Methods of determining selectivity of test compounds

Abstract

The present invention relates to methods for determining the selectivity of a test compound and related methods, such as methods for determining whether a subject having a cancer will respond to or is responsive to treatment with a test compound or a composition comprising more than one test compound.

Description

[0001] This invention relates to methods and related methods for determining the selectivity of a test compound, such as methods for determining whether a subject with cancer will respond to or is responsive to treatment with a test compound. Specifically, the method includes the following steps: (a) providing a sample comprising at least two distinguishable cell subpopulations in a total cell population; (b) dividing the sample into at least two portions; (c) incubating at least one portion obtained in (b) in the absence of the test compound, and incubating at least one portion obtained in (b) in the presence of the test compound; (d) determining, in (i) the at least one portion incubated in the presence of the test compound and (ii) the at least one portion in the absence of the test compound, the number of cells exhibiting one of the at least two subpopulations displaying a distinguishable phenotype, which is relative to the number of cells exhibiting a distinguishable phenotype. The number of cells in the total population of cells exhibiting the same phenotype; and (e) determining the selectivity of the test compound in inducing the phenotype mentioned in (d) relative to all other phenotypes in a subpopulation mentioned in step (d) by dividing (i) by (ii), wherein if (i) divided by (ii) is greater than 1, preferably greater than 1.05, 1.1, 1.5, 2, 3, and most preferably greater than 5, then the test compound selectively induces the phenotype mentioned in (d), and if (i) divided by (ii) is less than 1, preferably less than 0.95, 0.9, 0.7, 0.5, 0.3, and most preferably less than 0.2, then the test compound selectively inhibits or reduces the phenotype mentioned in (d). The present invention also provides a method for determining whether a subject with cancer will respond to or is responsive to treatment with a test compound, wherein the method comprises (a) providing a sample from the subject, the sample comprising at least two cell subpopulations of a total population of cells, wherein at least one subpopulation corresponds to cancer cells and at least one subpopulation corresponds to non-cancer cells; (b) dividing the sample into at least two portions; (c) incubating at least one portion obtained in (b) in the absence of the test compound and incubating at least one portion in the presence of the test compound; (d)(i) incubating the sample with the test compound. In the at least portion incubated in the presence of the test compound and in the at least portion in the absence of the test compound, the number of live cells in at least one of the subpopulations corresponding to cancer cells is determined relative to the number of live cells in the total cell population; and (e) whether the subject will respond to treatment with the test compound or is responsive to treatment is determined by dividing (i) by (ii), wherein if the obtained value is less than 1, preferably less than 0.95, 0.9, 0.8, 0.6, 0.4, and most preferably less than 0.2, then the subject will respond to treatment or is responsive to treatment.

[0002] The identification of drugs for treating diseases in humans and / or animals requires the identification of molecules that selectively induce desired biological effects in specific cell types without affecting other cells and thus causing undesirable side effects. Conversely, drugs capable of selectively inducing desired biological effects in the desired cell types of an individual patient are likely to provide medical benefit to that patient. Drugs with lower selectivity may cause serious side effects, may require reduced treatment doses, and may therefore fail to produce the desired medical outcomes.

[0003] In the past, drug discovery relied heavily on the use of cell line model systems. However, it is increasingly understood that these models merely do not fully reproduce the complex processes of higher organisms, which often involve interactions between different cell types. In particular, in such systems, selectivity as an ability to induce effects only in the desired target cell type cannot be studied. A feasible solution in the art is to independently test molecules in different cell lines. However, this does not account for the possible interactions between different cell types. An alternative is to establish co-culture model systems in which different cell lines are mixed to reproduce more realistic environments. Additionally, chemicals can be directly tested ex vivo in pristine materials such as PBMCs or bone marrow composed of multiple cell types. Therefore, in the context of drug discovery and development, there is a need for effective methods to determine how a chemical selectively affects one cell type relative to others in a cell mixture containing at least two different cell types.

[0004] It is becoming increasingly clear that different patients with the same medical condition may respond very differently to the same medication. It is estimated that up to 90% of all prescribed drugs benefit only 25% of patients. Treating patients with drugs that are likely ineffective can not only cause unnecessary suffering due to side effects and a lack of medical benefit, but also waste valuable healthcare resources. Therefore, methods for accurately predicting treatment outcomes for individual patients are needed so that physicians can give the right medication to the right patient at the right time.

[0005] Methods in this field for personalized prediction of treatment response can be broadly categorized into methods that infer treatment response from indirect biomarkers (e.g., inferring from mutations in BCR-ABL that a patient is likely not to respond to imatinib) and methods that directly measure drug action in raw patient material to predict treatment outcomes (i.e., functional ex vivo drug testing).

[0006] Known functional ex vivo drug assays in the art include the MiCK assay (Kravtsov et al., Blood. 92(3): 968–80), the method described in US20100298255A1, or the Cell Titer Glo assay from Promega Corporation. These methods focus on testing whether target cell populations extracted from patients respond to a specific drug under appropriate ex vivo incubation conditions. Here, we present evidence that, contrary to current understanding in the art, it is insufficient to merely measure drug response in target cell populations; in fact, the selectivity of drug effects on target cells, in contrast to effects on off-target cells, is crucial for predicting treatment outcomes (Examples 2–4). Therefore, similarly for predicting clinical drug response using functional ex vivo drug response assays, effective means are needed to determine how a chemical substance (e.g., an FDA-approved drug) selectively affects one cell population relative to others in a cell mixture containing at least two different cell subpopulations.

[0007] The selectivity of a drug to another cell population is traditionally determined by comparing the concentration (EC50) at which 50% of the desired or on-target effect is achieved in the target cell population with the concentration at which the same effect is achieved in an off-target cell population.

[0008] One approach implemented in this art involves isolating target and off-target cell populations, or providing isolated cell lines representing the target and off-target cell populations, and determining drug responses at different concentrations within these isolated cell populations. A drawback of this approach is that target cells cannot be analyzed in their natural environment, which can affect the results in several ways. Furthermore, if isolating target cells from complex cell mixtures (e.g., PBMCs) is required, this process introduces additional interference to the system, potentially affecting the assay results. Moreover, effects dependent on cell-cell interactions via direct physical interaction or on action at a distance via soluble messengers (e.g., the immune system's cellular clearance of damaged but not apoptotic cancer cells) cannot be reproduced in isolated cell model systems.

[0009] To determine the selectivity of a drug or other chemical test compound in a complex mixture composed of two or more cell types, new methods are needed to distinguish different cell populations and selectively determine the efficacy of the drug against each cell population. The standard method in the art for analyzing complex cell mixtures is flow cytometry. Here, individual cell populations can be distinguished, and the effect of the test compound can be determined using fluorescent dyes and markers (e.g., by staining with fluorescently labeled antibodies, fluorescent live-death staining). Another method has been described in WO2016 / 046346.

[0010] To determine the EC50 of a chemical test compound (e.g., a drug that acts on a specific cell subpopulation in a complex mixture of cells) using flow cytometry, a person skilled in the art would count (i.e., determine the absolute cell count) the target subpopulation of cells exhibiting the desired phenotype after treatment with the chemical compound (typically four or more concentrations of the chemical compound), plot the number of cells exhibiting the desired phenotype after treatment with the chemical compound against the concentration of the chemical compound, and fit a 4-parameter logistic or other sigmoid model to determine the EC50. Alternatively, a representative assay proportional to the phenotype, such as the determination of ATP levels for the total number of viable cells, can be used. This is evidenced by a large body of prior art literature and application descriptions (e.g., Hernandez et al., SLAS Technology 2017, Vol. 22(3), 325–337 (where the phenotype is viable leukemia cells) or Ross et al., Cancer Research 49, 3776–3782. July 15, 1989) and considerable effort invested in validating and optimizing the ability to count absolute cell numbers using flow cytometry.

[0011] To determine the effect of a test compound on the selectivity of one cell type relative to another in a complex mixture of cells, a person skilled in the art will therefore determine the EC50 of the test compound for the two cell populations, and take the ratio of the EC50 values ​​or the difference of the log EC50 values ​​as a measure of selectivity (see the definition of the therapeutic index used by the FDA).

[0012] However, this method is subject to the following major limitations: absolute cell counts are inherently difficult to determine using flow cytometry and other single-cell analysis techniques. The absolute cell count seeded into the assay plate can vary significantly, especially when using automated cell dispensing machines. Cells may be differentially lost from different assay wells during subsequent staining and washing steps. Furthermore, absolute cell quantification typically requires benchmarking against bead standards, which introduces another source of error and represents an additional effort required. Finally, selective assays require determining the effect of the test compound at different concentrations, which significantly increases sample requirements and assay time.

[0013] In view of the above, there is a need in the art for a method to determine the selectivity of chemical compounds in complex cellular mixtures.

[0014] Therefore, the technical problem to be solved by the present invention is to provide an improved method for determining the selectivity of one or more test compounds and a related method for determining selectivity based on the improvement.

[0015] Therefore, in a first embodiment, the present invention relates to a method for determining the selectivity of a test compound, the method comprising the following steps:

[0016] (a) Provide a sample which contains at least two distinguishable cell subpopulations in the total cell population;

[0017] (b) Divide the sample into at least two parts;

[0018] (c) Incubating at least one portion obtained in step (b) in the absence of the test compound, and incubating at least one portion obtained in step (b) in the presence of the test compound;

[0019] (d) in

[0020] (i) the at least portion incubated in the presence of the test compound and

[0021] (ii) Of the at least portion incubated in the absence of the test compound

[0022] Determine the number of cells in one of at least two subpopulations exhibiting a distinguishable phenotype, relative to the number of cells in the total population of cells exhibiting the same phenotype;

[0023] (e) Determine the selectivity of the test compound in inducing the phenotype mentioned in (d) relative to all other phenotypes in a subgroup mentioned in step (d) by dividing (i) by (ii), wherein if (i) divided by (ii) is greater than 1, preferably greater than 1.05, 1.1, 1.5, 2, 3, and most preferably greater than 5, then the test compound selectively induces the phenotype mentioned in (d), and if (i) divided by (ii) is less than 1, preferably less than 0.95, 0.9, 0.7, 0.5, 0.3, and most preferably less than 0.2, then the test compound selectively inhibits or reduces the phenotype mentioned in (d).

[0024] Unlike methods practiced in the art for determining the selectivity of a test compound in inducing or inhibiting a distinguishable cell population's phenotype relative to other cell populations in a total cell population, the method of the present invention does not require the determination of absolute cell counts, but rather relies on determining the fraction of cells exhibiting the desired phenotype out of the total cell population exhibiting the same phenotype. Therefore, the method of the present invention is robust to variations in the precise number of cells seeded into the assay plate, which can vary significantly, especially when using automated cell dispensing machines. The method of the present invention is even more robust to cell loss during subsequent staining and washing steps, where cells may be lost differentially from different assay wells. The method also eliminates the need to set a baseline for bead standards, which introduces another source of error and represents an additional effort required. Therefore, the method of the present invention is internally controlled. Furthermore, the method of the present invention obtains selectivity information with as few as one concentration point of the test compound, while competing methods require determination of multiple concentration points; see, for example, Example 8.

[0025] Furthermore, unlike methods practiced in the art, the method of the present invention does not require the separation of the cell population contained in the total cell population before determining the selectivity of the test compound for the cell population contained in the total cell population. Therefore, the method of the present invention does not rely solely on the effect of the test compound on the separated target cell population to determine its selectivity, but takes into account the interactions of the cells included in the complex cell population. Thus, the method of the present invention surprisingly enables the determination of the selectivity of the test compound in a cell mixture, the resulting selectivity being inherently robust to variations in cell number between different assays, internally controlled, and taking into account the interactions between the different cell populations contained in the total cell population.

[0026] Furthermore, the method of the present invention does not rely on determining EC50 from dose-response curves. This is in contrast to prior art methods that require determining EC50 to determine selectivity. EC50 is the concentration of a compound at which the half-maximal effect induced by or inhibited by the test compound is obtained. EC50 is often also referred to as IC50 (“50% inhibition concentration”) or GI50 (“50% growth inhibition”), depending on the context. Alternatively, concentrations that achieve or inhibit other percentage effects (e.g., EC90, EC80, etc.) are sometimes used. EC50 is typically obtained by measuring the cell response to four or more concentrations of the test compound and fitting the data with a suitable sigmoid curve. To determine the selectivity of a compound in affecting one cell population relative to another, the EC50 of the compound affecting both cell populations must be determined independently, and the difference in log EC50 is used as a measure of selectivity. To determine EC50, an assay proportional to the response of the test compound is required, such as the number of cells with a specific phenotype or the magnitude of the effect. Such an assay is inherently sensitive to changes in the absolute number of cells incubated with the test compound before determining the desired phenotype. In contrast, the method of the present invention relies on the fraction of cells exhibiting a specific phenotype out of the total number of cells with the same phenotype to determine selectivity. Therefore, the method of the present invention does not rely on quantification of absolute cell counts, but is internally controlled, and allows for the quantitative testing of compound selectivity by determining the response to the test compound at fewer concentration points than methods in the art require, which necessitate determining the EC50 and thus the full dose-response curve (Example 1). The method of the present invention is particularly advantageous when the amount of sample available is limited, as this is often the case with primary patient samples.

[0027] As described in Example 12, the EC50 value, which is crucial for determining selectivity in implementing the method of interest, cannot be determined from the fraction of cells exhibiting the desired phenotype out of the total number of cells exhibiting that phenotype. Therefore, it is not obvious to those skilled in the art that using the fraction of cells exhibiting a specific phenotype out of the total number of cells exhibiting the same phenotype to obtain information about selectivity is not obvious.

[0028] In contrast to existing methods, in the method of this invention, to evaluate the selectivity of a test compound in affecting one cell subpopulation relative to another, the selectivity of the test compound is determined based on the number of cells in a specific cell subpopulation exhibiting the particular phenotype of interest relative to the number of cells in the total cell population exhibiting the same phenotype. Therefore, the resulting selectivity is inherently robust to changes in absolute cell numbers between different assays, as shown in Example 13, is internally controlled, and takes into account interactions between different cell populations contained within the total cell population. Furthermore, selectivity can be determined by measuring the cellular response to the test compound at fewer concentration points than those required by methods in the art.

