Method for predicting response to bone marrow stem cell therapy

Abstract

The present invention relates to a method for predicting a response to disease treatment, specifically tissue regeneration, preferably cardiovascular regeneration, comprising the use of biomarkers. Furthermore, the present invention relates to a combination of biomarkers for use in a method for predicting a response to disease treatment, specifically tissue regeneration, preferably cardiovascular regeneration, a computer device for performing the method according to the present invention, and a device suitable for performing the method of the present invention.

Description

Technology Field

[0001] The present invention relates to a method for predicting a response to disease treatment, specifically tissue regeneration, preferably cardiovascular regeneration, comprising the use of biomarkers. Furthermore, the present invention relates to a combination of biomarkers for use in a method for predicting a response to disease treatment, specifically tissue regeneration, preferably cardiovascular regeneration, a computer device for carrying out the method according to the present invention, and a device suitable for carrying out the method of the present invention. Background Technology

[0002] Regenerative therapy for the recovery of organ tissues, specifically cardiac tissue, has been at the forefront of preclinical and clinical development for the past 17 years. In the context of cardiac tissue, among other approaches, the direct application of bone marrow stem cells (BMSCs) to cardiac tissue has received the most dedicated clinical development interest since its first application and early promising clinical trials in 2001 (Stamm C, Westphal B, Kleine HD, et al. Lancet. 2003; 361(9351):45-46; Tse HF, Kwong YL, Chan JK, Lo G, Ho CL, Lau CP. Lancet 2003; 361(9351):47-9; Stamm C, Kleine HD, Choi YH, et al. J Thorac Cardiovasc Surg 2007;133(3):717-25). However, in these trials, clinically relevant improvements in LVEF (Left Ventricular Ejection Fraction) and non-responders could be observed in both the treatment and placebo groups (Timothy DH, Lem M, Jay HT, Circulation Research 2016; 119:404-406; Nasseri BA, Ebell W, Dandel M, et al. Eur Heart J. 2014, 35(19):1263-74; Bartunek J, Terzic A, Davison BA et al. Eur Heart J. 2016 Dec 23. pii: ehw543. doi: 10.1093 / eurheartj / ehw543).

[0003] Hematopoietic stem cells (HCSs) are stem cells that generate other blood cells. In vertebrates, the majority of hematopoiesis occurs in the bone marrow (BM) and originates from a limited number of pluripotent hematopoietic stem cells capable of extensive self-renewal. The hematopoietic system has traditionally been considered unique among phenotype-characterized adult stem / progenitor cells in that it is a hierarchical system organized into pluripotent, self-regenerating stem cells at the top, lineage-requested progenitor cells in the middle, and lineage-restricted progenitor cells generating terminally differentiated cells at the bottom (Weissmann IL. Cell 2000; 100:157-168) (Slack JM. Science 2000; 287:1431-1433; Blau HM, Brazelton TR, Weimann JM. Cell 2001;105:829-841; Korbling M, Estrov Z. New England Journal of Medicine 2003; 349:570-582). However, clonal hematopoietic disorders have been described in patients with hematological and cardiovascular diseases and are associated with congenital or somatic DNA mutations (Moehrle BM, Geiger H. Exp Hematol 2016; Oct; 44(10):895-901. doi: 10.2016 / j.exphem.2016.06.253. Epub 2016 Jul 8; Jaiswal S, Natarajan P, Silver AJ et al. N Engl J Med 2017; Jul 13; 377(2):111-121. doi: 10.1056 / NEJMoa1701719. Epub 2017 Jun 21.). Questions arise as to which mutations in innate or somatic regulatory genes induce hematopoietic clonal advantage and affect heart disease pathology (Machiela MJ, Chanock SJ. Curr Opin Genet Dev 2017; Feb;42:8-13. doi: 10.1016 / j.gde.2016.12.002. Epub 2017 Jan 6.).

[0004] One of the major genes associated with hematopoietic stem cell (HSC) proliferation disorders, such as myelodysplastic syndrome, erythrocytosis, or leukemia caused by somatic mutations, is SH2B3, which codes for the lymphocyte adapter protein (Lnk, LNK) (Elias HK, Bryder D, Park CY. Semin Hematol 2017; Jan; 54(1):4-11. doi: 10.1053 / j.seminhematol.2016.11.002. Epub 2016 Nov 20.). However, little is known about the regulation of Lnk / SH2B3 expression in human bone marrow (BM) and clonal blood stem and progenitor cells regarding cardiovascular regeneration (Steinhoff G, Nesteruk J, Wolfien M, Große J, Ruch U, Vasudevan P, Muller P. Adv Drug Deliv Rev 2017; Oct 1; 120:2-24. doi: 10.1016 / j.addr.2017.10.007. Epub 2017 Oct 18; Zhu X, Fang J, Jiang DS, Zhang P, Zhao GN, Zhu X, Yang L, Wei X. 2-B3Hypertension 2015; Sep; 66(3):571-81. doi: 10.1161 / HYPERTENSIONAHA.115.05183. Epub 2015 Jun 22.).

[0005] Lnk / SH2B3 shares a plextrin homologous domain, a Src homologous 2 domain, and a potential tyrosine phosphorylation site with APS and SH-2B. It belongs to the family of adapter proteins involved in the integration and cleavage of multiple signaling events (Ahmed Z, Pillay TS. Biochem J 2003; 371:405-412., Huang X, Li Y, Tanaka K, Moore KG, Hayashi JI. Proc Natl Acad Sci USA 1995; 92:11618-11622; Takaki S, Watts JD, Forbush KA, Nguyen NT, Hayashi J, Alberola. Ila J, Aebersold R, Perlmutter RM. Biol Chem 1997; 272:14562-14570.) and has been proposed to act as a negative regulator of the Stem Cell Factor (SCF) / -c-Kit signaling pathway (Moehrle BM, Geiger H. Exp Hematol 2016; Oct;44(10):895-901. doi: 10.1016 / j.exphem.2016.06.253. Epub 2016 Jul. 8., Takaki S, Sauer K, Iritani BM, Chien S, Ebihara Y, Tsuji K, Takatsu K, Perlmutter RM. Immunity 2000; 13:599-609.). Lnk / SH2B3, KSL CD34 + BM c-Kit, an HSC subgroup more immature than cells + / Sca-1 + / Lineage (Lin) - (KSL) CD34 -In cell-based studies, it has been reported to play an important role in maintaining the function of HSCs in self-renewal (Ema H, Sudo K, Seita J, Matsubara A, Morita Y, Osawa M, Takatsu K, Takaki S, Nakauchi H. Dev Cell 2005; 8:907-914.).

[0006] Takaki et al. reported that Lnk / SH2B3 is expressed in mouse hematopoietic cell lineages and that BM cells from Lnk / SH2B3-deficient mice are competitively superior to those of WT mice in the hematopoietic population (Takaki S, Morita H, Tezuka Y, Takatsu K. Lnk / SH2B3. J Exp Med 2002; 195:151-160.). They also noted that Lnk / SH2B3 affects not only the number of HSCs / hematopoietic progenitor cells (HPCs) but also the self-renewal ability of some HSCs / HPCs. - / - They clarified that it was significantly increased in mice (Ema H, Sudo K, Seita J, Matsubara A, Morita Y, Osawa M, Takatsu K, Takaki S, Nakauchi H. Dev Cell 2005; 8:907-914). Furthermore, they identified the functional domain of Lnk / SH2B3 and developed a dominant-negative Lnk / SH2B3 mutant that enhances HPCs upon inoculation by inhibiting the function of Lnk / SH2B3 intrinsically expressed in HSCs / HPCs (Takizawa H, Kubo-Akashi C, Nobuhisa I, Kwon SM, Iseki M, Taga T, Takatsu K, Takaki S. Blood 2006; 107:2968-2975.).

[0007] The inventors have identified a method for predicting a response to disease treatment, specifically tissue regeneration, preferably cardiovascular regeneration, comprising the determination of a biomarker. Accordingly, the present invention relates to a method for predicting a response to cardiovascular regeneration, comprising the determination of a biomarker, according to claim 1 of the present application. Furthermore, the present invention relates to a combination of biomarkers for use in a method for predicting a response to disease treatment, specifically tissue regeneration, preferably cardiovascular regeneration, a computer device for carrying out such a method, and a device suitable for carrying out the method of the present invention. Brief explanation of the drawing

[0008] Further features of the present invention are derived from the embodiments together with the claims and drawings. In certain embodiments, a single feature may be realized in combination with other features and does not limit the scope of protection of the present invention. The following description of embodiments according to the present invention may be related to the drawings, and Fig. 1 Figure [Illegible] illustrates a summary of targeted DNAseq and RNAseq variants of SH2B3 according to the present invention. The plot shows the proportion of identified SNP / deletion sites and possible amino acid transitions in responders (first percentage in parentheses) and non-responders (second percentage in parentheses). Fig. 2 Figure 2 illustrates a comparison of machine learning accuracy for supervised prediction of patient response using only preoperative data. Results are obtained after feature selection and subsequent prediction using two independent classifiers. The graph shows the true positive prediction results of the two ML models (AdaBoost for feature selection and RF and SVM for the study). Error bars represent the corresponding accuracy standard deviation for the generated models obtained after 100 iterations. Fig. 3Figure 2 shows the top 20 biomarkers obtained from machine learning feature selection based on a random forest classifier. The primary initial data consisted of a PERFECT trial involving RT-PCR data as well as study data and patient sequencing data (specifically differentially expressed transcripts, co-expressed transcripts, and transcripts associated with SH2B3 over time). Combinations and subsets of these 20 features were subsequently used to train a final model containing the eight features indicated by dotted lines. Importance indicates the hierarchy of the most relevant features required for classification. Specific details for implementing the invention

[0009] Accordingly, the present invention relates to a method for predicting a response to disease treatment, specifically cardiovascular regeneration, and said method

[0010] (i) a step of determining gene and / or -gene expression in a sample of an individual as a biomarker, wherein said gene and / or -gene expression includes a mutant variant,

[0011] (ii) a step of comparing the determined gene and / or -gene expression mutant variant with a reference value and / or reference, preferably a non-disease type reference value and / or reference or individual reference cells without somatic mutant gene and / or -gene expression,

[0012] (iii) a step of predicting whether, based on the above comparison results, a response to disease treatment, specifically tissue regeneration, preferably cardiac recovery, is expected, is not expected, or is ambiguous in the subject.

