available machine learning (ML) algorithms, as noted by Rafique et al. (in: “Machine learning in the prediction of cancer therapy.” Computational and Structural Biotechnology Journal 19 (2021): 4003–4017), the predictive power of ML algorithms is severely limited by the amount of primary patient data, since most ML algorithms are trained and developed using cell line data.Another challenge in developing machine learning algorithms is curating data from diverse clinical data sources, as it is difficult to incorporate inconsistent data formats. Responsive analysis using patient-derived tissues, particularly patient-derived 3D microtissues, addresses both of these problems by generating primary patient sample data at all levels with minimal sample size, providing sufficient and readily accessible datasets for training and developing machine learning algorithms.

[0090] Another aspect of the present invention is the prediction of treatment outcomes by implementing an algorithm, preferably a machine learning-based algorithm, which is trained on response data collected and stored in an accessible database. The machine learning algorithm can be a deep learning algorithm that uses advanced machine learning techniques such as deep neural networks or ensemble learning to process response data from subjects or patients and then, for example, predict the treatment efficacy of identified drugs or combinations of drugs. The deep neural networks enable the algorithm to be trained on complex relationships between patient data and treatment outcomes, while ensemble learning ensures that the algorithm is robust to noisy or incomplete data.

[0091] In a preferred embodiment of the method according to the invention, the machine learning algorithm is a supervised learning algorithm, wherein the collected response data form the dataset for training the algorithm. These inputs can be supplemented with other data, e.g., with genomic data from patients, such as mutations / biomarkers, patient parameters (physiology, chemistry), patient characteristics (age, sex, ethnicity, etc.), and previous therapies and responses.

[0092] A further advantage of the method according to the invention is the cross-validation of the accuracy of the prediction by the machine learning algorithm with additional response data that are intentionally not used as a training dataset. This is only possible with the present invention, since the additional validation is based on a surplus of input data made available by generating an array of microtissues from patient-derived samples.

[0093] A further aspect of the present invention relates to a method for identifying adverse effects associated with treatment with a patient-specific drug or a combination of drugs in a patient, wherein the method is carried out according to the present invention and further comprises the step of testing and analyzing said patient-specific drug or combination of drugs for adverse effects in said patient. In this aspect, the present method ultimately enables the creation of a risk profile for the patient and the effect of the tested treatments, whereby the benefits of a drug or drug combination can be compared and weighed against the possible side effects.

[0094] Similar to the above, another aspect of the present invention relates to a method for stratifying a patient with regard to treatment with a patient-specific drug or combination of drugs, comprising carrying out the method according to the present invention, which includes identifying at least one subject-specific drug or combination of drugs based on the response profile and optionally further based on the molecular pathway, as determined, and stratifying the subject based on the identified subject-specific drug or combination of drugs.In this respect, the patient can be divided into one or more treatment groups that are treated with a specific drug according to the present invention and for which this treatment offers the greatest benefit or even the lowest risk of side effects.

[0095] Another aspect of the present invention relates to a method for monitoring the treatment of a proliferative disease with a subject-specific drug or a combination of drugs, comprising carrying out the method according to the invention, treating the patient with the at least one identified patient-specific drug or combination of drugs, and repeating the method according to the invention on a sample of the patient following treatment, and optionally adjusting the treatment based on the result(s) of the repetition.

[0096] A preferred method according to the invention is one in which the observed changes in the reaction profile can be analyzed by comparing samples taken at different times with other samples from the same patient.This analysis can include the following steps: i) collection of responseomic raw data, wherein preselected physiological parameters in the responseomic profile are determined according to the method of the present invention and added to a central server for storage; ii) data processing, wherein the raw data can be subjected to annotation, clustering, sorting, reformatting, compression, and the like; iii) data input, wherein the raw data are stored in the database; iii) data input into analysis software, wherein the processed raw data can be fed into at least one algorithm, which is implemented, for example, by automated computer software, thereby identifying a preferred drug or drug combination; and iv) post-processing of the previously performed data analysis to evaluate the validity of the identified drug or drug combination.

[0097] As mentioned above, the method according to the present invention is preferred, wherein the identification and / or selection step includes the use of additional clinical parameters, such as patient-specific data and / or treatment history.

[0098] In a preferred embodiment of the method according to the invention, the transcriptome changes in the responseome profile are analyzed to interpret the effect of the applied treatment on the gene expression of the subject or of the cell and / or tissue samples derived from the patient. Within the framework of the method according to the invention, a specific treatment (drug or combination of drugs) can be preferred and selected as a suitable treatment for the patient if the treatment either upregulates genes that are generally known to be associated with the survival of the diseased tissues and / or cells, or, for example, downregulates genes that are known to be associated with supporting the survival of the diseased tissues and / or cells. Various genes are known to those skilled in the art whose expression levels correlate with cell survival and uncontrolled proliferation.Examples of genes whose expression levels are generally associated with the suppression of neoplastic cell survival include genes involved in cell proliferation, such as KRAS, BRAF, MYC, EGFR, HER2; genes involved in DNA repair mechanisms, such as TP53; genes involved in cell growth, division, and differentiation, such as PIK3CA, PTEM, NF1; genes associated with the cell cycle, such as CDKN2A; and genes associated with apoptosis, such as BCL2.