[0029] Unlike existing methods for determining whether a subject with cancer will respond to or is responsive to treatment with a test compound, the method of the present invention links the effect of the test compound on a complex population of cancer cells included in a variety of cell populations from a patient with the effect of the same test compound on the complex cell population as a whole. Therefore, the method of the present invention does not rely solely on the effect of the test compound on the target cancer cell population to determine its selectivity and draw conclusions about whether a patient treated with the test compound is responsive, but also takes into account the undesirable effects of the test compound on other cells included in the complex cell population. In other words, the method of the present invention quantifies the selective ability of an anticancer drug to kill cancer cells relative to non-cancer cells in order to determine whether a subject with cancer will respond to or is responsive to treatment with the test compound or drug. Compared to existing methods that only measure the effect of the test compound on cancer cells, the method of the present invention provides more accurate information about whether a subject with cancer will respond to or is responsive to treatment with the test compound as described in Examples 2-4. Specifically, as shown in the appended embodiments, using the method of the present invention, the area under the ROC curve (AUROC) value, a measure of quality (therefore, one method can distinguish between two types), is 0.97, while the AUROC obtained using the method according to WO2016 / 046346 is only 0.91, and the AUROC based on cell number is 0.86 (see...). Figure 5 Similarly, reduced classification accuracy was observed in Examples 2 and 3, leading to... Figure 4Accordingly, the method of the present invention produces a classification accuracy of 0.85, while if the drug response is determined solely based on the sensitivity of cancer cells or the sensitivity of the total cell population, a classification accuracy of 0.65 or less is obtained. Figure 4 (See Figures 1 and 2, respectively). Based on the comparative data provided in this application, it is clear that the method of the present invention provides improved accuracy.

[0030] Furthermore, unlike existing methods for determining whether a subject with cancer will respond to or is responsive to treatment with the test compound, the method of the present invention does not rely on the quantification of absolute cell numbers, dose-response curves, or the separation of cell populations contained in the total cell population. Therefore, the method of the present invention is more robust to variations in total cell numbers between different assays and to the loss of selectivity of cells from each assay. The method of the present invention can take into account the interactions of different cell populations that may affect the response of cell populations to the test compound (e.g., the recognition of damaged rather than dead cancer cells by cells of the immune system) and requires fewer assay concentration points.

[0031] The method of the present invention includes, in step (a), providing a sample comprising at least two distinguishable cell subpopulations within a total cell population. In this invention, the term "distinguished subpopulation" refers to cells that are part of a larger population and can be distinguished from other cells in the population by cellular markers. That is, the cells in the two distinguishable subpopulations may belong to the same or different cell types, as long as the cells exhibit different expression properties of cellular markers, making them distinguishable using imaging techniques such as confocal microscopy. To facilitate determination of whether a cell belongs to a cell subpopulation, it is preferable to provide cells in monolayer form. As shown in the appended embodiments, the formation of the monolayer can be performed using methods known in the art. For samples containing only non-adherent cells or a mixture of adherent and non-adherent cells, monolayer formation is preferably performed using the methods taught in WO2016 / 046346. Therefore, in a preferred embodiment of the invention, the method of the present invention further includes forming a monolayer containing the cells of the cell sample used after step (b) and before further analysis. In this respect, there is no particular limitation on the type of cell sample used in the method of the present invention, as long as it comprises at least two distinguishable cell subpopulations. However, in a preferred embodiment of the present invention, the cell sample used is a PBMC sample or a bone marrow sample.

[0032] Cellular markers are proteins expressed by specific cell types that, alone or in combination with other proteins, allow this type of cell to be distinguished from other cell types. That is, by using cellular markers expressed on the cell surface or within the cell (including in the cytoplasm or the inner membrane), cells contained in a total cell population as used herein, such as those contained in a sample obtained from a donor, can be distinguished and thus classified into distinguishable subpopulations. Therefore, the two or more distinguishable cell subpopulations are not limited to cells belonging to different cell types. Rather, the cells in two or more distinguishable subgroups can be of the same cell type, provided that the subgroups can be distinguished using cellular markers, such as those expressed on their surfaces, for example, cells of the same cell type at different disease stages, and / or cells of the same type but with different activation states and / or cells of the same type at different differentiation stages.

[0033] Peripheral blood mononuclear cells (PBMCs) are blood cells with round nuclei (as opposed to lobe nuclei). PBMCs include lymphocytes (B cells, T cells (CD4 or CD8 positive), and NK cells), monocytes (dendritic cells and macrophage precursors), macrophages, and dendritic cells. These blood cells are key components of the immune system in fighting infection and adapting to invaders. In some embodiments of the invention, PBMCs purified using a Ficoll density gradient, preferably human PBMCs, are preferably used to produce a PBMC monolayer of the invention or a cell culture apparatus containing a PBMC monolayer, or for use in the methods provided in some aspects of the invention. The invention can use any mononuclear cell. In a preferred embodiment, the invention can determine, but is not limited to, the selectivity of the test compound relative to target cells contained in PBMCs or bone marrow cell samples, specifically selectivity for cells in the following cell groups and cell lineages (including terminal cell states): hematopoietic stem cells (including, but not limited to, common lymphoid progenitor cells, common bone marrow progenitor cells, and their mature lineages and terminal states, including progenitor B cells, B cells, double-negative T cells, positive T cells, plasma B cells, NK cells, monocytes (macrophages, dendritic cells)). These can be found, but are not limited to, in peripheral blood, bone marrow (locally flattened bone), umbilical cord blood, spleen, thymus, lymphoid tissue, and any fluid accumulation resulting from disease, such as pleural fluid. Cells can be in any healthy or diseased state.

[0034] PBMCs used in the methods described herein can be isolated from whole blood using any suitable method known in the art or described herein. For example, the protocol described by Panda et al. can be used (Panda, S. and Ravindran, B. (2013). Isolation of Human PBMCs. Bio-protocol 3(3):e323). Preferably, density gradient centrifugation is used for separation. This density gradient centrifugation separates whole blood into components separated by layers, such as an upper plasma layer, followed by a PBMC layer and a bottom polymorphonuclear cell layer (e.g., neutrophils and eosinophils) and erythrocyte fraction. Polymorphonuclear cells can be further separated by lysing erythrocytes, i.e., anucleate cells. Common density gradients used for this centrifugation include, but are not limited to, Ficoll (a hydrophilic polysaccharide, e.g., -Paque (GE Healthcare, Upsalla, Sweden) and SepMate TM (StemCell Technologies, Inc.) Germany).

[0035] Bone marrow cells used in the methods described herein can be isolated from bone marrow using any suitable method known in the art. In particular, density gradient centrifugation and magnetic beads can be used to separate bone marrow cells from other components of these samples. For example, the MACS cell separation reagent (Miltenyi Biotec, Bergisch Gladbach, Germany) can be used.

[0036] As is known in the art, such isolated cultures may contain a small percentage of one or more other cell types, such as anucleate cells like erythrocytes. PBMCs can be further isolated and / or purified from these other cell populations known in the art and / or described herein; for example, methods for lysing erythrocytes are commonly used to remove these cells from isolated PBMCs. However, the methods of the present invention do not rely on further purification methods and can be used directly from the PBMCs isolated herein. Therefore, the methods disclosed herein may or may not include lysing erythrocytes from isolated PBMC samples. However, when present, anucleate cells, such as erythrocytes (typically smaller than PBMCs), are believed to be present on the culture surface beneath and between the PBMCs and potentially interfere with the formation of a monolayer suitable for imaging. Therefore, it is preferred that the concentration of anucleate cells, such as erythrocytes, relative to the PMBCs is about 500 to 1, more preferably about 250 to 1, most preferably about 100 to 1, and preferably the lowest possible concentration. That is, most preferably, the isolated PBMC sample used for the methods disclosed herein contains fewer than about 100 anucleate cells, such as erythrocytes, per PBMC.

[0037] After providing a sample, particularly a sample containing at least two distinguishable cell subpopulations within the total cell population as described above, especially a sample containing PBMCs or bone marrow cells, the sample is divided into at least two portions. Alternatively, instead of separating the sample provided in (a), at least two samples of the same origin and type can be provided, i.e., samples that do not require further separation. The two portions may have the same size or different sizes. However, it is preferred that each of the at least two portions contains cells of each distinguishable cell subpopulation in similar, preferably identical, proportions.

[0038] After the sample is split into at least two portions, at least one portion is incubated in the absence of the test compound (i.e., the test compound whose selectivity is to be determined). That is, this portion is used as a control / reference portion.

[0039] Incubate at least one remaining portion obtained in step (b) in the presence of the test compound. In this respect, there are no particular limitations on the test compound or multiple test compounds, provided that it / they are generally suitable for use as a medicine. However, it is preferred that the test compound be selected from compounds known to be effective in the treatment of diseases, particularly hematologic malignancies and / or malignancies of bone marrow and / or lymphoid tissue, inflammatory diseases, and autoimmune diseases. Compounds known to be effective in treating these diseases include chemical compounds and biological compounds, such as antibodies. Examples of compounds known to be effective in treating these diseases include, but are not limited to, alenmab, anagrelide, arsenic trioxide, asparaginase, ATRA, azacitidine, bendamustin, bonnetumab, bortezomib, bosutinib, brentuximab vedotin, busulfan, histamine dihydrochloride (Ceplene), chlorambucil, cladribine, clofarabine, cyclophosphamide, cytarabine, dasatinib, daunorubicin, decitabine, and denileukin-toxin conjugate. Diftitox, Dexamethasone, Doxorubicin, Duvelisib, EGCG (Epigallocatechin Gallate), Etoposide, Filgraxazone, Fludarabine, Gemtuzumab (Ozomicin), Histamine Dihydrochloride, Homoharringtonine, Hydroxyurea, Ibrutinib, Idarubicin, Adelaide, Ifosfamide, Imatinib, Interferon A-2a (Recombinant), Interferon A-2b (Recombinant), Intravenous Immunoglobulin, L-Asparaginase, Lenalidomide, Macitinib, Melphalan, Mercaptopurine, Methotrexate, Midotaurin, Mitoxantrone, MK-3475 (Pembrolizumab), Nilotinib, Pegaspargase, Pegylated Interferon Alpha-2a, Prednisolone, Ponatinib, Prednisolone, Prednisolone, R115777, RAD001 (Everolimus) Rituximab, Ruxolotinib, Selinexor (KPT-330), Sorafenib, Sunitinib, Thalidomide, Topotecan, Retinoic Acid, Vincristine, Vincristine, Vorinostat, Zoledronic Acid, ABL001, ABT-199 (Venetoc), ABT-263 (Navitoclax), ABT-510, ABT-737, ABT-869 (Rinivani), AC220 (Quizatinib), AE-941 (Neovastatin), AG-858, AGRO100, Aminopterin, Erwinia chrysanthemi asparaginase, AT7519, AT9283, AVN-944, Bafetinib, Betumomab, Betadine, Betaalethine, Bexarotin, BEZ235, BI2536, Buparlisib (BKM120), Carfilzomib, Carmustine, Ceritinib, CGC-11047, CHIR-258, CHR-2797, CMC-544 (Izizumab / Ozomicin), CMLVAX100, CNF1010, CP-4055, Crenolanib, Crizotinib, Ellagic Acid, Exarucin, Epoetin Zeta, Ipatizumab, FAV-201, FavId, Flavonoidopyridamole, G4544, Galiximab, Gallium Maltolate, Gallium Nitrate, Givinostat, GMX1777, GPI-0100, Grn163l, GTI 2040, IDM-4, ​​Interferon Alfacon-1, IPH 1101, ISS-1018, Ixaspiron, JQ1, Lentatinib, Nitrogen Mustard, MEDI4736, MGCD-0103, MLN-518 = Tandotinib, Motexafen Gadolinium, Natural Alpha Interferon, Neraribine, Olbacram, Obinutuzumab, OSI-461, Pabistat, PF-114, PI-88, Pivaloyloxymethyl butyrate, Pianthraquinone, Pomalidomide, PPI-2458, Pralatrexate, Proleukin, PU-H71, Ranolazine, Rabastinib, Samarium (153sm) Lexidronam, SGN-30, Bone-targeted Radiotherapy, Tadenafil, Tamibarbitine, Tammarolimus, Thioguanine, Traxatabin, Vindysine, VNP 40101M, Volasertib, XL228, Hydroxychloroquine (Plaquenil), Leflunomide (Arava), Methotrexate (Trexall), Sulfasalazine (Azulfidine), Minocycline (Minocin), Abatacept (Orencia), Rituximab (Rituxan), Tocilizumab (Actemra), Kinetetin (Kineret), Adalimumab (Humira), Etanercept (Enbrel), Infliximab (Remicade), Sertolizumab, Pegol (Cimzia), Golimumab (Simponi), Tofacitinib (Xeljanz, Xeljanz) XR), baricitinib, celecoxib (Celebrex), ibuprofen (prescription-strength), naproxen (Relafen), naproxen sodium (Anaprox), naproxen (Naprosyn), piroxicam (Feldene), diclofenac (Voltaren, Diclofenac Sodium)XR, Cambia, diflunisal, indomethacin, ketoprofen (Orudis, Ketoprofen ER, Oruvail, Actron), lodine, fenofofen (Nalfon), flurbiprofen, toradol, meclofenamic acid, mefenamic acid (Ponstel), meloxicam (Mobic), oxaprazin (Daypro), sullinic acid (Clinoril), disalcidol (Disalcid, Amigesic, Marthritic, Salflex, Mono-Gesic, Anaflex, Salsitab), tolectin, betamethasone, prednisone (Deltasone, Sterapred, Liquid) Pred, dexamethasone (Dexpak, Taperpak, Decadron, Hexadrol), cortisone, hydrocortisone (Cortef, A-Hydrocort), methylprednisolone (Medrol, Methacort, Depopred, Predacorten), prednisolone, cyclophosphamide (Cytoxan), cyclosporine (Gengraf, Neoral, Sandimmune), azathioprine (Azasan, Imuran), and hydroxychloroquine (Plaquenil).