[0013] Includes

[0014] The present invention also relates to a method for predicting a response to disease treatment, specifically cardiovascular regeneration, said method

[0015] (i) a step of determining SH2B3-gene and / or -gene expression in a sample of an individual as a biomarker, wherein said SH2B3-gene and / or -gene expression includes a mutant variant,

[0016] (ii) a step of comparing the determined SH2B3-gene and / or -gene expression mutant variants with reference values ​​and / or reference values, preferably SH2B3 non-disease type reference values ​​and / or reference values, or individual reference cells without somatic mutant gene and / or -gene expression,

[0017] (iii) a step of predicting whether, based on the above comparison results, a response to disease treatment, specifically tissue recovery, preferably cardiac recovery, is expected, is not expected, or is ambiguous in the subject.

[0018] Includes

[0019] Preferably, the method of the present invention ex vivo / In a test tube It is a method. Additionally, it may include steps in addition to those explicitly mentioned above. For example, additional steps may relate to sample preprocessing or further evaluation or use of results obtained by the method. The method may be performed manually or supported by automation. Preferably, step (i) may be supported wholly or partially by automation, such as appropriate robots and sensory equipment. Steps (ii) and / or (iii) may be supported by a data processing unit that performs the respective comparison and / or prediction.

[0020] The aforementioned SH2B3 gene encodes a member of the SH2B adapter family of proteins involved in various signaling activities mediated by growth factor and cytokine receptors. The encoded protein is a major negative regulator of cytokine signaling and plays a crucial role in hematopoiesis. Mutations in this gene are associated with susceptibility to celiac disease type 13 and insulin-dependent diabetes mellitus. Alternative spliced ​​transcriptomic variants encoding different isoforms of this gene have been discovered. SHB3 is also HGNC: 29605 , Entrez Gene: 10019 , Ensembl: ENSG00000111252 , OMIM: 605093 , UniProtKB: Q9UQQ2 It is confirmed in.

[0021] The term "prediction" as used herein means evaluating the probability that an individual will benefit from disease treatment, specifically tissue recovery, preferably cardiac recovery, more preferably cardiac stem cell therapy, in that there is a functional improvement of the cardiovascular system as defined in detail herein following the above-mentioned cardiac treatment.

[0022] As will be understood by those skilled in the art, such an assessment is generally not intended to be accurate for 100% of the subjects to be diagnosed. However, the term requires that the assessment be accurate for a statistically significant portion of the subjects (e.g., a cohort in a cohort study). Whether a portion is statistically significant can be determined without further deliberation by those skilled in the art using various known statistical assessment tools, such as determining confidence intervals, determining p-values, Student's t-test, and the Mann-Whitney test. Details are provided in Dowdy and Wearden, Statistics for Research, John Wiley & Sons, New York 1983. Preferred confidence intervals are 90% or greater, 95% or greater, 97% or greater, 98% or greater, or 99% or greater. Preferably, p-values ​​are 0.1, 0.05, 0.01, 0.005, or 0.0001.

[0023] The term "cardiovascular regeneration" as used herein includes the regeneration and / or treatment and / or improvement of diseases related to the cardiovascular system.

[0024] The term "biomarker" refers to a molecule present, absent, or present in an amount associated with a medical condition or predisposition. A biomarker may be any molecule and / or cell or subgroup thereof occurring in an individual. Typically, a biomarker is a protein, peptide, small molecule, or nucleic acid such as DNAs or RNAs, or a cell or subgroup thereof. According to the present invention, the term preferably refers to a gene and / or -gene expression, said gene and / or -gene expression including mutant variants. Such gene and / or -gene expression may be analyzed by DNA and / or RNA sequencing. The analysis of said gene and / or -gene expression may also include the co-expression and / or -gene expression of additional genes. The determination of such gene and / or -gene expression may be performed, for example, in myocardial cells. According to a specific embodiment, the biomarker is selected from SH2B3-gene and / or -gene expression, and said SH2B3-gene and / or -gene expression includes mutant variants.

[0025] The step of determining the amount of a myomarker mentioned herein relates to measuring the amount or concentration, preferably semi-quantitatively or quantitatively. Measurements may be performed directly or indirectly. Direct measurement relates to measuring the amount or concentration of a biomarker based on a signal obtained from the biomarker itself and an intensity directly related to the number of molecules of the biomarker present in the sample. This intensity—sometimes referred to as signal intensity in this specification—may be obtained, for example, by measuring the intensity value of a specific physical or chemical property of the biomarker. Indirect measurement involves measuring a signal obtained from a secondary component (i.e., a component other than the biomarker itself) or a biological reader, for example, a measurable cellular response, ligand, label, or enzyme reaction product.

[0026] According to the present invention, the step of determining the amount of the biomarker can be achieved by any known means for determining the amount of nucleic acids, such as peptides, proteins, small molecules, DNAs, or RNAs, or cells or subgroups thereof, in a sample. Such means include translocation analysis and methods that may utilize molecules labeled in various sandwich, competition, or other analysis formats. Such analysis is preferably based on a detector, such as an antibody, that specifically recognizes the biomarker to be determined. The detector must be able to generate a signal indicating the presence or absence of the biomarker, either directly or indirectly. Additionally, the signal intensity may be correlated, preferably, directly or indirectly (e.g., inversely) with the amount of the biomarker present in the sample. Further suitable methods include measuring physical or chemical properties specific to the biomarker, such as accurate molecular mass or NMR spectrum. Such methods preferably include analytical devices such as biosensors, optical devices coupled to immunoassays, FACS analyzers, biochips, mass spectrometers, NMR analyzers, or chromatography devices. In addition, the method includes a micro-plate ELISA-based method, a fully automated or robotic immunoassay, an enzyme cobalt binding assay, and a latex agglutination assay.

[0027] Preferably, the step of determining the amount of the biomarker comprises the steps of: (a) contacting a cell with which an intensity indicating the amount of the biomarker can induce a cellular response for an appropriate time with the biomarker; and (b) measuring the cellular response. To measure the cellular response, the sample or treated sample is preferably added to a cell culture medium, and an internal or external cellular response is measured. The cellular response may include the measurable expression of a reporter gene or, for example, the secretion of a substance that is a peptide, polypeptide, or small molecule. The expression or substance must generate an intensity signal associated with the amount of the biomarker.

[0028] Additionally, preferably, the step of determining the amount of the biomarker includes the step of measuring a specific intensity signal obtainable from the biomarker in the sample. As previously mentioned, this signal may be an m / z variable specific to the biomarker observed in a mass spectrum or a signal observed in an NMR spectrum specific to the biomarker.

[0029] As used herein, the term "quantity" includes the absolute amount of a biomarker, the relative amount or concentration of said biomarker, and any values ​​or parameters associated with or derived therefrom. Such values ​​or parameters include intensity signal values ​​from any specific physical or chemical properties obtained from said peptide by direct measurement, e.g., intensity values ​​in a mass spectrum or NMR spectrum. Additionally, it includes any values ​​or parameters obtained by indirect measurement as specified elsewhere in this specification, e.g., reaction levels determined by a biological reading system in response to the peptide, or intensity signals obtained from a specifically bound ligand. It should be understood that values ​​corresponding to the aforementioned quantities or parameters can also be obtained by any standard mathematical operation and may be used without dimensions, for example, in a scoring system as described elsewhere in this specification.

[0030] As used herein, the term “comparison” involves comparing an amount of a biomarker contained in a sample to be analyzed with an amount of appropriate reference material specified elsewhere in this specification. It should be understood that as used herein, the comparison refers to the value of a corresponding parameter, e.g., an absolute amount is compared to an absolute reference amount, while a concentration is compared to a reference concentration, or a signal intensity obtained from a test sample is compared to the same type of signal intensity of a reference sample. However, according to the present invention, it is also expected that a value be calculated based on a measured amount of the biomarker. Such value is compared to a reference interval derived from a multivariate discriminant analysis performed on the amount from a population of subjects including predefined responders and non-responders to cardiac stem cell therapy. Further details can be found in the attached examples below.

[0031] The comparison mentioned in the method of the present invention may be performed manually or with computer assistance. For computer-assisted comparison, the value of a determined quantity may be compared by a computer program with a value corresponding to an appropriate reference stored in a database. The computer program may further evaluate the comparison results, that is, automatically provide the desired quantity in an appropriate output format. Preferably, this evaluation is performed by machine learning (ML). Based on the comparison of the determined quantity and the reference, the response to cardiac regeneration, for example, functional improvement of the heart after cardiac stem cell therapy, can be predicted. Specifically, it must be possible to predict a high probability (i.e., the subject will be a responder), a low probability (i.e., the subject will be a non-responder), or the subject will be ambivalent. Therefore, the reference quantity must be selected so that the difference or similarity in the comparison quantity can identify these test subjects belonging to a group of subjects exhibiting symptoms of acute inflammation with ischemic or non-ischemic brain damage. The above method enables the exclusion (rule-out) or identification (rule-in) of an individual as having (i) ischemic brain injury or (ii) non-ischemic brain injury.

[0032] As used herein, the term “reference” refers to a value, threshold, or interval based on the amount of a biomarker that enables an individual to be assigned to one of the groups of individuals who can expect to benefit from expected disease treatment, specifically tissue recovery, preferably cardiac treatment, or individuals who cannot expect to benefit from disease treatment, specifically tissue recovery, preferably cardiac treatment, or whose benefits are ambiguous.

[0033] Preferably, these references are individual reference cells that are non-disease type or free from somatic mutant gene and / or -gene expression. More preferably, these references are individual reference cells that are non-disease type or free from SH2B3 somatic mutant gene and / or -gene expression.