[0099] In a preferred embodiment of the method according to the invention, the proteomic changes in the responsome are analyzed to determine the effect of the applied treatment on the protein composition of the patient-derived cell and / or tissue samples. In the method according to the invention, a particular treatment (drug or combination of drugs) is considered a preferred treatment for the patient if this treatment either upregulates proteins generally associated with suppressing the survival of diseased tissues and / or cells, or degrades proteins generally associated with supporting the survival of diseased tissues and / or cells. Various proteins associated with cell survival and uncontrolled proliferation are known to those skilled in the art.Examples of proteins that are critical for the survival and growth of neoplastic cells and / or tissues include proteins involved in apoptosis, such as Bcl-2, Akt, Nuclear Factor Kappa B (NF-κB), cMyc, Murine Double Minute 2 (MDM2), p53, Survivin and Phosphatidylinositol 3-kinase (PI3K); proteins involved in angiogenesis and / or glycolysis, such as hypoxia-inducible factor 1 alpha (HIF-1α) and vascular endothelial growth factor (VEGF); and proteins involved in signaling processes for cell survival, such as human epidermal growth factor receptor 2 (HER2), focal adhesion kinase (FAK) and heat shock protein 90 (Hsp90).

[0100] In a preferred embodiment of the method according to the invention, metabolomic changes in the responsome are analyzed to interpret the effect of the applied treatment on the metabolism of patient-derived cell and / or tissue samples. Neoplastic cells can exhibit a significantly altered metabolism. A specific example of this is neoplastic tissue, which has increased glycolysis and is therefore enriched with glucose. When analyzing the responsome profile to determine the optimal therapy, the metabolome can serve as a special criterion, with a particular therapy being preferred if it induces metabolomic changes in the patient-derived cells and / or tissues such that they resemble the metabolome of healthy cells and / or tissues.

[0101] In a preferred embodiment of the method according to the invention, changes in cell composition and / or morphological alterations are analyzed to interpret the effect of a particular treatment on the protein composition of the patient or of patient-derived cell and / or tissue samples. Other cell types coexisting in the same microenvironment may interact with said patient-derived diseased cells and / or tissues. The interactions between the different cell types in the tumor microenvironment can have both tumor-promoting and tumor-inhibiting effects. Examples of cell types that may favor the survival of cancer cells include fibroblasts, which can produce an abnormal extracellular matrix or growth factors that promote the survival and invasion of cancer cells, and endothelial cells.Cells that line blood vessels to supply cancer cells with nutrients and oxygen, thereby supporting their growth, and cancer-associated adipocytes (CAAs), i.e., adipocytes that accumulate in the tumor microenvironment and can secrete growth factors that promote cancer cell survival, are examples of cell types that can inhibit cancer cell survival. Examples of cell types that can inhibit cancer cell survival include certain immune cells, such as immune cells and natural killer cells, which can recognize and attack cancer cells. In particular, engineered immune cells, such as CAR-T cells, which can be added to the patient-derived tissue sample as a target therapy, can also be identified and characterized by analyzing the cell composition of said cells and / or tissue sample. In the method according to the invention, a therapy is preferred if it reduces the occurrence of pro-cancer cell types or increases the occurrence of anti-cancer cell types.

[0102] In a preferred embodiment of the method according to the invention, changes in preselected biomarkers are analyzed at the molecular level in the reaction data to determine the effect of the applied treatment on the patient-derived cell and / or tissue samples. In particular, some molecular markers can indicate the suitability of a therapeutic method for the patient. For example, the expression of PD-L1 (program death-ligand 1) can be detected and used as an indicator of the efficacy of an immune checkpoint inhibitor-based therapy. Another example of a preselected biomarker is tumor mutational burden (TMB), which is an indicator of the applicability of cancer immunotherapy.

[0103] In a preferred embodiment of the method according to the invention, the method is at least partially, preferably fully, automated and includes the use of at least one robot and / or a data processing system, as described above. A preferred method according to the present invention is one in which a plurality of drugs or combinations of drugs are tested on the array of patient-derived 3D microtissues. Further details of a preferred apparatus and a preferred method for systematic, automated high-throughput screening of drugs or drug combinations in human-derived tissue samples, in particular 3D microtissues, are disclosed, for example, in WO 2020 / 104549 (incorporated herein by reference) and are known to those skilled in the art.

[0104] Another aspect of the present invention relates to a computer program for carrying out the method according to the invention on a suitable computer, such as the one described above. Preferably, the computer program according to the present invention comprises a deep learning algorithm, for example a deep neural network algorithm, as also described herein.

[0105] A further aspect of the present invention relates to a medicament or a combination of medicaments for use in the prevention or treatment of a proliferative disease, wherein the treatment comprises carrying out the method according to the present invention, preferably further comprising the step of providing subject-specific and / or personalized prevention or treatment. Preferably, the medicament or combination of medicaments for use according to the present invention is characterized in that the neoplastic disease or tumor is a drug-resistant neoplastic disease or a drug-resistant tumor as described above.

[0106] According to the invention, the term “personalized prevention” or “personalized treatment” refers to a medical model in which patients or subjects are divided into different groups, with medical decisions, practices, interventions and / or products being tailored to the individual patient or subject based on their predicted response or disease risk, preferably based on the method described herein.

