Therapeutic evaluation of hematopoietic organ cancer

Abstract

The present invention relates to the evaluation of the treatment of cancer, particularly hematological cancer. In particular, the present invention is a method for evaluating, preferably predicting, the response of a subject suffering from cancer, preferably hematological cancer, to treatment with a BCL family inhibitor, the method comprising determining the amounts of the biomarkers BCL-2, BCL-xL, and MCL-1 in a tumor driving cell population, preferably a leukemia stem cell (LSC) population, in a sample from the subject, and comparing the amounts of the biomarkers to a reference, whereby the response to treatment with a BCL family inhibitor is evaluated. Further, the present invention relates to a BCL-2 inhibitor, preferably Venetoclax, a BCL-xL and / or MCL-1 inhibitor, preferably Navitoclax, or a BCL-2 inhibitor in combination with at least one MCL-1 inhibitor, for use in the treatment of cancer, preferably hematological cancer, in a subject evaluated to benefit from treatment with said inhibitor by using the method of the present invention.

Description

Technical Field

[0001] The present invention relates to the evaluation of the treatment of cancer, particularly hematological cancer. In particular, the present invention is a method for evaluating, and preferably predicting, the response of a subject suffering from cancer, preferably hematological cancer, to treatment with a BCL family inhibitor, comprising determining the amounts of the biomarkers BCL-2, BCL-xL, and MCL-1 in a tumor driving cell population, preferably a leukemia stem cell (LSC) population, in a sample of the subject, and comparing the amounts of the biomarkers to a reference, whereby the response to treatment with a BCL family inhibitor is evaluated. Further, the present invention relates to a BCL-2 inhibitor, preferably Venetoclax, a BCL-xL and / or MCL-1 inhibitor, preferably Navitoclax, or a BCL-2 inhibitor in combination with at least one MCL-1 inhibitor, for use in the treatment of cancer, preferably hematological cancer, in a subject evaluated to benefit from treatment with said inhibitor by using the method of the present invention.

Background Art

[0002] Acute myeloid leukemia (AML) remains a cancer with a poor prognosis, particularly in elderly or frail patients who cannot receive high-dose chemotherapy and in patients with high-risk disease. The survival of AML cells depends on the expression of anti-apoptotic factors such as BCL-2.

[0003] In recent years, as a standard treatment for AML patients who are not suitable for intensive induction chemotherapy, a potent BCL-2 inhibitor, Venetoclax, in combination with a hypomethylating agent (HMA), has replaced HMA alone (Konopleva et al., 2016) (DiNardo et al., 2020a). Furthermore, recently, success has been achieved in adding Venetoclax to various high-dose induction protocols, showing further evidence of the effectiveness of Venetoclax in AML treatment beyond combinations with HMA (DiNardo et al., 2021; Garcia et al., 2021).

[0004] Currently, HMA in combination with venetoclax is also being evaluated as a first-line treatment option for adult AML patients eligible for intensive induction chemotherapy, such as cytarabine and daunorubicin. Therefore, longitudinal studies correlating treatment response with molecular and cytogenetic abnormalities are essential to identify the most appropriate therapy and predict early resistance and relapse after initial response. The European LeukemiaNet (ELN) risk classification currently used to guide treatment decisions in AML patients was established based on data collected prior to venetoclax-based treatments and may therefore not accurately predict response to HMA / venetoclax (Cherry et al., 2021; Dohner et al., 2017). Some common features of venetoclax sensitivity, such as the origin cell (Cai et al., 2020), apoptosis priming (Bhatt et al., 2020), and monocytic differentiation of blasts (Cherry et al., 2021; Kuusanmaki et al., 2020; Pei et al., 2020) have been proposed. The latter has received particular attention in several studies and refers to AML samples previously classified as myelomonocytic (M4) or monocytic (M5) based on the French-American-British (FAB) classification and / or AML samples containing blast cells with high levels of CD11b+, CD64+, or CD68+ expression detected by flow cytometry. Furthermore, ex vivo treatment and transcriptome data suggest that monocytic AML is a distinct class of AML associated with high resistance to HMA / venetoclax treatment.

[0005] Furthermore, recent studies have claimed that this resistance can be observed in an AML population with increased LSCs and low reactive oxygen species (ROS) in M4 / 5 patients (Pei et al., 2020). Importantly, it has been suggested that the dependence on MCL-1 rather than BCL-2 in LSCs derived from monocytic AML underlies the resilience to 5-AZA / venetoclax. However, two recent independent clinical trials have shown that monocytic differentiation in AML is not associated with a worsening of patient outcomes after treatment with HMA / venetoclax, and thus the hypothesis related to the above-mentioned M4 / 5 AML could not be proven (DiNardo et al., 2020b; Stahl et al., 2021).

Summary of the Invention

Problems to be Solved by the Invention

[0006] Therefore, there is a need to evaluate and predict the response and efficacy to BCL family inhibitor therapy, more specifically, venetoclax therapy, with greater certainty.

[0007] The technical problem underlying the present invention can be regarded as providing means and methods to meet the aforementioned need. The technical problem is solved by the embodiments characterized in the following claims and the present specification.

Means for Solving the Problems

[0008] Accordingly, the present invention relates to a method for evaluating the response of a subject suffering from cancer, preferably hematological cancer, to BCL family inhibitor therapy, comprising: (a) determining the amounts of the biomarkers BCL-2, BCL-xL, and MCL-1 in a tumor-driving cell population, preferably a leukemia stem cell (LSC) population, in a sample of the subject; and (b) comparing the amounts of the biomarkers to a reference, whereby the response to BCL family inhibitor therapy is evaluated.

[0009] In this specification and the claims, "a" or "an" should be understood to mean one or more of the next-mentioned items, depending on the context in which it is used. Thus, for example, a reference to "an" item can mean that at least one item may be utilized.

[0010] As used hereinafter, the terms "have", "comprise", or "include" are intended to have either a non-limiting or a limiting meaning. Thus, when having a limiting meaning, these terms can refer to a situation where there are no other features in the described embodiment other than the features introduced by these terms. That is, these terms have a limiting meaning in the sense of "consisting of" or "essentially consisting of". When having a non-limiting meaning, these terms refer to a situation where there are one or more other features in the described embodiment in addition to the features introduced by these terms.

[0011] Furthermore, as used hereinafter, the terms "preferably", "more preferably", "most preferably", "particularly", "more particularly", "typically", and "more typically" are used in conjunction with features to indicate that these features are preferred features. That is, these terms are intended to indicate that alternative features may also be contemplated in the present invention.

[0012] Furthermore, it will be understood that, as used herein, the term "at least one" means that one or more of the items recited following these terms can be used in the present invention. For example, when these terms indicate that at least one item is to be used, this can be understood to mean one item or more than one item, i.e., two, three, four, five, or any other number. Depending on the items these terms refer to, those skilled in the art will understand what upper limit (if any) these terms can refer to.

[0013] In the context of the present invention, the term "about" means + / - 20%, + / - 10%, + / - 5%, + / - 2%, or + / - 1% from the indicated parameter or value. This takes into account normal deviations due to, for example, measurement techniques.

[0014] The method of the present invention may include further steps before step a), after step b), between or within those steps. Typically, the method may include a step of pretreating a sample for determination of a biomarker before step a). Furthermore, the method may include, for example, a step of recommending a treatment method after step b) based on an evaluation.

[0015] As used herein, the term "assessing" refers to determining or predicting the response of a subject afflicted with a hematopoietic cancer to treatment with a BCL family inhibitor. Thus, the response can be determined for a treated subject or predicted for a subject prior to initiation of the treatment. With respect to response prediction, it will be understood that the prediction is made within a prediction window. Typically, the window begins at the time the sample being assayed by the methods of the invention is taken. The prediction window is preferably in the range of at least 1 month, 2 months, 3 months, 6 months, 9 months, 1 year, 2 years, or 3 years. As will be understood by those of skill in the art, assessing is not usually intended to be correct for 100% of the subjects being assayed. However, the term requires that the assessment be correct for a statistically significant proportion of the subjects (e.g., the cohort of a cohort study). Whether the proportion is statistically significant can be readily determined by those of skill in the art using a variety of well-known statistical assays, such as determination of confidence intervals, p-value determination, Student's t-test, Mann-Whitney test, etc. Details can be found in Dowdy and Wearden, Statistics for Research, John Wiley & Sons, New York 1983. Preferred confidence intervals are at least 90%, at least 95%, at least 97%, at least 98%, or at least 99%. The p-value is preferably 0.1, 0.05, 0.01, 0.005, or 0.0001.

[0016] Preferably, said evaluation of response to BCL family inhibitor treatment comprises identifying whether the subject will benefit from treatment with a BCL-2 inhibitor. Also preferably, said evaluation of response to BCL family inhibitor treatment comprises identifying whether the subject will benefit from treatment with a BCL-xL and / or MCL-1 inhibitor. More preferably, said evaluation of response to BCL family inhibitor treatment comprises identifying whether the subject will benefit from treatment with a BCL-2 inhibitor and at least one MCL-1 inhibitor. As will be appreciated by those skilled in the art, the methods of the invention enable identification of subjects who will benefit from treatment ("rule-in"), or who will not benefit from treatment ("rule-out"), or both.

[0017] As used herein, the term "BCL family inhibitor" refers to an inhibitor that affects the biological function of a BCL family member, preferably an anti-apoptotic BCL family member. Typically, such an inhibitor binds to an anti-apoptotic BCL family member, thereby affecting its biological function, and in particular reducing or inhibiting its biological activity. The anti-apoptotic BCL family includes several regulatory factor proteins involved in the inhibition of apoptosis. Anti-apoptotic BCL family members include BCL-2, BCL-xL, MCL-1, CED-9, A1, and Bfl-1. Of particular importance are BCL-2, BCL-xL, and MCL-1. Thus, by reducing or inhibiting their activities, BCL family inhibitors cause cells to enter apoptotic cell death. The anti-apoptotic activity of BCL family proteins is important for the development of many cancer entities that become immortalized due to said activity and other factors. Preferably, the BCL family inhibitors referred to in the present invention are selected from the group consisting of obatoclax, subatoclax, maritoclax, gossypol, apogossypol, TW-37, UMI-77, BDA-366, ABT-737, navitoclax, venetoclax, S64315 (MIK665), AZD5991, AMG176, AMG379, and ABBV467. More preferably, the BCL family inhibitor is an inhibitor of BCL-2 preferably selected from the group consisting of venetoclax and ABT-737, and most preferably venetoclax. Even more preferably, the BCL family inhibitor is an inhibitor of BCL-xL preferably selected from the group consisting of navitoclax, ABT-737, A-1155463, and A-1331852, and most preferably navitoclax. Also, more preferably, the BCL family inhibitor is an inhibitor of MCL-1 preferably selected from the group consisting of navitoclax, S64315 (MIK665), AZD5991, AMG176, AMG379, and ABBV467, and most preferably navitoclax.Inhibitors, such as navitoclax, may also inhibit other BCL family members, such as BCL-2 or MCL-1. However, navitoclax is referred to herein as a central BCL-xL inhibitor.

[0018] It will be understood that the BCL family inhibitors referred to in the present invention may be used as monotherapy or in combination with other drugs. Preferably, the BCL family inhibitor is used in combination with an additional cancer treatment agent, such as a classical chemotherapeutic agent, more preferably a hypomethylating agent, preferably 5-azacitidine (5-AZA), decitabine, or cytarabine, or an antibody, such as rituximab, or a targeted therapeutic agent, such as midostaurin.

[0019] The term "response to BCL family inhibitor treatment" as used herein refers to any response or symptom of a hematological cancer that becomes clinically apparent in response to BCL family inhibitor treatment. Thus, the response of the hematological cancer may be beneficial to a subject suffering from the cancer in that the cancer or its symptoms improve, or it may be harmful to the subject in that no response is observable or the hematological cancer or its symptoms worsen. Preferably, the response to BCL family inhibitor treatment in the present invention is any improvement in the hematological cancer or its symptoms that becomes clinically apparent in response to BCL family inhibitor treatment and from which the subject suffering from the cancer benefits.

[0020] The term "subject" as used herein relates to animals, preferably mammals, more preferably humans. In the present invention, the subject is assumed to be suffering from a hematological cancer as described in other parts of the present specification.

[0021] As used herein, the term "cancer" refers to hematopoietic cancers and solid tumors, such as colorectal cancer, pancreatic cancer, liver cancer, lung cancer, cancers of the nervous system, etc. The term "cancer", as used herein, also encompasses pre-cancerous stages that occur in various cancer entities. Preferably, hematopoietic cancers may have pre-cancerous stages, such as myelodysplastic syndromes (MDS) that can progress to AML.

[0022] As used herein, the term "hematopoietic cancer" refers to any cancer that involves or affects hematopoietic cells. This term also includes any type of hematopoietic malignancy. Preferably, said hematopoietic cancer is acute myeloid leukemia (AML), other B-cell or T-cell lymphomas or leukemias, or plasma cell neoplasms.

[0023] As used herein, the term "BCL-2" refers to the B-cell lymphoma 2 protein encoded in humans by the BCL-2 gene. BCL-2 is an early member of the Bcl-2 family of apoptosis regulatory proteins. These proteins control apoptosis by either inhibiting apoptosis, i.e., being anti-apoptotic, or inducing apoptosis, i.e., being pro-apoptotic. The BCL-2 protein inhibits apoptosis. Thus, it is anti-apoptotic. There are two known isoforms of BCL-2 in humans. Several orthologs of BCL-2 have been reported in various animal species. The BCL-2 protein is localized to the outer mitochondrial membrane and plays an important role in promoting cell survival and inhibiting the action of pro-apoptotic proteins. Pro-apoptotic proteins of the BCL family, such as Bax and Bak, typically permeabilize the mitochondrial membrane and release cytochrome C and ROS, which are important signals in the apoptotic cascade. These pro-apoptotic proteins are in turn activated by BH3-only proteins and inhibited by the functions of BCL-2 and its relative BCL-Xl. In various cancer entities, the homeostatic balance between cell proliferation and cell death is disrupted. Overexpression of the anti-apoptotic BCL-2 protein alone does not cause cancer. However, cancer may occur when anti-apoptotic BCL-2 is overexpressed simultaneously with oncogenes.