[0034] Such references may be threshold amounts separating these groups from one another. An appropriate threshold amount separating the two groups may be calculated without further interference by statistical tests mentioned elsewhere in this invention, based on the amount of biomarkers from individuals or groups of individuals known to benefit from disease treatment, specifically tissue recovery, preferably cardiac treatment, or individuals or groups of individuals known not to benefit from said treatment, or those known to be ambiguous.

[0035] In principle, by applying standard statistical methods, a reference to a cohort of individuals as specified above may be calculated based on the mean or mean value for a given biomarker. Specifically, the accuracy and sensitivity of a test, such as a method for diagnosing a response, are best evaluated by the receiver-operation characteristic (ROC) (see Zweig 1993, Clin. Chem. 39:561-577 in particular). Accordingly, a reference for use in the method of the present invention described above can be generated by establishing the ROC for the cohort as described above and deriving a threshold amount therefrom. Depending on the desired sensitivity and specificity for the diagnostic method, the ROC plot can derive an appropriate threshold amount.

[0036] The term "sample" refers to a body fluid sample, preferably a sample of (whole blood), plasma, or serum. However, the term also includes all samples derived from the aforementioned whole blood, plasma, or serum by a pretreatment step, for example, such as fractions of blood, plasma, or serum obtained by partial purification.

[0037] The term "individual" as used herein refers to an animal, preferably a mammal, and, most preferably, a human. The individual requires treatment for a disease, preferably cardiac treatment. Preferably, the individual requiring cardiac treatment suffers from a heart disease, such as heart failure or coronary artery disease, for example, after a myocardial infarction, and more preferably, heart failure.

[0038] The prediction made according to the method of the present invention also enables an assessment of whether the probability is high, and therefore, whether functional improvement of the heart system in the individual is expected to occur, or whether the probability is ambiguous regarding treatment success, or whether the probability is low, and therefore, whether functional improvement of the heart system in the individual is expected not to occur.

[0039] The present invention also relates to one or more biomarkers selected from lymphocyte adapter protein expression from SH2B3-gene and / or -gene expression mutant variants and / or SH2B3-gene and / or -gene expression mutant variants for use in a method for treating a disease, specifically for predicting a response to tissue recovery, preferably for predicting a response to cardiovascular regeneration.

[0040] In addition, the present invention relates to a computer device comprising a processor and a memory encoding one or more machine learning (ML) models connected to the processor, wherein the program causes the processor to perform a method, and the method

[0041] (i) a step of comparing the determined biomarker above with a reference value and / or reference, preferably a reference value and / or reference of SH2B3-gene and / or -gene expression without mutant variants,

[0042] (ii) a step of predicting, based on the above comparison results, whether a response to disease treatment, specifically tissue regeneration, preferably cardiovascular regeneration in the subject is expected, not expected, or ambiguous.

[0043] Includes

[0044] In addition, the present invention relates to an apparatus suitable for carrying out the method of the present invention,

[0045] (i) an analysis unit for determining the amount of each biomarker according to the present invention in a sample of an individual, and

[0046] (ii) Computer device according to the present invention (as described above)

[0047] Includes

[0048] As used herein, the term “device” relates to a system comprising the aforementioned units operably connected to one another to allow for diagnosis according to the method of the present invention. Preferred detectors that may be used in the analysis unit are disclosed elsewhere in this invention. Preferred detectors are antibodies or other proteins that specifically bind to a biomarker to form a detection complex. The analysis unit preferably comprises said detector in a form immobilized on a solid support to come into contact with a sample containing an amount of the biomarker to be determined. Additionally, the analysis unit may also include a detector that determines the amount of the detector specifically bound to the biomarker(s). The determined amount may be transmitted to an evaluation unit. The evaluation unit includes a data processing element, such as a computer, having an implemented algorithm for performing a comparison between the determined amount and an appropriate reference. As described elsewhere in this specification, an appropriate reference may be derived from a sample of an object used to generate the reference amount. The result may be provided as an output of parametric diagnostic raw data, preferably in absolute or relative amounts. It should be understood that such data requires clinical interpretation. However, the output is also expected to be a specialized system device containing processed raw diagnostic data, and its interpretation does not require a specialist clinician.

[0049] Other preferred embodiments of the present invention are derived from dependent claims together with the following description, so that a specific category of patent claim may be formed by dependent claims of other categories, and features of other examples may be combined with new examples. It should be understood that the definitions and descriptions of terms above and below are therefore appropriately applied according to all embodiments described in this specification and the appended claims. Below, specific embodiments of the method of the present invention are further specified.

[0050] According to a preferred embodiment, the SH2B3-gene and / or -gene expression is a knockout-gene and / or -gene expression variant. Preferably, such knockout-gene and / or -gene expression variant is a dominant-negative SH2B3 - / LNK - It is an LNK / SH2B3 deficiency that inhibits the function of variants or endogenously expressed LNK / SH2B3.

[0051] Specifically, these preferred variants are SH2B3-gene and / or -gene expression mutants comprising at least one mutation of SH2B3-gene and / or -gene expression mutant 1 (transcript: NM_005475.2) and / or SH2B3-gene and / or -gene expression mutant 2 (transcript: NM_001291424.1), as illustrated in FIG. 1. These SH2B3-gene and / or -gene expression mutants may be caused by SNP deletions and / or nucleotide substitutions at the level of RNA and / or DNA and, for example, may become LNK proteins exhibiting amino acid substitutions in the range of 70 to 90%, preferably in the range of 80%, specifically about 83%.

[0052] Preferably, the SH2B3-gene and / or -gene expression variant 1 comprises a sequence selected from SEQ ID NO. 1; and the SH2B3-gene and / or -gene expression variant 2 comprises a sequence selected from SEQ ID NO. 2. SEQ ID NO. 2 comprises a portion of the 5' region, wherein two alternate exons are missing from the 5' region, and, compared to SEQ ID NO. 1, translation begins at an alternate start codon in the alternate exons. The coded isoform 2 has a distinct N-terminus and is shorter than isoform 1.

[0053] By using the SH2B3-gene or -gene expression mutant variant together with additional markers, specifically biomarkers selected from CD133 gene expression in peripheral blood and / or CFU-Hill and / or CD133+ / ml MNC cells, the method according to the present invention can advantageously increase the sensitivity and specificity of prediction accuracy to about 60% or more.

[0054] A more preferred method analyzes the inhibition and / or alteration of LNK function due to amino acid substitutions or deletions by using LNK protein expression analysis and the function of SH2B3-gene and / or -gene expression variants.

[0055] According to a particularly preferred embodiment, the method further uses one or more biomarkers, said additional biomarkers are selected from the group of angiogenesis factors and / or survival factors and / or chemokines and / or circulating endothelial progenitor cells (EPCs) and / or receptor / ligand expression in circulating endothelial cells and subpopulations, MNC subpopulations and / or whole-genome sequence RNA.

[0056] According to a particularly preferred embodiment, the advantageous method further uses one or more biomarkers, said additional biomarkers are selected from PLCG1, EPO, LPCAT2, GRB2, AP1B1, AFAP1, KLF8, MARK3, REX1BD, SACM1L, PDGFRB, VEGF, BEX3, Delta_CT_SH2B3, ZNF205, LTB, EMG1, CD34+ cells / ml PB, BAZ1A and / or CD133+ cells / ml PB.

[0057] Preferably, PLCG1, LPCAT2, GRB2, AP1B1, AFAP1, KLF8, MARK3, REX1BD, SACM1L, PDGFRB, BEX3, ZNF205, EMG1, BAZ1A and / or LTB are determined at the RNA level and / or EPO and / or VEGF are determined at the protein level.

[0058] More preferably, the determination of EPO, VEGF, Delta_CT_SH2B3, CD34+ cells / ml PB and / or CD133+ cells / ml PB is performed on a preoperative sample.

[0059] According to a more preferred embodiment, additional biomarkers are selected from PLCG1, EPO, LPCAT2, GRB2, AP1B1, AFAP1, KLF8, MARK3, REX1BD, SACM1L, PDGFRB and / or VEGF.

[0060] The combination of additional biomarkers mentioned above may be selected on an individual basis, depending on each of the disease type and / or disease state of the individual or patient. For example, the selection may include a combination of biomarkers including PLCG1, EPO, LPCAT2, GRB2, AP1B1, KLF8, MARK3, and / or VEGF (as shown in FIG. 3).

[0061] In addition to the additional biomarkers mentioned above, according to the following embodiments, for example, an angiogenesis factor is selected from VEGF, preferably VEGF-B, and / or FGF, preferably FGF-4, and / or HGF and / or Ang-1, a survival factor is preferably selected from IGF-1, and a chemokine is selected from IGF-2 and / or SDF-1.

[0062] In the research forming the basis of the present invention, it was advantageously found that analyzing the amount of a combination of biomarkers in a sample taken prior to previous cardiac treatment from an individual requiring cardiac treatment, e.g., an individual suffering from heart failure, allows for the prediction of whether the individual will benefit from treatment in terms of improved cardiac regeneration or cardiovascular recovery after treatment. Techniques for measuring the amount of individual biomarkers are all well known in the art.

[0063] Advantageously, by implementing the method according to the present invention, the sensitivity and specificity of the prediction accuracy may be greater than about 90%, preferably greater than about 92%, more preferably greater than about 93%, and more preferably greater than about 94%. Accordingly, the method of the present invention improves the decision-making of clinicians before the application of expensive and cumbersome treatment means, such as cardiac treatment. Making the right decision—that is, applying treatment only when it is effective—will certainly be advantageous to the public health system in light of individual patients and cost outcomes.

[0064] The above method is preferably further supported because, by using ML, there is no need to perform individual comparisons. In addition, the machine learning approach is advantageous for balancing and weighting parameters so that the reliability of the prediction can be further improved. Therefore, the advantageous method of the present invention is preferably supported by machine learning to facilitate and improve overall prediction accuracy. According to the present invention, the term "machine learning" relates to the study and construction of algorithms that learn from and predict from data—wherein the algorithm overcomes strictly static program instructions by making data-driven predictions or decisions by building a model from sample inputs.