[0040] Therefore, in the method of the present invention, at least a portion of the cell sample is incubated in the absence of the test compound, and at least one sample is incubated in the presence of the test compound.

[0041] In this regard, in some embodiments of the method of the present invention, cells, particularly PBMCs, are subsequently incubated at a density of about 100 cells / mm² to about 30,000 cells / mm² for separation. Preferably, cells, particularly PBMCs, are incubated at densities of about 500 cells / mm² to about 20,000 cells / mm², about 1,000 cells / mm² to about 10,000 cells / mm², about 1,000 cells / mm² to about 5,000 cells / mm², or about 1,000 cells / mm² to about 3,000 cells / mm². Most preferably, cells, particularly PBMCs, are incubated at a density of about 2,000 cells / mm². The term "about" should have the meaning of within 10% of a given value or range, more preferably within 5%. Therefore, in some embodiments, cells, particularly PBMCs, are incubated in the method of the present invention to have a density of about 100 (i.e., 90 to 110) cells / mm² growth zone to about 30,000 (i.e., 27,000 to 33,000) cells / mm² growth zone in the culture device. More preferably, cells, particularly PBMCs, are incubated at the following densities: about 500 (i.e., 450 to 550) cells / mm² growth zone to about 20,000 (i.e., 18,000 to 22,000) cells / mm² growth zone; about 1,000 (i.e., 900 to 1,100) cells / mm² growth zone to about 10,000 (i.e., 9,000 to 11,000) cells / mm² growth zone; about 1,000 (i.e., 900 to 1,100) cells / mm² growth zone to about 5,000 (i.e., 4,500 to 5,500) cells / mm² growth zone; or about 1,000 (i.e., 900 to 1,100) cells / mm² growth zone to about 3,000 (i.e., 2,700 to 3,300) cells / mm² growth zone. Most preferably, cells, particularly PBMCs, are incubated at a density of about 2,000 (i.e., 1,800 to 2,200) cells / mm² growth zone.

[0042] The number of cells, particularly PBMCs, can be determined using standard methods known in the art. Specifically, the number of PBMCs can be determined by cell counting using a hematology counter or the methods described by Chan et al. (Chan et al. (2013) J. Immunol. Methods 388(1-2), 25-32). The number of bone marrow cells can also be determined using methods well known in the art. Specifically, bone marrow cells can be determined using cell counting. Other cells can also be counted using methods well known in the art.

[0043] Incubation is performed in a culture medium. Methods for maintaining cell viability, particularly PBMCs or bone marrow cells, are well known to those skilled in the art. However, there are no particular limitations on the culture medium used in the methods of this invention. In this respect, culture medium represents a liquid containing the nutrients and substances necessary for cell culture. Liquid culture media for culturing eukaryotic cells are known to those skilled in the art (e.g., DMEM, RPMI 1640, etc.). A suitable culture medium can be selected depending on the type of cells to be cultured. For example, PBMCs or bone marrow cells can be cultured in RPMI 1640 10% FCS. Any suitable culture medium can be selected; however, a culture medium component known not to artificially affect the response of PBMCs and / or bone marrow cells should be selected. Supplements describe substances added to the culture medium to induce or alter cell function (e.g., cytokines, growth and differentiation factors, mitogens, serum). Supplements are known to those skilled in the art. An example of serum commonly used with eukaryotic cells is fetal bovine serum. Antibiotics, such as penicillin, streptomycin, ciprofloxacin, etc., may also be added to the culture medium. In one embodiment, test substances and / or stimulants may be added separately to the live cell material in each individual unit. Test substances may be drugs or drug components. Stimulants can include any substance that supports the maintenance, growth, or differentiation of cells. In one specific embodiment, the stimulant is a substance that acts on immune cells, for example, by activating them. Stimulants used to activate immune cells are known in the art. These activators can be polypeptides, peptides, or antibodies and other stimulants. Examples include OKT-3, interferon-α, interferon-β, and interferon-γ, oligoCPGs, cell mitogens (e.g., PWM, PHA, LPS), etc. The test substance and stimulant can be injected into the cell culture medium. Preferably, PBMCs are cultured in RPMI supplemented with 10% FBS / FCS (preferably, but not necessarily, a low endotoxin level to minimize activation). The PBMC culture may also contain human serum from a PBMC donor.

[0044] As used in this invention, the term "growth zone" refers to the surface of a culture apparatus on which cells reside. As used in this invention, "density" refers to the number of cells per unit area of ​​the surface of the apparatus on which cells reside. The culture apparatus can be made of any material compatible with the cell culture, particularly materials for non-cytotoxic cell culture assays. Examples are plastic materials, such as thermoplastic or rigid plastics. Examples of suitable plastics are polyethylene, polypropylene, polysulfone, polycarbonate, polyetheretherketone (PEEK), or polytetrafluoroethylene (PTFE). In particular, the apparatus is suitable for the culture and / or maintenance of PBMCs. Typical culture apparatuses known in the art and used in this invention include culture flasks, dishes, plates, and multiwell plates. Multiwell plates are particularly used because they provide the ability to maintain multiple cultures individually with minimal material requirements (e.g., minimal culture medium requirements), for example, for multiple perturbations. Preferred culture apparatuses include 96-well plates, 384-well plates, and 1536-well plates. As is known in the art, for imaging analysis of cultures, particularly fluorescence imaging, it is particularly preferred to use a specially designed black panel for imaging, which reduces background fluorescence / background optical interference while having minimal light scattering and reduced crosstalk. The culture device can be sterile. In a most preferred embodiment, a multi-well imaging plate is used, the plate comprising a plurality of holes, wherein at least some of the holes include a first chamber formed by one or more first sidewalls and a bottom wall; a second chamber formed by one or more second sidewalls and including an opening for introducing liquid, wherein the second chamber is disposed on top of the first chamber; an intermediate bottom plate disposed between the first and second chambers, which forms an interference-blocking structure; wherein the intermediate bottom plate is provided with at least one through hole providing liquid connection between the first chamber and the second chamber; wherein the through hole is configured to insert the tip of a pipette from the second chamber into the first chamber through the through hole.

[0045] This device is particularly useful for automated imaging systems and analysis. Therefore, it is preferably suited for such systems. In a non-limiting example, the culture device may be semi-transparent. Culture dishes and plates for imaging, such as fluorescence imaging, are well known in the art and are commercially available. A non-limiting example of a commercially available culture plate used in the practice of this invention is... 384-well, tissue culture treated black cap, transparent substrate (Corning Inc., Massachusetts, USA) or 384-well flat-bottomed, transparent-backed, black polystyrene TC-treated microplate (Product #3712). Another example: Perkin Elmer.

[0046] As described above, a surprising technical advantage of the method of the present invention is that it determines / depends on the selectivity of the test compound for target cells, which is determined in an environment that more closely represents the native environment of the target cells within a complex cell population. That is, in the method of the present invention, naturally occurring cell-cell interactions are preferably maintained. In this regard, those skilled in the art know the means and methods for determining / evaluating / tracking / verifying cell-cell interactions. In particular, those skilled in the art can distinguish between naturally occurring cell-cell interactions and interactions introduced during the preparation of cell samples. Therefore, those skilled in the art understand that cells of the same type and / or different types interact in a living organism. Furthermore, those skilled in the art understand that cells comprising distinguishable subgroups of cells within a total cell population interact in a living organism. In the present invention, preferably, the majority of cells contained in the cell sample maintain their naturally occurring cell-cell interactions. That is, the majority of cells in the cell sample, particularly at least 50%, preferably 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 100% of the cells in the cell sample, interact with the same cells or cells of the same cell type or the same distinguishable subgroup of cells, as in vivo. Cell-cell interactions can be verified / evaluated / determined using methods well known in the art. For example, confocal microscopy can be used to assess / determine / verify whether cell-cell interactions are between cells that interact in their natural environment or between cells that do not exhibit interactions in their natural environment. Such non-natural cell-cell interactions may be particularly due to reasons such as cell aggregation.

[0047] Furthermore, in the method of the present invention, it is preferable that the majority of cells are in a physiologically relevant state, meaning that preferably 60%, 70%, 80%, 90%, 95%, or 100% of the cells are in a physiologically relevant state. The percentage of cells in a physiologically relevant state used in the method of the present invention is determined / measured / assessed using methods well known in the art. In particular, whether a cell sample contains cells in a physiologically relevant state is determined by quantifying the cells contained in the cell sample. This can be done using methods well known in the art. Specifically, quantification can be performed by image analysis comparing cells to those in a reference sample, such as cells in the peripheral blood or bone marrow of a reference individual or multiple reference individuals (e.g., one or more healthy donors), wherein the cell sample, particularly PBMCs or bone marrow cell samples, is from a diseased donor. Cell quantification is a standard diagnostic tool. Thresholds for cell subpopulations contained in cell samples (particularly PBMCs and / or bone marrow cells) for both healthy and diseased donors have been well established. Therefore, physiological relevance can be determined based on the differences in samples assessed using the means and methods of the present invention. For example, literature on cell subsets contained in hematopoietic cells can be found in Hallek et al. (2008) Blood 111(12). Therefore, quantitative and further means and methods, such as using microscopy to determine cell-cell interactions, make it possible to determine whether a cell sample represents a physiologically relevant state.

[0048] The inventors have provided a method for analyzing samples containing cells that maintain naturally occurring cell-cell interactions and are primarily in a physiologically relevant state. Therefore, in a preferred embodiment of the invention, cells are analyzed in the form of a monolayer. In this regard, the inventors have found that incubation at the cell density disclosed above results in the formation of an imageable cell monolayer, particularly PBMCs or bone marrow cells. The monolayer is formed after the sample is fractionated into at least two parts (i.e., step (b)) and before further analysis. Therefore, the method of the present invention also allows for imaging and / or microscopic analysis of cell populations, particularly PBMC populations and / or bone marrow populations. As used herein, a monolayer refers to a layer of cells found primarily within the same focal plane of an imaging device, such as a microscope or automated camera known in the art or described herein. The term "layer" is used to refer to a culture within which the cell formation is primarily two-dimensional, i.e., the culture is primarily a single layer of cells. That is, within the culture, most cells are found not to remain on or above other cells and are not present in aggregates (e.g., groups of cells that extend above a single-cell layer by including cells that remain on or above other cells). Therefore, the cell monolayers, particularly PBMC monolayers, within the scope of this invention preferably comprise a horizontal layer of cells, particularly PBMC cells, with a thickness equal to the height of a single cell, particularly the height of a PBMC. Similarly, the bone marrow cell monolayers within the scope of this invention preferably comprise a horizontal layer of bone marrow cells with a thickness equal to the height of a single bone marrow cell. As used herein, the term monolayer does not exclude the possibility of cell aggregates or multilayer constructs (i.e., regions of cell cultures having a height greater than a single cell (particularly PBMC cells or a single bone marrow cell)) or cell-free regions within the culture vessel. Rather, the term is used to refer to a culture of the present invention in which the majority of its imageable or visible area (e.g., by microscopic methods) consists of a single layer of cells. This is most readily achieved by providing a single layer of cells on the cell culture surface. However, as those skilled in the art will understand, other forms of cell samples may also be used in the methods of the present invention.

[0049] When using non-adherent cells, such as PBMCs or bone marrow cells, these cells are known to typically not form strong contact with the cell culture surface or strong intercellular contact. Therefore, the cell monolayers used in this invention, particularly the PBMC monolayers used in various aspects of this invention, are not contemplated as necessarily equivalent to adherent cell monolayers as understood in the art, i.e., cell layers that are firmly attached, uniformly spread, and cover most of the culture surface. Rather, in some embodiments, the cell monolayers used in this invention, particularly the PBMC monolayers, may comprise high-density cultures comprising the majority of cells in direct contact with one or more other cells but not necessarily attached to the culture surface, or may comprise low-density cultures in which cells are within the monolayer but not in direct physical contact with any other cells in the culture. Cell monolayers used in certain aspects of this invention may also comprise medium-density cultures with discrete regions of cell contact with one or more cells and other regions of cells not in contact with other cells.

[0050] Cells used in the methods of the present invention, particularly PBMCs, bone marrow cells, or other adherent and non-adherent primary cells, can be isolated from samples obtained from healthy subjects (i.e., those not suspected of having the disease or suspected of being susceptible to it), or from samples obtained from subjects known to have the disease or suspected of having the disease. The diagnosis of the subject's disease state can be performed using standard methods routinely performed by those skilled in the art, such as physicians. These conventional methods can be supplemented or replaced by the methods of the present invention. For example, to determine whether a subject has or is likely to have a disease, samples from subjects known to have the disease are used to determine the characteristic cell-cell interaction patterns of each disease. Additionally or optionally, cell-cell interaction patterns from healthy donors can be used to determine differences likely caused by the corresponding disease. Cell interaction patterns herein refer to the natural tendency (propensity) of one or more different cell types or cell populations to interact with each other, as determined according to the present invention.

[0051] Following incubation, in the at least portion incubated with (i) the test compound and in the at least portion incubated without the test compound, the number of cells exhibiting one of at least two subpopulations displaying a distinguishable phenotype is determined relative to the number of cells in the total cell population exhibiting the same phenotype. Those skilled in the art are familiar with various methods that can be used to count cells with a specific phenotype. In this regard, "phenotype" refers to an observable characteristic or trait of a cell. A phenotype arises from the expression of an organism's genetic code, its genotype, and the influence of environmental factors and the interaction between the two. A cell's phenotype includes, but is not limited to, specific cell morphology and size, cell viability, protein expression, localization of specific proteins at specific locations, protein colocalization, post-translational modifications of proteins such as phosphorylation, nutrient uptake and consumption, etc. A phenotype is also functional in nature, as a specific trait is only expressed in response to certain stimuli, such as cytokines, pathogens, or some other extracellular or intracellular stimuli.

[0052] In a preferred embodiment of the invention, the distinguishable phenotype in step (d) is the survival rate, and (i) if the selectivity determined in step (e) is <1, then the test compound is determined to selectively reduce the number of live cells in one subpopulation described in step (d), and (ii) if the selectivity determined in step (e) is >1, then the test compound is determined to selectively increase the survival rate of said subpopulation and / or selectively reduce the survival rate of one or more subpopulations other than the subpopulation described in step (d).