[0024] The BCL-2 protein referred to in the present invention is preferably human BCL-2 having the amino acid sequence deposited under UniProt accession number P10415, or mouse BCL-2 having the amino acid sequence deposited under UniProt accession number P10417. It will be understood that the term "BCL-2" also relates to variants of the protein. Such variants have at least the same essential biological and immunological properties as the aforementioned BCL-2 protein. In particular, such variants share the same essential biological and immunological properties when they can be detected by the same specific assays referred to herein. Furthermore, the variants referred to in the present invention have an amino acid sequence that differs by at least one amino acid substitution, deletion, and / or addition, and the amino acid sequence of the variant still preferably corresponds to the specific amino acid sequence of the human or mouse BCL-2 protein, preferably over the entire length of each of the BCL-2 proteins, and is preferably at least 50%, 60%, 70%, 80%, 85%, 90%, 92%, 95%, 97%, 98%, or 99% identical.

[0025] In the present invention, the degree of identity between two amino acid sequences can be determined by algorithms well-known in the art. Preferably, the degree of identity is determined by comparing two optimally aligned sequences over a comparison window, wherein a fragment of the amino acid sequence in the comparison window may include additions or deletions (e.g., gaps or overhangs) for optimal alignment compared to a reference sequence (which does not include additions or deletions). The percentage is calculated by determining the number of positions at which identical amino acid residues occur in both sequences to obtain the number of matching positions, dividing the number of matching positions by the total number of positions in the comparison window, and multiplying the result by 100 to obtain the percentage of sequence identity. Optimal alignment of sequences for comparison can be performed by the local homology algorithm disclosed by Smith, the homology alignment algorithm of Needleman, the similarity search method of Pearson, computer implementations of these algorithms (GAP, BESTFIT, BLAST, FAST, PASTA, and TFASTA in the Wisconsin Genetics Software Package, Genetics Computer Group (GCG), 575 Science Dr., Madison, WI), or by visual inspection. Since two sequences are specified for comparison, GAP and BESTFIT are preferably used to determine their optimal alignment and, thus, the degree of identity. Preferably, a default value of 5.00 is used for the gap weight and 0.30 is used for the length of the gap weight. The variants mentioned above may be allelic variants or any other species-specific homologs, paralogs, or orthologs. The variants mentioned above may be allelic variants or any other species-specific homologs, paralogs, or orthologs.

[0026] As used herein, the term "BCL-xL" refers to the B-cell lymphoma extra-large protein encoded in humans by the BCL-2-like 1 gene. Similar to BCL-2, BCL-xL is a member of the Bcl-2 family of apoptosis regulatory proteins and inhibits apoptosis, i.e., is anti-apoptotic. Several orthologs of BCL-xL have been reported in various animal species. The BCL-xL protein is a transmembrane protein of the outer mitochondrial membrane and plays a role in promoting cell survival and inhibiting the action of apoptosis-promoting proteins. Similar to BCL-2, BCL-xL has been reported to be involved in the development of various cancers due to its anti-apoptotic activity.

[0027] The BCL-xL protein referred to in the present invention is preferably human BCL-xL having the amino acid sequence deposited under UniProt accession number Q07817, or mouse BCL-xL having the amino acid sequence deposited under UniProt accession number Q64373. It will be understood that the term "BCL-xL" also relates to variants of the protein. Such variants have at least the same essential biological and immunological properties as the aforementioned BCL-xL protein. In particular, such variants share the same essential biological and immunological properties if they can be detected by the same specific assays referred to herein. Furthermore, the variants referred to in the present invention have an amino acid sequence that differs by at least one amino acid substitution, deletion, and / or addition, and the amino acid sequence of the variant is still preferably at least 50%, 60%, 70%, 80%, 85%, 90%, 92%, 95%, 97%, 98%, or 99% identical to the specific amino acid sequence of the human or mouse BCL-xL protein, preferably over the entire length of each of the BCL-xL proteins.

[0028] As used herein, the term "MCL-1" refers to the induced myeloid leukemia cell differentiation protein, which is a protein encoded by the MCL-1 gene in humans. Similar to BCL-2, MCL-1 is an anti-apoptotic member of the Bcl-2 family of apoptosis regulators. MCL-1 acts on the outer mitochondrial membrane, similar to BCL-2 or BCL-xL. In humans, two isoforms have been reported. Furthermore, several orthologs of MCL-1 have been reported in various animal species. The MCL-1 protein plays a role in promoting cell survival and inhibiting the action of apoptosis-promoting proteins. MCL-1 has also been reported to be involved in the development of various cancers due to its anti-apoptotic activity.

[0029] The MCL-1 protein referred to in the present invention is preferably human MCL-1 having the amino acid sequence deposited under UniProt accession number Q07820, or mouse MCL-1 having the amino acid sequence deposited under UniProt accession number P97287. It will be understood that the term "MCL-1" also relates to variants of the protein. Such variants have at least the same essential biological and immunological properties as the aforementioned MCL-1 protein. In particular, such variants share the same essential biological and immunological properties if they can be detected by the same specific assays referred to herein. Furthermore, the variants referred to in the present invention have an amino acid sequence that differs by at least one amino acid substitution, deletion, and / or addition, and the amino acid sequence of the variant is still preferably at least 50%, 60%, 70%, 80%, 85%, 90%, 92%, 95%, 97%, 98%, or 99% identical to the specific amino acid sequence of the human or mouse MCL-1 protein, preferably over the entire length of each of the MCL-1 proteins.

[0030] As used herein, the term "biomarker" refers to a biomarker protein or any precursor protein, fragment, or derivative thereof that is naturally produced and reflects the amount of the biomarker protein. Additionally, the term encompasses any nucleic acid molecule that reflects the amount of the biomarker protein. Preferably, such transcribed nucleic acid molecules are messenger RNA molecules (mRNA), or any precursor or variant thereof including pre-mRNA or mRNA of splice variants. Those RNA nucleic acid molecules may likewise be determined as biomarkers in the present invention. Thus, for example, if BCL-2 is determined as a biomarker in the present invention, it will be understood that either the BCL-2 protein can be determined or the transcribed nucleic acid molecule encoding the BCL-2 protein, such as BCL-2 mRNA, can be determined. Unless otherwise specified, the same applies to BCL-xL and MCL-1 as biomarkers and all other biomarkers mentioned herein.

[0031] As used herein, the term "tumor-initiating cell population" refers to any cell population in the subject that serves as a reservoir for the development of cancer and, preferably, further cancer cells of hematopoietic cancer. The tumor-initiating cell population is preferably not a population of quiescent cancer cells, but rather a population of cancer cells that have long-term self-renewal ability and can therefore start from a single cell and divide infinitely, contributing to tumorigenesis, such as cancer cells that divide frequently and / or cancer cells that have metastatic ability. Thus, the population may consist of cancer cells or cancer cell precursors. Preferably, the tumor-initiating cell population is a leukemia stem cell (LSC) population, which may also be referred to herein as an LSC-like population. Preferably, the LSC population is characterized by an increase in the expression of at least one biomarker selected from the group consisting of GPR56, CD34, and BCL-2. Typically, the LSC population is characterized by an increase in the expression of at least two or at least three of the aforementioned biomarkers. Most preferably, the LSC population is a population of cells that express all of the aforementioned biomarkers. More preferably, the expression is increased compared to the expression of at least one biomarker in monocyte-like AML cells. The LSC population useful in the present invention can be determined by determining the expression of at least one of the aforementioned biomarkers or at least one, preferably at least five, of the biomarkers shown in Table 1 below. The expression of those biomarkers has been shown to correlate with GPR56 expression in LSCs. Typically, the amount of the biomarker is determined by quantification of RNA expression, preferably a PCR-based method, or quantification based on specific antibodies, preferably by flow cytometry techniques, Western blot, or immunofluorescence measurement. Those skilled in the art are well aware of how those techniques can be applied. Preferably, individual cells contained in the sample are investigated for the expression of the aforementioned biomarkers characteristic of the tumor-initiating cell population, preferably the LSC population, and simultaneously for the expression of BCL-2, BCL-xL, and MCL-1, which are used in the evaluation carried out by the method of the present invention.Therefore, particularly preferred in the present invention are methods that enable simultaneous measurement of all of these biomarkers in individual cells, such as flow cytometry or single cell real-time PCR technology. When using such techniques, advantageously, it is not necessary to physically separate the tumor-driven cell population from the sample. Identification of said population and determination of the expression levels of BCL-2, BCL-xL, and MCL-1 can be performed in a digital environment after measurement.

[0032]

Table 1

[0033] The term "sample" refers to a sample of body fluid, a sample of isolated cells, or a sample derived from tissue or an organ that contains or is suspected of containing a tumor-driven cell population. Samples of body fluid can be obtained by well-known techniques and preferably include samples of blood. Tissue or organ samples, such as bone marrow samples, can be obtained, for example, by biopsy. Isolated cells can be obtained from body fluid or tissue or an organ by separation techniques, such as centrifugation or cell sorting.

[0034] Determination of the amount of one or more biomarkers referred to in the present invention encompasses preferably semi-quantitative or quantitative measurement of the amount or concentration.

[0035] The measurement is carried out either directly or indirectly. Direct measurement relates to the measurement of the amount or concentration of a biomarker based on a signal obtained directly from the biomarker molecule itself and the intensity of the signal that directly correlates with the number of biomarker molecules present in the sample. Such a signal, which may sometimes be referred to in the present invention as an intensity signal, can be obtained, for example, by measuring the intensity value of a specific physical or chemical property of the biomarker molecule. Indirect measurement includes the measurement of a signal obtained from a second component, i.e., a component other than the biomarker molecule itself.

[0036] In the present invention, the determination of the amount of a biomarker can be achieved by any known means for measuring such an amount in a sample. The said means includes immunoassay devices and methods that can utilize labeled molecules in various sandwich, competitive, or other assay formats. The said assay will generate a signal indicating the presence or absence of a peptide or polypeptide. Furthermore, the signal intensity can preferably be directly or indirectly (e.g., inversely) correlated with the amount of the biomarker present in the sample. Further suitable methods include the measurement of specific physical or chemical properties of the biomarker. The said methods preferably include biosensors, optical devices linked to immunoassays, biochips, or other analytical devices, such as chromatography devices or single-cell analysis devices, such as FACS analyzers or devices for single-cell PCR analysis.

[0037] Preferably, the biomarker determined in the present invention can be determined to be a protein. For this purpose, typically, a binding molecule that specifically binds to the biomarker protein and can be detected either by a detectable label present on the binding molecule or by a second binding molecule that specifically binds to the first binding molecule and contains a detectable label is applied.

[0038] The binding molecule referred to in this context may be any molecule capable of specifically binding to the biomarker to be detected. Preferably, such a binding molecule may be an antibody or an antibody mimetic or an aptamer.

[0039] In the present invention, the antibody can include any type of antibody that specifically binds to the biomarker protein. Preferably, the antibody of the present invention is a monoclonal antibody, a polyclonal antibody, a single-chain antibody, a chimeric antibody, or any fragment or derivative of such an antibody that can still specifically bind to the biomarker protein. Such fragments and derivatives included by the term antibody as used herein include bispecific antibodies, synthetic antibodies, Fab, F(ab)2, Fv or scFv fragments, or chemically modified derivatives of any of these antibodies. Specific binding as used in the context of the antibody of the present invention means that the antibody does not cross-react with other molecules present in the sample being investigated. Specific binding can be verified by various well-known techniques. The antibody or its fragment can generally be obtained by using the methods described in standard textbooks, for example, Harlow and Lane "Antibodies, A Laboratory Manual", CSH Press, Cold Spring Harbor, 1988. Monoclonal antibodies can be prepared by techniques involving the fusion of mouse myeloma cells with spleen cells derived from an immunized mammal, preferably an immunized mouse. Preferably, an immunogenic peptide is applied to the mammal. The peptide is preferably conjugated to a carrier protein, such as bovine serum albumin, thyroglobulin, and keyhole limpet hemocyanin (KLH). Depending on the host species, various adjuvants can be used to increase the immunological response. Such adjuvants preferably include Freund's adjuvant, mineral gels, such as aluminum hydroxide, and surfactants, such as lysolecithin, Pluronic® polyol, polyanion, peptide, oil emulsion, keyhole limpet hemocyanin, and dinitrophenol. Monoclonal antibodies that specifically bind to the analyte can then be prepared using well-known hybridoma techniques, human B cell hybridoma techniques, and EBV hybridoma techniques. The detection system using the antibody is based on the very specific binding affinity of the antibody for a specific antigen, namely the biomarker protein.The binding event results in a physicochemical change that can be detected as described in other parts of this specification.

[0040] In the present invention, antibody mimetics include peptide or protein molecules that have antibody-like binding properties but are not structurally related to antibodies. Such antibody mimetics typically have a molecular weight of up to 20 kDa. Preferably, in the present invention, antibody mimetics are affibody molecules, affilins, affimers, affitins, alphabodies, anticalins, avimers, DARPins, finomers, gastrobodies, knotted domain proteins, monobodies, nanoclamps, repebodies, centryns, or obodies.

[0041] In the present invention, the aptamer may be a nucleic acid or a peptide aptamer. Specific aptamers can be generated by techniques well known in the art, including, for example, systematic evolution of ligands by exponential enrichment (SELEX) technology. A peptide aptamer consists of a variable peptide loop that binds to a protein scaffold at both ends. This dual structural constraint enhances the binding affinity of the peptide aptamer to the nanomolar range. The length of the variable peptide loop typically consists of 10 to 20 amino acids, and the scaffold may be any protein with improved solubility and compactness properties, such as thioredoxin A. The selection of peptide aptamers can be performed using various systems, including, for example, the yeast two-hybrid system. The term also includes optimized or modified aptamers, such as optimers, split aptamers, or X aptamers.

[0042] Detectable labels referred to herein that can be used in the present invention include gold particles, latex beads, acridan esters, luminol, ruthenium, enzyme activity labels, radioactive labels, magnetic labels including paramagnetic and superparamagnetic labels, such as magnetic beads, and fluorescent labels. Enzyme activity labels include, for example, horseradish peroxidase, alkaline phosphatase, beta-galactosidase, luciferase, and their derivatives. Suitable substrates for detection include di-amino-benzidine (DAB), 3,3'-5,5'-tetramethylbenzidine, NBT-BCIP (4-nitroblue tetrazolium chloride), and 5-bromo-4-chloro-3-indolyl-phosphate. Appropriate enzyme-substrate combinations can result in a color reaction product, fluorescence, or chemiluminescence, which can be measured by methods known in the art (e.g., using light-sensitive film or an appropriate camera system). The above-mentioned criteria apply similarly for the measurement of enzyme reactions. Typical fluorescent labels include fluorescent proteins (e.g., GFP and its derivatives), Cy3, Cy5, Texas Red, fluorescein, and Alexa dyes. The use of quantum dots as fluorescent labels is also contemplated. Typical radioactive labels include 35S, 125I, 32P, 33P, etc. Radioactive labels can be detected by any known and appropriate method, such as light-sensitive film or a phosphor imager. Suitable labels can also be tags, such as biotin, digoxigenin, His-, GST-, FLAG-, GFP-, MYC-tags, influenza A virus hemagglutinin (HA), maltose-binding protein, etc., or can include these.