[0065] In another embodiment of the above method, the method further uses one or more biomarkers, said one or more biomarkers being zinc finger proteins, ZDHHC2 and / or MYL4 / ALC1 and / or INPP4B and / or RBM38 and / or CD133+ cells / ml PB (peripheral blood) and / or CD34+ cells / ml PB and / or CD45+184+ cells / ml PB and / or CD45+ cells / ml PB and / or CD184+ cells / ml PB and / or MNC in PB and / or CFU-Hill in PB and / or RIOK3-gene expression in PB and / or ABCC13-gene expression in PB and / or COPS3-gene expression in PB and / or GUK1-gene expression in PB and / or AP3B1-gene expression in PB and / or TBC1D22B-gene expression in PB and / or in PB It is selected from RBM38-gene expression and / or ZBTB33-gene expression in PB and / or AGO2-gene expression in PB.

[0066] In another preferred embodiment, the method is used for preoperative prediction of response to stem cell therapy and / or induction of angiogenic response and / or tissue regeneration in the context of cardiovascular diseases including myocardial infarction, stroke and peripheral ischemic vascular disease, heart disease and / or ischemic disease. Such therapy may include treatment for the use of cardiovascular implants or stents, ventricular assist devices (VADs), pacemakers and / or occlusion devices or appropriate occlusion devices. Additionally, such therapy may include treatment for diabetes mellitus, oncological diseases, E. coli, rheumatic diseases, infectious diseases, sepsis and / or hypertension. Specifically, the above method is used to predict the improvement of left ventricular heart function preoperatively after treatment with, for example, acute percutaneous coronary intervention (PCI) and secondary coronary artery bypass graft (CABG) vascular regeneration, and / or ischemia-reperfusion intervention following deterioration of the left ventricular ejection fraction (LVEF) after acute myocardial ST-segment elevation infarction (STEMI) and coronary artery 3-vessel disease.

[0067] In another preferred embodiment, a method for predicting the response to stem cell therapy and / or the induction of angiogenic response and / or tissue recovery in the context of cardiac regeneration, particularly cardiovascular disease, uses samples taken from an individual suffering from heart disease and / or arteriosclerosis. Such samples may be taken particularly from an individual suffering from angina pectoris, acute myocardial injury, cell necrosis, myocardial hypertrophy, heart failure, preferably ischemic heart failure, non-ischemic heart failure, myocarditis, atrial fibrillation, ventricular fibrillation and / or arteriosclerosis.

[0068] According to a preferred embodiment, the method comprises a step of profiling comparison results at least 2, 3, 4, 5, 6 or more time points. Advantageously, these time points include samples taken before surgery and at 1, 3, 10, 90, 180, and / or 730 days after surgery. By analyzing at least 2, 3, 4, 5, 6 or more samples, the specificity of the prediction accuracy can be advantageously increased to and beyond.

[0069] Further embodiments of the advantageous method include the use of clinical diagnostic parameters. Specifically, these parameters are selected from colony-forming units (CFU) Hill assay, Matrigel Plug assay, weight, and / or left ventricular systolic volume (LVESV).

[0070] According to the following preferred embodiment, the method is used for profiling angiogenic reactions.

[0071] Another preferred embodiment includes an advantageous method further comprising analyzing functional RNA and / or non-coding RNA and / or SNPs, such as RNA and / or DNA and / or mRNA sequences and / or microRNAs, which contain diagnostic signatures. In the context of the present invention, the analysis of the signatures of RNA and / or DNA and / or SNPs may be performed or / or supported by machine learning and / or pathway analysis. In the context of the present invention, pathway analysis is used to identify relevant RNA and / or DNA and / or SNPs within a pathway derived from relevant RNA, DNA and / or SNPs or within a constructed pathway.

[0072] Additionally, according to a further preferred embodiment, the advantageous method may include the analysis of pharmacokinetic and pharmacogenetic data, including RNA and / or DNA sequencing and / or network pathway analysis. These pharmacokinetic data may be obtained specifically from subjects administered drugs from a group selected from statins, acetylsalicylic acids (ASS), β-blockers, angiotensin-converting enzyme (ACE) inhibitors, angiotensin II (ATII) receptor antagonists, aldosterone antagonists, diuretics, Ca-blockers, anti-arrhythmic agents, Digitalis, Marcumar, and / or nitrates.

[0073] The following preferred embodiment may additionally use phenotypic analysis of an object, such as weight analysis.

[0074] According to a more preferred embodiment, a method for predicting cardiovascular recovery includes stem cell therapy using the transplantation of CD133-positive stem cells. Such stem cell therapy may be combined with CABG angiogenesis, which is optionally used for the induction of cardiac regeneration and / or PCI. However, a particularly preferred embodiment includes cardiac stem cell therapy additionally comprising coronary artery bypass surgery.

[0075] Advantageously, cardiac regeneration and / or functional improvement of the heart after cardiac stem cell therapy is accompanied by an increase in LVEF of at least 5%.

[0076] The method of the present invention described above is not limited to application only to human subjects and may include animals. However, the method is preferably applied to human subjects. According to the present invention, samples of such subjects may be derived from blood, specifically peripheral blood, and / or serum and / or plasma samples and / or tissue biopsy samples and / or samples of circulating (stem) cells such as endothelial progenitor cells (EPCs).

[0077] As described above, the present invention also relates to one or more biomarkers selected from SH2B3-gene and / or -gene expression mutant variants and / or lymphocyte adapter protein expression from SH2B3-gene and / or -gene expression mutant variants for use in a method for predicting the response to disease treatment, specifically tissue regeneration, preferably cardiovascular regeneration.

[0078] According to a preferred embodiment, the biomarker is used in combination with one or more additional biomarkers, the additional biomarkers are selected from the group of angiogenesis factors, survival factors, chemokines, circulating EPCs, CECs, circulating platelets, circulating proteolytic cells and subpopulations, expression of receptors / ligans in MNC subpopulations and / or whole-genome sequence RNAs.

[0079] According to a particularly preferred embodiment, the advantageous combination additionally uses one or more biomarkers, said additional biomarkers are selected from PLCG1, EPO, LPCAT2, GRB2, AP1B1, AFAP1, KLF8, MARK3, REX1BD, SACM1L, PDGFRB, VEGF, BEX3, Delta_CT_SH2B3, ZNF205, LTB, EMG1, CD34+ cells / ml PB, BAZ1A and / or CD133+ cells / ml PB.

[0080] Preferably, PLCG1, LPCAT2, GRB2, AP1B1, AFAP1, KLF8, MARK3, REX1BD, SACM1L, PDGFRB, BEX3, ZNF205, EMG1, BAZ1A and / or LTB are determined at the RNA level and / or EPO and / or VEGF are determined at the protein level.

[0081] More preferably, the determination of EPO, VEGF, Delta_CT_SH2B3, CD34+ cells / ml PB and / or CD133+ cells / ml PB is performed from a preoperative sample.

[0082] According to a more preferred embodiment, a favorable combination of additional biomarkers is selected from PLCG1, EPO, LPCAT2, GRB2, AP1B1, AFAP1, KLF8, MARK3, REX1BD, SACM1L, PDGFRB and / or VEGF.

[0083] Combinations of these additional biomarkers may be selected on individual criteria according to each of the disease forms and / or disease states of the individual or patient. For example, the selection may include a combination of biomarkers including PLCG1, EPO, LPCAT2, GRB2, AP1B1, KLF8, MARK3, and / or VEGF (as shown in FIG. 3).

[0084] In addition to the additional biomarkers mentioned above, according to the following embodiments, for example, an angiogenesis factor is selected from VEGF and / or FGF, more preferably from VEGF-B and / or FGF-4, and / or HGF and / or Ang-1, a survival factor is preferably selected from IGF-1, and a chemokine is selected from IGF-2 and / or SDF-1.

[0085] Generally or additionally, these additional biomarkers are zinc finger proteins ZDHHC2 and / or MYL4 / ALC1 and / or INPP4B and / or RBM38, CD133+ cells / ml PB and / or CD34+ cells / ml PB and / or CD45+184+ cells / ml PB and / or CD45+ cells / ml PB and / or CD184+ cells / ml PB and / or MNC in PB and / or CFU-Hill in PB and / or RIOK3-gene expression in PB and / or ABCC13-gene expression in PB and / or COPS3-gene expression in PB and / or GUK1-gene expression in PB and / or AP3B1-gene expression in PB and / or TBC1D22B-gene expression in PB and / or RBM38-gene expression in PB and / or ZBTB33-gene expression in PB and / or includes one or more biomarkers selected from AGO2-gene expression in PB. Advantageously, the method ex vivo / In a test tube It is a method.

[0086] Preferably, a combination of biomarkers is used in a method for predicting a response to disease treatment, specifically tissue recovery, preferably cardiovascular regeneration, and said method

[0087] (i) A step of determining the amount of each biomarker in a sample of an individual,

[0088] (ii) a step of comparing the above-determined amount with a reference value and / or reference,

[0089] (iii) a step of predicting, based on the above comparison results, whether the response to cardiac recovery in the subject is expected, not expected, or ambiguous.

[0090] Includes

[0091] According to a preferred embodiment of the present invention, the combination of biomarkers is used in a method for predicting a response to cardiovascular regeneration, and the method further comprises clinical diagnostic data, and / or analysis of functional RNA and / or non-coding RNA and / or SNPs such as RNA and / or mRNA and / or microRNA, and / or analysis of pharmacokinetic data and / or analysis of phenotypes such as body weight, for example.

[0092] More preferably, an advantageous method and / or advantageous combination of the above biomarker is used for preoperative prediction of the response to stem cell therapy, and said stem cell therapy is accompanied by CABG.