[0107] A further aspect of the present invention relates to a test system comprising means for carrying out the method according to the present invention, comprising: a) a unit for cultivating an array of 3D microtissues, such as microtumors or organoids, based on dissociated cells of a tissue sample obtained from the provided subject; b) a drug testing unit for contacting the array of 3D microtissues with at least one drug or combination of drugs to be tested; c) a unit for determining a response profile for the 3D microtissues, in particular for the 3D microtumors or organoids of the array, and the drugs and / or combinations of drugs and their predefined doses and times as tested; and i) a first analysis unit for non-disruptive kinetic-based functional analysis of the 3D microtissues, in particular the 3D microtumors or organoids of the array.ii) a second analysis unit for determining a molecular parameter-based analysis of the 3D microtissues, in particular the 3D microtumors or organoids of the array, and d) a unit for identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject based on the determined response profile. Preferably, the test system according to the invention further comprises at least one database for collecting and / or storing data on the determined response profile and optionally additional data regarding the preselection and / or grouping of the drug combination, physiological parameters, data regarding the effect(s) of the drugs or drug combinations, data regarding the cellular and / or tissue-specific response analysis, data regarding adverse effects, and data regarding additional clinical parameters.such as patient-specific data and / or treatment history, data relating to stratification and / or monitoring, and data for system automation, for example, for controlling at least one robot. Further details of a preferred device and a preferred method for systematic, automated high-throughput screening of drugs or drug combinations in human tissue samples, in particular 3D microtissues, are disclosed, for example, in WO 2020 / 104549 (incorporated herein by reference) and are known to those skilled in the art.

[0108] Another aspect of the present invention relates to the use of the test system or computer program according to the invention for identifying at least one subject-specific drug or combination of drugs and / or for at least one cellular and / or tissue pathway-specific response analysis based on the parameter-based analysis at the molecular level in step c) and for identifying at least one functional relationship between genes and / or proteins of a pathway and the neoplastic disease or tumor and / or the drug or combination of drugs according to the method described above according to the present invention.