[0043] Also, preferably, the biomarker determined in the present invention can be determined as a nucleic acid molecule, preferably a transcript, such as mRNA. If a transcript encoding a biomarker protein is detected, it will be understood that typically a nucleic acid molecule, either RNA or DNA, can be used as a detection agent for detection in the present invention.

[0044] The nucleic acid molecules useful as detection agents in the present invention refer to DNA or RNA molecules that can specifically interact with the transcripts of biomarkers. Biomarker transcripts are also nucleic acid molecules, and specific binding can be achieved by the specific interaction of complementary or reverse-complementary nucleotide strands. Typically, the nucleic acids useful as detection agents are selected from the group consisting of antisense RNA, ribozymes, siRNA, or microRNA. Also preferably, oligonucleotides having complementary and reverse-complementary sequences can also be used as target transcript-specific primers for PCR-based detection techniques.

[0045] As used herein, antisense RNA refers to RNA containing a nucleic acid sequence that is essentially or completely complementary to the target transcript. Typically, the antisense nucleic acid molecule consists essentially of a nucleic acid sequence complementary to at least 100 consecutive nucleotides, more preferably at least 200, at least 300, at least 400, or at least 500 consecutive nucleotides of the target transcript. Methods for generating and using antisense nucleic acid molecules are well known in the art.

[0046] As used herein, ribozymes refer to catalytic RNA molecules that have a well-defined tertiary structure that allows specific binding to the target RNA and catalyze either the hydrolysis of one of their own phosphodiester bonds (self-cleaving ribozymes) or the hydrolysis of the bond in the target RNA, although they have also been found to catalyze the aminotransferase activity of ribosomes. Methods for generating and using such ribozymes are well known in the art.

[0047] As used herein, siRNA refers to small interfering RNA (siRNA) that is complementary to a target RNA (encoding a gene of interest) and reduces or abolishes gene expression by RNA interference (RNAi). RNAi is commonly used to target mRNA to stop the expression of a gene of interest. Briefly described, the process of RNAi in cells is initiated by double-stranded RNA (dsRNA) that is cleaved by ribonucleases, thus generating siRNA duplexes. The siRNA binds to another intracellular enzyme complex, which is thereby activated to target any mRNA molecule homologous (or complementary) to the siRNA sequence. The function of the complex is to target homologous mRNA molecules by base-pairing interactions between one of the siRNA strands and the target mRNA. Therefore, siRNA molecules can specifically bind and can be used as a detection agent in the present invention.

[0048] As used herein, microRNA refers to a self-complementary single-stranded RNA containing sense and antisense strands linked via a hairpin structure. MicroRNA contains a strand complementary to an RNA target sequence contained in a down-regulated transcript. MicroRNA is processed into smaller single-stranded RNAs and therefore probably also acts by the RNAi mechanism. Methods for designing and synthesizing microRNA that specifically bind to and degrade a target transcript are known in the art. Due to its specific nucleic acid binding ability, microRNA can be used as a detection agent in the present invention.

[0049] A detection system using a nucleic acid as a detection molecule may be based on complementary base-pairing interactions. The recognition process is based on the principle of complementary nucleic acid base-pairing. When the target nucleic acid sequence is known, a complementary sequence can be synthesized and labeled for detection. Hybridization events can be detected by known means. Furthermore, PCR-based techniques can be used to determine and quantify even small amounts of transcripts. The implementation methods of such PCR-based techniques are well known to those skilled in the art.

[0050] As used herein, the term "quantity" encompasses the absolute quantity of a biomarker, the relative quantity or relative concentration of a biomarker, and any value or parameter that can correlate with or be derived from them. Such values or parameters include intensity signal values from all specific physical or chemical properties obtained from the biomarker or detection molecule and / or detectable label. It should be understood that values correlating with the aforementioned quantity or parameter can also be obtained and / or modified by all standard mathematical operations.

[0051] Typically, the quantities referred to herein are normalized quantities, i.e., measured values of the quantity of a biomarker, and quantities calculated based on a normalization parameter that enables correction of physiological variations between different samples and / or technical variations due to technical differences or irregularities between different measurements. Preferably, the normalization of the measured quantity of an individual biomarker in a test sample will be performed using the measured value of the biomarker in one or more normalization samples, or a normalization value calculated based on such measured values. Preferably, the median quantity of a biomarker determined in a plurality of normalization samples obtained from subjects responsive to BCL family inhibitor treatment is used as the normalization value in the present invention. The plurality of normalization samples used to derive the median preferably includes at least 10, at least 100, or at least 1,000 samples. Further, normalization can also take into account the general expression level in the sample by correcting the measured quantity of a biomarker by the measured quantity of one or more housekeeping proteins, such as IgG.

[0052] The determined quantities of the biomarkers BCL-2, BCL-xL, and MCL-1 are referred to and compared in the method of the present invention.

[0053] As used herein, the term "reference" relates to any amount or value that enables the evaluation of a subject's response to BCL family inhibitor treatment by comparison to a determined amount of a biomarker. Thus, a reference amount or value can be obtained from a subject or group of subjects known to respond to BCL family inhibitor treatment. In such a case, if a similar amount of the biomarker is present in the test sample, the test subject is also evaluated as a responder, whereas if an amount of the biomarker different from the reference is determined in the test sample, the subject may be evaluated as a non-responder. A reference amount or value can also be obtained from a subject or group of subjects (non-responders) known not to respond to BCL family inhibitor treatment. In such a case, if a similar amount of the biomarker is present in the test sample, the test subject is also evaluated as a non-responder, whereas if an amount of the biomarker different from the reference is determined in the test sample, the subject may be evaluated as a responder.

[0054] Furthermore, the reference can also be a score that integrates reference amounts or values derived from those of various biomarkers. Preferably, the score used in the present invention integrates the amounts of BCL-2, BCL-xL, and MCL-1. This allows for consideration of possible avoidance mechanisms for a particular BCL family inhibitor. For example, large amounts of BCL-xL and MCL-1 and low amounts of BCL-2 in the cells of a subject's tumor-driven cell population may drive an avoidance mechanism for BCL-2 inhibitor treatment, and thus the subject is a non-responder. Conversely, large amounts of BCL-2 and low amounts of BCL-xL and MCL-1 indicate that the BCL-2 inhibitor acts efficiently and thus the subject is a responder. When considering possible avoidance pathways for BCL family inhibitors, a score integrating the three components, BCL-2, BCL-xL, and MCL-1, has been found to be particularly useful for evaluating the response to BCL family inhibitor treatment in LSC-like cells. Preferably, the score contemplated in the present invention is a response score, also referred to herein as a prediction score, which is the ratio of the determined amount of BCL-2 to the combined determined amounts of BCL-xL and MCL-1.

[0055] Preferably, the response score for evaluating the response of a subject to a BCL-2 inhibitor, preferably venetoclax, investigated by the method of the present invention can preferably be calculated as follows: Response score = [determined amount of BCL-2] / ([determined amount of BCL-xL]+[determined amount of MCL-1])

[0056] Preferably, the response score for evaluating the response of a subject to a BCL-xL inhibitor, preferably navitoclax, investigated by the method of the present invention can preferably be calculated as follows: Response score = 0.5([determined amount of BCL-2]+[determined amount of BCL-xL]) / [determined amount of MCL-1]

[0057] Preferably, the response score for evaluating the response of a subject to an MCL-1 inhibitor investigated by the method of the present invention can preferably be calculated as follows: Response score = 0.5([determined amount of MCL-1]+[determined amount of BCL-2]) / [determined amount of BCL-xL]

[0058] As discussed in other parts of this specification, the determined amount of the biomarker is preferably the normalized amount.

[0059] More preferably, the reference for the response score may be a reference score calculated as described above, and the reference is derived from a non-responder population. More preferably, the reference is from about 0.6 to about 1.0, preferably about 0.8.

[0060] More preferably, the method of the present invention involves determining the response scores of a BCL-2 inhibitor, a BCL-xL inhibitor, and an MCL-1 inhibitor as described above. Most preferably, the three response scores are compared with each other and evaluated. For the evaluation, the three determined response scores may be compared, and the highest response score will indicate a beneficial BCL family inhibitor treatment. For example, if the BCL-2 response score is the highest among the three scores, BCL-2 inhibitor treatment (e.g., Venetoclax treatment) is beneficial; if the BCL-xL or MCL-1 response score is the highest among the three scores, BCL-xL inhibitor treatment (e.g., Navitoclax treatment) is beneficial; if the MCL-1 score is the highest, BCL-2 inhibitor and MCL-1 inhibitor treatments are beneficial. Thereby, the evaluation can be further improved.

[0061] Based on the comparison, the response to BCL family inhibitor treatment is evaluated. Preferably, the evaluation includes identifying whether the subject examined by the method of the present invention is a responder or a non-responder to BCL family inhibitor treatment. Further, the evaluation enables identification of whether the subject benefits from the treatment if the subject is a responder or does not benefit from the treatment if the subject is a non-responder. As used herein, the term "comparing" includes comparison of amounts, values, or response scores as defined above. The comparison referred to in step (b) of the method of the present invention can be performed manually or using a computer. When the step is performed in a computer-utilizing format, the evaluation can also be automatically performed based on the result of the comparison. Preferably, the computer program for performing the evaluation will provide the result of the evaluation in an appropriate output format.

[0062] In a further step, the method may also make recommendations regarding BCL family inhibitor therapy. That is, the method can recommend to continue, discontinue, or modify the treatment. The step of making the recommendations can also be implemented in computer-implemented form using an expert system and an appropriate database integral to the recommendations, for example, a relational database that assigns possible recommendations to the evaluation.

[0063] Advantageously, AML is evaluated at the bulk level, and monocytic AML is dominated by monocytic blasts, which was confirmed in the studies underlying the present invention to confer resistance to 5-AZA / venetoclax treatment ex vivo. Paradoxically, a retrospective analysis of 54 AML patients receiving HMA / venetoclax as frontline treatment at the University Hospital Heidelberg did not show a prognostic value for bone marrow / monocyte differentiation. The monocytic blast subpopulation was shown to be insensitive to 5-AZA / venetoclax by MCL-1 dependence rather than BCL-2 dependence, but to have no significant functional LSC ability. This suggests that other leukemia cells must be playing a role in explaining the clinical data, and that the LSC population of immature GPR56+ stem cell-like cells present in all investigated AML specimens, regardless of the tumor-driven cell population and, in particular, the overall phenotypic presentation, can be used for the evaluation of responsiveness to BCL family inhibitor therapy. GPR56+ stem cell-like cells are very rich in BCL-2-dependent functional LSCs and rapidly disappear in AML patients responsive to 5-AZA / venetoclax treatment. This generally demonstrates the effectiveness of venetoclax against LSCs. Based on the BCL-2, BCL-xL, and MCL-1 expression levels in GPR56+ LSCs, the findings of the present invention have established a response score for identifying subjects who will benefit from BCL family inhibitor therapy. Furthermore, the established method can be easily implemented in a clinical flow cytometry facility.

[0064] Thanks to the present invention and the method for evaluating BCL family inhibitor therapy, it is possible to do the following: - Decide between standard chemotherapy and BCL family inhibitor treatment, i.e., the first choice - Perform risk stratification of elderly patients who need to decide whether to receive chemotherapy in the first place - Decide on a second-choice treatment using intensive chemotherapy for patients who do not respond to standard chemotherapy - Decide on any of various members of the BCL family inhibitor as the therapeutic agent.

[0065] All the explanations and definitions of the terms made above shall apply mutatis mutandis to the following embodiments.

[0066] In a preferred embodiment of the method of the present invention, the evaluation of the response to BCL family inhibitor treatment includes identifying whether the subject will benefit from treatment with a BCL-2 inhibitor, preferably venetoclax.

[0067] More preferably, the comparison of the biomarker with the reference includes calculating the ratio of the amount of BCL-2 to the combined amount of BCL-xL and MCL-1 to obtain a prediction score, and comparing the prediction score with the reference. Preferably, a prediction score greater than the reference indicates a subject who will benefit from treatment with a BCL-2 inhibitor, while a prediction score lower than the reference indicates a subject who will not benefit from treatment with a BCL-2 inhibitor. More preferably, the reference is a reference value derived from a non-responder population, and most preferably, the reference is from about 0.6 to about 1.0, preferably about 0.8.

[0068] In another preferred embodiment of the method of the present invention, the evaluation of the response to BCL family inhibitor treatment includes identifying whether the subject will benefit from treatment with a BCL-xL and / or at least one MCL-1 inhibitor, preferably navitoclax.

[0069] More preferably, the comparison between the biomarker and the reference includes calculating a ratio of half the combined amount of BCL-2 and BCL-xL to the amount of MCL-1 to obtain a prediction score, and comparing the prediction score with the reference. Preferably, a prediction score greater than the reference indicates a subject who will benefit from treatment with a BCL-xL and / or MCL-1 inhibitor, while a prediction score lower than the reference indicates a subject who will not benefit from treatment with a BCL-xL and / or MCL-1 inhibitor. More preferably, the reference is a reference value derived from a non-responder population, and most preferably, the reference is from about 0.6 to about 1.0, preferably about 0.8.

[0070] In a more preferred embodiment of the method of the present invention, the evaluation of the response to BCL family inhibitor treatment includes determining whether a subject will benefit from treatment with a BCL-2 inhibitor and at least one MCL-1 inhibitor, preferably venetoclax (and the at least one MCL-1 inhibitor is AZD5991 or MIK665).

[0071] More preferably, the comparison between the biomarker and the reference includes calculating a ratio of the amount of BCL-2 to the combined amount of BCL-xL and MCL-1 to obtain a prediction score, and comparing the prediction score with the reference. Preferably, a prediction score greater than the reference indicates a subject who will benefit from treatment with a BCL-2 inhibitor and at least one MCL-1 inhibitor, while a prediction score lower than the reference indicates a subject who will not benefit from treatment with a BCL-2 inhibitor and at least one MCL-1 inhibitor. More preferably, the reference is a reference value derived from a non-responder population, and most preferably, the reference is from about 0.6 to about 1.0, preferably about 0.8.