[0053] Subsequently, the selectivity of the test compound in inducing the cell phenotype counted as part of a subpopulation in the previous step is determined. In this invention, this is accomplished by dividing (i) by (ii) as immediately preceding above, wherein if (i) divided by (ii) is greater than 1, preferably greater than 1.05, 1.1, 1.5, 2, 3, and most preferably greater than 5, then the test compound selectively induces the phenotype mentioned in (d), and if (i) divided by (ii) is less than 1, preferably less than 0.95, 0.9, 0.7, 0.5, 0.3, and most preferably less than 0.2, then the test compound selectively inhibits or reduces the phenotype mentioned in (d).

[0054] As shown in the accompanying examples, the selectivity of the obtained test compound to the target cell population is more robust to changes in the total cell number in different assays performed during the implementation of this method, requires fewer concentration points to be measured, and takes into account the interactions of different cell populations contained in the total cell population.

[0055] In another embodiment of the invention, a method is provided for determining whether a subject with cancer will respond to or is responsive to treatment with a test compound, wherein the method includes steps (a) providing a sample obtained from the subject comprising at least two cell subpopulations in a total population of cells, wherein at least one subpopulation corresponds to cancer cells and at least one subpopulation corresponds to non-cancer cells; (b) dividing the sample into at least two portions; (c) incubating at least one portion obtained in step (b) in the absence of the test compound and incubating at least one portion in the presence of the test compound; (d) in the presence of (i) In (ii) the at least one portion incubated with the test compound and in the at least one portion in the absence of the test compound, the number of live cells in at least one of the subpopulations corresponding to cancer cells is determined, relative to the number of live cells in the total cell population; and (e) whether the subject will respond to treatment with the test compound or is responsive to the treatment is determined by (i) dividing by (ii), wherein if the obtained value is less than 1, preferably less than 0.95, 0.9, 0.8, 0.6, 0.4, and most preferably less than 0.2, then the subject will respond to the treatment or is responsive to the treatment.

[0056] Therefore, the present invention provides a method for a diagnostic approach to determine whether a subject will respond to or is responsive to treatment with a test compound, particularly a therapeutic agent. As mentioned above, a given test compound, particularly a therapeutic agent, such as one of the therapeutic agents further listed above, may exhibit different therapeutic effects in individual subjects suffering from the same or similar diseases, such as cancer. Therefore, it is advantageous to determine the selectivity of the test compound, particularly the therapeutic agent, individually for a given subject. In the above-described method of the present invention, the selectivity of the test compound, particularly the therapeutic agent, can be determined in a highly reliable manner, with the potential for improvement compared to prior art methods, and the test compound (particularly the therapeutic agent) identified as having improved selectivity compared to one or more alternative test compounds (particularly therapeutic agents) will show advantageous effects in the in vivo environment.

[0057] In other words, using the methods provided herein to analyze the selectivity of test compounds to cell subpopulations within a sample / image can predict the response of a disease state to a therapy tested in a donor; in this respect, the methods of the present invention offer advantages over existing methods in the art.

[0058] In one implementation, the method for determining whether a subject with cancer will respond to or is responsive to treatment using a test compound is repeated for at least two test compounds, and the subject will respond to or is responsive to treatment using a combination of at least two test compounds is determined as follows: the value of each of the at least two test compounds obtained in (e) is subtracted from 1.0, and the resulting values ​​of the at least two test compounds are summed, wherein if the sum is greater than -1, preferably greater than -0.5, 0, 0.5, and most preferably greater than 1, then the subject is determined to respond to or is responsive to treatment using a combination of the at least two test compounds.

[0059] In one embodiment of the invention, the test compound used in the method of the invention may comprise more than one chemical substance. That is, in one embodiment, the invention relates to the method of the invention disclosed herein, wherein the test compound comprises one or more chemical substances. The presence of more than one chemical substance in the test compound used in the method of the invention can provide useful information about, for example, the synergistic effect between at least two of the chemical substances. That is, the chemical substances may influence each other in terms of their selectivity for a given target cell. Therefore, it is advantageous to use a test compound comprising more than one chemical substance in order to reliably determine the selectivity and / or to use the determined selectivity in the method of the invention.

[0060] In another embodiment of the invention, at least one portion obtained in step (b) of the method of the invention is further divided into at least two portions, each of which is incubated with a different concentration of the test compound. Determining the selectivity of different concentrations of the test compound and / or determining whether a subject with cancer will respond to treatment with different concentrations of the test compound or to said treatment can provide further improved results, as the effective concentration in vivo can vary depending on the dose and timing of the administered test compound. That is, to account for potential effects associated with the concentration of the test compound, in one embodiment of the invention, the test compound may be incubated with at least two portions obtained in step (b) of the method of the invention at different concentrations. Those skilled in the art are aware of typical concentrations used in methods known in the art to determine the selectivity of the test compound. That is, concentrations are typically from 100 μM to 100 pM, preferably from 10 μM to 1 μM, and more preferably from 10 μM, 1 μM, and 100 nM.

[0061] In a preferred embodiment of the invention, particularly in the method of the invention where the test compound is incubated at different concentrations with one or more portions obtained in step (b) of the method of the invention, an average selectivity may be calculated in step (e) and used to determine the final selectivity. The average selectivity can provide a more reliable measure, representing a further improvement over selectivity determined by methods known in the art.

[0062] Similarly, in one embodiment of the invention, the method includes step (c) incubating at least two portions in the absence of a test compound and / or in the presence of a test compound, and step (d) determining the average number of cells in one of at least two subpopulations relative to the number of cells in the total cell population, in (i) the at least two portions in the presence of the test compound and / or (ii) the at least two portions in the absence of the test compound. Averaging multiple assays reduces potential undesirable effects that may occur in a single assay due to natural sample variations. That is, averaging can significantly reduce the expected error in the obtained results and thus lead to more reliable results from the method of the invention.

[0063] Therefore, in one embodiment, the present invention relates to a method according to the invention, wherein at least one portion obtained in step (b) is further divided into at least two portions, wherein each of the at least two portions is incubated with a test compound of different concentrations in step (c), and wherein steps (d) and (e) are repeated independently for each concentration of the test compound to determine a value at each concentration of the test compound, thereby calculating an average of all concentrations after step (e) and using it to determine a final value.

[0064] In another embodiment, the invention relates to a method according to the invention, wherein in step (b), the sample is divided into at least three portions, in step (c), at least two portions are incubated in the absence of a test compound, and / or at least two portions are incubated in the presence of a test compound, such that each portion incubated in the presence of the test compound is incubated in the presence of the same concentration of the test compound, and wherein in step (d), for each portion incubated independently in the presence of the test compound and / or (ii) in the absence of the test compound, the number of cells in one of at least two subpopulations exhibiting a distinguishable phenotype is determined relative to the number of cells in the total cell population exhibiting the same distinguishable phenotype, and the average of the relative numbers obtained in (i) and / or (ii) is determined.

[0065] In another embodiment, the invention relates to a method according to the invention, wherein in step (b), the sample is divided into at least three portions, and in step (c), at least one portion is incubated in the absence of a test compound, and / or at least two portions are incubated in the presence of at least two different concentrations of the test compound, and in step (d), for each portion (i) incubated independently in the presence of the test compound and / or (ii) incubated independently in the absence of the test compound, the number of cells exhibiting one of at least two subpopulations showing a distinguishable phenotype is determined relative to the number of cells in the total cell population showing the same distinguishable phenotype, wherein the average value of (i) and / or the average value of (ii) for each concentration is determined independently, and is used for further steps, and wherein in step (e), the selectivity / value of each concentration of the test compound is determined by dividing the average value of (i) for each concentration by the average value of (ii), and the final selectivity / value is obtained by averaging the selectivity / value of each concentration.

[0066] In another embodiment, the present invention relates to a method according to the invention, wherein the method is repeated for at least two test compounds, and the test compound having the lowest value obtained in step (e) is selected for treating a subject with cancer.

[0067] In another embodiment, the present invention relates to a method according to the invention, wherein the method is repeated for at least three test compounds, and a combination of at least two of the at least three test compounds having the highest value is selected for treating a subject with cancer, the highest value being obtained by subtracting the value obtained in (e) for each of the at least two test compounds in the combination from 1.0, and summing the resulting values ​​of the at least two test compounds in the combination.

[0068] Therefore, the present invention also relates to a method for determining which of a plurality of test compounds is most likely to provide the best clinical benefit to cancer patients, thereby repeating the method of the present invention with two or more test compounds, and the test compound having the lowest value determined in step (e) of the method of the present invention will be the best test compound most likely to provide the greatest clinical benefit to cancer patients. Therefore, the present invention also relates to the use of the resulting test compounds in the treatment of cancer and the use of the test compounds in the preparation of pharmaceutical compositions for the treatment of cancer.

[0069] In another embodiment, the present invention relates to a method for determining which of two or more different combinations of test compounds (each comprising two or more test compounds) will most likely provide the greatest clinical benefit to a cancer patient, thereby repeating the method of the invention with two or more test compounds comprising two or more different combinations of each test compound. The combination of two or more test compounds having the highest obtained sum is the combination of test compounds that will most likely provide the highest clinical benefit to a patient with cancer, said highest obtained sum is obtained by subtracting the selectivity value of each of the at least two test compounds comprising each combination obtained in (e) from 1.0, and summing the result values ​​of said at least two test compounds. Therefore, the present invention also relates to the use of combinations of test compounds for the treatment of cancer.

[0070] As detailed above, the method of the present invention can be used to determine the selectivity of a test compound for cells contained in a total cell population, wherein the total cell population comprises at least two distinguishable cell subpopulations. In principle, any cell population can be used in the method of the present invention. However, PBMCs or bone marrow cells are preferred. There are various diseases associated with cells contained in PBMC or bone marrow cell samples, particularly proliferative diseases such as cancer. Therefore, in the method of the present invention, particularly in the method for determining whether a subject with cancer will respond to treatment with the test compound or to said treatment, said cancer is preferably a cancer associated with PBMCs or bone marrow cells or cells derived from PBMCs or bone marrow cells. Those skilled in the art know cancerous diseases that fall under this definition, i.e., cancer types associated with PBMCs or bone marrow cells or cells derived from PBMCs or bone marrow cells. However, the method of the present invention is not limited to cancer. In other words, the method of the present invention can be used to determine whether a subject will respond to or is responsive to treatment for the following diseases coded in ICD-10 (but not limited to): A00-B99 - certain infectious and parasitic diseases; C00-C97 - malignant tumors; D70-77 - other diseases of the blood-forming system; D80-89 - certain diseases involving immune mechanisms, not classified elsewhere; D82 - immunodeficiency associated with other major deficiencies; D83 - common variant immunodeficiency; D84 - other immunodeficiency; G35-37 - diseases of the central nervous system; I00-I03 - acute rheumatic fever; I05-I09 - chronic rheumatic heart disease; I01 - rheumatic fever with cardiac involvement; I06 - rheumatic aortic valve disease; I 09 - Rheumatic myocarditis; I70 - Atherosclerosis; K50 - Crohn's disease; K51 - Colitis; K52 - Other non-infectious gastroenteritis and colitis; M00-M19 - Joint diseases; M05 - Seropic rheumatoid arthritis; M06 - Other rheumatoid arthritis; M10 - Gout; M11 - Other crystalline arthropathy; M35 - Sjögren's syndrome; M32 - Systemic lupus erythematosus; N70-77 - Inflammatory diseases of the female pelvic organs; P35-39 - Perinatal-specific infections; P50-P61 - Hemorrhagic and hematologic disorders of the fetus and newborn; Z22 - Carriers of infectious diseases; Z23 - Requires immunity against a single bacterial disease; and / or Z24 - Requires immunity against certain single viral diseases.

[0071] To more reliably determine the selectivity of the test compound and / or whether a subject will respond to or is responsive to treatment, in a more preferred embodiment, the method of the present invention utilizes a tissue sample containing at least 1% cancer cells and / or at least 1% non-cancer cells. More preferably, the tissue sample contains at least 2% cancer cells and / or at least 2% non-cancer cells, even more preferably at least 5% cancer cells and / or at least 5% non-cancer cells. Most preferably, the tissue sample contains at least 10% cancer cells and / or at least 10% non-cancer cells.

[0072] As further disclosed above, in the method of the present invention, it is preferable to culture samples, particularly tissue samples, as a monolayer. When the sample originates from PBMCs containing non-adherent cells or cells contained in bone marrow, it is preferable to culture the tissue sample as a non-adherent cell monolayer. Therefore, the present invention provides a method for high-throughput determination of the following in imaging studies using physiologically relevant multipopulation cell samples, particularly primary hematopoietic samples: 1) the effect of testing compounds, e.g., based on single-cell analysis, at the whole-cell level, the effect of testing compounds for chemotherapy / immunotherapy / immunosuppressive therapy on in vitro cell population diagnostic markers or other markers; 2) the ability of this technique, based on in vitro determination in patient samples, to provide predictions about which chemotherapy will be beneficial to which patient; 3) the ability of this technique to determine the effect of numerous stimuli or stimulants (e.g., drugs) on immune function; and 4) the integration of numerous patient datasets over time to determine patterns of treatment assessment. In principle, any cell sample can be used in the method of the present invention, such as mononuclear cells from blood, bone marrow, pleural effusion, spleen homogenate, lymphoid tissue homogenate, or skin homogenate. However, mononuclear cells are preferred. As those skilled in the art will recognize, the mononuclear cell samples used in the methods of the present invention particularly include PBMCs and bone marrow cells, as well as other cells. Therefore, cell samples, preferably monolayers of primary mononuclear cells used in the methods of the present invention, may include PBMCs and / or bone marrow cells. That is, while the methods provided herein are described with respect to cells or PBMCs in general, those skilled in the art will understand that these methods are also provided with respect to bone marrow cells or other cells. Therefore, this document provides methods using bone marrow cells for determining the selectivity of a test compound for cells contained in a bone marrow sample, and for determining whether a subject with or susceptible to a disease will respond to treatment with the test compound or is responsive to said treatment, including the use of bone marrow cells.