[0109] The present invention relates to the following points: Subject matter 1. A method for identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject, comprising the steps a) providing an array of i) primary tissue samples of a proliferative disease and / or ii) 3D microtissues derived from a proliferative disease, in particular 3D microtumors or organoids, from a subject who has or is diagnosed with a proliferative disease, in a multi-well format in a suitable culture medium, wherein the multi-well format comprises between 5 wells and a number of wells corresponding to the drugs and / or combinations of drugs and their doses plus controls to be tested, b) contacting the 3D microtissues,in particular 3D microtumors or organoids of the array with a predefined number of drugs and / or combinations of drugs and their predefined doses for a predefined period or predefined different periods, c) determining a response profile for the 3D microtissues, in particular for the 3D microtumors or organoids of the array and the tested drugs and / or combinations of drugs and their predefined doses and times, based on i) a first step of a non-disruptive kinetic-based functional analysis of the 3D microtissues, in particular the 3D microtumors or organoids of the array, wherein the analysis includes at least one analysis selected from the group consisting of the absolute number of cells, the proliferation rate, the growth kinetics, the growth rate and the number of cells, proliferation rate, growth kinetics, cell size,cellular composition of the cells, tissues, tumors or organoids and metabolic rate, and ii) a second step of a molecular-level parameter-based analysis of the 3D microtissues, in particular the 3D microtumors or organoids of the array, wherein the analysis includes at least one analysis selected from the group consisting of transcriptome, proteome, genome, metabolome, gene mutation and selected biomarker analyses, wherein i) and ii) are performed on the same 3D microtissue, in particular the 3D microtumor or organoid of the array, and d) identification of a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject based on the responseome profile determined in step c). Point 2. Method according to point 1, wherein the determination of the response profile further comprises at least one analysis selected from the group consisting of i) for non-disruptive kinetic-based functional analysis, cell count, 3D microtumor or organoid size determination, LDH release in the 3D microtumor or organoid and preselected biomarkers in the 3D microtumor or organoid, intracellular calcium level, physiological parameters directly indicating the absolute number of cells, proliferation rate and metabolic rate, wherein preferably the 3D microtumor or organoid size determination comprises at least one parameter selected from the diameter, circumference, volume and area of ​​the optical cross-section, and wherein preferably the 3D microtumor or organoid size determination comprises the use of an imaging device.and, where appropriate, further includes the analysis of growth kinetics, and ii) for the analysis of parameters at the molecular level: gene expression level, protein level, metabolite level / amount, histochemistry, preselected biomarker levels, such as PD-L1 expression level, tumor mutational burden (TMB), microsatellite instability (MSI) and mismatch repair deficiency (dMMR). Point 3. The procedure according to point 1 or 2, wherein the identification in step d) is based on the response profile determined in step c), wherein a drug or combination of drugs is identified if: i) according to the non-disruptive kinetic-based functional analysis, it substantially inhibits the growth of the 3D microtissue, in particular the 3D microtumor or organoid of the array, as determined by a reduction in tissue size, slower growth kinetics and / or LDH release; ii) according to the parameter analysis at the molecular level, the tissue shows no resistance to the drug, the tissue exhibits no resistance to the drug or combination of drugs, as determined, for example, by a mutation in specific genes targeted by the drug or combination of drugs, or by a lower level of expression of the products of the genes targeted by the drug or combination of drugs.and / or the absence of drug-resistant mutations is detected. Point 4. Method according to any of points 1 to 3, wherein the method further comprises at least one spatial biology test to assess the spatial context of cells within the tissue(s), and wherein the identification is further based at least partially on the data generated in the at least one spatial biology test. Point 5. The procedure according to any of points 1 to 4, which further includes step e) of selecting a medicinal product or combination of medicinal products as specific and effective, and in particular personalized, against a proliferative disease in a subject on the basis of the identification in step d). Point 6. The procedure according to one of points 1 to 5, wherein steps a) to d) are repeated at least once, combining the medicinal products identified in the first round of the procedure. Item 7. Procedure according to one of items 1 to 6, wherein the drug is from the group of cancer drugs, such as alkylating agents, antimetabolites, natural products, hormones, tyrosine inhibitors, chemotherapeutic compounds, anticancer antibodies, in particular gemcitabine, abraxame, trametinib, olaparib, oxaliplatin, erlotinib, 5-FU, docetaxel and pemetrexed, small molecule drugs, chemotherapeutic cancer drugs, protein-containing drugs, antibodies and genetically modified variants thereof, nucleic acid drugs, antibody-drug conjugates and genetically modified cells, such as CAR-T cells. Point 8. The procedure according to any of points 1 to 7, wherein the drug combination comprises at least two drugs that are administered together or sequentially. Point 9. Method according to one of points 1 to 8, wherein the 3D microtissues, in particular 3D microtumors or organoids of the array, are brought into contact with the drug or drug combination in the Cmax concentration(s). Item 10. Method according to one of points 1 to 9, wherein the active substance or combination of active substances is brought into contact with the 3D microtissues, in particular 3D microtumors or organoids of the array, for a period of time which depends at least partially on their t½ value(s). Item 11. Procedure according to any of items 1 to 10, wherein prior to contacting in step b) the primary proliferative disease samples or the 3D microtissues derived from the proliferative disease, in particular 3D microtumors or organoids, comprise cells that reflect the composition of the main cell types of native tumor tissue, such as connective tissue cells, CD4+ T lymphocytes, epithelial cancer cells, macrophages, natural killer cells, B cells and CD8+ T lymphocytes. Item 12. Procedure according to any of items 1 to 11, wherein the patient is a mammal, such as a human being, and wherein the patient preferably suffers from or is diagnosed with a neoplastic disease or tumor. Item 13. Procedure according to one of items 1 to 12, wherein the proliferative disease is selected from neoplastic diseases, benign proliferative diseases, malignant proliferative diseases, psoriasis, cancer, such as lung cancer, cervical cancer, brain cancer, stomach cancer, liver cancer, bone cancer, head and neck cancer, benign tumors, adenomas, endometriosis, hyperplasia (e.g., prostate), thyroid cancer (e.g., prostate), thyroid nodules, hemangioma, lymphangioma, keloids, warts, and colon cancer. Item 14. The method according to any of items 1 to 13, wherein the multi-well format comprises a multi-well plate with at least 5, at least 16 or at least 32 wells. Item 15. Method according to any one of items 1 to 14, wherein the provision of the 3D microtissues, in particular 3D microtumors or organoids, comprises a maturation period of about 6 hours to 7 days, preferably about 1 to 6 days, particularly preferably about 2 to 5 days, and / or wherein the generated 3D microtumors have a size of 100-450 µm, preferably 350 µm + / - 100 µm. Point 16. The method according to any of points 1 to 15, wherein steps a) to d) and, if applicable, a) to e) are carried out within about 1 to 14 days, preferably about 3 to 12 days. Item 17. The procedure according to any of points 1 to 16, which further includes at least one cellular and / or tissue-specific response analysis based on the molecular-level parameter-based analysis in step c) and the identification of at least one functional relationship between genes and / or proteins of a pathway and the neoplastic disease or tumor and / or the drug or combination of drugs. Item 18. The procedure according to item 17, which further includes the step of identifying genes and / or proteins of the signaling pathway as drug targets. Item 19. Procedure according to any of items 1 to 18, wherein the identified Responsom profile is stored on a centralized server and / or the identification includes at least one of the following steps: i) comparing the Responsom profile with a Responsom profile generated from tissue samples of a healthy subject, ii) comparing the Responsom profile with a Responsom profile generated from tissue samples of other subjects diagnosed with a treated or untreated proliferative disorder. Point 20. The procedure according to point 19, wherein the identification comprises a procedure using a deep learning algorithm, the algorithm preferably being trained using the stored response profiles. Item 21. Procedure for identifying adverse effects associated with treatment with a patient-specific medicinal product or combination of medicinal products in a patient, comprising carrying out the procedure according to any one of items 1 to 20, and further comprising the step of testing and analyzing the patient-specific medicinal product or combination of medicinal products for adverse effects in the patient. Item 22. Procedure for stratifying a patient with regard to treatment with a patient-specific medicinal product or combination of medicinal products, comprising carrying out the procedure according to any one of items 1 to 20, comprising identifying at least one subject-specific medicinal product or combination of medicinal products on the basis of the response profile and, where appropriate, further on the basis of the molecular pathway, as determined, and stratifying the patient on the basis of the identified subject-specific medicinal product or combination of medicinal products. Item 23. Procedure for monitoring treatment with a subject-specific medicinal product or combination of medicinal products, comprising carrying out the procedure according to any one of items 1 to 20, treating the patient with the at least one identified patient-specific medicinal product or combination of medicinal products and repeating the procedure according to any one of items 1 to 20 on a sample from the patient after treatment and, where appropriate, adjusting the treatment based on the result(s) of the repeat. Item 24. Procedure according to one of points 1 to 20, wherein the step of identifying and / or selecting includes the use of additional clinical parameters, such as patient-specific data and / or treatment history. Point 25. Method according to any of points 1 to 24, wherein the method is at least partially, preferably fully, automated and includes the use of at least one robot and / or a computing device. Item 26. A computer program for carrying out the procedure according to one of points 17 to 20 on a suitable computer. Point 27. The computer program according to point 26, wherein the machine learning algorithm includes a deep learning algorithm, for example a deep neural network algorithm. Item 28. A medicinal product or combination of medicinal products for use in the prevention or treatment of a proliferative disease, wherein the treatment comprises carrying out the procedure according to any one of items 1 to 23, preferably with the further step of providing subject-specific and / or personalized prevention or treatment. Item 29. The medicinal product or combination of medicinal products for use in accordance with item 28, wherein the neoplastic disease or tumor is a drug-resistant neoplastic disease or a drug-resistant tumor. Item 30. Test system with means for carrying out the procedure according to any one of items 1 to 20, comprising: a) a unit for cultivating an array of 3D microtissues, such as microtumors or organoids, based on dissociated cells of a tissue sample obtained from the individual as intended; b) a drug testing unit for contacting the array of 3D microtissues with at least one drug or combination of drugs to be tested; c) a unit for determining a response profile for the 3D microtissues, in particular for the 3D microtumors or organoids of the array, and the drugs and / or combinations of drugs and their predefined doses and times as tested; and i) a first analysis unit for non-disruptive kinetic-based functional analysis of the 3D microtissues, in particular the 3D microtumors or organoids of the array.ii) a second analytical unit for determining a parameter-based analysis of the 3D microtissues, in particular the 3D microtumors or organoids of the array, and d) a unit for identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject based on the determined response profile. Point 32. The test system according to point 31 further comprises at least one database for collecting and / or storing data relating to the determined response profile and, where applicable, additional data relating to the preselection and / or grouping of the drug combination, physiological parameters, data relating to the effect(s) of the drugs or the drug combination, data relating to cellular and / or tissue-specific response analysis, data relating to adverse effects, data relating to additional clinical parameters, such as patient-specific data and / or treatment history, data relating to stratification and / or monitoring, and data relating to the automation of the system, for example, to control at least one robot. Item 33. Use of the test system according to item 31 or 32 or the computer program according to item 26 or 27 for the identification of at least one subject-specific medicinal product or combination of medicinal products and / or for at least one cellular and / or tissue-specific response analysis based on the parameter-based analysis at the molecular level in step c) and for the identification of at least one functional relationship between genes and / or proteins of a signaling pathway and the neoplastic disease or tumor and / or the medicinal product or combination of medicinal products according to the procedure according to any one of items 1 to 20.