[0072] The present invention further contemplates a BCL-2 inhibitor, preferably venetoclax, for use in the treatment of cancer, preferably hematologic cancer, in a subject evaluated to benefit from treatment with the BCL-2 inhibitor by the method of the present invention.

[0073] As used herein, the term "treating" relates to ameliorating and / or curing a disease referred to herein, preventing the progression of a disease, or at least ameliorating at least one symptom associated with said disease. It will be understood that the treatments referred to herein will not likely be successful in all subjects treated. However, it is contemplated that the treatments will be effective in at least a statistically significant proportion of the subjects treated. Whether a statistically significant proportion (e.g., of a cohort of subjects) can be successfully treated can preferably be determined by statistical tests more fully discussed in other parts of the present invention.

[0074] The present invention further relates to BCL-xL and / or MCL-1 inhibitors, preferably navitoclax, for use in treating cancer, preferably hematologic cancer, in a subject evaluated to benefit from treatment using said BCL-xL and / or MCL-1 inhibitor by the method of the present invention.

[0075] The present invention relates to a BCL-2 inhibitor in combination with at least one MCL-1 inhibitor, preferably venetoclax, for use in treating cancer, preferably hematologic cancer, in a subject evaluated to benefit from treatment using said BCL-2 inhibitor in combination with at least one MCL-1 inhibitor by the method of the present invention.

[0076] Furthermore, the present invention also relates to a method of treating a subject suffering from cancer, preferably hematologic cancer, with a BCL family inhibitor treatment, comprising evaluating the response of said subject to the BCL family inhibitor treatment by performing the method of the present invention, and administering a BCL family inhibitor to the subject if the subject is evaluated to benefit from the treatment at a therapeutically effective amount.

[0077] A therapeutically effective amount refers to the amount of the BCL family inhibitor used to prevent, ameliorate, or treat the symptoms associated with the diseases or conditions referred to herein. The therapeutic efficacy and toxicity of a compound can be determined by standard pharmacological procedures in cell cultures or experimental animals, e.g., ED 50 (the dose that has a therapeutic effect on 50% of the population) and LD 50 (the dose that is lethal to 50% of the population). The dose ratio of the therapeutic effect to the toxic effect is the therapeutic index, and the therapeutic index can be expressed as the ratio LD 50 / ED 50 . The dosing regimen will be determined by the attending physician and other clinical factors. As is well known in the medical field, the dosage for any individual patient depends on many factors, including the patient's size, surface area, age, the particular compound administered, sex, time and route of administration, general health status, and other drugs being administered concurrently. Progression can be monitored by regular evaluation. In the present invention, the dosage and dosing regimen of the BCL family inhibitor are preferably those known in the art.

[0078] In a preferred embodiment of the aforementioned method of treating a subject, the BCL family inhibitor is a BCL-2 inhibitor, preferably venetoclax, a BCL-xL and / or MCL-1 inhibitor, preferably navitoclax, or a BCL-2 inhibitor, preferably venetoclax, in combination with at least one MCL-1 inhibitor.

[0079] The present invention also relates to an apparatus for evaluating the response of a subject suffering from cancer, preferably hematological cancer, to treatment with a BCL family inhibitor, comprising (a) an analysis unit capable of determining the amounts of the biomarkers BCL-2, BCL-xL, and MCL-1 in a tumor-initiating cell population, preferably a leukemia stem cell (LSC) population, in a sample of the subject, and (b) a scoring unit comprising a data processor capable of comparing the amounts of the biomarkers to a reference, whereby the response to treatment with the BCL family inhibitor is evaluated.

[0080] As used herein, the term "device" refers to a system that includes the aforementioned units operatively connected to each other so as to enable the determination of the presence, absence, or amount of a biomarker and its assessment in the methods of the present invention so as to provide an evaluation.

[0081] The analysis unit typically includes at least one detection element capable of detecting a biomarker present in a sample. Before introducing the sample into the detection element, the sample may be pretreated with a detection molecule to generate a detectable signal, for example, by forming a biomarker-antibody complex, whereby the antibody as the detection molecule includes a detectable label that can be detected by the detection element. The detection element can also include a reaction zone that enables the performance of a chemical detection reaction, such as PCR. The detection element must be suitable for determining the amount of the biomarker. The determined amount can then be transmitted to the assessment unit.

[0082] The assessment unit includes a data processing element, such as a computer, implementing an algorithm for determining the amount of biomarker present in a sample. The processing unit referred to in the method of the present invention typically includes a central processing unit (CPU) and / or one or more graphics processing units (GPU) and / or one or more application-specific integrated circuits (ASIC) and / or one or more tensor processing units (TPU) and / or one or more field programmable gate arrays (FPGA), etc. The data processing element may be, for example, a general-purpose computer or a portable computing device. It should also be understood that multiple computing devices may be used together, for example, via a network or other means of transferring data, to perform one or more steps of the methods disclosed herein. Exemplary computing devices include desktop computers, laptop computers, personal data assistants ("PDAs"), cellular devices, smart devices, or mobile devices, tablet computers, servers, etc. Generally, the data processing element includes a processor (e.g., a software program) capable of executing multiple instructions. The assessment unit typically includes or can access a memory. The memory is a computer-readable medium and may be composed of, for example, a single storage device or multiple storage devices arranged to be accessible to the computing device locally or through a network. The computer-readable medium may be any available medium that can be accessed by a computing device and includes both volatile and non-volatile media. Further, the computer-readable medium may be one or both of removable media and non-removable media. By way of example, but not limitation, the computer-readable medium may include computer storage media.Exemplary computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or any other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be accessed by a computing device and used to store a plurality of instructions executable by a processor of the computing device. The assessment unit can also include, or can access, an output device. Exemplary output devices include, for example, fax machines, displays, printers, and files. According to some embodiments of the present disclosure, a computing device can perform one or more steps of the methods disclosed herein and then provide an output regarding the results, metrics, ratios, or other elements of the method via an output device.

[0083] Preferably, the device is adapted to implement the method of the present invention.

[0084] The present invention further relates to a kit for evaluating the response of a subject suffering from cancer, preferably hematopoietic cancer, to treatment with a BCL family inhibitor, the kit comprising detection molecules for determining the amounts of the biomarkers BCL-2, BCL-xL, and MCL-1 in a tumor-driven cell population, preferably a leukemia stem cell (LSC) population, in a sample of the subject.

[0085] As used herein, the term "kit" typically refers to an assembly of the foregoing components provided separately or in a single container. The container typically also includes instructions for practicing the method of the invention. These instructions may be in the form of a manual or, when executed on a computer or data processing device, may be provided by computer program code capable of performing or supporting the determination of biomarkers referred to in the method of the invention. The computer program code may be provided on a data storage medium or device, such as an optical storage medium (e.g., a compact disc), or may be provided directly to a computer or data processing device, or may be provided in a downloadable format, such as a link to an accessible server or cloud. Further, the kit may typically include a reference amount standard of a biomarker for calibration purposes, as described in detail in other parts of this specification. The kit according to the invention may also include additional components necessary for practicing the method of the invention, such as solvents, buffers, washing solutions, and / or reagents required for the detection of the released second molecule. Further, the kit may include either part or all of the device of the invention.

[0086] The following embodiments are specific preferred embodiments according to the present invention.

[0087] Embodiment 1: A method for evaluating the response of a subject suffering from cancer, preferably hematopoietic cancer, to treatment with a BCL family inhibitor, comprising: (a) determining the amounts of the biomarkers BCL-2, BCL-xL, and MCL-1 in a tumor-initiating cell population, preferably a leukemia stem cell (LSC) population, in a sample from the subject; and (b) comparing the amounts of the biomarkers to a reference, whereby the response to treatment with a BCL family inhibitor is evaluated.

[0088] Embodiment 2: The method according to Embodiment 1, wherein the hematopoietic cancer is acute myeloid leukemia (AML), other lymphoma or leukemia of B-cell or T-cell origin, or a plasma cell neoplasm.

[0089] Embodiment 3: The method according to embodiment 1 or 2, wherein said evaluation of the response to BCL family inhibitor treatment comprises determining whether a subject will benefit from treatment with a BCL-2 inhibitor.

[0090] Embodiment 4: The method according to embodiment 3, wherein said comparison of the biomarker to the reference comprises calculating a ratio of the amount of BCL-2 to the combined amount of BCL-xL and MCL-1 to obtain a prediction score, and comparing said prediction score to the reference.

[0091] Embodiment 5: The method according to embodiment 4, wherein a prediction score greater than the reference indicates a subject who will benefit from treatment with a BCL-2 inhibitor.

[0092] Embodiment 6: The method according to embodiment 4, wherein a prediction score lower than the reference indicates a subject who will not benefit from treatment with a BCL-2 inhibitor.

[0093] Embodiment 7: The method according to any one of embodiments 4 to 6, wherein said reference is a reference value derived from a non-responder population.

[0094] Embodiment 8: The method according to embodiment 7, wherein said reference is from about 0.6 to about 1.0, preferably about 0.8.

[0095] Embodiment 9: The method according to any one of embodiments 3 to 8, wherein said BCL-2 inhibitor is venetoclax.

[0096] Embodiment 10: The method according to embodiment 9, wherein said venetoclax is used in combination with an additional cancer treatment agent, such as a classical chemotherapeutic agent, more preferably a hypomethylating agent, preferably 5-azacytidine (5-AZA), decitabine or cytarabine, or an antibody, such as rituximab, or a targeted therapeutic agent, such as midostaurin.

[0097] Embodiment 11: The method according to embodiment 1 or 2, wherein the evaluation of the response to BCL family inhibitor treatment comprises identifying whether a subject will benefit from treatment with a BCL-xL and / or MCL-1 inhibitor.

[0098] Embodiment 12: The method according to embodiment 11, wherein the comparison of the biomarker with the reference comprises calculating a ratio of half the combined amount of BCL-2 and BCL-xL to the amount of MCL-1 to obtain a prediction score, and comparing the prediction score with the reference.

[0099] Embodiment 13: The method according to embodiment 12, wherein a prediction score greater than the reference indicates a subject who will benefit from treatment with a BCL-xL and / or MCL-1 inhibitor.

[0100] Embodiment 14: The method according to embodiment 12, wherein a prediction score lower than the reference indicates a subject who will not benefit from treatment with a BCL-xL and / or MCL-1 inhibitor.

[0101] Embodiment 15: The method according to embodiment 12 or 14, wherein the reference is a reference value derived from a non-responder population.

[0102] Embodiment 16: The method according to embodiment 15, wherein the reference is from about 0.6 to about 1.0, preferably about 0.8.

[0103] Embodiment 17: The method according to any one of embodiments 11 to 16, wherein the BCL-xL and / or MCL-1 inhibitor is navitoclax.

[0104] Embodiment 18: The method according to embodiment 1 or 2, wherein the evaluation of the response to BCL family inhibitor treatment comprises identifying whether a subject will benefit from treatment with a BCL-2 inhibitor and at least one MCL-1 inhibitor.

[0105] Embodiment 19: The method according to embodiment 18, wherein the comparison between the biomarker and the reference includes calculating a ratio of the amount of BCL-2 to the combined amount of BCL-xL and MCL-1 to obtain a prediction score, and comparing the prediction score with the reference.

[0106] Embodiment 20: The method according to embodiment 19, wherein a prediction score greater than the reference indicates a subject who benefits from treatment with a BCL-2 inhibitor and at least one MCL-1 inhibitor.

[0107] Embodiment 21: The method according to embodiment 19, wherein a prediction score lower than the reference indicates a subject who does not benefit from treatment with a BCL-2 inhibitor and at least one MCL-1 inhibitor.

[0108] Embodiment 22: The method according to embodiment 19 or 21, wherein the reference is a reference value derived from a non-responder population.

[0109] Embodiment 23: The method according to embodiment 22, wherein the reference is from about 0.6 to about 1.0, preferably about 0.8.

[0110] Embodiment 24: The method according to any one of embodiments 18 to 23, wherein the BCL-2 inhibitor is venetoclax and the at least one MCL-1 inhibitor is AZD5991 or MIK665.

[0111] Embodiment 25: The method according to any one of embodiments 1 to 24, wherein the LSC population is characterized by an increase in the expression of at least one biomarker selected from the group consisting of GPR56, CD34, and BCL-2.

[0112] Embodiment 26: The method according to embodiment 25, wherein the expression is increased compared to the expression of at least one biomarker in monocyte-like AML cells.

[0113] Embodiment 27: The method according to any one of Embodiments 1 to 26, wherein the amount of the biomarker is determined by quantification of RNA expression or specific antibody-based quantification, preferably by flow cytometry.

[0114] Embodiment 28: A BCL-2 inhibitor, preferably venetoclax, for use in the treatment of cancer, preferably hematological cancer, in a subject evaluated to benefit from treatment with a BCL-2 inhibitor by the method according to any one of Embodiments 3 to 10 and 24 to 27.

[0115] Embodiment 29: A BCL-xL and / or MCL-1 inhibitor, preferably navitoclax, for use in the treatment of cancer, preferably hematological cancer, in a subject evaluated to benefit from treatment with a BCL-xL and / or MCL-1 inhibitor by the method according to any one of Embodiments 11 to 17 and 24 to 27.

[0116] Embodiment 30: A BCL-2 inhibitor in combination with at least one MCL-1 inhibitor, preferably venetoclax, for use in the treatment of cancer, preferably hematological cancer, in a subject evaluated to benefit from treatment with a BCL-2 inhibitor in combination with at least one MCL-1 inhibitor by the method according to any one of Embodiments 18 to 27.

[0117] Embodiment 31: A method of treating a subject suffering from cancer, preferably hematological cancer, with a BCL family inhibitor therapy, comprising evaluating the response of the subject to the BCL family inhibitor therapy by performing the method of the present invention, and administering a BCL family inhibitor to the subject if the subject is evaluated to benefit from the treatment.

[0118] Embodiment 32: The method according to embodiment 31, wherein the BCL family inhibitor is a BCL-2 inhibitor, preferably venetoclax, a BCL-xL and / or MCL-1 inhibitor, preferably navitoclax, or a BCL-2 inhibitor, preferably venetoclax, in combination with at least one MCL-1 inhibitor.