[0093] As used herein, the term "stem cell therapy" refers to any therapeutic approach involving the transplantation of exogenous cardiomyocytes into the heart of a patient. These cardiomyocytes may be generated by reprogramming non-cardiomyocyte progenitor cells into cardiomyocytes. The cells being reprogrammed may be embryonic stem cells, induced pluripotent stem cells, pluripotent cardiac progenitor cells, skeletal myoblasts, or bone marrow-derived stem cells. Additionally, mature cardiomyocytes may be used, specifically when stimulated to re-enter the mitotic cell circulation. More preferably, cardiac stem cell therapy according to the present invention involves the transplantation of CD133-positive cells, most preferably CD133-positive bone marrow mononuclear cells. The cells may be transplanted as isolated single cells or in a formed array, such as a tissue block formed by a tissue engineering process. Preferably, the cells are transplanted by intramural injection according to the present invention. Furthermore, the term cardiac stem cell therapy may also include additional therapeutic means, such as drug therapy or surgery and other therapeutic means, that accompany the transplantation process. Preferably, the cardiac stem cell therapy according to the present invention further includes coronary artery bypass surgery as described in the attached examples below.

[0094] The term "functional improvement of the heart" as used herein refers to a significant increase in the heart's LVEF observed when comparing LVEF before and after treatment of the individual. Preferably, a significant increase is an increase of 5% or more in LVEF observed after treatment. An increase of less than 5% is considered insignificant. Additional parameters that may be considered to detect functional improvement are a reduction of 10% or more in the size of the perfusion defect, a reduction of 10% or more in left ventricular end-systolic volume (LVESV) quantified by MIBI SPECT, and an increase of 10% or more in peak systolic velocity measured by thoracic echocardiography.

[0095] As used herein, heart failure refers to any functional impairment of the heart, including left-sided failure, right-sided failure, or ventricular failure. Typically, the term heart failure referred to herein is left-sided failure resulting in reduced ejection fractions, such as a significantly reduced LVEF. Additional symptoms of heart failure are well known to clinicians. The term heart failure referred to herein includes acute and chronic forms of heart failure and any severe stages, for example, all stages of left-sided failure according to the New York Heart Association (NYHA) classification system, NYHA I through IV.

[0096] The term "PLCG1" as used herein refers to a protein encoded by the corresponding gene and catalyzes the formation of inositol 1,4,5-triphosphate and diacylglycerol from phosphatidylinositol 4,5-bisphosphate. This reaction plays a crucial role in the intracellular transduction of receptor-mediated tyrosine kinase activators. Two transcriptomic variants encoding different isoforms for this gene have been discovered.

[0097] The term "LPCAT2" as used herein refers to a gene encoding a member of the lysophospholipid acyltransferase family. The encoded protein may act on membrane biosynthesis and the production of platelet-activating factors in inflammatory cells. The enzyme may be localized to the endoplasmic reticulum and the Golgi.

[0098] As used herein, the term "GRB2" refers to Growth Factor Receptor-Binding Protein 2, known as Grb2, which is an adapter protein involved in signal transduction / cell communication. In humans, the GRB2 protein is encoded by the GRB2 gene. The protein encoded by this gene binds to receptors such as the epidermal growth factor receptor and contains one SH2 domain and two SH3 domains.

[0099] The term "AP1B1" as used herein refers to the AP-1 complex subunit beta-1, which is the protein encoded by the AP1B1 gene in humans. Adapter protein complex 1 is found on the cytoplasmic surface of coated vesicles located in the Golgi complex, where it mediates both the recruitment of clathrins to the membrane and the recognition of signal sorting within the cytoplasmic tails of transmembrane receptors. This complex is a heterotemer composed of two large, one intermediate, and one small adaptin subunits. The protein encoded by this gene acts as one of the large subunits of this complex and is a member of the adaptin protein family.

[0100] As used herein, the term "AFAP1" refers to Actin Filament-Associated Protein 1, a protein encoded by the AFAP1 gene in humans. The protein encoded by this gene is a Src binding partner. It can represent a potential regulator of the complete form of the actin filament in response to cellular signals and can function as an adapter protein by linking Src family members and / or other signaling proteins to the actin filament. Two alternative transcripts encoding the same protein have been identified.

[0101] The term "KLF8" as used herein refers to Krueppel-like factor 8, a protein encoded by the KLF8 gene in humans. KLF8 belongs to the family of KLF proteins. KLF8 is activated by KLF1 along with KLF3, and KLF3 inhibits KLF8.

[0102] The term "MARK3" as used herein refers to MAP / microtubule affinity-regulating kinase 3, an enzyme encoded by the MARK3 gene in humans. MARK3 has been shown to interact with stratipin.

[0103] The term "REX1BD" as used herein refers to a protein-coding gene that is 'required for inclusion of the resection 1-B domain'. It interacts with 3-isobutyl-1-methyl-7H-xanthine, aflatoxin B1, and aflatoxin B2.

[0104] The term "SACM1L" as used herein refers to Staphylococcus aureus cowan 1 phosphatidylinositide phosphatase, an enzyme encoded by the SACM1L gene in humans.

[0105] As used herein, the term "PDGFRB" refers to the PDGFRB gene, which encodes a typical tyrosine kinase belonging to the type III tyrosine kinase receptor (RTK) family and is structurally characterized by five extracellular immunoglobulin-like domains, a single membrane-spanning helical domain, and an intracellular membrane-spanning domain, a split tyrosine kinase domain, and a carboxylic acid tail. Upon PDGF binding, receptor dimerization releases an inhibitory form resulting from the autophosphorylation of regulatory tyrosine residues in a trans-mode. Tyrosine residues 857 and 751 are the major phosphorylation sites for the activation of PDGFRβ.

[0106] The term "BEX3" as used herein refers to brain-expressed X-linked 3, which is a protein-coding gene. Among its related pathways are GPCR-mediated p75 NTR receptor-mediated signaling and signaling.

[0107] The term "ZNF205" as used herein refers to the zinc finger protein 205, which is a protein-coding gene. The gene ontology (GO) annotations associated with this gene include nucleic acid binding and DNA-binding transcription factor activity.

[0108] The term "LTB" refers to lymphotoxin beta, a protein-coding gene. Among its associated pathways are the innate immune system and the innate lymphocyte differentiation pathway. Gene ontology (GO) annotations associated with this gene include binding and tumor necrosis factor receptor binding.

[0109] As used herein, the term "EMG1" refers to EMG1 N1-specific pseudouridine methyltransferase, which is a protein-coding gene. Its associated pathways include rRNA processing in the nucleus and cytoplasm and ribosome biosynthesis in eukaryotes. The gene ontology (GO) annotation associated with this gene includes methyltransferase activity.

[0110] The term "BAZ1A" as used herein refers to a bromodomain adjacent to zinc finger domain 1A and is a component of the Hispon-folding protein CHRAC complex, which facilitates nucleosome sliding by the ACF complex and enhances ACF-mediated chromatin assembly. The C-terminal regions of CHRAC1 and POLE1 are required for these functions.

[0111] As used herein, the term "Vascular Endothelial Growth Factor (VEGF)" refers to a soluble polypeptide growth factor that stimulates angiogenesis, angiogenesis, and vascular permeability. It is produced in various cell types. There are five different types of VEGF polypeptides: VEGF-A, Placental Growth Factor (PGF), VEGF-B, VEGF-C, and VEGF-D. Preferably, VEGF is selected from VEGF-B. In contrast to VEGF-A, VEGF-B plays a less prominent role in the vascular system. While VEGF-A is important for angiogenesis, such as during development or in pathological conditions, VEGF-B appears to play a role only in the maintenance of newly formed blood vessels during pathological conditions. VEGF-B also plays an important role in various forms of neurons. It is important for neurons in the retina and cerebral cortex during stroke and for protecting motor neurons during motor neuron diseases such as amyotrophic lateral sclerosis. VEGF-B exerts its effects through the FLT1 receptor (also known as vascular endothelial growth factor receptor 1). VEGF-B has also been found to regulate the endothelial uptake and transport of fatty acids in the heart and skeletal muscle.

[0112] As used herein, the term "fibroblast growth factor (FGF)" refers to a family of growth factors, including members involved in angiogenesis, wound healing, embryonic development, and various endocrine signaling pathways. Preferably, FGF is selected from FGF-4. As used herein, the term "FGF-4" refers to the fibroblast growth factor 4 protein encoded by the FGF4-gene in humans.

[0113] As used herein, the term "HGF" refers to Hepatocyte Growth Factor (HGF) or Scattering Factor (SF), which is a secreted cell growth, motility, and morphogenesis factor. It is secreted by mesenchymal cells and acts primarily on epithelial and endothelial cells, as well as on hematopoietic progenitor cells and T cells.

[0114] The term "Ang-1" as used herein refers to Angiopoietin 1, which is a type of angiopoietin encoded by the gene ANGPT1. Angiopoietin is part of the vascular growth factor family that plays a role in embryonic and postnatal angiogenesis. Angiopoietin signaling corresponds most directly to angiogenesis, the process in which new arteries and veins are formed from existing blood vessels. Angiogenesis proceeds through budding, endothelial cell migration, proliferation, and vascular destabilization and stabilization. They play a role in assembling and breaking down the endothelium of blood vessels. Angiopoietin cytokines are involved in controlling microvascular permeability, vasodilation, and vasoconstriction by signaling smooth muscle cells surrounding blood vessels.

[0115] The term "survival factor" as used herein includes a group of substances that exhibit anti-apoptotic effects.

[0116] The term "IGF-1" (insulin-like growth factor 1), also known as somatomedin C, refers to the protein encoded by the IGF-1 gene in humans. IGF-1 is a hormone with a molecular structure similar to insulin. It plays a crucial role in growth during childhood and continues to have anabolic effects in adulthood. IGF-1 consists of 70 amino acids in a single chain with three intramolecular disulfide crosslinks. IGF-1 has a molecular weight of 7,649 daltons.

[0117] As used herein, the term "chemokine" refers to a family of small cytokines or signaling proteins secreted by cells. Their name is derived from their ability to induce chemotaxis induced in nearby response cells; they are chemotactic cytokines.