[0110] The invention will now be further described in the following examples with reference to the accompanying figures, without, however, being limited thereto. For the purposes of the present invention, all cited references are incorporated in their entirety by reference. Fig. Figure 1 shows a schematic representation of a preferred embodiment of the method according to the present invention. Here, A) a lung cancer tissue resection was taken, dissociated to generate a single-cell solution, and distributed with a defined number of cells in a non-adherent U-bottom multiwell plate to enable gravity-enhanced tumor tissue formation. B) After a microtumor maturation period of 5 days, the microtumors C) were treated either with immune checkpoint inhibitors as monotherapy or in combination with chemotherapy, and the response was observed for 14 days. The response to the drugs was analyzed D) by image-based (non-disruptive) measurement of growth kinetics and E) by subsequent RNA isolation of the same sample for analysis of transcriptomic changes. The RNA was isolated using the RNAeasy method and subjected to quality control. Fig. This paper presents an overview of the method used to generate microtumors from fresh tumor tissue while preserving the original cell composition. A tissue sample from non-small cell lung cancer was enzymatically digested. The resulting cell suspension was dispersed in non-adherent, round-bottomed, multiwell plates to facilitate gravity-enhanced cell alignment. Over six days, the cells regenerated tumor microtissue measuring 250–300 µm at a consistent size. Prior to administration, some microtumors were re-dissociated and used for FACS analysis to determine the presence of the various cell types from the original tissue. The FACS analysis revealed that, in addition to the epithelial cancer cells, the stromal connective tissue cells and the immune system were preserved. Fig. This shows an example of a pathway-specific response analysis of NSCLC cancer. Evaluation of the relative differences in expression levels (log(treatment) / log(control)) of a subset of genes in non-small cell lung cancer (NSCLC) with respect to the analysis of functional response determined by the effect of various drug / drug combinations of immuno-oncology drugs (Immuno), chemotherapy combinations (Chemo Combo), or combinations of immuno-oncology drugs and chemotherapies (Chemo / Immuno) on the growth of 3D microtumors. Fig. This shows an example of pathway-specific response analysis. Ras-associated protein 1 (Rap1), a small GTPase from the Ras-related protein family, is an important regulator of fundamental cellular functions and plays a major role in cell invasion and metastasis in various cancers. Differential expression analysis of control samples compared to different cancer therapies (two immuno-oncology drugs, two combinations of two chemotherapeutic agents (1.1 / 1.2 with different concentration ratios), and two combinations of one chemotherapeutic agent and one immuno-oncology drug) reveals the most striking alterations (genes marked in gray boxes) in parts of the Rap1 signaling pathway, which regulates cell adhesion, migration, and polarity, in samples treated with chemotherapeutic agents but not in samples treated with immuno-oncology drugs. Fig. demonstrates the reaction principle of the method according to the invention in comparison to a conventional analysis. Fig. shows an example of an analysis of the drug response compared to drug-induced expression. Fig. shows an example of an analysis of DNA compared to drug-induced expression. Examples: Identification of the elimination half-life (t½) for drugs under investigation