[0119] Embodiment 33: An apparatus for evaluating the response of a subject suffering from cancer, preferably hematological cancer, to treatment with a BCL family inhibitor, comprising: (a) an analysis unit capable of determining the amounts of the biomarkers BCL-2, BCL-xL, and MCL-1 in a tumor-driving cell population, preferably a leukemia stem cell (LSC) population, in a sample of the subject; and (b) an assessment unit comprising a data processor capable of comparing the amounts of the biomarkers to a reference, whereby the response to treatment with a BCL family inhibitor is evaluated.

[0120] Embodiment 34: A kit for evaluating the response of a subject suffering from cancer, preferably hematological cancer, to treatment with a BCL family inhibitor, the kit comprising detection molecules for determining the amounts of the biomarkers BCL-2, BCL-xL, and MCL-1 in a tumor-driving cell population, preferably a leukemia stem cell (LSC) population, in a sample of the subject.

[0121] All references cited throughout this specification are hereby incorporated by reference in their entirety for the specific disclosure to which they relate.

Brief Description of the Drawings

[0122]

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Mode for Carrying Out the Invention

[0123] [Examples] Examples are merely for explaining the present invention. Examples should not be construed as limiting the scope in any case.

[0124] [Example 1] General Method Primary AML Patient Samples AML samples were collected from diagnostic peripheral blood (PB) punctures at the University Hospital of Heidelberg in accordance with the Declaration of Helsinki after obtaining written consent from each patient. The project was approved by the Ethics Committee of the Heidelberg Faculty of Medicine (S-169 / 217, S-648-2021). PB mononuclear cells (MNCs) were isolated by density gradient centrifugation using Ficoll Paque Plus (GE Healthcare, catalog number GE17-1440-03) and stored in liquid nitrogen until further use. Experienced hematologists retrospectively reviewed all pathology reports and flow cytometry reports to classify all patients as responders and non-responders. Responders were defined as those who achieved a blast reduction of <5% according to the ELN.

[0125] Treatment of primary AML cells Viably cryopreserved AML PB samples were thawed at 37 °C in Iscove's Modified Dulbecco's Medium (IMDM) containing 10% FBS and treated with DNase I for 15 minutes (100 μg / ml).

[0126] Ex vivo drug screening in primary leukemia cells The recovered cells were cultured using a previously established protocol with IMDM, 15% BIT (bovine serum albumin, insulin, transferrin; Stem Cell Technologies, catalog number 09500), 100 ng / ml SCF (PeproTech, catalog number 300-07), 50 ng / ml FLT3-L (PeproTech, catalog number 300-19), 20 ng / ml IL-3 (PeproTech, catalog number 200-03), 20 ng / ml G-CSF (PeproTech, catalog number 300-23), 100 μM β-mercaptoethanol (ThermoFisher, catalog number 31350010), 500 nM SR1 (StemRegenin 1, STEMCELL Technologies, catalog number 72342), 1 μM UM729 (STEMCELL Technologies, catalog number 72332), and 1% penicillin-streptomycin (Sigma, catalog number P4458-100ML). For the drug assay in Figure 1, 0.5×10 5 AML cells / well were seeded in a flat-bottom 96-well plate and treated with escalating concentrations of azacitidine (0.5 μM, 1.5 μM, 4.5 μM, 13.5 μM, 40.5 μM) and venetoclax (0.3 nM, 0.9 nM, 2.7 nM, 8.1 nM, 24.3 nM, 72.9 nM, 218.7 nM, 656.1 nM, 1968.3 nM) alone and in combination for 72 hours. After 72 hours, viability was assessed using the CellTiter-Glo (Promega, catalog number G7571) Luminescent Cell Viability Assay. For the drug assay in Figure 3, 0.5×10 5AML cells / well were seeded in a flat-bottom 96-well plate and the cells were treated with 1.5 μM of azacitidine and 100 nM of venetoclax for 24 hours. After 24 hours, the cells were stained with a total of 11 fluorescent cell surface antibodies. An equal amount of CountBright Absolute Counting Beads (Thermo Fisher Scientific, catalog number C36950) and 7-AAD (BD Biosciences, catalog number 559925) were added to each sample and then analyzed with a BD LSRFortessa Cell Analyzer.

[0127] Intracellular staining of BCL-2 family members The thawed cells were stained with Zombie NIR Fixable Viability stain (BioLegend, catalog number 423105) in PBS and then stained with a total of 11 fluorescent cell surface antibodies. The stained cells were fixed and permeabilized using a Fixation / Permeabilization Solution Kit (BD Biosciences, catalog number 554714) according to the manufacturer's instructions, and then a second permeabilization step to enhance intracellular staining was performed using Permeabilization Buffer Plus (BD Biosciences, catalog number 561651). The fixed and permeabilized cells were separately stained for anti-human BCL-2-PE (clone 124, Cell Signaling, catalog number 26295S), anti-human MCL-1-PE (clone D2W9E, Cell Signaling, catalog number 65617S), and anti-human MCL-1-PE (clone 54H6, Cell Signaling, catalog number 13835S). The samples were analyzed with a BD LSRFortessa Cell Analyzer.

[0128] BH3 profiling of primary AML samples The thawed cells were stained with Zombie NIR Fixable Viability stain (BioLegend, catalog number 423105) in PBS and then stained with a total of nine fluorescent cell surface antibodies. BH3 profiling was performed as previously described. After permeabilizing the cell membrane with digitonin (0.002%), the cells were exposed to synthetic BH3 peptides and BH mimetics at increasing concentrations in MEB buffer (150 mM mannitol, 10 mM HEPES-KOH pH 7.5, 50 mM KCl, 0.02 mM EGTA, 0.02 mM EDTA, 0.1% BSA, and 5 mM succinic acid) for 60 minutes. After 60 minutes of peptide / mimetic exposure at room temperature, the cells were fixed with 4% formaldehyde for 15 minutes and then neutralized with N2 buffer (1.7 M Tris, 1.25 M glycine pH 9.1) for 10 minutes. Sensitivity to BH3 peptides / mimetics was measured as cytochrome c release using an anti-cytochrome C FITC antibody (clone 6H2.B4, Biolegend, catalog number 612302) on a BD FACSymphony A3 Cell Analyzer. DMSO was used as a negative control for cytochrome c retention, while alamethicin (ALM) was used as a positive control for 100% cytochrome c release. The decrease in cytochrome c was calculated using the following formula: [Cytochrome c release = 1 - (《MFI》_sample - 《MFI》_ALM) / 《MFI》_DMSO]. Cytochrome c release was evaluated in each FACS gate population. To evaluate the variation in cytochrome c release as a function of BH3 peptide / mimetic concentration, the area under the curve (AUC) for each BH3 peptide / mimetic was calculated using Prism v.9.

[0129] Long-term collected primary AML samples Peripheral blood (PB) was collected from three newly diagnosed AML patients (AML55, AML61, and AML62) before treatment with 5-AZA / Ven (day 0), and then blood was collected during treatment (days 1 - 6). PB-MNCs were isolated as described above. Before freezing, 0.2×10 6Cells were stained with a total of 11 types of fluorescent cell surface antibodies and analyzed using a BD LSRFortessa Cell Analyzer.

[0130] Treatment of AML cell lines Twenty-four types of AML cell lines were cultured at 37°C in a humidified incubator with 5% CO2. All cell lines were authenticated.

[0131] In vitro drug screening in leukemia cell lines 0.1×10 5 Cells / well were seeded in a flat-bottom 96-well plate and treated with increasing concentrations of venetoclax (1 nM, 3 nM, 9 nM, 27 nM, 81 nM, 243 nM, 729 nM, 2187 nM) combined with a single dose of azacitidine (1.5 μM) for 72 hours. After 72 hours, cell viability was evaluated using the CellTiter-Glo (Promega, catalog number G7571) Luminescent Cell Viability Assay.

[0132] Fluorescence-activated cell sorting (FACS) Primary AML cells were stained with a total of 10 types of fluorescent cell surface antibodies. Cells were sorted into four populations according to the expression of CD11b, CD64, and GPR56 within the lineage-negative gate. Cells from each population and the lineage-negative bulk were sorted directly into RNA extraction buffer (Thermo Fisher, catalog number KIT0214), snap-frozen, and stored at -80°C until RNA extraction. For xenotransplantation, cells from each population were sorted into PBS.

[0133] Determination of leukemogenic ability in vivo NOD.Prkdcscid.Il2rgnull (NSG) mice were housed and maintained under specific pathogen-free conditions at the Central Animal Facility of the German Cancer Research Center (DKFZ) in Germany. All animal experiments were performed in compliance with all relevant ethical regulations. All experiments were approved by the Regierungsprasidium Karlsruhe under animal experiment application numbers (Tierversuchsantrag numbers) G-140-21 and G42 / 18.

[0134] Xenotransplantation Prior to the xenotransplantation assay, female mice aged 8 - 12 weeks were irradiated with sublethal radiation (175 cGy) for 24 hours. Up to 1x10 6 cells from the FACS-sorted primary AML samples (see above) were injected into the femur BM cavity of the sublethally irradiated mice. Human leukemia engraftment in mouse BM was assayed by flow cytometry using anti-human CD45-FITC (clone HI30), anti-human CD34-BUV395 (clone 581), anti-human GPR56-PE (clone CG4), anti-human CD19-FITC (clone HIB19), anti-human CD33-PE-Cy7 (clone WM53), CD64-APC, CD11b-BV711, and anti-mouse CD45-Alexa700 (clone 30-F11) (up to 45 weeks maximum, unless the endpoint criteria were reached earlier). At the endpoint time point, the bones were crushed to recover BM cells from the tibia, femur, and iliac crest. The spleen was minced finely with a plunger to recover spleen cells. After erythrocyte lysis, the cells were resuspended in FBS + 10% DMSO (Sigma, catalog number D2650-100) and stored in liquid nitrogen until further use.

[0135] RNA sequencing of the AML population RNA extraction and purification of FACS-sorted cells were performed using the PicoPure RNA Isolation kit according to the manufacturer's instructions (Thermo Fisher, catalog number KIT0214). RNA quality assessment and quantification were performed on a Bioanalyzer using the Agilent RNA 6000 Pico kit (Agilent, catalog number 5067-1513). Whole transcriptome amplification was performed using 5 μl of modified RT buffer containing 1×SMART First Strand buffer (Takara Bio Clontech, catalog number 639538), 1 mM dithiothreitol (Takara Bio Clontech), 1 μM template switching oligo (IDT), 10 U μl-1 SMARTScribe (Takara Bio Clontech, catalog number 639538), and 1 U μl-1 RNasin Plus RNase inhibitor (Promega, catalog number N2615) using the modified smart-seq2 protocol. cDNA tagmentation was performed using the Nextera XT DNA Library Preparation kit (Illumina, catalog number FC-121-1030). All RNA libraries were pooled and sequenced together on an Illumina NextSeq 550 high-output sequencer (1.4 pM, 1% PhiX loading concentration, single-end 75 bp read configuration).

[0136] Raw data processing and quality control of RNA sequencing data Reads were demultiplexed and FASTQ files containing reads for individual samples were aligned by two-pass alignment using STAR aligner v.2.5.3a. Reads were aligned to a STAR index generated from the 1000 Genomes Project human genome assembly (hs37d5) using the GENCODE v.19 gene model. The default alignment calling parameters were used with the following modifications: --outSAMtype BAM Unsorted SortedByCoordinate --limitBAMsortRAM 100000000000 --outBAMsortingThreadN=1 --outSAMstrandField intronMotif --outSAMunmapped Within KeepPairs --outFilterMultimapNmax 1 --outFilterMismatchNmax 5 --outFilterMismatchNoverLmax 0.3 --chimSegmentMin 15 --chimScoreMin 1 --chimScoreJunctionNonGTAG 0 --chimJunctionOverhangMin 15 --chimSegmentReadGapMax 3 --alignSJstitchMismatchNmax 5 -1 5 5 --alignIntronMax 1100000 --alignMatesGapMax 1100000 --alignSJDBOverhangMin 3 --alignIntronMin 20。

[0137] Sambamba v.0.6.5 was used for alignment file sorting, duplicate marking, and BAM index generation using eight thresholds. Quality control analysis was performed using the sambamba flagstat command and rnaseqc v.1.1.8 with the hs37d5 assembly and GENCODE v.19 gene model. The coverage depth analysis of rnaseqc was turned off. Gene-specific gene counting across exon features based on the GENCODE v.19 gene model was performed using featureCounts v.1.5.1. The quality threshold was set to 255, indicating that STAR found unique alignments. Strand-unspecific counting was used.

[0138] DESeq2 was used for statistical analysis of read counts to identify differentially expressed genes between the LSC population and the mature population in Prim-AML and Mono-AML. Genes with an FDR-corrected p-value ≤ 0.05 and a change in expression of at least 1.5 log-fold (|log2FC| ≥ 1.5) were considered differentially expressed. Gene expression values for PCA visualization were adjusted by variance stabilization. Gene set enrichment analysis for hallmark gene sets between LSC and mature cells was performed based on a gene list ranked by log-fold change order. The LSC17 score was calculated for each AML sample as follows: LSC17 score = 1 / 17 × (DNMT3B + ZBTB46 + NYNRIN + ARHGAP22 + LAPTM4B + MMRN1 + DPYSL3 + KIAA0125 + CDK6 + CPXM1 + SOCS2 + SMIM24 + EMP1 + NGFRAP1 + CD34 + AKR1C3 + GPR56)

[0139] Quantification and statistical analysis Flow cytometry data analysis was performed using FlowJo v.10.5.3. tSNE plots of BCL-2 family members (Figure 3) and 5-AZA / VEN sensitivity (Figure 4) were performed using the FlowJo TSne plugin v.2.0.0. All statistical analyses except for RNA sequencing data were performed using Prism v.9.