[0118] Some chemokines are considered pro-inflammatory and are induced during immune responses to recruit cells of the immune system to the site of infection, while others are considered homeostatic and are involved in controlling cell migration during the normal processes of tissue maintenance or development. Chemokines are found in all vertebrates, some viruses, and some bacteria, but are unknown in other invertebrates. Chemokines have been classified into four major subfamily: CXC, CC, CX3C, and XC. All of these proteins exert their biological effects by interacting with G protein-coupled membrane-penetrating receptors found selectively on the surface of target cells.

[0119] As used herein, the term "erythropoietin (EPO)" refers to a soluble polypeptide that is a cytokine. EPO is generally produced by kidney cells under hypoxic conditions. The term also includes variants of the human EPO polypeptide mentioned above. These variants have at least the same essential biological and immunological properties as the EPO polypeptide mentioned above. Specifically, they share the same essential biological and immunological properties when detected by the same specific assay mentioned herein, for example, an ELISA assay using a polyclonal or monoclonal antibody that specifically recognizes the EPO polypeptide. Additionally, it should be understood that the variants mentioned according to the present invention have different amino acid sequences due to at least one amino acid substitution, deletion, and / or addition, and that the amino acid sequence of said variant is still, preferably, at least 50%, 60%, 70%, 80%, 85%, 90%, 92%, 95%, 97%, 98%, or 99% or more identical to the amino acid sequence of a specific IL-6. The variants may be allelic variants, splice variants, or any other species-specific homologs, paralogs, or orthologs. Additionally, the variants mentioned herein comprise fragments of a specific EPO or fragments or subunits of the type of variant mentioned above, provided that the fragments possess the essential immunological and biological characteristics as mentioned above. Such fragments may be, for example, degradation products of EPO. Variants are considered to share the same essential biological and immunological characteristics if they are detected by the same specific assay mentioned herein, for example, an ELISA assay using a polyclonal or monoclonal antibody that specifically recognizes the EPO polypeptide. Preferred assays are described in the appended examples. Different variants are further included due to post-translational modifications such as phosphorylation or myristylation.

[0120] As used herein, the term "SH2B adapter protein 3 (SH2B3)" refers to the lymphocyte adapter protein (LNK). SH2B3 is a protein encoded by the SH2B3 gene on chromosome 12 in humans. It is ubiquitously expressed in many tissues and cell types (Li Y, He X, Schembri-King J, Jakes S, Hayashi J, May 2000. Journal of Immunology. 164(10):5199-206).

[0121] As used herein, the term "Interleukin-6 (IL-6)" refers to a soluble polypeptide that is a pro-inflammatory cytokine and an anti-inflammatory myokine. It is produced by T cells and macrophages.

[0122] Preferably, IL-6 refers to human IL-6 as described, for example, in Wong 1988, Behring Inst. Mitt 83: 40-47. More preferably, human IL-6 has an amino acid sequence as shown in Genbank accession number p05231.1, GI: 124347. The term also includes variants of the aforementioned human IL-6 polypeptide. These variants have at least the same essential biological and immunological properties as the aforementioned IL-6 polypeptide. Specifically, they share the same essential biological and immunological properties when detected by the same specific assay mentioned herein, for example, an ELISA assay using a polyclonal or monoclonal antibody that specifically recognizes the IL-6 polypeptide. Additionally, it should be understood that the variants mentioned according to the present invention have different amino acid sequences due to at least one amino acid substitution, deletion, and / or addition, and that the amino acid sequence of said variant is still, preferably, at least 50%, 60%, 70%, 80%, 85%, 90%, 92%, 95%, 97%, 98%, or 99% or more identical to the amino acid sequence of a specific IL-6. The variants may be allelic variants, splice variants, or any other species-specific homologs, paralogs, or orthologs. Furthermore, the variants mentioned herein include fragments of a specific IL-6 or types of variants mentioned above, provided that the fragments possess the essential immunological and biological characteristics as mentioned above. Such fragments may be, for example, degradation products of IL-6. Variants are considered to share the same essential biological and immunological characteristics when detected by the same specific assay mentioned herein, for example, an ELISA assay using a polyclonal or monoclonal antibody that specifically recognizes the IL-6 polypeptide.Preferred analytical methods are described in the attached examples. Different variants are additionally included due to post-translational modifications such as phosphorylation or myristylation.

[0123] The term "insulin-like growth factor (IGF-2)" as used herein refers to one of three protein hormones that share structural similarities with insulin. IFG-2 is believed to be secreted by the liver and circulate in the blood. It possesses growth-regulating, insulin-like, and mitotic activities. The growth factor has a major, but not absolute, dependence on somatotropin. It is considered an important fetal growth factor.

[0124] The term "stromal cell-derived factor 1 (SDF-1)," also known as CXC motif chemokine 12 (CXCL12), is a chemokine protein encoded by the CXCL12 gene on chromosome 10 in humans. It is ubiquitously expressed in many tissues and cell populations. SDF-1-alpha and 1-beta are small cytokines belonging to the chemokine family, and their members activate leukocytes and are often induced by pro-inflammatory stimuli such as lipopolysaccharides, tumor necrosis factor (TNF), or IL1. Chemokines are characterized by the presence of four conserved cysteines forming two disulfide bonds.

[0125] As used herein, the term "zinc finger protein (ZNFs)" refers to proteins that possess a wide range of molecular functions. Given their diverse zinc-finger domains, ZNFs can interact with DNA, RNA, PAR (poly-ADP-ribose), and other proteins. Therefore, ZNFs are involved in the regulation of various cellular regulatory processes. ZNFs are associated with transcriptional regulation, ubiquitin-mediated proteolysis, signal transduction, actin targeting, DNA repair, cell migration, and numerous other processes. As used herein, the term "ZDHHC2" refers to a zinc finger DHHC type.

[0126] The term "MYL4 / ALC1" as used herein refers to atrial light chain-1 (ALC-1), also known as the essential light chain. ALC-1 is a protein encoded by the MYL4 gene in humans.

[0127] The term "INPP4B" as used herein refers to inositol polyphosphate 4-phosphatase, which is a regulator of the phosphoinositide 3-kinase (PI3K) signaling pathway and is implicated as a tumor suppressor in epithelial carcinomas.

[0128] The term "RBM38" refers to RNA-binding motif protein 38. Also known as RNPC1, RBM38 induces cell-cycle arrest in the G1 phase, at least in part, through binding to and stabilizing the mRNA of the cyclin-dependent kinase inhibitor p21.

[0129] As used herein, the term "MNC" refers to a monocyte, which is any cell possessing a single round nucleus; examples of monocytes include lymphocytes, monocytes, and dendritic cells. These cells are important components of the immune system and are involved in both humoral and cell-mediated immunity. MNCs are widely used in research and clinical applications such as microbiology, virology, oncology, vaccine development, transplantation and regenerative biology, and toxicology.

[0130] The term "CFU-Hill" as used herein refers to the initial growth formed by plating peripheral blood mononuclear cells on a fibronectin-coated plate, allowing them to adhere and deplete non-adherent cells, and separating distinct colonies.

[0131] The term "ABCC13" as used herein refers to ATP-binding cassette carrier sub-series C member 13, which is a protein encoded by the ABCC13 gene in humans.

[0132] As used herein, the term "COPS3" refers to the protein encoded by the COPS3 gene in humans. It encodes a subunit of the COP9 signaling molecule. The protein complex is suggested to exhibit isopeptidase activity. Among its associated pathways are transcription-linked nucleotide removal repair (TC-NER) and clathrin-mediated endocytosis. It is found in progressive organisms, including humans.

[0133] The term "GUK1" as used herein refers to guanylic acid kinase, an enzyme encoded by the GUK1 gene in humans.

[0134] As used herein, the term "AP3B1" refers to AP-3 complex subunit beta-1, a protein encoded by the AP3B1 gene in humans. This gene encodes a protein that may play a role in the formation of organelles associated with melanosomes, platelet density granules, and lysosomes. The encoded protein is part of a heterotraminated AP-3 protein complex that interacts with the scaffolding protein clathrin.

[0135] The term "TBC1D22B" as used herein refers to TBC1D22B (TBC1 domain family member 22B), which is a protein-coding gene and acts as a GTPase-activating protein for Rab (Ras-associated protein) family proteins. The gene ontology (GO) annotation associated with this gene contains GTPase-activating activity. The paralog of this gene is TBC1D22A.

[0136] The term "ZBTB33" as used herein refers to a transcriptional regulatory kaiso, which is a protein encoded by the ZBTB33-gene in humans. This gene encodes a transcriptional regulator with bimodal DNA-binding specificity, which binds to methylated CGCG and also to the unmethylated matching KAISO-binding site TCCTGCNA. The protein contains an N-terminal POZ / BTB domain and three C-terminal zinc finger motifs. It recruits the N-CoR repressor complex to promote histone deacetylation and the formation of repressive chromatin structures at target gene promoters. It may contribute to the repression of target genes in the Wnt signaling pathway (the term "Wnt" encompasses a diverse family of secreted lipid-modifying signaling glycoproteins of 350–400 amino acids in length) and may also activate the transcription of a subset of target genes by catenin delta-2 (CTNND2). Its interaction with catenin delta-1 (CTNND1) inhibits binding to both methylated and non-methylated DNA. It also directly interacts with the nuclear import receptor importin-α2 (also known as caryoperin alpha2 or RAG cohort 1), which can mediate the nuclear import of this protein. Spliced ​​transcript variants encoding largely the same protein have been identified.

[0137] The term "AGO2" as used herein refers to a member of the Argonaut protein that plays a central role as an essential component of the RNA-induced silencing complex (RISC) in the RNA splicing process. Endonuclease activity and, therefore, RNAi-dependent gene silencing belong exclusively to AGO2. Given the sequence conservation of the RAZ and PIWI domains throughout the family, the uniqueness of AGO2 is presumed to occur at the N-terminus or in the gap region connecting the PZA and PIWI motifs.