[0111] The elimination half-life or drug half-life t 1 / 2 This generally refers to the time required to eliminate half of the administered drug dose from the body. For the drugs under investigation, for which no suitable t exists in the literature, 1 / 2-Wert can be specified, and / or for the formation of groups or panels of medicines, the t 1 / 2-Werte as follows.

[0112] According to existing guidelines, the speed (C max , t max ) and the extent (AUC) of absorption of the drug from a test formulation (compared to the reference formulation) is assessed by a single-dose study in healthy volunteers, followed by measurement of the blood / plasma concentration of the parent drug and major active metabolites (if any) over a period of ≥ 3 t ½.

[0113] The t max This is usually related to the duration of t½, as drugs with short half-lives tend to reach their peak and are rapidly eliminated, often requiring more frequent dosing or redosing to keep a drug within its clinically effective therapeutic range.

[0114] To determine the parameters, blood samples are taken at 0, 1, 2, 3, 4, 5, 6, 7, and 8 hours, possibly followed by 10 and 12, 18, 24, 30, 36, and 42 hours after administration. Generally, the more frequently samples are taken around the expected time of maximum concentration, the more accurate the value of t1 / 2 (and t2) will be. max and C max The above-mentioned determinations can be made for several people, and the median values ​​can be used as parameters, e.g., for grouping and testing. Production of microtumors from fresh tumor tissue while maintaining the original cell composition

[0115] A tissue sample from non-small cell lung cancer was enzymatically digested. The resulting cell suspension was spread in non-adherent, round-bottomed, multiwell plates to promote gravity-enhanced cell arrangement. Over six days, the cells formed tumor microtissue measuring 250 to 300 µm. Prior to administration, some microtumors were re-dissociated and used for FACS analysis to determine the presence of the various cell types from the original tissue. FACS analysis revealed that, in addition to the epithelial cancer cells, the stromal connective tissue cells and the immune system were also preserved. Example of a colon cancer response analysis

[0116] A colon primary tumor tissue sample was taken, dissociated to produce a single-cell solution, and distributed with a defined number of cells in a non-adherent 32-well U-bottom plate to allow the formation of tumor tissue by gravity.

[0117] After a maturation period of 5 days, the microtumors have a uniform size of approximately 250 µm and were treated either with immunotherapy monotherapy or in combination with chemotherapy in the respective C max The treatment groups were treated with specific concentrations and their response was observed over 14 days. The control groups were not treated.

[0118] To determine the response profile for the array's 3D microtumors and the drugs and / or drug combinations, the non-disruptive kinetic-based functional analysis consisted of the absolute number of cells, the proliferation rate and growth kinetics, as well as the cell size and compactness.

[0119] Subsequently, a molecular parameter-based analysis of the 3D microtumors on the array was performed, and the analysis included a transcriptome analysis, in particular of genes known to be involved in resistance, and a pathway-specific responseome analysis with respect to the Ras-associated protein 1 (Rap1) (see also Fig. and Looi CK, et al. The Role of Ras-Associated Protein 1 (Rap1) in Cancer: Bad Actor or Good Player? Biomedicines. 2020 Sep 7;8(9):334. doi: 10.3390 / biomedicines8090334. PMID: 32906721; PMCID: PMC7555474).

[0120] The response profile was then determined. While the immunotherapy monotherapies or combination chemotherapies were effective in the respective C cases... maxWhile concentrations showed more or less the same effects on the absolute cell count (reduction), the proliferation rate (reduction), and growth kinetics (slower), as well as cell size (smaller and more irregularly shaped) and compactness (looser), the resistance genes were also equally distributed. Only the inclusion of pathway-specific response analysis with regard to Ras-associated protein-1 (Rap1) showed a clear preference for chemotherapy, but not for immuno-oncological treatment. This treatment bias would not have been detected in a standard analysis.

[0121] Thus, a specific and effective chemotherapy combination of drugs was identified in connection with potentially metastatic colon cancer.