[0073] In this respect, bone marrow is the flexible tissue inside bone. In humans, red blood cells are produced by the bone marrow core in the epiphysis of long bones during a process called hematopoiesis. Bone marrow transplantation can be performed to treat serious bone marrow diseases, including certain forms of cancer such as leukemia. Additionally, bone marrow stem cells have been successfully converted into functional nerve cells and can also be used to treat diseases such as inflammatory bowel disease. Therefore, bone marrow cells represent a valuable target for treating a variety of diseases, such as cancer or inflammatory diseases like inflammatory bowel disease. Thus, the methods described herein for using bone marrow samples obtained from donors are very useful for assessing / determining whether a donor has a disease or is predisposed to having one. Furthermore, the methods for using bone marrow cells described herein offer several advantages in areas such as high-throughput drug screening.

[0074] WO2016 / 046346 overcomes the notion that adherent cells (macrophages, HeLa, etc.) need to form stainable and imageable monolayers by providing a monolayer. Prior to the monolayer described above, research groups could not implement image-based single-cell screening techniques in raw patient samples for high-throughput determination of chemotherapy-induced molecular (biomarker) changes, assessment of oncoblast viability, and cell-cell contact, particularly where disease states are manifested or reflected in non-adherent cells, such as in hematologic diseases or conditions like lymphoma and leukemia. To address this problem, the inventors of WO2016 / 046346 provide means and methods, as well as methodologies and image analysis circuitry, referred to herein as "pharmacoscopy," which allows the visualization of adherent and non-adherent cells in a single image, typically requiring only 1 / 10th the perturbation time compared to methods known in the art. thThe pharmacoscopy method provides the same information collected by known methods (e.g., flow cytometry) but offers additional advantageous information, such as the determination of subcellular phenotypes (protein localization / co-localization) and cellular microenvironment / proximity relationships. Furthermore, the method described in WO2016 / 046346 requires fewer cells, thus requiring less patient material, less liquid volume, and virtually no human intervention; therefore, pharmacoscopy significantly increases the number of molecular perturbations that can be tested in parallel and produces a more detailed assessment. Moreover, without the need to sort diseased cells from an inherently healthy population, pharmacoscopy can track drug-mediated phenotypic changes while controlling for off-target drug effects in parallel. These important controls are performed by correlating the effect of the test compound on target cells (e.g., cancer cells) with total cells (e.g., healthy cells) from the same donor present in the same well and the same imaging field. The method of the present invention uses the method of WO2016 / 046346 but includes additional surprising and unexpected advantages. In particular, it is now possible to more reliably determine the selectivity of the test compound and / or whether a subject with a disease, particularly cancer, will respond to or be responsive to treatment with the test compound. This is achieved by taking into account the nonspecific effects of the test compound on off-target cells, particularly monolayers, contained in the sample representative.

[0075] Using the method of this invention, a large number of test compounds can be analyzed efficiently and rapidly, i.e., their selectivity can be determined, using a large number of monolayers, which can be derived from a single sample obtained from, for example, a patient / subject, particularly PBMC or bone marrow samples. Typically, the effects of at least 1000, at least 4000, at least 8000, at least 12000, at least 16000, at least 20000, at least 24000, at least 50000, at least 75000, or up to 90000 or more test compounds can be studied in multiple monolayers obtained from such a single sample. In some embodiments, the monolayers provided herein can be imaged and analyzed using multiple channels with high-content data simultaneously. The number of available data channels depends only on the specific imaging software and available staining methods, a field that is rapidly evolving. Currently available methods allow for the simultaneous imaging, processing, and analysis of high-content data from at least two channels, more typically four, five, or eight channels.

[0076] In the methods of this invention, cells, preferably in monolayer form, can be imaged according to any methods known in the art and / or described herein, and the methods provided herein can use any imaging techniques known in the art. Specific imaging methods are not critical and can be determined based on the knowledge of those skilled in the art. Imaging may or may not require the use of dyes or staining agents, and may include imaging of stained and unstained components, and / or may include imaging under conditions where the staining agent is visible or invisible (e.g., imaging in a bright field (where fluorescent staining agents would be invisible) and imaging under UV irradiation (where fluorescent staining agents are visible), or combinations thereof. Imaging under bright field conditions is well-known and conventional in the art and can be performed according to standard methods and / or as described herein. Additionally or alternatively, any other label-free imaging can be used according to the invention. Such label-free methods are known, including, for example, PhaseFocus imaging (Phase Focus Ltd, Sheffield, UK).

[0077] In a preferred embodiment of the invention, the number of live cancer cells and non-cancer cells is determined using an automated microscope. In an even more preferred embodiment, the number of live cells is determined as the number of unfragmented nuclei. Methods for determining nucleus fragmentation include, but are not limited to, staining the nuclei with dyes such as DAPI or Hoechst series nuclear dyes and evaluating their morphology under a fluorescence microscope.

[0078] Implementation of the invention may further include adding detectable markers to cells, preferably monolayers, particularly PBMC monolayers (with or without relation to label-free methods), which can be detected using microscopic methods to selectively label cells with specific phenotypes (such as viable cells) and / or distinguishable cell subpopulations. Detectable markers can label discrete cellular structures, components, or proteins known in the art. Markers can also be linked to antibodies for specific labeling and allow detection of antibody antigens. In a preferred embodiment, the detectable markers allow visualization under visible or ultraviolet light. Thus, detectable markers can be fluorescent. Many visual tags are known in the art and are suitable for this invention. Markers may be detectable without further action, or may become detectable only after a second step, such as the addition of a substrate, exposure to an enzyme reaction, or exposure to a specific wavelength of light.

[0079] Cellular subpopulations, i.e., distinguishable subpopulations as used herein, particularly PBMC or myeloid cell subpopulations, can be identified by detectable markers by the expression of one or more markers on or within the target cell surface. Alternatively or additionally, a subpopulation can be defined by the absence of expression of one or more markers on or within the target cell surface. It may be necessary to test for the expression or non-expression of one or more markers (e.g., two, three, four, etc.) to further ensure that cells expressing or not expressing the markers are indeed target cells, e.g., members of the desired cell subpopulation. For example, a “mixture” of antibodies against different markers can each be conjugated (either directly or indirectly) to the same or different markers. As an example, a mixture of antibodies against different markers can each contain a binding motif that binds to the same marker (e.g., each can contain an Fc of the same species recognized by the same secondary antibody, or each can be biotinylated and specifically bound to the same avidin-conjugated marker). Optionally, two or more different antibodies or mixtures of antibodies can be used. Preferably, cells are stained using at least two mutually distinguishable markers, allowing identification of cells expressing at least two different markers of the target cell type. Cells may also be stained using at least three, four, five, or more mutually distinguishable markers, allowing detection of cells expressing a greater number of markers of the target cell type. Optionally, cells can be identified as target type cells if they express a preselected number of markers or certain preselected combinations of markers, or if they do not express preselected markers. Furthermore, the markers for the target cell type need not be unique to the target cell, as long as they allow differentiation of the target cell from other cells in the population. In the case of PBMCs, the major components of the PBMC cell population are represented by CD11C of dendritic cells, CD14 of macrophages, CD3 (CD4 or CD8 with CD3) of T cells, and CD19 of B cells. Although the aforementioned markers overlap across these major PBMC subgroups, staining with these markers to identify PBMC subpopulations is widely accepted in the art. Other markers applicable to the methods of this invention can be found in the CD Marker Manual (Becton, Dickinson and Co. 2010, CA, USA). The major cell subsets contained in bone marrow cells are neutrophils, metamyelocytes, segmented neutrophils, normal erythrocytes, and lymphocytes.

[0080] In the practice of this invention, antibodies conjugated to detectable markers are preferred. Such antibodies allow targeting discrete cellular structures, and therefore, mixtures of such antibodies (each carrying different markers) can be used to simultaneously visualize multiple targets / cellular structures / cellular components. Care must be taken during staining to avoid monolayer disruption. As those skilled in the art will understand, this is particularly problematic for the use of antibody-based markers, as their use typically requires one or more washing steps to remove unbound markers that will interfere with accurate visualization, i.e., will result in nonspecific staining and / or measurement “noise.” Therefore, this invention includes a method for staining cellular monolayers with detectable markers, particularly antibody-based markers, which minimizes or eliminates the washing requirement after staining. The method of this invention may include adding the detectable marker at a concentration that avoids generating noise signals without washing, which can be determined by methods well known in the art and / or the methods described herein. Therefore, this invention includes the use of labeled antibodies at concentrations higher or lower than those recommended by the antibody manufacturer.

[0081] For some exemplary cell populations, cells are considered positive for a given marker only if it exhibits a characteristic localization or pattern within the cells. For example, cells may be considered "positive" if cytoskeletal markers are present in the cytoskeleton, and "negative" if some diffuse cytoplasmic staining is present. In such cases, cells can be cultured under suitable conditions (e.g., as adherent cultures) to establish the characteristic localization or pattern within the cells. Those skilled in the art can readily determine the appropriate culture conditions and times for cytoskeleton assembly (or other processes for establishing subcellular tissues) that may be necessary for robust detection of a given marker. Additionally, markers can be readily selected that reduce or eliminate the need for adherent culture as a prerequisite for robust staining.

[0082] Dyes for labeling proteins are known in the art. Generally, dyes are molecules, compounds, or substances that can provide optically detectable signals, such as colorimetric, luminescent, bioluminescent, chemiluminescent, phosphorescent, or fluorescent signals. In a preferred embodiment of the invention, the dye is a fluorescent dye. Non-limiting examples of dyes (some of which are commercially available) include CF dyes (Biotium, Inc.), Alexa Fluor dyes (Invitrogen), DyLight dyes (Thermo Fisher), Cy dyes (GE Healthscience), IRDyes (Li-Cor Biosciences, Inc.), and HiLyte dyes (Anaspec, Inc.). In some embodiments, the excitation and / or emission wavelengths of the dye are between 350 nm and 900 nm, or between 400 nm and 700 nm, or between 450 and 650 nm.

[0083] For example, staining may include the use of various detectable markers, such as antibodies, autoantibodies, or patient serum. The staining agent can be observed under visible and ultraviolet light. The staining agent may contain antibodies or enzymes capable of producing the colored reagent, directly or indirectly coupled to it. When an antibody is used as a component of the staining agent, the marker may be directly or indirectly coupled to the antibody. Examples of indirect coupling include avidin / biotin coupling, coupling via secondary antibodies, and combinations thereof. For example, cells may be stained with a primary antibody that binds to a target-specific antigen, and a secondary antibody that binds to the primary antibody or a molecule coupled to the primary antibody may be coupled to a detectable marker. Using indirect coupling can improve the signal-to-noise ratio, for example, by reducing background binding and / or providing signal amplification.

[0084] The staining agent may also include a first or second antibody directly or indirectly conjugated (as described above) to a fluorescent label. The fluorescent label may be selected from: Alexa Fluor 350, Alexa Fluor 405, Alexa Fluor 430, Alexa Fluor 488, Alexa Fluor 514, Alexa Fluor 532, Alexa Fluor 546, Alexa Fluor 555, Alexa Fluor 568, Alexa Fluor 594, Alexa Fluor 610, Alexa Fluor 633, Alexa Fluor 635, Alexa Fluor 647, Alexa Fluor 660, Alexa Fluor 680, Alexa Fluor 700, Alexa Fluor 750 and Alexa Fluor 790, FITC, Texas Red, SYBR. Green, DyLightFluors, Green Fluorescent Protein (GFP), TRIT (Tetramethylrhodamine isothiol), NBD (7-Nitrobenzo-2-oxa-1,3-diazole), Texas Red dye, Phthalic acid, Terephthalic acid, Isophthalic acid, Cresyl violet, Cresyl blueviolet, Brilliant cresyl blue, p-aminobenzoic acid, Erythrosine, Biotin, Digoxinyl lignin, 5-Carboxy-4',5'-Dichloro-2',7'-Dimethoxyfluorescein, TET (6-Carboxy-2',4,7,7'-Tetrachlorofluorescein), HEX (6-Carboxy-2',4,4',5',7,7'-Hexachlorofluorescein), Joe (6-Carboxy-4',5'-Dichloro-2',7'-Dimethoxyfluorescein) 5- Carboxy-2',4',5',7'-tetrachlorofluorescein, 5-carboxyfluorescein, 5-carboxyrhodamine, Tamra (tetramethylrhodamine), 6-carboxyrhodamine, Rox (carboxy-X-rhodamine), R6G (rhodamine 6G), phthalocyanine, azocyanine, anthocyanins (e.g., Cy3, Cy3.5, Cy5), xanthine, succinylfluorescein, N,N-diethyl-4-(5'-azobenzotriazolyl)aniline, acridine and quantum dots.