[0140] [Example 2] Ex vivo resistance of AML to 5-AZA / VEN is associated with monocytic differentiation To identify predictive parameters for response to 5-AZA / VEN treatment, the in vitro sensitivity of 19 AML cell lines treated for 72 hours was assayed. After stratifying these into monocytic-like AML (Mono-AML) and primitive-like AML (Prim-AML) based on the mean fluorescence intensity (MFI) of the monocyte marker CD64, it was observed that Mono-AML cell lines were highly resistant to 5-AZA / VEN, while most of the Prim-AML cell lines were sensitive even to low concentrations of VEN (Figure 1A). Overall, the mean IC 50 of Mono-AML cell lines was 155-fold higher compared to Prim-AML cell lines (1901 nM vs. 12 nM) (Figure 5A). To verify the 5-AZA / VEN resistance of Mono-AML observed in cell lines, primary cells from 12 AML patients were treated with increasing concentrations of 5-AZA and VEN (Figure 1B). Bias-free clustering of cell viability measured 72 hours after ex vivo treatment revealed two independent clusters. The cluster associated with treatment resistance contained only samples with >40% CD64 + CD11b + cells prior to treatment (Figure 1B, bottom cluster), while specimens with <20% CD64 + CD11b + cells were highly sensitive to VEN-based treatment and were clustered separately (Figure 1B, top cluster). This data implies that monocytic differentiation of bulk AML cells is associated with ex vivo resistance to 5-AZA / VEN treatment. CD64 obtained from bias-free clustering +CD11b + The percentages were subsequently used to stratify the AML patient samples into Mono-AML (>40%) and Prim-AML (<20%).

[0141] [Example 3] The clinical response of AML to 5-AZA / VEN is independent of monocytic differentiation To determine the significance of the above findings in the clinical context, between 2019 and 2022, a cohort of 54 newly diagnosed, previously untreated AML patients who received HMA / VEN (e.g., 5-AZA / VEN) at the University Hospital of Heidelberg for parameters related to treatment refractoriness was analyzed. In contrast to our ex vivo results, CD64 + The frequency of blasts was not a significant predictor of refractoriness (Figs. 1C–E). In univariate logistic regression analysis, the only significant factors predicting the risk of refractory disease were (1) previous myelodysplastic syndrome or myeloproliferative neoplasm (MDS / MPN), (2) adverse risk according to the 2017 ELN classification, and (3) complex karyotype (CK) (Figs. 1C–D). CK and MDS / MPN also showed a tendency to be independent predictors in multivariate analysis, and 50% of these patients were refractory. The ELN “adverse” was not reproduced as an independent predictor because of the high response rate in RUNX1- and ASXL1-mutated AML (Figs. 1C–D). This finding was demonstrated by quantifying the percentage of CD64 + CD11b + AML cells, and there was no difference in CD64 between responding and refractory patients + CD11b +Equivalent distribution of cells (Figure 1E) was confirmed. Finally, to investigate the proliferation of monocyte clones, we studied the available longitudinal flow cytometry reports of 13 patients with primary refractory diseases. CD64 surface expression in bone marrow (BM) AML blasts on day 15 and / or day 30 after treatment was not associated with treatment resistance (Figure 5C). These data conflict with the ex vivo data reported above and by others, indicating that monocyte differentiation is not a strong predictor of response to HMA / VEN (e.g., 5-AZA / VEN).

[0142] [Example 4] In monocytic and primitive AML, LSCs are immature GPR56 + rich in blasts The discrepancy between clinical and preclinical data suggests that the analysis of bulk AML patient samples is not suitable for predicting response to HMA / VEN treatment (e.g., 5-AZA / VEN treatment). Instead, we hypothesized that LSCs drive resistance and relapse after HMA / VEN treatment and are therefore an appropriate population for further investigation. However, it has been demonstrated that it is difficult to reliably detect LSCs across genetic subclasses because AML is a very heterogeneous disease. Therefore, the goal was to functionally and transcriptionally characterize LSCs in Mono-AML and Prim-AML samples. Cell surface expression of CD64 and CD11b easily discriminated two dominant cell populations in a cohort of 72 diagnostic AML samples. Mature CD64 + CD11b +Cells (mature) were the dominant population in Mono-AML but were also present in Prim-AML, although at much lower frequencies (40 - 97.6% vs 0.1 - 20% of leukemic blasts) (Figs. 2A - B, 6A). To study subpopulations within immature blasts, functional LSCs in Mono-AML and Prim-AML samples were further enriched, including GPR56 expression, a marker of LSCs and poor outcome in AML. GPR56 was identified in 0.4 - 92.6% of all AML cells in all samples examined, including NPM1 wild-type and mutant patient samples, where classical LSC surface markers, such as CD34, often fail to define leukemic initiating cells (Figs. 2A - B). + To verify the association between GPR56 expression and leukemogenicity in Mono-AML and Prim-AML, mature, immature GPR56 - (non-LSC) and immature GPR56 + (LSC-like) populations sorted by FACS from 12 patient samples were injected into NSG mice and evaluated for leukemogenic potential (Fig. 2C). In 12 / 12 AMLs, leukemia engraftment (CD45 + CD33 + ) was initiated by the LSC-like population, but only in 2 / 12 patients did the non-LSC-like and mature fractions consistently result in associated leukemia engraftment (Figs. 2D - E). Importantly, since both AML classes showed superior engraftment of the LSC-like population, no difference in leukemogenic potential of LSC-like cells derived from Mono-AML and Prim-AML was detected.

[0143] [Example 5] The immature GPR56 + fraction is rich in stem cell-related molecular programs Due to the similarity in leukemogenic ability observed in Mono-AML and Prim-AML, RNA sequencing was performed on cell populations selected from 17 AML patients to further characterize LSC-like cells and mature cells. Interestingly, dimensionality reduction using principal component analysis (PCA) revealed clear clustering of samples based on the population (LSC-like and mature), but not based on the two AML classes (Figure 2F). Differential gene expression analysis between LSC-like cells and mature cells showed that the upregulated genes in LSC-like cells contain known cancer stem cell markers including KIT, ERG, GPR56, and PROM1, while the upregulated genes in mature cells contain monocyte markers such as S100A9, S100A8, and CD14, indicating an abundance of pathways related to myeloid differentiation (Figures 6B - C). Furthermore, several stemness scores, including the LSC17 score, were significantly higher in LSC-like cells compared to mature cells regardless of the AML class (Figure 2G). Since no significant differences were observed between LSC-like cells from AML samples stratified based on monocyte-like surface expression, we also evaluated whether genetics could explain the minor differences observed between different LSC-like samples in PCA. Indeed, RUNX1 mutant LSC-like cells and NPM1 mutant LSC-like cells formed separate clusters (Figure 6E). These findings emphasize the stemness characteristics of functionally defined LSC-like cells at the transcriptome level, indicating that transcriptome clustering at the LSC level is mainly determined by underlying mutational events rather than the differentiation state (e.g., of progeny of blasts).

[0144] [Example 6] LSC-like cells mainly express BCL-2, while MCL-1 is highly expressed in mature blasts Interested in the indistinguishable clinical responses of Mono-AML and Prim-AML to HMA / VEN treatment (e.g., 5-AZA / VEN treatment), the expression of BCL-2 family members, such as BCL-2, MCL-1, and BCL-2L1, within the LSC-like subpopulation of either AML class was correlated. Consistent with previous studies, BCL-2 was highly expressed in the LSC-like population compared to the mature population (4.7-fold higher) (FDR < 0.01, Figure 2I). On the other hand, MCL-1 expression was higher in the mature population compared to LSC-like cells (2.3-fold higher) (FDR < 0.01, Figure 2I). When comparing the expression of BCL-2 and MCL-1 in LSC-like cells and mature cells, no difference was found between Mono-AML samples and Prim-AML samples. In contrast, BCL-2L1 expression, which encodes BCL-xL, was not biased towards a specific subpopulation (Figure 2J). Furthermore, transcriptome analysis that expanded the BCL-2 family also did not form clusters based on AML classes (Figure 6F). These results suggest that LSC-like cells express higher levels of BCL-2 and lower levels of MCL-1 than more mature cells, regardless of the overall AML differentiation state. To confirm the expression levels of BCL-2 family members at the protein level, the inventors established an intracellular staining protocol (Figure 2K) that combined their surface marker panel with antibodies specific to BCL-2, MCL-1, and BCL-xL. Without gating on the LSC-like or mature subpopulation, bulk AML cells from Prim-AML showed 1.8-fold higher expression of BCL-2 protein compared to Mono-AML (Figure 2L). However, analysis of pre-gated subpopulations showed that BCL-2 was highly expressed in LSC-like cells regardless of the AML class but not in mature cells. This nullifies the difference due to AML class observed in bulk (2.6-fold higher in LSC-like compared to mature; Figure 2M). Conversely, bulk assessment of MCL-1 protein revealed a 2.1-fold higher level in Mono-AML than in Prim-AML (Figure 2N).Again, after gating the LSC-like and mature subpopulations, MCL-1 protein expression was not different between the Mono-AML and Prim-AML samples, and MCL-1 was highly expressed in the mature populations of both AML classes (2.1-fold higher than LSC-like cells; Fig. 2O). On the other hand, BCL-xL did not show a clear difference specific to the subpopulation or AML class (Fig. 6J). Representative t-distributed stochastic neighbor embedding (tSNE) plots for both cases show that Prim-AML contains more immature LSC-like cells that co-express higher levels of CD34 and GPR56 with BCL-2, but also contains a smaller fraction of mature cells that express CD64 and MCL-1 (Fig. 2P). In contrast, Mono-AML is rich in more mature cells that express high levels of CD64 and MCL-1 and contains only a small immature LSC-like population that expresses CD34, GPR56, and BCL-2 (Fig. 2Q). Collectively, the evaluation of BCL-2, MCL-1, and BCL-xL protein levels supports the findings of the transcriptional data and emphasizes the similarity of the subpopulations of Prim-AML and Mono-AML, despite the fact that their frequencies are very different in the two different AML classes.

[0145] [Example 7] BH3 profiling corroborates that LSC-like cells of both AML classes are BCL-2 dependent Next, we investigated whether the differential expression of subsets of BCL-2 family members leads to functional consequences. To address this, we performed BH3 profiling to measure the activity of the apoptotic pathway in the same AML samples. Similar to the intracellular staining of BCL-2 family members, surface markers were combined with BH3 profiling to assess mitochondrial apoptotic priming and survival-promoting BCL-2 family protein dependency in bulk AML as well as pre-gated LSC-like cells and mature cells. Briefly, cells were exposed to apoptosis-promoting BH3 peptides and various mimetics, and the release of mitochondrial cytochrome C, which irreversibly induces apoptosis in cells, was evaluated (Figures 3A - B). In bulk AML cells, the dependency on BCL-2 when exposed to the BH3 mimetic VEN was higher in Prim-AML compared to Mono-AML (Figure 3C). However, differential analysis in pre-gated LSC-like cells and mature cells nullified this difference, while revealing that LSC-like cells were more dependent on BCL-2 compared to mature cells in both AML classes (Figure 3D). After evaluating the dependency on MCL-1 by cytochrome C release when exposed to the BH3 peptide MS-1, the opposite behavior was found (Figures 3E - F). Here, mature cells showed 1.7-fold higher MS-1-mediated apoptotic priming compared to LSC-like cells, regardless of the AML class. Collectively, this data suggests that differences in BCL-2 and MCL-1 mRNA and protein expression predict subset (LSC-like / mature)-specific dependencies independent of the AML class. Generally, LSC-like cells are most dependent on BCL-2.

[0146] [Example 8] Ex vivo 5-AZA / VEN treatment eradicates LSC-like blasts and spares mature blasts in both AML classes Since BH3 profiling showed that LSC-like cells are dependent on BCL-2 and mature cells are dependent on MCL-1, we hypothesized that the differential ex vivo treatment responses of bulk Prim / Mono-AML are mainly driven by these subpopulations. Therefore, bulk AML cells from 16 newly diagnosed patients were exposed to 5-AZA / VEN for 24 hours, and the viability of bulk cells and subpopulations compared to untreated controls was analyzed by flow cytometry (Figure 3G). As found in Figure 1B, bulk cells from Mono-AML patient samples showed significantly higher resistance to treatment (compared to Prim-AML). Analysis of subpopulation viability after 5-AZA / VEN revealed that 5-AZA / VEN only slightly reduced the mature population by 40+ / -32% (Prim-AML) and 22.5+ / -11.5% (Mono-AML) in both AML classes (Figure 3H). In comparison, LSC-like cells derived from both the Prim-AML class and the Mono-AML class were efficiently eliminated at 90+ / -10.6% and 79+ / -19% respectively (Figure 3H). Representative tSNE plots of live cells from two AML samples were overlaid on heatmaps of CD64 and GPR56 expression to identify the mature fraction and the LSC-like fraction respectively (Figures 2I-J). Irrespective of the AML class, the differential response between mature cells and LSC-like cells was further confirmed in the remaining live cells as the LSC-like cells replaced the enrichment of mature cells (Figures 2I-J). Consistent with the results of BH3 profiling and BCL-2 expression, these data indicate that the difference in bulk AML sensitivity is mainly brought about by the initial difference in the proportion of the treatment-resistant but disease-spreading mature subpopulation between Prim-AML samples and Mono-AML samples. Importantly, the number of LSC-like cells is effectively eliminated by 5-AZA / VEN in both AML classes.

[0147] [Example 9] HMA / VEN rapidly eliminates LSC-like cells in responsive patients Ex vivo treatment of frozen AML samples is notoriously difficult and prone to misinterpretation. Therefore, the goal was to confirm the potential of HMA / VEN (e.g., 5-AZA / VEN) to eradicate a patient's LSC-like cells. For this purpose, PBMCs were collected from three AML patients together with peripheral blasts before treatment initiation (day 0) and during HMA / VEN (e.g., 5-AZA / VEN) treatment on days 1–6 (Figure 3K), and the mature and LSC-like population sizes were normalized to the pre-treatment cell numbers at each time point. All three patients responded to treatment, achieving a marked decrease in peripheral blast counts within the first 24 hours of treatment. The relative number of LSC-like cells decreased and remained low, but a substantial proportion of mature cells remained in all three patients during the first few days of treatment (Figures 3L–M). Finally, the ability of ex vivo 5-AZA / VEN treatment to predict clinical response was evaluated. Therefore, the viability of the LSC-like and mature fractions was compared after 24-hour ex vivo 5-AZA / VEN treatment in 26 patients with known clinical responses to first-line HMA / VEN (Figure 3N). LSC-like cells from non-responsive patients were significantly resistant to treatment, while mature cells remained largely unaffected by ex vivo treatment regardless of the patient's clinical response (Figures 3O–P). This data emphasizes the need to study disease-driving subpopulations in AML and demonstrates the functional properties of LSC-like cells as predictors of response to HMA / VEN treatment (e.g., 5-AZA / VEN).