[0122] Similar experiments with essentially identical results were conducted with samples of sarcomas, liver cancer, and cervical cancer. Example of a lung cancer response analysis

[0123] A lung cancer tissue resection was taken, dissociated to generate a single-cell solution, and distributed with a defined cell count in a non-adherent, U-shaped multi-well plate to allow gravity-driven tumor tissue formation. After a 5-day microtumor maturation period, the microtumors were treated with either immune checkpoint inhibitors as monotherapy or in combination with chemotherapy, and the response was monitored for 14 days. Treatment response was analyzed by image-based (non-disruptive) measurement of growth kinetics and subsequent RNA isolation of the same sample to analyze transcriptomic changes. The RNA was isolated using the RNAeasy method and subjected to quality control. Pathway-specific response analysis of NSCLC cancer pathways EGFR to RAS and ERK signaling pathways

[0124] Evaluation of the relative differences in expression levels (log(treatment) / log(control)) of a subset of genes of non-small cell lung cancer (NSCLC) with respect to the analysis of functional response determined by the effect of various drug / substance combinations of immuno-oncology drugs (Immuno), chemotherapy combinations (Chemo-Combo) or combinations of immuno-oncology drugs and chemotherapies (Chemo / Immuno) on the growth of 3D microtumors. Example of a pathway-specific response analysis: Rap1 signaling pathway

[0125] Ras-associated protein-1 (Rap1), a small GTPase from the Ras-related protein family, is an important regulator of basic cellular functions and plays a variety of roles during cell invasion and metastasis in various cancers ( Fig. Differential expression analysis of control samples compared to various cancer therapies (2 immuno-oncology drugs, 2 combinations of 2 chemotherapeutic agents (1.1 / 1.2 with different concentration ratios) and 2 combinations of one chemotherapeutic agent and one immuno-oncology drug) shows the most striking changes (genes marked in grey boxes) in parts of the Rap1 signaling pathway, which regulates cell adhesion, migration and polarity, in samples treated with chemotherapies, but not in samples treated with immuno-oncology drugs ( Fig. .