[0085] Other exemplary embodiments of the method of the present invention utilize antibodies directly or indirectly coupled to fluorescent molecules, such as ethidium bromide, SYBR Green, FITC (fluorescein isothiocyanate), DyLight Fluors, green fluorescent protein (GFP), TRIT (tetramethylrhodamine isothiol), NBD (7-nitrobenzo-2-oxa-1,3-diazole), Texas Red dye, phthalic acid, terephthalic acid, isophthalic acid, cresol violet, and cresyl blue. Violet, Brilliant Cryocyanate Blue, p-Aminobenzoic Acid, Erythrosine, Biotin, Digoxin, 5-Carboxy-4',5'-Dichloro-2',7'-Dimethoxyfluorescein, TET (6-Carboxy-2',4,7,7'-Tetrachlorofluorescein), HEX (6-Carboxy-2',4,4',5',7,7'-Hexachlorofluorescein), Joe (6-Carboxy-4',5'-Dichloro-2',7'-Dimethoxyfluorescein) Carboxy-2',4',5',7'-tetrachlorofluorescein, 5-carboxyfluorescein, 5-carboxyrhodamine, Tamra (tetramethylrhodamine), 6-carboxyrhodamine, Rox (carboxy-X-rhodamine), R6G (rhodamine 6G), phthalocyanine, azomethyl, anthocyanins (e.g., Cy3, Cy3.5, Cy5), xanthine, succinylfluorescein, N,N-diethyl-4-(5'-azobenzotriazolyl)aniline, and aminoacridine. Other exemplary fluorescent molecules include quantum dots, which are described in patent literature [see, for example, USPat.Nos.6,207,299,6,322,901,6,576,291,6,649,138 (Surface modification methods in which a mixture of hydrophobic / hydrophilic polymer transfer agents is bound to the surface of quantum dots), USPat.Nos.6,682,596,6,815,064 (For alloying or hybrid shells), each of which is incorporated herein by reference] and technical literature [such as "Alternative Routestoward High Quality CdSe Nanocrystals," (Qu et al., Nano Lett., 1(6):333-337(2001)]. Quantum dots with various surface chemistry and fluorescence properties are commercially available from companies such as Invitrogen Corporation, Eugene, Oreg., Evident Technologies (Troy, NY) and Quantum Dot Corporation (Hayward, Calif.)."Quantum dots" also includes alloyed quantum dots such as ZnSSe, ZnSeTe, ZnSTe, CdSSe, CdSeTe, ScSTe, HgSSe, HgSeTe, HgSTe, ZnCdS, ZnCdSe, ZnCdTe, ZnHgS, ZnHgSe, ZnHgTe, CdHgS, CdHgSe, CdHgTe, ZnCdSSe, ZnHgSSe, ZnCdSeTe, ZnHgSeTe, CdHgSSe, CdHgSeTe, InGaAs, GaAlAs, and InGaN. Alloyed quantum dots and their preparation methods are disclosed, for example, in US Application Publication No. 2005 / 0012182 and PCT Publication WO 2005 / 001889.

[0086] After labeling the cells used in the method of the present invention (preferably in monolayer form), the method may further include detecting the signal of the detectable marker. The detection method can be appropriately adjusted depending on the type of signal emitted by the detectable marker. A detection method suitable for detecting fluorescently emitted markers is preferred. The detection method can also be automated according to standard methods known in the art. For example, various computational methods exist that enable those skilled in the art to analyze and interpret microscopic images of cells or to develop automated schemes for their analysis. For primary image analysis, including correction for illumination bias in microscope images, identification of individual cells from microscope images, and determination of marker intensity and texture, as well as nuclear and cell size, shape, and position parameters, the OpenSource software CellProfiler (e.g., version 2.1.1) can be used. Identification of marker-positive cells (such as CD34+ progenitor cells or viable dye-positive cells) can be performed using machine learning with the OpenSource software CellProfilerAnalyst (e.g., version 2.0), and double-positive or triple-positive cells can be identified using a sequential gating strategy. Plate-overviews for further analysis and selection can also be created using CellProfilerAnalyst.

[0087] The cellHTS package in Bioconductor (e.g., version 2.14) or Pipeline Pilot (e.g., version 9.0; Accelrys) can be used for data analysis after primary image analysis, including plate effect normalization, control-based normalization, and hit selection.

[0088] Commercial automated microscope systems can also be used in the practice of this invention, such as the PerkinElmer Operetta automated microscope (PerkinElmer Technologies GmbH & Co. KG, Walluf, Germany), which may include corresponding image analysis software, such as PerkinElmer's Harmony software (e.g., version 3.1.1). According to the method of the invention, such automated and / or commercial systems can be used for primary image analysis, positive cell selection, and hit selection from microscopic images.

[0089] Following this initial analysis, the method of the present invention is performed to determine the selectivity of the test compound for a cell population with a specific phenotype contained in a sample containing at least two distinguishable cell subpopulations, or to determine whether a subject with a disease, particularly cancer, will respond to or is responsive to treatment with the test compound, wherein the method includes determining the selectivity of the test compound based on its ability to induce the aforementioned phenotype, particularly cell viability.

[0090] A treatment decision can be made based on the results of a method for determining whether a subject with cancer will respond to treatment with or to the test compound of the present invention. That is, the subject can be selected to be treated with the test compound that has the most favorable outcome in terms of whether the subject will respond to or to the treatment with the test compound.

[0091] When calculating the “average” or “mean” of quantities in the method of the present invention, it should be understood that this can refer to the arithmetic mean, geometric mean, and / or related statistical measures, the purpose of which is to estimate the true value of a variable based on repeated measurements associated with random error. Those skilled in the art will also understand that in some cases, using the median instead of the average may be advantageous (e.g., where outliers exist but the underlying random variable is normally distributed). In a preferred embodiment, the arithmetic mean is used whenever the method of the present invention involves the “average” or “mean”.

[0092] When a test compound contains more than one chemical substance, the concentration of the test compound refers to a specific combination of chemical substances at different concentrations, and a test compound at different concentrations refers to a test compound containing at least one chemical substance at different concentrations. A test compound containing more than one chemical substance at a specific concentration means that all chemical substances containing the test compound have a specific, but not necessarily identical, concentration.

[0093] "Treatment" or "treatment" refers to therapeutic interventions and preventative or preventive measures aimed at preventing, improving, or alleviating (reducing) a target pathological condition or symptom, or one or more symptoms associated with it. Similarly, "responsive" or "response" and similar terms refer to indications that a target pathological condition or one or more symptoms associated with it has been prevented, improved, or reduced. These terms are also used herein to indicate delaying the onset of a disease (especially myeloproliferative disorders) or an indication that has been completed, suppressing the disease or indication (e.g., reducing or preventing the development of the disease or indication), mitigating the effects of the disease or indication, or prolonging the life of a patient with the disease or indication. Patients requiring treatment include those diagnosed with the disease, those suspected of having the disease, those predisposed to having the disease, and those seeking prevention of the disease. Therefore, the mammals to be treated herein may have been diagnosed with the condition or may be predisposed to or susceptible to the disease.

[0094] A “response” or “having a response” refers to a subject who exhibits at least one altered characteristic after treatment. The altered characteristic of the subject can be an improvement or reduction in the target pathological condition or symptom.

[0095] As used herein, the terms “prevention” or “avoidance” refer to the prevention of the occurrence and / or recurrence or onset of one or more symptoms of cancer by administering a preventive or therapeutic agent to a subject.

[0096] The techniques and methods described herein are primarily for primary hematopoietic cells or all monocytes. As will be understood by those skilled in the art, primary hematopoietic cells include, in particular, PBMCs and bone marrow cells. Therefore, the techniques and methods for PBMCs described herein are also disclosed for bone marrow cells and any other monocytes.

[0097] The test compound used in the method of this invention can be a therapeutic agent used in treatment / a therapeutic agent approved for treating diseases (especially cancer). In this respect, "test compound" as understood in this invention refers to molecules, including but not limited to polypeptides, peptides, glycoproteins, nucleic acids, synthetic and natural drugs, peptide mimics, polyenes, macrocyclic compounds, glycosides, terpenes, terpenoids, aliphatic and aromatic compounds and their derivatives. In a preferred embodiment, the test compound is a chemical compound, such as a synthetic and natural drug. In another preferred embodiment, the test compound achieves improvement and / or cure of a disease, condition, pathology and / or related symptoms. Polymers may encapsulate one or more test compounds used in the method of this invention.

[0098] As detailed immediately above, test compounds may also be selected from known therapeutic agents. In this regard, suitable therapeutic agents include, but are not limited to, those presented by Goodman and Oilman in *The Pharmacological Basis of Therapeutics* (e.g., 9th edition) or *The Merck Index* (e.g., 12th edition). The genera of therapeutic agents include, but are not limited to, drugs affecting inflammatory responses, drugs affecting body fluid composition, drugs affecting electrolyte metabolism, chemotherapeutic agents (e.g., for hyperproliferative diseases, particularly cancer, for parasitic infections, and for microbial diseases), antitumor agents, immunosuppressants, drugs affecting blood and blood-forming organs, hormones and hormone antagonists, vitamins and nutrients, vaccines, oligonucleotides, and gene therapy. It should be understood that the invention also includes compositions comprising combinations, such as mixtures or blends of two or more active agents (e.g., two drugs).

[0099] In one embodiment, the therapeutic agent may be a drug or prodrug, antibody, or vaccine. The method of the present invention can be used to assess whether administration of a therapeutic agent to a patient elicits a response to the therapeutic agent or components of a delivery medium, excipient, carrier, etc., administered together with the therapeutic agent.

[0100] The exact nature of the therapeutic agent does not limit the invention. In a non-limiting embodiment, the method of the invention can be used to evaluate the response to synthetic small molecules, naturally occurring substances, naturally occurring or synthetically produced biological agents, or any combination of two or more of the foregoing, optionally in combination with excipients, carriers, or delivery media.

[0101] Methods well known in the art can be used to determine / evaluate / verify the viability of cells contained in a sample to be analyzed, particularly in a monolayer. That is, those skilled in the art are well aware of methods for determining / evaluating / verifying cell state (stadium), such as whether cells are viable, alive, dead, or undergoing a process that alters their state, such as death in apoptosis or necrosis. Therefore, known markers / dyes that specifically identify / label cells in a particular state can be used in the methods of the present invention. These include dyes / labels selective for cells with incomplete membranes or selective for late cell death or early apoptosis. For example, fixable live / dead green (ThermoFisher, catalogue number L-23101) or antibodies against cytochrome C can be used to determine DNA renewal or cell proliferation by using dyes. Other means and methods for determining / evaluating / verifying the viability of cells (particularly in monolayer form) contained in the cell samples used in the present invention are known to those skilled in the art.

[0102] Changes in the viability and / or cell-cell interactions of two or more distinguishable subpopulations contained in a cell sample (particularly a monolayer, particularly a PBMC monolayer or a bone marrow cell monolayer) can be determined / tracked / assessed / verified using methods well known in the art. For example, changes can be determined / tracked / assessed / verified by optical sensing using a microscope. However, for high-throughput applications, automated methods are preferred for determining / tracking / assessing / verifying changes in the viability and / or cell-cell interactions of the individual subpopulations contained in the monolayer. This method involves identifying the subpopulations contained in the cell sample, preferably a monolayer, for example, by means of detectable markers. It can then be determined whether the marked / detected subpopulations exhibit cell-cell interactions, which can include direct contact (as described above) or indirect contact via the plasma membrane. Therefore, a distance parameter between marked cells, i.e., a threshold defined above, is introduced, which determines the total number of interactions, i.e., how many cell-cell interactions are observed between marked cells. In this method, a marked cell of one distinguishable subgroup may interact with one or more cells of a second distinguishable subgroup, and each interaction is counted. The obtained quantity is compared with the quantity expected by a random distribution function (i.e., random cell-cell interactions). The interaction tendency, i.e., the interaction score, can then be calculated using the method of the present invention, which determines whether the interaction is random or directional. Following this approach, changes in cell-cell interactions caused by the one or more test compounds can be measured / tracked / evaluated / verified before and after the addition of one or more test substances to the cell sample of the present invention.

[0103] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. While similar or equivalent methods and materials described herein may be used in the practice or testing of this invention, suitable methods and materials are described below. In case of any conflict, this specification, including definitions, shall prevail. Furthermore, the materials, methods, and embodiments described are illustrative only and not restrictive.

[0104] Unless otherwise stated, the general methods and techniques described herein may be performed according to conventional methods well known in the art and as described in the various general and more specific references cited and discussed in this specification. See, for example, Sambrook et al., Molecular Cloning: A Laboratory Manual, 2nd ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY (1989); Ausubel et al., Current Protocols in Molecular Biology, Greene Publishing Associates (1992); and Harlow and Lane Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY (1990).

[0105] Although various aspects of the invention have been shown and described in detail in the accompanying drawings and the foregoing description, such illustrations and descriptions are to be considered illustrative or exemplary rather than restrictive. However, it should be understood that changes and modifications can be made by those skilled in the art within the scope and spirit of the appended claims. In particular, the invention covers further embodiments having any combination of features from the different embodiments described above and below.

[0106] The invention also covers all other features shown individually in the figures, although they may not be described in the preceding or following description. Furthermore, individual alternatives to the embodiments described in the figures and specification, and individual alternatives to their features, may be omitted from the subject matter of other aspects of the invention.

[0107] Furthermore, in the claims, the words "comprising" or "including" do not exclude other elements or steps, and the singular form of the term does not exclude multiple elements. A single unit can perform the function of several features described in the claims. Terms related to attributes or values, such as "substantially," "about," "approximately," etc., specifically and precisely define the attribute or precisely define the value. Any reference marks in the claims should not be construed as limiting the scope.

[0108] The patent or application document contains at least one color drawing. A copy of the patent or patent application publication with color drawings will be provided by the official authority upon request and payment of the necessary fees.

[0109] The present invention is also illustrated in some aspects with reference to the following figures.

[0110] Figure 1 A. Two hypothetical dose-response curves showing the viability of cancer A cells and non-cancer B cells as a function of the drug concentration of the cytotoxicity test compound. B. Showing total cell viability and the fraction of viable A cells among the total viable cells as a function of the test compound concentration. Here, when the total cell viability is below 5% of the initial value, the fraction of viable A cells is set to 0.8 (i.e., the fraction at zero test compound concentration), due to the fact that quantifying a small number of cells is associated with large errors. According to this method, when the steps of the method according to the invention are performed, the selectivity of 1 will be reliably attributed to the test compound with strong total cytotoxicity.

[0111] Figure 2 A. The selectivity / value of the cytotoxicity test compound for killing cancer cells, as determined by the present invention, is a function of the difference in log EC50 of the test compound against cancer A cells and non-cancer B cells, measured at three concentration points (see [reference]). Figure 1 B). B. The selectivity / value determined as described in this invention is a function of the difference in log EC50 between the test compound and cancer cells and non-cancer cells measured at 400 concentration points. Using this invention, measurements at three concentration points are sufficient to obtain selectivity information. However, the more concentration points, the more accurately the selectivity / value reflects the difference in log EC50.