[0148] [Example 10] Rapid and robust prediction of ex vivo response by BCL-2 family-based LSC-like response score Flow cytometry is a routine diagnostic tool for diagnosing and monitoring AML in the clinic and can support clinical decision-making within hours of sample collection. Motivated by the BCL-2 dependence observed in LSC-like cells and the correlation between the in vitro survival of LSC-like cells and clinical response, we hypothesized that BCL-2 family protein expression levels in LSC-like cells could predict patient response to HMA / VEN (e.g., 5-AZA / VEN) treatment at diagnosis. Ex vivo 5-AZA / VEN treatment and simultaneous intracellular staining of BCL-2, MCL-1, and BCL-xL (Figure 7A) were performed on 55 diagnostic AML patient samples. Stratification (<5% or >20%) based on the survival of LSC-like cells after 24 hours of ex vivo 5-AZA / VEN treatment showed significantly higher intracellular BCL-2 expression scores in AML specimens sensitive to ex vivo treatment (Figure 7B). While BCL-2 conveys sensitivity, MCL-1 and BCL-xL can inhibit apoptosis independently of BCL-2. Therefore, all three proteins were incorporated into a single response score of the normalized expression of the drug target to the normalized expression of the resistance factor: BCL-2 Norm. MFI / (MCL-1 Norm. MFI +BCL-xL Norm. MFI ). Inclusion of alternative inhibitors of apoptosis and thus potential mediators of VEN resistance further improved the discrimination between 5-AZA / VEN ex vivo responders and non-responders. This highlights the advantage of combinatorial evaluation of BCL-2 family proteins in LSC-like cells (Figures 7C-E).

[0149] [Example 11] Rapid and Robust Prediction of Clinical Response and Remission Duration by LSC-like Response Score Motivated by the aforementioned ex vivo results, we evaluated whether the response score in LSC-like cells could also be used to predict the clinical response to HMA / VEN treatment. The expression of BCL-2 family proteins BCL-2, MCL-1, and BCL-xL was analyzed together with surface staining of diagnostic samples. Here, BCL-2 was more highly expressed in LSC-like cells of patients achieving CR, CRi, or MLFS, while MCL-1 and Bcl-xL were higher in SD, PR, or PD patients. However, since all three levels had high variability, no clear distinction between responders and non-responders was shown by a single BCL-2 family protein alone (Figs. 8A - D). Therefore, instead of the predictive value of individual BCL-2 family proteins, we evaluated the combinatorial response score (Fig. 4A) in LSC-like cells of two independent patient cohorts receiving HMA / VEN as first-line treatment. Cohort 1 (n = 18) showed a significantly higher response score in LSC-like cells of patients responding to HMA / VEN treatment compared to non-responders (Fig. 4B). To evaluate the response duration, the number of HMA / VEN cycles was used as a surrogate for disease-free survival (DFS). The reason is that ongoing VEN treatment indicates ongoing response. Patients who progressed to receive allogeneic stem cell transplantation in CR or whose treatment was discontinued for reasons other than disease progression were excluded. Notably, a significantly longer HMA / VEN response (Fig. 4C) was observed in patients with a median response score above (>0.4) compared to those below (<0.4) the median response score. When these findings were validated in a second cohort (n = 19), the response score in LSC-like cells was also significantly higher in patients responding to HMA / VEN treatment (Fig. 4D). Furthermore, the duration of HMA / VEN treatment in Cohort 2 was also longer in patients with a median response score above, suggesting longer-lasting sensitivity (Fig. 4E). When the two cohorts were combined, the response score of responders remained significantly higher compared to non-responders (Fig. 4F). Additionally, the prognostic accuracy of the combined cohort response score by ROC analysis was evaluated, and an ROC value of 0.95 was observed. This indicates that the response score has high accuracy in predicting the HMA / VEN response (Fig. 4G).This was also observed in the probability that patients above the median score of the combined cohort's ongoing HMA / VEN treatment continued treatment longer (Figure 4H). When the response score was calculated specifically in the monocytic AML sample bulk, no distinction was observed between responders and non-responders (Figures 8G-I). Notably, the response score showed no difference between patients who responded or did not respond to standard induction therapy, highlighting its specificity in predicting response to VEN-based treatment (Figure 8J). Next, we evaluated whether differences in the response score within patients who showed an initial response to HMA / VEN also revealed differences regarding the response duration. Indeed, responders with a response score above the median (>0.4) continued HMA / VEN longer compared to responders with a lower response score (<0.4), indicating that the response score can be used to identify patients with a longer-lasting response to HMA / VEN (Figure 4I). Finally, a multivariate analysis including the response score as a variable was performed on all 37 study patients. In the univariate analysis, the response score and CK were the only significant parameters showing a trend for IDH1 / 2, splicing factor, and TET2 mutations. Multivariate linear regression was performed on these five parameters, and the response score was still found to be a significant predictor, with an odds ratio of 10.2 per 0.1 point (Figure 4J). In summary, the response score can predict the initial response of patients to HMA / VEN with an accuracy exceeding mutation profiling and can select patients with a longer response period. Due to these characteristics, the response score becomes a fast, easy, and globally accessible and inexpensive tool for guiding treatment decisions in choosing HMA / VEN as the first-line treatment for selected eligible patients.

[0150] [Example 12] Rapid and Robust Prediction of Clinical Response and Remission Duration by the LSC-like Response Score Finally, to gain insights into the gene profiles of AML patients with different response scores, clinical structural variant (SV) analysis and targeted mutation profiling were correlated with response scores in 73 diagnostic AML patient samples (Figure 4K, Figure 8K). Patients with low response scores were observed to have more SVs compared to patients with high response scores (Figure 4L). Additionally, only mutations in JAK2 or CALR had low response scores (Figure 4M). This is consistent with the previous association between clinical non-responsiveness to MDS / MPN and HMA / VEN, although the sample size is small (Figure 1C - D). On the other hand, patients with high response scores were rich in IDH1 / 2 and splicing mutations (Figure 4M), suggesting that specific genetic changes play a role in differential dependence on BCL-2 family proteins. Importantly, not all patients with CK showed low response scores, or all IDH1 / 2 mutants had high response scores, highlighting the value of individual patient assessment. In summary, the response score provides the molecular rationale underlying HMA / VEN treatment response, elevating the influence of the correlated mutational background and other non-genetic, potentially elusive factors to the forefront.

[0151] In Table 2 below, the response scores calculated according to the method mentioned above are compared with the individual responses of 14 patients.

[0152]

Table 2

[0153] The amounts used in calculating the response score are typically normalized amounts as described in other parts of the present invention.

[0154] In Table 2 above, the response scores for venetoclax, navitoclax, and MCL-1-venetoclax are all calculated for all three. By comparing the response scores, it is clear that the prediction can be further improved compared to the calculation of a single score. As is clear from Table 2 above, the highest response score (shown in bold) among the three scores is also the score for a beneficial BCL family inhibitor treatment, i.e., if the venetoclax response score is the highest among the three scores when confirmed by the percentage of LSCs surviving after 24 hours compared to untreated controls, venetoclax is beneficial, if the navitoclax response score is the highest among the three scores, navitoclax is beneficial, and so on.

[0155] [Example 13] Response scores in LSC-like cells predict the clinical response and remission duration of 5-AZA / VEN While BCL-2 conveys sensitivity to VEN, MCL-1 and BCL-xL can promote survival independently of BCL-2. Therefore, to further explain these factors contributing to resistance, all three proteins were incorporated into a single response score and named "Response Score". The "Response Score" can be calculated for subpopulations defined by flow cytometry. The response score is calculated as the ratio between the normalized MFI of the drug target (BCL-2) and the normalized MF of the resistance factors (the sum of MCL-1 and BCL-xL) as follows: BCL-2 Norm. MFI / (MCL-1 Norm. MFI +BCL-xL Norm. MFI ) (For details, refer to Example 1, General Methods above.)

[0156] To ensure comparable MFI measurements of consistent samples processed and analyzed on separate days, reference AML samples were processed and analyzed with each cohort. The detector voltage was adjusted to keep the MFI of each BCL-2, MCL-1, and BCL-xL of the LSC-like population in the reference sample constant. Minor variations in the reference sample MFI were adjusted by normalizing the reference sample of the measurement day to match the previous reference sample measurement value. For each sample, the normalized MFI of each BCL-2, MCL-1, and BCL-xL of the LSC-like population was divided by the median MFI of each AML patient classified as a responder within the cohort to obtain the relative MFI value (rel. MFI).

[0157] By explaining alternative inhibitors of apoptosis and potential mediators of VEN resistance, the response score further improved the discrimination between ex vivo 5-AZA / VEN-sensitive AML samples compared to individual protein levels (Figs. 9B–E). These data clearly highlight the advantage of the combinatorial evaluation of BCL-2 family proteins in LSC-like cells.

[0158] Next, it was evaluated whether individual BCL-2 family protein levels or the "response score" predict the clinical response to 5-AZA / VEN treatment. The expression of the BCL-2 family proteins BCL-2, MCL-1, and BCL-xL was analyzed together with cell surface expression profiling in 35 diagnostic samples from two independently processed multi-center cohorts (Fig. 10A, cohort 1 + 2). Here, BCL-2 was more highly expressed in the LSC-like cells of patients who achieved CR, CRi, or MLFS (responders), while MCL-1 and BCL-xL were more highly expressed in patients with SD, PR, or PD (non-responders) (Fig. 10B). A similar trend was observed in non-LSC cells and total blasts, but there was no difference in BCL-2 family expression in mature cells between responders and non-responders. However, the expression of individual BCL-2 family proteins alone did not show a clear distinction between responders and non-responders, either in LSC-like cells or in other subpopulations, indicating high variability among patients in the levels of all three proteins (Fig. 10B).

[0159] In predicting the in vitro 5-AZA / VEN response, since the "response score" was superior to individual BCL-2 family proteins, the "response score" was calculated in LSC-like cells, non-LSC cells, mature cells, and total blasts in these two patient cohorts (Figures 10C-D). Both cohorts, individually and together, showed significantly higher response scores in LSC-like cells derived from patients who responded to 5-AZA / VEN treatment compared to LSC-like cells analyzed from non-responder patients (Figures 10C-D). This phenomenon was also detected in total blasts, but to a lesser extent and did not clearly distinguish responders from non-responders (Figure 10D). These findings indicate that the response score in LSC-like cells is superior to the expression of individual BCL-2 family proteins as a binary predictor of clinical response to 5-AZA / VEN.

[0160] To examine the response duration, the event-free survival (EFS) of these two cohorts was evaluated. Patients who discontinued treatment for reasons other than disease progression were excluded. In both cohorts, a significantly longer 5-AZA / VEN response was observed in patients with a median response score >0.4 compared to those with a median “response score” <0.4 in LSC-like cells (Figure 10E). When analyzed in LSC-like cells, although high BCL-2 levels alone predicted a longer EFS and high MCL-1 levels predicted a shorter EFS, the BCL-2 family proteins alone did not reach the same predictive value as the response score (Figure 10E). Furthermore, neither the response scores determined from other subpopulations nor the individual protein levels of the three BCL-2 members showed reasonable predictive power, and all exceeded the response score in LSC-like cells. When these findings were verified in a third independently processed cohort (n = 24), the response score in LSC-like cells was again significantly higher in patients who responded to 5-AZA / VEN treatment and lower in non-responding patients (Figure 10F). The EFS was also longer in patients above the median response score, suggesting an improvement in treatment response (Figure 10G). Collectively, these findings support the concept that the response score of LSC-like cells can accurately predict the response to first-line treatment with 5-AZA / VEN in elderly frail patients and is superior to predictions based on individual BCL-2 family proteins alone.

[0161] 5-AZA / VEN is currently under investigation for first-line treatment in younger patients and has shown efficacy as a salvage therapy option for relapsed refractory patients. Therefore, we investigated whether response to 5-AZA / VEN could be predicted in younger relapsed refractory patients by evaluating response scores in 23 AML samples receiving 5-AZA / VEN as salvage therapy (Figure 10H). Samples were deposited in the biobank at diagnosis or prior to the start of 5-AZA / VEN treatment. In this salvage setting, response scores above the median also highly predicted binary clinical response and longer EFS, opening the way for prospective evaluation of biomarker-based selection of induction regimens (Figures 10I-J). In summary, the response score serves as a step to reliably identify patients who will benefit from 5-AZA / VEN as first-line and salvage therapy and to select the best treatment for each patient on an individual basis.

[0162] [Example 14] The response score is superior to BH3 profiling in LSC-like cells Measurement of apoptosis dependency by BH3 profiling has been previously reported as a predictor of response to 5-AZA / VEN. To compare response scores with BH3 profiling, 15 samples (7 non-responders vs. 8 responders) were selected from the above three cohorts initially treated with 5-AZA / VEN, and both readings were evaluated. As expected, the response score highly predicted the binary clinical response (Figure 12A). Furthermore, when stratified by either the median response score of the 15 selected samples or a previously established cutoff value of 0.4, EFS showed a robust difference (Figures 12B–C). In contrast, BH3 profiling in LSC-like cells based on VEN, HRK, or MS1-induced cytochrome C release did not predict clinical response (Figures 12D–F). Evaluation of the sum of HRK and MS1-induced cytochrome C release as previously reported did not improve prediction (Figure 12G). BH3 profiling in LSC-like cells also did not correlate with EFS in these patients (Figure 12H). When evaluating total blasts, only VEN-induced cytochrome C release predicted the binary clinical response, but only showed a tendency towards longer EFS (Figures 12I–M). Importantly, the observed discrimination was weak compared to the response scores in this patient cohort. In summary, the response score is a robust means to replace BH3 profiling for predicting VEN response.

[0163] [Example 15] Integrated analysis of response scores in LSC-like cells identifies promoters of VEN resistance To further evaluate the prognostic accuracy of the response score, the response scores of LSC-like cells were integrated from all three first-choice treatment cohorts (cohorts 1, 2, and 3), and the analysis was extended to 59 patients who had been treated with 5-AZA / VEN as the first choice. As expected, the response score was significantly higher in responders compared to non-responders (Figure 11A). The prognostic accuracy of the response score of the integrated cohort was evaluated by ROC analysis, and an ROC value of 0.95 was observed, revealing a high accuracy in predicting the 5-AZA / VEN treatment response of patients (Figure 11B). The combined EFS analysis of all 59 patients who had 5-AZA / VEN as the first-choice treatment showed a four-fold extension of EFS in patients with scores above the median score, with an EF of 3 months versus 12 months (Figure 11C). Since some patients with a response score < 0.4 responded to 5-AZA / VEN, next, it was evaluated whether the response score within responders could discriminate regarding the response duration. Indeed, responders with a response score > 0.4 had a longer EFS with 5-AZA / VEN treatment compared to responders with a response score < 0.4, with a median EFS of 6 months versus 12 months (Figure 11D). This indicates that the response score can be used to identify patients with a long-lasting response to 5-AZA / VEN.