Claims

[1] Method for identifying a specific and effective, in particular personalized, drug or a combination of drugs against a proliferative disease in a patient, comprising the steps a) providing an array of i) primary tissue samples of a proliferative disease and / or ii) 3D microtissues derived from a proliferative disease, in particular 3D microtumors or organoids, from a patient having or diagnosed with a proliferative disease in a multiwell format in a suitable culture medium, wherein the multi-well format comprises between 6 wells and a number of wells corresponding to the drugs and / or combinations of drugs to be tested and their dosages plus controls, b) contacting the 3D microtissues, in particular the 3D microtumors or organoids of the array, with a predefined number of drugs and / or combinations of drugs and predefined doses thereof for a predefined period of time or predefined different periods of time, c) Determining a response profile for the 3D microtissues, in particular for the 3D microtumors or organoids of the array and the drugs and / or combinations of drugs and their predefined doses and times, as tested, based on i) a first step of a non-disruptive kinetic-based functional analysis of the 3D microtissues, in particular the 3D microtumors or organoids of the array, wherein the analysis comprises at least one analysis selected from the group consisting of the absolute number of cells, the proliferation rate, the growth kinetics, the density or compactness of the cells, the degree of dissolution of the cells, the morphological changes of the cells, the size of the cells, the cellular composition of the cells, tissues, tumors or organoids and the metabolic rate, and ii) a second step of an analysis of the 3D microtissues, in particular the 3D microtumors or organoids of the array, based on parameters at the molecular level, wherein the analysis comprises at least one analysis selected from the group consisting of transcriptome, proteome, genome, metabolome, gene mutation and selected biomarker analyses, wherein i) and ii) are carried out on the same 3D microtissue, in particular the 3D microtumor or Organoid of the array, further comprising at least one cellular and / or tissue-specific metabolic pathway analysis based on the analysis based on parameters at the molecular level and an identification of at least one functional relationship between genes and / or proteins of a metabolic pathway and the neoplastic disease or tumor and / or the drug or combination of drugs, optionally further comprising the step of identifying genes and / or proteins of the metabolic pathway as drug targets, and d) identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject on the basis of the responsesome profile determined in step c). [2] The method of claim 1, wherein determining the responsesome profile further comprises at least one analysis selected from the group consisting of i) for the non-disruptive kinetic-based functional analysis, the cell count, the sizing of the 3D microtumor or organoid, the determination of LDH release in the 3D microtumor or organoid, and the determination of preselected biomarkers in the 3D microtumor or organoid, the intracellular calcium level, physiological parameters that directly indicate the absolute number of cells, the proliferation rate, and the metabolic rate, preferably the absolute number of cells, the proliferation rate, physiological parameters that directly indicate the absolute number of cells, the proliferation rate, and the metabolic rate, preferably the sizing of the 3D microtumor or organoid comprising at least one parameter selected from the diameter, circumference, volume, and area of the optical cross-section, and preferably the sizing of the 3D microtumor or organoid comprising the use of an imaging device,and optionally further comprises the analysis of growth kinetics, and, ii) for the analysis of molecular-level parameters: gene expression level, protein level, metabolite level / amount, histochemistry, preselected biomarker levels, such as PD-L1 expression level, tumor mutational burden (TMB), microsatellite instability (MSI), and mismatch repair deficiency (dMMR). [3] The method of claim 1 or 2, wherein the method further comprises at least one spatial biology test to assess the spatial context of cells within the tissue(s), and wherein the identification is further based at least in part on the data generated in the at least one spatial biology test. [4] A method according to any one of claims 1 to 3, further comprising the step e) selecting a drug or a combination of drugs as specific and effective, and in particular personalized, against a proliferative disease in a subject based on the identification in step d). [5] A method according to any one of claims 1 to 4, wherein steps a) to d) are repeated at least once by combining the drugs identified in the first round of the method. [6] A method according to any one of claims 1 to 5, wherein the combination of medicaments comprises at least two medicaments which are applied together or sequentially. [7] Method according to one of claims 1 to 6, wherein the 3D microtissues, in particular 3D microtumors or organoids of the array are treated with the drug or drug combination at the C maxconcentration(s) and / or wherein the drug or drug combination is / are brought into contact with the 3D microtissues, in particular 3D microtumors or organoids of the array for a period of time that depends at least in part on their t½ value(s). [8] Method according to one of claims 1 to 7, wherein, prior to contacting in step b), the primary proliferative disease samples or the 3D microtissues derived from the proliferative disease, in particular 3D microtumors or organoids, comprise cells that reflect the composition of the main cell types of the native tumor tissue, such as connective tissue cells, CD4+ T lymphocytes, epithelial cancer cells, macrophages, natural killer cells, B cells and CD8+ T lymphocytes. [9] The method according to any one of claims 1 to 8, wherein the proliferative disease is selected from neoplastic diseases, benign proliferative diseases, malignant proliferative diseases, psoriasis, cancer such as lung cancer, cervical cancer, brain cancer, gastric cancer, liver cancer, bone cancer, head and neck cancer, benign tumors, adenomas, endometriosis, hyperplasia (e.g. prostate), thyroid cancer (e.g. prostate), tumors of the thyroid and liver. e.g. prostate), thyroid nodules, hemangioma, lymphangioma, keloids, warts and colon cancer. [10] Method according to one of claims 1 to 9, wherein the provision of the 3D microtissues, in particular the 3D microtumors or organoids, comprises a maturation time of about 6 hours to 7 days, preferably of about 1 to 6 days, more preferably of about 2 to 5 days, and / or wherein the 3D microtumors produced have a size of 100-450 µm, preferably of 350 µm + / - 100 µm. [11] Method according to one of claims 1 to 10, wherein the identified responsesome profile is stored on a centralized server and / or the identifying comprises at least one of the following steps: i) comparing the responsesome profile with a responsesome profile generated from tissue samples of a healthy subject, ii) comparing the responsesome profile with a responsesome profile generated from tissue samples of other subjects diagnosed with a treated or untreated proliferative disease, wherein the identifying preferably comprises a method using a deep learning algorithm, wherein the algorithm is preferably trained using the stored responsesome profiles. [12] A method according to any one of claims 1 to 11, wherein the step of identifying and / or selecting includes the use of additional clinical parameters, such as patient-specific data and / or treatment history. [13] A method for stratifying a patient with respect to treatment with a patient-specific drug or combination of drugs, comprising performing the method according to any one of claims 1 to 11, comprising identifying at least one patient-specific drug or combination of drugs based on the response profile, and optionally further based on the determined molecular pathway, and stratifying the patient based on the identified patient-specific drug or combination of drugs. [14] Computer program for carrying out the method according to claim 11 on a suitable computer, wherein the machine learning algorithm preferably comprises a deep learning algorithm, for example a deep neural network algorithm. [15] Test system with means for carrying out the method according to one of claims 1 to 11, comprising a) a unit for cultivating an array of 3D microtissues, such as microtumors or organoids, based on dissociated cells of a tissue sample derived from the provided subject, b) a drug testing unit for contacting the array of 3D microtissues with at least one drug or combination of drugs to be tested, c) a unit for determining a response profile for the 3D microtissues, in particular for the 3D microtumors or organoids of the array and the tested drugs and / or combinations of drugs and predefined doses and times thereof, comprising i) a first analysis unit for a non-disruptive kinetic-based functional analysis of the 3D microtissues, in particular the 3D microtumors or organoids of the array, (ii) a second analysis unit for determining an analysis of the 3D microtissues, in particular the 3D microtumors or organoids of the array, based on parameters at the molecular level, and d) a unit for identifying a specific and effective, in particular personalized, drug or a combination of drugs against a proliferative disease in a subject on the basis of the determined responsesome profile, wherein the unit optionally further comprises at least one database for collecting and / or storing data relating to the identified responsesome profile and optionally additional data relating to the pre-selection and / or grouping of the combination of drugs, physiological parameters, data relating to the effect(s) contained in the combination of drugs, and data relating to the effect(s), physiological parameters, data relating to the effect(s) of the drugs or the combination of drugs, data relating to the cellular and / or tissue-specific responsesome analysis, data relating to adverse effects, data relating to additional clinical parameters, such aspatient-specific data and / or treatment history, data relating to stratification and / or monitoring, and data for automating the system, for example for controlling at least one robot.

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