[0112] Figure 3 The selectivity / value for daunorubicin in killing AML blast cells (defined as CD34+, CD117+, or CD34+ / CD117+), determined using this invention for various daunorubicin concentrations, is shown as a function of daunorubicin concentration. At different daunorubicin concentrations, patients responding to therapies containing daunorubicin had lower selectivity / values ​​as determined using this invention than patients unresponsive to daunorubicin-based 3+5+7 induction therapy.

[0113] Figure 4 Top: Selectivity / value determined according to the present invention for killing cancer cells (defined here as cells expressing CD34 or CD117) in bone marrow samples from AML patients using a “3+5+7” induction therapy consisting of daunorubicin, cytarabine, and etoposide, in both responders and non-responders. A cutoff value of 0.92 allows patients to be classified as responders and non-responders with an overall classification accuracy of 0.85. Middle: Number of cancer cells, averaged over all drug concentrations and combinations, relative to the number of cancer cells at zero drug concentration. This metric only allows patients to be classified as responders and non-responders with an overall classification accuracy of 0.65. Bottom: Similar to the middle metric, but based on the total cell count. Accurate classification is not possible using this metric.

[0114] Figure 5 When predicting the response of AML patients to 3+5+7 induction therapy containing daunorubicin based on the selectivity of daunorubicin in killing AML blast cells determined according to the invention and using downstream data processing as described in Example 4, the area under the recipient operating curve was highest (AUROC = 0.97). This indicates that determining the killing selectivity / value according to the invention is advantageous compared to response predictions based, for example, on the number of cancer cells (AUROC = 0.91). AML blast cells are described here as follows... Figure 2 Defined in [the document / reference].

[0115] Figure 6 Left: Treatment of patients with hematologic malignancies using a combination of two or more FDA-approved drugs. For each patient, combination selectivity is calculated as the sum of 1 minus the individual selectivity of the drugs administered to the patient as determined according to the present invention. Combination selectivity is plotted against response (PD = progressive disease, SD = stable disease, PR = partial remission, CR = complete remission) and correlated with response. Based on the integrated selectivity, patients can be categorized as responders (CR and PR) and non-responders (PD and SD) with 92% accuracy and an AUROC of 0.84.

[0116] Figure 7 The selectivity / value of an FDA-approved drug, as determined according to this invention, in killing CD20+ cells relative to CD20- cells in patients with diffuse large B-cell lymphoma is [value missing]. The patients responded to ibrutinib treatment.

[0117] Figure 8 The selectivity / value of an FDA-approved drug, as determined according to this invention, in killing CD20+ cells relative to CD20- cells in patients with B-cell lymphoblastic lymphoma. Patients responded to treatment with the combination of bortezomib and 6-mercaptopurine.

[0118] Figure 9 The selectivity / value of an FDA-approved drug for killing CD79a+ cells relative to CD79a- cells in patients with diffuse large B-cell lymphoma, as determined according to this invention. The patients responded to combination therapy with bortezomib, cladribine, and dexamethasone.

[0119] Figure 10Cell viability was calculated for populations A and B after treatment with compound X at different concentrations [X] (log EC50 for A = -2 and log EC50 for B = 3, arbitrary concentration scale). Therefore, the number of viable cells in population A, as a fraction of the total viable cells (population A + B), was determined as Aviable / (Aviable + Bviable). The logarithmic dose-response curve was fitted to the S-shaped curve (black line) obtained as a function of concentration [X], Aviable / (Aviable + Bviable), with log EC50 as the inflection point. Neither log EC50A nor log EC50B corresponds to the log EC50 obtained from the curve fitting (i.e., the log EC50 of the black S-shaped curve), indicating that cell viability EC50A or EC50B cannot be obtained by fitting the logarithmic curve to Aviable / (Aviable + Bviable). Example

[0120] Example 1

[0121] Synthetic data simulating the response of a cell mixture consisting of cell populations A and B to the cytotoxic drug X are provided. X is expressed as log EC50 of EC50A (e.g., ...). Figure 1 2.5 in A) affects A, in log EC50B (e.g., Figure 1 In B, 3) affects B, on an arbitrary concentration scale. Based on these parameters, assuming a standard 4-parameter logistic (i.e., sigmoid) dose-response curve, the number of viable cells and the total number of each cell type in a mixture of cells A and B can be calculated. At a concentration of [X] = 0, assuming a total number of 10,000 cells, the ratio A:B = 0.8:0.2 is used to simulate the determination of the selectivity of X in killing A relative to killing B at only 3 drug concentrations in total.

[0122] Drug selectivity is calculated using this invention. Specifically, for each measured drug concentration, the number of viable A cells and the number of viable B cells are calculated. According to step (d) of the method of the invention, the number of viable cells in one of at least two subpopulations (here, A) exhibiting a distinguishable phenotype (here, viability) when X is present at three different concentrations is determined, relative to the number of cells in the total cell population exhibiting the same phenotype (here, viable A + viable B), which is Rx = Ax / (Ax + Bx), where Ax and Bx represent the number of viable A and B cells at the three different concentrations of [X], and (ii) for a concentration [X] = 0, R0 = A0 / (A0 + B0) is given. Then, the selectivity at each concentration of [X] is determined as Sx = Rx / R0, and the average of all Sx is calculated to obtain the final selectivity Sfinal = (S1 + S2 + S3) / 3.

[0123] When using this invention to determine the drug selectivity of different pairs of EC50A and EC50B, surprisingly, it is linearly proportional to the difference between the log EC50 of X for A and the log EC50 of X for B. Figure 2 ).

[0124] Example 2

[0125] Monocytes were extracted from 20 bone marrow samples from newly diagnosed, untreated patients with acute myeloid leukemia (AML) using Ficoll density gradient centrifugation. Following bone marrow sample collection, all 20 patients were treated with a “3+5+7” regimen of daunorubicin, etoposide, and cytarabine; 10 patients responded and 10 did not.

[0126] Monocytes were suspended in RPMI + 10% FCS + penicillin / streptomycin and seeded into Perkin Elmer Cell Carrier 384-well cell culture plates at a concentration of 20,000 cells per well (50 μL of medium). The wells were pre-loaded with different concentrations of cytarabine, daunorubicin, and etoposide. All possible drug and concentration combinations were represented on the plates at concentrations of 0, 1, 3, 10, and 20 μM for cytarabine, 0, 0.1, 1, 3, and 10 μM for daunorubicin, and 0, 1, 3, 10, and 20 μM for etoposide, thus obtaining a 3D drug titration matrix. Following WO2016 / 046346, cells were formed into monolayers and incubated for 18 hours. The monolayers were then fixed by adding 15 μL of PBS solution containing 0.5% Tritox 114 in 4% formaldehyde, gently flicked, and stained with DAPI and fluorescently labeled antibodies to label CD34 and CD117 positive cells. After 1 hour of incubation, images of each well were taken using an Opera Phenix automated confocal microscope (Perkin Elmer).

[0127] Cells positive for the marker were considered cancer cells, while those negative were considered non-cancer cells. The total number of viable cells was quantified by counting intact DAPI-stained nuclei using CellProfiler computational image analysis software, while fragmented nuclei were discarded due to death or near-death. Similarly, the number of viable cancer cells was determined by antibody-stained cells with intact DAPI-stained nuclei.

[0128] According to the present invention, the fraction of live cancer cells in the total live cells is determined by the fraction of live cancer cells relative to the control well (no drug, DMSO only). (For different concentrations of daunorubicin only) Figure 3This allows for the determination of the selectivity of each drug in killing cancer cells. Surprisingly, this measure can distinguish between responders and non-responders individually.

[0129] To account for the contribution of all drugs, according to the present invention, the selectivity of each drug in killing the cancer cell population is determined by taking the fraction of viable cancer cells in the total viable cells relative to the control well (no drug, DMSO only), at each drug combination and concentration, and averaging over all drug concentrations and combinations. A cutoff value of 0.92 can be used to distinguish patients who have a clinical response to the drug combination from those who do not. Figure 4 (See the image above), the overall classification accuracy is 0.85.

[0130] Example 3

[0131] Similar to Example 2, if the drug response is determined solely based on the sensitivity of cancer cells (here: CD34 or CD117 positive cells) or the sensitivity of the total cell population, a classification accuracy of 0.65 or less is obtained. Figure 4 (These are the middle and bottom images, respectively).

[0132] Example 4

[0133] Similar to Examples 2 and 3, this example illustrates how the selectivity determined according to the invention can be used for downstream analysis to obtain a drug response score that allows for more accurate classification of patients into responders and non-responders. For each patient sample and different drug concentration combinations, selectivity is calculated according to the invention, and the average is taken for responders and non-responders at each concentration point. The resulting data points span the dose-response surfaces in a four-dimensional dose-response space, assigning one surface to responders and one to non-responders. The surface that optimally separates the two dose-response surfaces is determined by identifying the cutoff point at each point in the dose-response space, allowing for optimal classification of responders and non-responders at that particular drug dose combination. A response score for each patient is calculated by assigning 1 to each point in the dose-response space, i.e., on the responder side of the separated surface, and -1 to the opposite side. These indices, weighted by total classification accuracy, are summed at each point in the concentration space to obtain the final drug response score. For example, this response score allows for classification of AML patients receiving 3+5+7 induction therapy with over 90% total classification accuracy. Figure 5 The area under the receiver operating curve (AUROC) was 0.97 for responders and non-responders, while the same model based on the number of cancer cells normalized only to the number of cancer cells at zero drug concentration yielded an AUROC of only 0.91, and when it was based on the number of cells, it yielded an AUROC of 0.86.

[0134] Example 5

[0135] Bone marrow aspirate, peripheral blood, pleural effusion, ascites, or excised lymph node samples consisting of cells commonly found in PBMCs or bone marrow were purified via Ficoll gradient (bone marrow, peripheral blood, pleural effusion, ascites) (GE Healthcare), or homogenized and filtered (lymphoid tissue), and resuspended in RPMI + 10% FCS and penicillin / streptomycin. According to WO2016 / 046346, a single-cell suspension of the resulting monocytes commonly found in PBMCs was seeded at a concentration of 20,000 cells per well in 50 μL of culture medium in 384-well Perkin Elmer cell carrier imaging plates to form a non-adherent monolayer. The plates were pre-loaded with 140 different clinically used anticancer drugs in 50 nL of DMSO or 50 nL of DMSO as a control, such that each drug was present at a final concentration of 1 or 10 μM after the addition of 50 μL of culture medium and cells, with at least three technical replicates for each drug and concentration, and the DMSO concentration was 0.1% v / v.

[0136] Monolayers were cultured overnight (18 hours). All biopsies used in the study were freshly obtained and not cryopreserved. Immunofluorescence staining, imaging via automated microscopy (Opera Phenix, Perkin Elmer), image analysis (CellProfiler), and data analysis (Matlab) were performed as previously described in Vladimer et al., Nat Chem Biol 2017. Antibodies used to identify the target cancer cell population were selected based on clinicopathological reports and antibody reactivity assessments, including CD3 (HIT3a), CD19 (HIB19), CD20 (2H7), CD79a (HM47), CD34 (4H11), CD117 (104ED2), and CD138 (DL-101) from eBiosciences. Unstained cells were considered non-cancerous cells.

[0137] According to the present invention, for each drug and concentration, the selectivity of the drug in killing cancer cells relative to non-cancer cells is determined by taking the average fraction of live cancer cells in the total live cells relative to the average fraction of live cancer cells in the control well (no drug, DMSO only). These quotients are determined for the average fraction of each drug at the two concentrations.

[0138] Patients treated with drugs whose value / selectivity is <1 according to the invention have a higher chance of response (i.e., achieving complete or partial remission) compared to patients treated with drugs selected without taking into account the value determined according to the invention or with drugs whose value / selectivity is >1 according to the invention.

[0139] Furthermore, when a combination of drugs is given to a patient, the higher the sum of the individual values ​​of the drugs given to the patient (1), the better. Figure 6 The higher the chance of a patient responding, the better.

[0140] Example 6

[0141] A 69-year-old male patient with diffuse large B-cell lymphoma (DLBCL) relapsed after prior seven-lineage therapy. The lymphoma cells in the sample were resistant to most of the 104 drugs tested, as indicated by drug selectivity >1 relative to non-cancer cells killing cancer cells as determined according to the present invention, while only six compounds showed significant in vitro on-target effects. Figure 7 Cisplatin and oxaliplatin were considered infeasible given the patient's history, age, and comorbidities; however, the BTK inhibitor ibrutinib showed the second strongest ex vivo efficacy (value / selectivity according to the invention = 0.61, P < 0.00048). Figure 7 A PET-CT scan performed on day 49 of ibrutinib treatment confirmed complete remission in the patient.

[0142] Example 7

[0143] A 51-year-old woman with precursor B-cell lymphoblastic lymphoma (B-LBL) received trilineage-first therapy, and the disease was progressive following immunotherapy with the bispecific CD3-CD19 antibody blinatumomab. A cell mixture containing cells commonly found in PBMCs was isolated from the woman's pleural effusion. The ability of 266 compounds to selectively kill cancer cells relative to non-cancer cells contained in the cell mixture was determined using this invention. It was revealed that the proteasome inhibitor bortezomib was able to selectively kill cancer cells (selectivity determined according to this invention = 0.50, P < 0.001). Figure 8 ) and thiopurine 6-mercaptopurine, 6-MP (value / selectivity determined according to the present invention = 0.58, P < 0.001; Figure 8 6-MP and bortezomib were combined with anti-CD20 oxetuzumab. A partial response was confirmed by PET-CT 28 days later.

[0144] Example 8

[0145] Lymph nodes excised from patients with diffuse large B-cell lymphoma were dissected into single cells, resulting in a complex cellular mixture containing cells typically found in PBMCs. The ability of 266 compounds to selectively kill cancer cells relative to non-cancer cells in the cellular mixture was determined according to the present invention. Patients showed varying responses to a single, most potent ex vivo drug, bortezomib (selectivity = 0.59, P < 0.0001), cladribine (selectivity = 0.73, P < 0.0003), and dexamethasone (value / selectivity = 0.87, P < 0.05). Figure 9 The combination of ) achieved complete relief ( Figure 9 ).