[0164] Finally, to identify predictors of response to 5-AZA / VEN, logistic regression analysis was performed on all 59 first-choice treatment patients, including the response score as one of the evaluation variables. Univariate analysis was used to identify the response score and complex karyotype as statistically significant parameters in this set of samples (Figure 11E). Subsequently, multivariate analysis was performed on all parameters with p < 0.15, and it was observed that the response score remained as the only predictor of response, with an odds ratio of 5.1 per 0.1 point (Figure 11E). The response score predicted EFS even within genetic subgroups such as those with complex karyotype, RUNX1, or NPM1 mutations, highlighting its potential role in stratifying patients beyond genetics (Figure 11F). Notably, the response score showed no difference between patients who responded or did not respond to standard induction therapy, highlighting its specificity in predicting response to venetoclax-based therapy (Figure 11G).

[0165] In summary, the response score can predict the initial response of patients to 5-AZA / VEN with an accuracy exceeding mutation profiling and identify patients with a long response duration.

[0166] [Example 16] The response score unravels the heterogeneity of responses within the mutation pattern To gain insights into the gene profiles of AML patients with different response scores, clinical structural variant (SV) analysis and targeted mutation profiling were correlated with the response scores (Figure 11H) of 95 diagnostic AML patient samples that were treatment-naive. Patients with low response scores were observed to have more SVs compared to those with high response scores (Figure 11I). Furthermore, only AML patients with mutations in TET2 or JAK2 and CALR had low response scores (Figure 11J). This is consistent with the observed association between previous MDS / MPN diagnosis and clinical non-responsiveness to 5-AZA / VEN, although the sample size remained small (Figure 1C). In contrast, patients with high response scores were enriched for IDH1 / 2, DNMT3A, and splicing mutations (Figure 11J), suggesting that specific genetic changes influence differential dependence of LSC-like cells on BCL-2 family proteins. Importantly, the heterogeneity of response scores within complex karyotype or IDH1 / 2 mutant AML and the associated 5-AZA / VEN response emphasizes the value of individual patient assessment. In summary, response scoring in LSC-like cells aggregates the effects of correlated mutational backgrounds and other still-elusive non-genetic factors by providing information based on causal relationships that predict patient response to AZA / VEN treatment (Figure 11K).

[0167] References Konopleva, M., Pollyea, D.A., Potluri, J., Chyla, B., Hogdal, L., Busman, T., McKeegan, E., Salem, A.H., Zhu, M., Ricker, J.L., et al. (2016). Efficacy and Biological Correlates of Response in a Phase II Study of Venetoclax Monotherapy in Patients with Acute Myelogenous Leukemia. Cancer Discov 6, 1106-1117 DiNardo, C.D., Jonas, B.A., Pullarkat, V., Thirman, M.J., Garcia, J.S., Wei, A.H., Konopleva, M., Dohner, H., Letai, A., Fenaux, P., et al. (2020a). Azacitidine and Venetoclax in Previously Untreated Acute Myeloid Leukemia. N Engl J Med 383, 617-629. DiNardo, C.D., Lachowiez, C.A., Takahashi, K., Loghavi, S., Xiao, L., Kadia, T., Daver, N., Adeoti, M., Short, N.J., Sasaki, K., et al. (2021). Venetoclax Combined With FLAG-IDA Induction and Consolidation in Newly Diagnosed and Relapsed or Refractory Acute Myeloid Leukemia. J Clin Oncol 39, 2768-2778.; Garcia, J.S., Kim, H.T., Murdock, H.M., Cutler, C.S., Brock, J., Gooptu, M., Ho, V.T., Koreth, J., Nikiforow, S., Romee, R., et al. (2021). Adding Venetoclax to fludarabine / busulfan RIC transplant for high-risk MDS and AML is feasible, safe, and active. Blood Adv 5, 5536-5545 Cherry, E.M., Abbott, D., Amaya, M., McMahon, C., Schwartz, M., Rosser, J., Sato, A., Schowinsky, J., Inguva, A., Minhajuddin, M., et al. (2021). Venetoclax and azacitidine compared with induction chemotherapy for newly diagnosed patients with acute myeloid leukemia. Blood Adv 5, 5565-5573. Dohner, H., Estey, E., Grimwade, D., Amadori, S., Appelbaum, F.R., Buchner, T., Dombret, H., Ebert, B.L., Fenaux, P., Larson, R.A., et al. (2017). Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood 129, 424-447. Cai, S.F., Chu, S.H., Goldberg, A.D., Parvin, S., Koche, R.P., Glass, J.L., Stein, E.M., Tallman, M.S., Sen, F., Famulare, C.A., et al. (2020). Leukemia Cell of Origin Influences Apoptotic Priming and Sensitivity to LSD1 Inhibition. Cancer Discov 10, 1500-1513. Bhatt, S., Pioso, M.S., Olesinski, E.A., Yilma, B., Ryan, J.A., Mashaka, T., Leutz, B., Adamia, S., Zhu, H., Kuang, Y., et al. (2020). Reduced Mitochondrial Apoptotic Priming Drives Resistance to BH3 Mimetics in Acute Myeloid Leukemia. Cancer Cell 38, 872-890 e876. Kuusanmaki, H., Leppa, A.M., Polonen, P., Kontro, M., Dufva, O., Deb, D., Yadav, B., Bruck, O., Kumar, A., Everaus, H., et al. (2020). Phenotype-based drug screening reveals association between Venetoclax response and differentiation stage in acute myeloid leukemia. Haematologica 105, 708-720. Pei, S., Pollyea, D.A., Gustafson, A., Stevens, B.M., Minhajuddin, M., Fu, R., Riemondy, K.A., Gillen, A.E., Sheridan, R.M., Kim, J., et al. (2020). Monocytic Subclones Confer Resistance to Venetoclax-Based Therapy in Patients with Acute Myeloid Leukemia. Cancer Discov 10, 536-551. DiNardo, C.D., Maiti, A., Rausch, C.R., Pemmaraju, N., Naqvi, K., Daver, N.G., Kadia, T.M., Borthakur, G., Ohanian, M., Alvarado, Y., et al. (2020b). 10-day decitabine with Venetoclax for newly diagnosed intensive chemotherapy ineligible, and relapsed or refractory acute myeloid leukaemia: a single-centre, phase 2 trial. Lancet Haematol 7, e724-e736. Stahl, M., Menghrajani, K., Derkach, A., Chan, A., Xiao, W., Glass, J., King, A.C., Daniyan, A.F., Famulare, C., Cuello, B.M., et al. (2021). Clinical and molecular predictors of response and survival following Venetoclax therapy in relapsed / refractory AML. Blood Adv 5, 1552-1564.

Claims

**Claim 1** A method for evaluating the response of a subject suffering from cancer, preferably hematopoietic cancer, to treatment with a BCL family inhibitor, comprising: (a) determining the amounts of the biomarkers BCL-2, BCL-xL, and MCL-1 in a tumor-driving cell population, preferably a leukemia stem cell (LSC) population, in a sample from the subject; and (b) comparing the amounts of the biomarkers to a reference, whereby the response to treatment with a BCL family inhibitor is evaluated. **Claim 2** The method according to claim 1, wherein the hematopoietic cancer is acute myeloid leukemia (AML), other lymphoma or leukemia of B-cell or T-cell origin, or a plasma cell neoplasm. **Claim 3** The method according to claim 1 or 2, wherein said evaluating the response to treatment with a BCL family inhibitor comprises determining whether the subject will benefit from treatment with a BCL-2 inhibitor. **Claim 4** The method according to claim 3, wherein said comparing the biomarker to the reference comprises calculating a ratio of the amount of BCL-2 to the combined amount of BCL-xL and MCL-1 to obtain a prediction score, and comparing the prediction score to the reference. **Claim 5** The method according to claim 4, wherein said calculating the prediction score is based on using the following formula: prediction score = BCL-2 / (MCL-1 + BCL-xL). **Claim 6** The method according to claim 4 or 5, wherein a prediction score greater than the reference indicates a subject who will benefit from treatment with a BCL-2 inhibitor. **Claim 7** The method according to claim 4 or 5, wherein a prediction score lower than the reference indicates a subject who will not benefit from treatment with a BCL-2 inhibitor. **Claim 8** The method according to any one of claims 4 to 7, wherein the reference is a reference value derived from a non-responder population. **Claim 9** The method according to claim 8, wherein the reference is from about 0.6 to about 1.0, preferably about 0.

8. **Claim 10** The method according to any one of claims 3 to 9, wherein the BCL-2 inhibitor is venetoclax. **Claim 11** The method according to claim 10, wherein the venetoclax is used in combination with an additional cancer treatment agent, such as a classical chemotherapeutic agent, more preferably a hypomethylating agent, preferably 5-azacitidine (5-AZA), decitabine, or cytarabine, or an antibody, such as rituximab, or a targeted therapeutic agent, such as midostaurin. **Claim 12** The method according to claim 1 or 2, wherein said evaluation of response to BCL family inhibitor treatment comprises determining whether a subject will benefit from treatment with a BCL-xL and / or MCL-1 inhibitor.

13. The method according to claim 12, wherein said comparison of the biomarker to the reference comprises calculating a ratio of half the combined amount of BCL-2 and BCL-xL to the amount of MCL-1 to obtain a prediction score, and comparing said prediction score to the reference.

14. The method according to claim 13, wherein said calculation of the prediction score is based on using the following formula: prediction score = 0.5(BCL-2 + BCL-xL) / MCL-1.

15. The method according to claim 13 or 14, wherein a prediction score greater than the reference indicates a subject who will benefit from treatment with a BCL-xL and / or MCL-1 inhibitor.

16. The method according to claim 13 or 14, wherein a prediction score lower than the reference indicates a subject who will not benefit from treatment with a BCL-xL and / or MCL-1 inhibitor.

17. The method according to any one of claims 13 to 16, wherein said reference is a reference value derived from a non-responder population.

18. The method according to claim 17, wherein said reference is from about 0.6 to about 1.0, preferably about 0.

8.

19. The method according to any one of claims 12 to 18, wherein said BCL-xL and / or MCL-1 inhibitor is navitoclax.

20. The method according to claim 1 or 2, wherein said evaluation of response to BCL family inhibitor treatment comprises determining whether a subject will benefit from treatment with a BCL-2 inhibitor and at least one MCL-1 inhibitor.

21. The method according to claim 20, wherein said comparison of the biomarker to the reference comprises calculating a ratio of the amount of BCL-2 to the combined amount of BCL-xL and MCL-1 to obtain a prediction score, and comparing said prediction score to the reference.

22. The method according to claim 21, wherein said calculation of the prediction score is based on using the following formula: prediction score = 0.5(MCL-1 + BCL-2) / BCL-xL.

23. The method according to claim 21 or 22, wherein a prediction score greater than the reference indicates a subject who will benefit from treatment with a BCL-2 inhibitor and at least one MCL-1 inhibitor.

24. The method according to claim 21 or 22, wherein a reference lower prediction score indicates a subject who does not benefit from treatment with a BCL-2 inhibitor and at least one MCL-1 inhibitor.

25. The method according to any one of claims 21 to 24, wherein the reference is a reference value derived from a non-responder population.

26. The method according to claim 25, wherein the reference is from about 0.6 to about 1.0, preferably about 0.

8.

27. The method according to any one of claims 20 to 23, wherein the BCL-2 inhibitor is venetoclax and the at least one MCL-1 inhibitor is AZD5991 or MIK665.

28. The method according to any one of claims 1 to 27, wherein the LSC population is characterized by an increase in the expression of at least one biomarker selected from the group consisting of GPR56, CD34, and BCL-2.

29. The method according to claim 28, wherein the expression is increased compared to the expression of at least one biomarker in monocyte-like AML cells.

30. The method according to any one of claims 1 to 29, wherein the amount of the biomarker is determined by quantification of RNA expression or specific antibody-based quantification, preferably by flow cytometry.

31. A BCL-2 inhibitor, preferably venetoclax, for use in the treatment of cancer, preferably hematological cancer, in a subject evaluated to benefit from treatment with a BCL-2 inhibitor by the method according to any one of claims 3 to 11 and 28 to 30.

32. A BCL-xL and / or MCL-1 inhibitor, preferably navitoclax, for use in the treatment of cancer, preferably hematological cancer, in a subject evaluated to benefit from treatment with a BCL-xL and / or MCL-1 inhibitor by the method according to any one of claims 12 to 19 and 28 to 30.

33. A BCL-2 inhibitor in combination with at least one MCL-1 inhibitor, preferably venetoclax, for use in the treatment of cancer, preferably hematological cancer, in a subject evaluated to benefit from treatment with a BCL-2 inhibitor in combination with at least one MCL-1 inhibitor by the method according to any one of claims 20 to 30.

34. A method of treating a subject suffering from cancer, preferably hematological cancer, with a BCL family inhibitor treatment, comprising evaluating the response of the subject to the BCL family inhibitor treatment by practicing the method of the present invention, and administering a BCL family inhibitor to the subject if the subject is evaluated to benefit from the treatment.

35. The method according to claim 34, wherein the BCL family inhibitor is a BCL-2 inhibitor, preferably venetoclax, a BCL-xL and / or MCL-1 inhibitor, preferably navitoclax, or a BCL-2 inhibitor, preferably venetoclax, in combination with at least one MCL-1 inhibitor.

36. An apparatus for evaluating the response of a subject suffering from cancer, preferably hematological cancer, to a BCL family inhibitor treatment, comprising (a) an analysis unit capable of determining the amounts of the biomarkers BCL-2, BCL-xL, and MCL-1 in a tumor-driving cell population, preferably a leukemia stem cell (LSC) population, in a sample of the subject, and (b) an assessment unit comprising a data processor capable of comparing the amounts of the biomarkers to a reference, whereby the response to the BCL family inhibitor treatment is evaluated.

37. A kit for evaluating the response of a subject suffering from cancer, preferably hematological cancer, to a BCL family inhibitor treatment, comprising detection molecules for determining the amounts of the biomarkers BCL-2, BCL-xL, and MCL-1 in a tumor-driving cell population, preferably a leukemia stem cell (LSC) population, in a sample of the subject.

Images