[0138] The step of determining the amount of a biomarker may preferably include the steps of (a) contacting the biomarker with a specific ligand, (b) (optional) removing the unbound ligand, and (c) measuring the amount of the bound ligand. The bound ligand will generate an intensity signal. The binding according to the present invention includes both covalent and non-covalent bonds. The ligand according to the present invention may be any compound, e.g., a peptide, polypeptide, nucleic acid, or small molecule, that binds to the peptide or polypeptide or protein or nucleic acid or cell described herein. Preferred ligands include, for example, peptides such as nucleic acids or peptide aptamers, and antibodies, nucleic acids, peptides, or polypeptides such as receptors or binding partners for peptides or polypeptides containing a binding domain for aptamers and fragments thereof. Methods for preparing such ligands are well known in the art. For example, the identification and production of suitable antibodies or aptamers are also provided by commercial suppliers. Those skilled in the art are familiar with methods for developing derivatives of such ligands with high affinity or specificity. For example, random mutations may be induced into nucleic acids, peptides, or polypeptides. Their derivatives may then be tested for binding according to screening methods known in the art, such as phage display. The antibodies mentioned herein include polyclonal and monoclonal antibodies, as well as fragments thereof, such as Fv, Fab, and F(ab)2 fragments, which can bind to antigens or haptens. The invention also includes single-chain antibodies and humanized hybrid antibodies, wherein non-human donor antibodies exhibiting desired antigen-specificity bind to the sequence of a human receptor antibody. The donor sequence generally includes at least the antigen-binding amino acid residues of the donor and may also include other structurally and / or functionally related amino acid residues of the donor antibody. Such hybrids may be prepared by various methods well known in the art.Preferably, the ligand or substance specifically binds to the peptide or polypeptide. The specific binding according to the invention must not substantially ("cross-react") with other peptides, polypeptides, or substances present in the sample to be analyzed. Preferably, the specifically bound peptide or polypeptide must bind with an affinity of at least 3 times, more preferably at least 10 times, and even more preferably at least 50 times greater than that of other related peptides or polypeptides. If non-specific binding can still be clearly distinguished and measured, for example, by its size in a Western blot or by a relatively higher abundance in the sample, it may be acceptable. The binding of the ligand can be measured by any method known in the art. Preferably, the method is semi-quantitative or quantitative. Further suitable techniques for the determination of biomarkers are as follows.

[0139] First, the binding of the ligand can be measured directly, for example, by NMR or surface plasmon resonance. Second, if the ligand also serves as a substrate for the enzymatic activity of the biomarker of interest, the enzymatic reaction product can be measured (for example, the amount of protease can be measured by measuring the amount of substrate cleaved in a Western blot). Alternatively, the ligand may exhibit enzymatic properties itself, and the "ligand / peptide or polypeptide" complex or the ligand itself, which is bound by the biomarker, may come into contact with a suitable substrate that can be detected by the generation of an intensity signal. For the measurement of the enzymatic reaction product, the amount of substrate is preferably saturated. The substrate may also be labeled with a detectable label prior to the reaction. Preferably, the sample is in contact with the substrate for an appropriate period of time. The appropriate period of time refers to the time required for the amount of product to be generated that is detectable, preferably measurable. Instead of measuring the amount of the product, the time required for the appearance of a specific (e.g., detectable) amount of the product is measured. Thirdly, the ligand may be bound covalently or non-covalently to a label that enables the detection and measurement of the ligand. Labeling may be performed by direct or indirect methods. Direct labeling involves the direct binding of a label (covalent or non-covalent) to the ligand. Indirect labeling involves the binding of a secondary ligand (covalent or non-covalent) to a primary ligand. The secondary ligand must bind specifically to the primary ligand. The secondary ligand may be bound to an appropriate label of a tertiary ligand that binds to the secondary ligand and / or may be a target (receptor). The use of secondary, tertiary, or even higher-order ligands is often used to amplify the signal. Appropriate secondary and higher-order ligands may include antibodies, secondary antibodies, and the well-known streptavidin-biotin system.The ligand or substrate may also be "tagged" with one or more tags known in the art. These tags may then be targeted by higher-order ligands. These tags include biotin, digoxygenin, His-Tag, glutathione-S-transferase, FLAG, GFP, myc-tag, influenza A virus hemagglutinin (HA), maltose that binds to proteins, etc. In the case of peptides or polypeptides, the tags are preferably located at the N-terminus and / or C-terminus. Suitable labels are any labels that can be detected by a suitable detection method. Typical labels include gold particles, latex beads, acridine esters, luminol, ruthenium, enzymatically active labels, radioactive labels, magnetic labels (including paramagnetic and superparamagnetic labels, e.g., magnetic beads), and fluorescent labels. Enzymatically active labels include, for example, osereish peroxidase, alkaline phosphatase, beta-galactosidase, luciferase, and derivatives thereof. 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-indoleyl-phosphate. Suitable enzyme-substrate combinations can induce a colored reaction, fluorescence emission, or chemiluminescence, which can be measured according to methods known in the art (e.g., using photosensitive films or suitable camera systems). The criteria presented above apply similarly to the measurement of the enzyme reaction. Typical fluorescent labels include fluorescent proteins (such as GFP and its derivatives), Cy3, Cy5, Texas Red, Fluorescein, and Alexa Red. Additional fluorescent labels are available, for example, from Molecular Probes (Oregon). The use of quantum dots as fluorescent labels is also considered. Typical radioactive labels are. 35 S, 125 I, 32P, 33 Includes P, etc. Radioactive labels can be detected by any known and appropriate method, for example, by a photosensitive film or a phosphorescent camera.

[0140] The amount of biomarker can also preferably be determined as follows: (a) contacting a solid support containing a ligand for the biomarker as specified above with a sample containing the biomarker, and (b) measuring the amount of biomarker bound to said support. The ligand, preferably selected from the group consisting of nucleic acids, peptides, polypeptides, antibodies, and aptamers, is preferably present on a solid support in an immobilized form. Materials for manufacturing the solid support include, well known in the art and particularly commercially available, corum materials, polystyrene beads, latex beads, magnetic beads, colloidal metal particles, glass and / or silicon chips and surfaces, nitrocellulose strips, membranes, sheets, duracytes, walls and walls of reaction trays, plastic tubes, etc. The ligand or agent can be bound to many different carriers. Examples of well-known carriers include glass, polystyrene, polyvinyl chloride, polypropylene, polyethylene, polycarbonate, dextran, nylon, amylose, natural and modified cellulose, polyacrylamide, agarose, and magnetite. For the purposes of the invention, the nature of the carrier may be soluble or insoluble. Suitable methods for immobilizing / fixing the ligand are well known and include, but are not limited to, ionic, hydrophobic, or covalent interactions.

[0141] Suitable measurement methods according to the present invention also include fluorescence-activated cell sorting (FACS) analysis, radioimmunoassay (RIA), urea-associated immunosorbent assay (ELISA), sandwich enzyme immunoassay, chemiluminescent sandwich immunoassay (ECLIA), dissociation-enhanced lanthanide fluoroimmunoassay (DELFIA), scintillation proximity assay (SPA), or solid-phase immunoassay. Additional methods known in the art (e.g., gel electrophoresis, 2D gel electrophoresis, SDS-polyacrylamide gel electrophoresis (SDS-PAGE), Western blotting, and mass spectrometry (MS)) may be used alone or in combination with labeling or other detection methods as described above.

[0142] To carry out the method of the present invention, a kit suitable for carrying out the method of the present invention is provided, and the kit according to the present invention comprises a detector for determining at least one amount of said biomarker in a sample of said individual. Such a kit advantageously enables the simple carrying out of the method of the present invention and / or the measurement and / or determination of the biomarker according to the present invention.

[0143] As used herein, the term “kit” refers to a set of the aforementioned components, preferably each component provided individually or in a single container. The kit may also include instructions for performing the method of the present invention. Such instructions may be in the form of a manual or may be provided by computer program code capable of performing the comparisons mentioned in the method of the present invention and, when implemented in a computer or data processing device, establishing a diagnosis accordingly. The computer program code may be provided on a data storage medium or device, such as a storage medium (e.g., a compact disc, a USB drive, or an external hard disk), or directly on a computer or data processing device. Additionally, the kit may include, preferably, a standard for reference quantities as described in detail elsewhere in this invention.

[0144] Examples

[0145] The following examples are merely illustrative of the invention. They should not be construed as limiting the scope of the invention in any way.

[0146] Example 1 Clinical study design and evaluation

[0147] Peripheral blood bone marrow responses were studied by whole-genome and circulating EPC analysis of biomarkers in a randomized phase 3 PERFECT trial at the Rostock clinical site using available biobank, clinical (per protocol), and biomarker data (n=23) (Steinhoff G, Nesteruk J, Wolfien M, et al. EBioMedicine 2017 Aug; 22:208-224. doi: 10.1016 / j.ebiom.2017.07.022. Epub 2017 Jul 29.). processed CD133 +The BMSC (n=13) and placebo control group (n=14) were equally distributed. Biomarker patient cohort of the PERECT trial (n=23; placebo / CD133 + In 9 / 14), the inventors identified responders (R) classified as LVEF≥5% (n=14; placebo / CD133 + Non-responders (NR) classified as LVEF < 5% after 7 / 7 and 180 days (n=9; placebo / CD133 + The systemic bone marrow stem cell response in peripheral blood was investigated in 5 / 4.

[0148] Lnk studied R / NR gene expression and biomarker data for myocardial regeneration after ischemia at different time points (0, 1, 3, 7, 14, 28 days) before and after MI - / - mouse big Lnk + / + It was compared with mouse Lnk / SH2B3 knockout deficiency conditions studied in an experimental setting of myocardial infarction in wild-type (WT) mice.