[0146] Example 9

[0147] This embodiment describes the practical application of the method described in claim (1) and subsequently, particularly claims (1) and (2). A tissue sample containing 40,000 cells from a patient with hematologic malignancies is provided. 20,000 cells are cancer cells and are positive for the cell surface marker CD19. The remaining cells are positive for other cell surface markers, including CD3, 4, 8, 11c, 14, 56, etc. The sample is divided into two portions, each containing 20,000 cells. The first sample is incubated in RPMI + 10% FCS in the presence of 10 μM bortezomib in DMSO (0.1% DMSO final concentration), while the second sample is incubated in RPMI + 10% FCS + 0.1% DMSO. After 24 hours of incubation, the viability of each cell in each sample is determined, wherein viability here is the “distinguished phenotype” referred to in step (d) of claim 1 and the dependent claims. In bortezomib-treated samples, 5,000 viable cells were found to stain for CD19, and 10,000 viable cells were found to be CD19-negative. In DMSO-treated samples, 10,000 viable cells were found to be CD19-positive and 10,000 were found to be CD19-negative. According to the present invention, the selectivity of bortezomib in reducing the viability of CD19-positive cells was calculated.

[0148] Following step (d), in at least a portion of incubation in the presence of bortezomib as the test compound (here: 5,000 / 15,000 = 0.33) and at least a portion of incubation in the absence of the test compound (here: 10,000 / 20,000 = 0.5), the number of cells in one of at least two subpopulations (here: CD19 positive cells) displaying a distinguishable phenotype (here: viability) is calculated relative to the number of cells in the total cell population (CD19 positive + CD19 negative cells) displaying the same phenotype (i.e., viability).

[0149] Following step (e), the selectivity of the test compound (bortezomib) in inducing the phenotype mentioned in step (d) (here: 0.33) relative to all other subpopulations is determined by dividing (i) (0.33) by (ii) (0.50). Since 0.33 / 0.50 = 0.66, which is less than 1, the test compound (bortezomib) selectively inhibits the phenotype (viability) of step (d) in the population explicitly mentioned in step (d) (here: CD19-positive cells). Therefore, according to the invention, we can conclude that bortezomib selectively reduces the viability of CD19-positive cells in the given embodiments.

[0150] Example 10

[0151] This embodiment describes a further practical application of the method of the present invention. A tissue sample from a patient with hematologic malignancies is provided, comprising 60,000 cells. 30,000 cells are cancer cells and are positive for the cell surface marker CD79a. The remaining cells are positive for other cell surface markers, including CD3, 4, 8, 11c, 14, 56, etc. An appropriately labeled antibody is used as the staining reagent. The sample is divided into two fractions: 40,000 cells and 20,000 cells. The first fraction of 40,000 cells is further divided into two fractions of 20,000 cells, denoted herein as [1a] and [1b]. Fractions [1a] and [1b] are incubated, respectively, in RPMI + 10% FCS, and in the presence of 10 μM and 1 μM bortezomib in DMSO (final concentration of 0.1% DMSO), while the second sample is incubated in RPMI + 10% FCS + 0.1% DMSO. For clarity, CD79a is chosen here as a hypothetical example, and it can be replaced by any other surface marker.

[0152] After 24 hours of incubation, the viability of each cell in each sample was determined, where viability is referred to herein as a “distinguishing phenotype”. In the bortezomib-treated sample [1a], 5,000 viable cells were found to be CD79a-stained, and 10,000 viable cells were found to be CD79a-negative. In the bortezomib-treated sample [1b], 8,000 viable cells were found to be CD79a-stained, and 10,000 viable cells were found to be CD79a-negative. In the DMSO-treated sample, 10,000 viable cells were found to be both CD79a-positive and CD79a-negative. The selectivity of bortezomib in reducing the viability of CD79a-positive cells was calculated according to the method of the invention.

[0153] For portions [1a] and [1b], in at least one portion incubated in the presence of bortezomib at the corresponding concentration of the test compound (here: 5,000 / 15,000 = 0.33 for [1a]; 8,000 / 18,000 = 0.44 for [1b]) and in at least one portion incubated in the absence of the test compound (here: 10,000 / 20,000 = 0.5), the number of cells in one of the subpopulations (here: CD79a-positive cells) showing at least two distinguishable phenotypes (here: viability) is calculated relative to the number of cells in the total cell population (CD79a-positive + CD79a-negative cells) showing the same phenotype (i.e., viability).

[0154] Following step (e), the selectivity of the test compound (here: bortezomib) for inducing the phenotype mentioned in step (d) relative to all other subpopulations is determined by dividing (i) (here: 0.33 for [1a] and 0.44 for [1b]) by (ii) (here: 0.50), and the average selectivity is calculated as a final value / selectivity of (0.33 / 0.50 + 0.44 / 0.50) / 2 = 0.77. Since 0.77 is less than 1, the test compound (here: bortezomib) selectively inhibits the phenotype (here: viability) of step (d) in the population explicitly mentioned in step (d) (here: CD79-positive cells). Therefore, according to the invention, it can be concluded that bortezomib selectively reduces the viability of CD79-positive cells in the given embodiments.

[0155] Patients from whom the sample was taken will respond to bortezomib treatment. For clarity, CD79a is chosen here only as a hypothetical example, and may be replaced by any other surface marker. Moreover, for illustrative purposes, only cell counts are chosen arbitrarily.

[0156] Example 11

[0157] A tissue sample from a patient with hematologic malignancies was provided, containing 60,000 cells. 30,000 cells were cancer cells and positive for the cell surface marker CD20. The remaining cells were positive for other cell surface markers, including CD3, 4, 8, 11c, 14, 56, etc. Appropriately labeled antibodies were used as staining reagents. The sample was divided into three fractions, each containing 20,000 cells. Two fractions of 20,000 cells each were incubated separately in RPMI + 10% FCS in the presence of 10 μM bortezomib in DMSO (DMSO final concentration 0.1%), while the third fraction was incubated in RPMI + 10% FCS + 0.1% DMSO. Note that, for clarity, CD20a is used here only as a hypothetical example and may be replaced by any other surface marker.

[0158] After 24 hours of incubation, cell viability was determined for each cell in each sample, where viability is defined as a “distinguishing phenotype.” In two samples treated with 10 μM bortezomib, 5,000 viable cells were found to be CD20a-positive and 10,000 viable cells were found to be CD20a-negative. In the DMSO-treated samples, 10,000 viable cells were found to be both CD20a-positive and CD20a-negative. The selectivity of bortezomib in reducing the viability of CD79a-positive cells was determined.

[0159] For the two fractions incubated in the presence of 10 μM bortezomib, (i) in each fraction incubated in the presence of bortezomib, the number of cells in one of at least two subpopulations (in this case, CD20-positive cells) showing a distinguishable phenotype (i.e., viability) was independently counted (i.e., 5,000 / 15,000 = 0.33 and 5,000 / 15,000 = 0.33), which is relative to the number of cells in the total cell population (CD20-positive + CD20-negative cells) showing the same phenotype (i.e., viability), and (ii) for each fraction incubated in the absence of the test compound, the number of cells incubated was independently determined (i.e., 10,000 / 20,000 = 0.5). Then the average of (i) and (ii) is formed, yielding 0.33 for (i) and 0.5 for (ii), and used in a further step, namely, in step (e), the value / selectivity is determined by dividing the average of (i) by the average of (ii), resulting in 0.33 / 0.5 = 0.66 as the final value / selectivity.

[0160] Example 12

[0161] This embodiment illustrates that a dose-response curve obtained from fitting the fraction of cells exhibiting a certain phenotype out of all cells exhibiting the same phenotype cannot yield an accurate EC50 value. Assume a mixture of type A and type B cells with a ratio A:B = 0.2:0.8. This cell mixture is treated with cytotoxic compound X. The ability of compound X to kill type A cells is quantified as log EC50 = 3, and the ability of compound X to kill type B cells is quantified as log EC50 = -2, using arbitrary concentration scales. The fraction of live A cells in the total number of live cells (i.e., live A + live B) is calculated to obtain... Figure 10 The S-shaped curve shown clearly indicates that the inflection point of this curve (solid line) is neither the EC50 information of the dose-response curve for the effect of X on A nor for the effect of X on B.

[0162] Example 13

[0163] This embodiment illustrates the effect of a 10% standard deviation of the total cell count when introducing a mixture of A and B cells into a microtiter plate to determine the selectivity of test compound X in killing A cells relative to killing B cells. When using the classical method of measuring the total number of A and B cells as a function of concentration [X] to determine selectivity, fitting an sigmoid dose-response curve, and measuring the EC50 of X against A and B, each measurement point will have a 10% standard deviation. Using this invention, a 10% change in the total cell count has no effect on the fraction of live A cells out of the total live cell count. Therefore, this invention makes it possible to determine selectivity with greater robustness to changes in cell seeding into the assay plate or cell loss during the procedure.

Claims

1. A method for determining whether a test compound is a therapeutic agent for cancer, the method comprising the steps of: (1) Provide a tissue sample from the subject, the tissue sample comprising cancer cells and non-cancer cells in a total population of cells; (2) The sample is divided into a first tissue sample portion and a second tissue sample portion, wherein the first tissue sample portion contains cancer cells and non-cancer cells, and wherein the second tissue sample portion contains cancer cells and non-cancer cells, wherein the ratio of cancer cells to non-cancer cells in the first tissue sample portion is similar to the ratio of cancer cells to non-cancer cells in the second tissue sample portion. (3) Incubate the first tissue sample portion in the absence of the test compound to obtain a non-compound-incubated tissue sample portion, wherein the first tissue sample is incubated in the first well; (4) Incubate the second tissue sample portion in the presence of the test compound to obtain a compound-incubated tissue sample portion, wherein the second tissue sample is incubated in the second well; (5) Using an automated microscope system, obtain the first set of microscope images of the tissue sample portion that has not been incubated with compounds; (6) Using an automated microscope system, a second set of microscope images of the tissue sample portion incubated with the compound were obtained; (7) Using an automated microscope system and based on the first set of microscope images, obtain the first number of cancer cells exhibiting a viable phenotype in a tissue sample portion that has not been incubated with compounds. (8) Using an automated microscope system and based on a second set of microscope images, a second number of cancer cells exhibiting a viable phenotype were obtained in tissue sample portions incubated with the compound. (9) The first number of cancer cells exhibiting a viable phenotype in a tissue sample portion that is not incubated with the compound is compared with the total number of cells exhibiting a viable phenotype in a tissue sample portion that is not incubated with the compound. (10) The second number of cancer cells exhibiting a viable phenotype in the tissue sample portion incubated with the compound was compared with the total number of cells exhibiting a viable phenotype in the tissue sample portion incubated with the compound. (11) Determine whether the test compound is a therapeutic agent for the subject’s cancer by dividing the value obtained in step (10) by the value obtained in step (9), wherein if the value obtained is less than 1, the test compound is a therapeutic agent for the subject’s cancer.

2. The method of claim 1, wherein if the obtained value is less than 0.95, the test compound is a therapeutic agent for the subject's cancer.

3. The method of claim 1, wherein if the obtained value is less than 0.9, the test compound is a therapeutic agent for the subject's cancer.

4. The method of claim 1, wherein if the obtained value is less than 0.8, the test compound is a therapeutic agent for the subject's cancer.

5. The method of claim 1, wherein if the obtained value is less than 0.6, the test compound is a therapeutic agent for the subject's cancer.

6. The method of claim 1, wherein if the obtained value is less than 0.4, the test compound is a therapeutic agent for the subject's cancer.

7. The method of claim 1, wherein if the obtained value is less than 0.2, the test compound is a therapeutic agent for the subject's cancer.

8. The method of claim 1, wherein the method is repeated for at least two test compounds, and whether the combination of the at least two test compounds is a therapeutic agent is determined by subtracting the respective value obtained in step (11) of claim 1 for each of the at least two test compounds from 1.0 and summing the resulting values ​​of the at least two test compounds, wherein if the sum of the results is greater than -1, the combination of the at least two test compounds is determined to be a therapeutic agent.

9. The method of claim 8, wherein if the sum of the results is greater than -0.5, then the combination of the at least two test compounds is determined to be a therapeutic agent.

10. The method of claim 8, wherein if the sum of the results is greater than 0, then the combination of the at least two test compounds is determined to be a therapeutic agent.

11. The method of claim 8, wherein if the sum of the results is greater than 0.5, then the combination of the at least two test compounds is determined to be a therapeutic agent.

12. The method of claim 8, wherein if the sum of the results is greater than 1, then the combination of the at least two test compounds is determined to be a therapeutic agent.

13. The method according to any one of claims 1 to 12, wherein the test compound comprises one or more chemical substances.

14. The method according to any one of claims 1 to 12, wherein the second tissue sample portion is further divided into at least two portions, wherein each of the at least two portions is incubated in the presence of different concentrations of the test compound, and wherein steps (9), (10) and (11) of claim 1 are repeated independently for each concentration of the test compound, thereby calculating the average selectivity / value at all concentrations and using it to determine whether the test compound is a therapeutic agent for the subject's cancer.

15. The method according to any one of claims 1 to 7, wherein the method is repeated for at least two test compounds, and the test compound having the lowest value obtained in step (11) of claim 1 is selected as a therapeutic agent for treating the subject's cancer.

16. The method according to any one of claims 8 to 12, wherein the method is repeated for at least three test compounds, and a combination of at least two of the at least three test compounds having the highest value is selected as a therapeutic agent for treating the subject's cancer, said highest value being obtained by subtracting the value obtained in step (11) of claim 1 for each of the at least two test compounds in the combination from 1.0, and summing the resulting values ​​for the at least two test compounds in the combination.

17. The method according to any one of claims 1 to 12, wherein the cancer is a cancer associated with PBMCs or bone marrow cells or cells derived from PBMCs or bone marrow cells.

18. The method according to any one of claims 1 to 12, wherein the sample is a tissue sample containing at least 1% cancer cells and / or at least 1% non-cancer cells.

19. The method according to any one of claims 1 to 12, wherein the tissue sample is cultured as a non-adherent cell monolayer.

20. The method according to any one of claims 1 to 12, wherein the number of living cells is determined as the number of non-fragmented cell nuclei.

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