[0149] Clinical trial setup

[0150] The PERFECT trial involved intramural CD133 along with coronary artery bypass graft (CABG) revascularization for post-infarct myocardial ischemia. +It was a randomized, multicenter, placebo-controlled, double-blind Phase 3 study investigating the myocardial regenerative effects of BMSC therapy (Steinhoff G, Nesteruk J, Wolfien M, et al. EBioMedicine 2017 Aug; 22:208-224. doi: 10.1016 / j.ebiom.2017.07.022. Epub 2017 Jul 29., Baughn LB, Meredith MM, Oseth L, Smolarek TA, Hirsch B. Cancer Genet. 2018 Oct; 226-227:30-35. doi: 10.1016 / j.cancergen.2018.05.004. Epub 2018 Jun 8.). Pre-operative designation and post-operative biomarker results analysis were published (Steinhoff G, Nesteruk J, Wolfien M, et al. EBioMedicine 2017; Aug;22:208-224. doi: 10.1016 / j.ebiom.2017.07.022. Epub 2017 Jul 29.). The inclusion criteria for the PERFECT study were (a) coronary artery disease after MI with indication for CABG surgery, (b) reduced LVEF (25–50%), and (c) the presence of localized exercise / reduction in exercise / reduction in reperfusion zones in the left ventricular (LV) myocardium defining the SC target area. Evaluations were performed preoperatively and at 1, 3, 10, 90, 180, and 730 days postoperatively.

[0151] Post-mortem gene expression analysis

[0152] RNAseq analysis and mRNA RT-PCR in PB: Before releasing the blind spots of the test, the analysis was performed while carefully adhering to data privacy protection (pseudonymization).

[0153] Transcriptome analysis of EDTA blood samples using NGS (Next-Generation Sequencing)

[0154] RNA from frozen EDTA blood samples was isolated using a three-step procedure: First, the GeneJET Stabilization and Fresh Whole Blood RNA Kit (Thermo Scientific) was used according to the manufacturer's instructions. Second, the isolated RNA was precipitated with 2.5 volumes of ethanol under high-salt conditions (10% 3 M sodium acetate, pH 5.2). After DNase digestion (Thermo Scientific), the RNA was finally purified using Agencourt RNAClean XP beads (Beckman Coulter). The isolated RNA was analyzed on a Bionalyzer (Agilent) using an RNA 6000 nanochip (Agilent). Quality control RNA was used to build a sequencing library using Universal Plus mRNA-Seq Technology (Nugen) according to the manufacturer's instructions. Summary: mRNA was selected with oligo d(T) beads, reverse transcribed, and Globin-derived cDNA was cleaved with a Globin-depleted module (Nugen). Quality-controlled and quantified libraries were sequenced on a HiSeq1500 system (Illumina) in single-end mode (100 nt read length). For RNAseq data analysis, the inventors performed adapter clipping and quality trimming procedures for data preprocessing and aligned reads to the hg19 genome with the help of kallisto. Differential expression analysis was performed using the likelihood ratio test of the SLURES package (genes with >2-fold change and q values ​​<0.05 are considered significantly differentially expressed). Gene annotation, including functional annotation clustering and functional classification, was performed with Enrichr (Kuleshov, MV et al., Nucleic Acids Res. 2016, vol 44(W1), doi: 10.1093 / nar / gkw377, Epub 2016 May 3).

[0155] Recalling variants from transcriptome data

[0156] Previously preprocessed RNAseq datasets (pre- and post-operative) were aligned to the hg19 reference using Star (2-pass mode). Variant calling was applied using the Gatk toolkit with special filters (e.g., a variant is considered a variant only if confirmed by 5 independent reads) (McKenna, A. et al., Genome Res. 2010, vol. 20(9): 1297-1303, doi: 10.1101 / gr.107524.110 Variant annotation was performed using reftool.

[0157] Experimental Lnk / SH2B3 - / - Mouse model

[0158] Lnk / SH2B3 - / - Mouse strains were generated as described above (Takaki S. et al., J. Exp. Med. 2002; 195:151-160). C57BL / 6 mice (CLEA Japan, Tokyo, Japan) were used as WT control mice. Mice with a GFP transplant gene (GFP-Tg mouse; C57BL / 6TgN [act EGFP] Osb Y01) were used as Wt mice or Lnk / SH2B3 - / - For mouse mating and BM transplantation (BMT) studies, WT / GFP mice or, respectively Lnk / SH2B3 - / - / GFP Mice were created. All experimental procedures were performed in accordance with the guidelines of the Japanese Physiological Society for the care and use of experimental animals, and the research protocol was approved by the Ethics Committee of the RIKEN Center for Developmental Biology.

[0159] Induction of myocardial infarction (MI)

[0160] Mice aged 8 to 12 weeks were anesthetized by intraperitoneal injection of 400 mg / kg 2,2,2-tribromoethanol (Avertin; Sigma, St.Louis, MO). MI was induced by ligating the left descending (LAD) coronary artery as previously described (Fabregat A, Sidiropoulos K, Garapati P, et al. Nucleic Acids Res 2016, Jan 4; 44 (D1):D481-7. doi: 10.1093 / nar / gkv1351. Epub 2015 Dec 9.).

[0161] Tissue harvesting

[0162] When not examined, OCT of the heart TM Compound (Tissue-Tek ® After being embedded in liquid nitrogen, the samples were rapidly frozen and sectioned at 6 μm using a cryostat (Leica microsystems, Wetzlar, Germany). Total RNA for RT-PCR analysis was isolated from the LV myocardium by selective dissection of fibrotic and infarcted regions. For the BrdU integration study, 100 μL of 10 mg / mL BrdU solution (BD Pharmingen) was intraperitoneally injected into mice 16 hours prior to sacrifice.

[0163] RT-PCR analysis

[0164] Total RNA was obtained from cardiac tissue 3 days before and after MI using Trizol (Invitrogen) according to the manufacturer's instructions. First-strand cDNA was synthesized using the PrimeScript RT reagent kit (Takara Bio, Otsu, Japan) and amplified with AmpliTaq Gold DNA polymerase (Applied Biosystems, Foster City, CA). PCR was performed using a PCR thermal cycler (MJ Research PTC-225, Bio-Rad, Hercules, CA). Mouse Lnk / SH2B3 and β-actin were amplified under the following conditions: Lnk / SH2B3 was maintained at 94 °C for 30 seconds, 57 °C for 30 seconds, and 72 °C for 30 seconds, followed by 42 cycles, and finally maintained at 72 °C for 5 minutes; β-actin was subjected to 35 cycles of 30 seconds at 94 °C, 30 seconds at 57 °C, and 30 seconds at 72 °C, followed by final maintenance at 72 °C for 5 minutes. Subsequently, the PCR products were visualized on a 1.5% ethidium bromide-stained agarose gel using a 100-bp DNA Ladder (Invitrogen). Primer sequences are shown in Supplementary Table 1S.

[0165] Quantitative Real-time RT-PCR Analysis

[0166] Total RNA was obtained from KSL cells using the RNeasy Mini Kit (QIAGEN, Hilden, Germany) according to the manufacturer's procedure. After the first cDNA was synthesized, real-time quantitative RT-PCR was performed using the SYBR Green Master Mix reagent (Applied Biosystems) with an ABI Prism 7700 (Applied Biosystems). Primer sequences are shown in Supplementary Table 1S.

[0167] Statistical analysis

[0168] The results were statistically analyzed using the software package (Statview 5.0, Abacus Concepts Inc, Berkeley, CA). All values ​​were expressed as mean ± standard deviation (mean ± SD). Comparisons between three or more groups were performed using one-way analysis of variance (ANOVA) in Prism 4 (GraphPad Software, San Diego, CA). Post-hoc analysis was performed using Tukey's multiple comparison test. Differences of P < 0.05 were considered statistically significant.

[0169] Data analysis with machine learning

[0170] Key features and classifications of the comprehensive patient data were obtained using supervised and unsupervised ML algorithms (Steinhoff G, Nesteruk J, Wolfien M, et al. EBioMedicine 2017 Aug; 22:208-224. doi: 10.1016 / j.ebiom.2017.07.022. Epub 2017 Jul 29.). The inventors preprocessed the data by removing features with low variance and high correlation for dimension reduction in accordance with best practice recommendations. The inventors compared the following supervised algorithms: AdaBoost, Gradient Boosting (GB), Support Vector Machines (SVM), and Random Forest (RF) (Steinhoff G, Nesteruk J, Wolfien M, et al. EBioMedicine 2017 Aug; 22:208-224. doi: 10.1016 / j.ebiom.2017.07.022. Epub 2017 Jul 29.). To compare features with little training, the inventors used classifiers suitable for training on small datasets and selected the most suitable algorithm based on accuracy and robustness against overfitting (Al-Naqeb D. Scientifica (Cairo). 2016; 2016:2079704. doi:10.1155 / 2016 / 2079704. Epub 2016 May 30.). The supervised ML model was cross-validated tenfold and repeated 100 times. The inventors then applied feature selection to AdaBoost, GB, and RF classifiers to reduce the number of features to <20. The inventors used t-distributed stochastic neighbor embeddings (t-SNE) for unsupervised machine learning classification and non-linear dimensionality reduction (Steinhoff G, Nesteruk J, Wolfien M, et al.EBioMedicine 2017 Aug; 22:208-224. doi: 10.1016 / j.ebiom.2017.07.022. Epub 2017 Jul 29.).

[0171] Strengthening time course networks

[0172] Using a Systems Biology approach, the inventors aimed to elucidate time-dependent expression response patterns within the investigated patient datasets. The identification of enhanced signaling pathways was performed using Reactome Functional Interaction (RFI) and the BisoGenenet Cytoscape application, which was designed to find enhanced pathway and network patterns within a curated and experimentally validated database (Fabregat A, Sidiropoulos K, Garapati P, et al. Nucleic Acids Res 2016; Jan 4; 44 D1:D481-7. doi: 10.1093 / nar / gkv1351. Epub 2015 Dec 9.). Additionally, the inventors subsequently used TiCoNE (Time Course Network Enricher) for time course network enrichment analysis of patient-specific expression data. This tool recognizes temporal patterns appearing in expression data for a given interaction network by utilizing various mathematical algorithms, specifically the Split Marginal Medoid (PAM) and Clustering for Large Applications (CLARA) algorithms, which are k-medoid clustering approaches. The error rate (FDR) and p-values ​​were calculated using global permutations with 1,000 iterations. Pearson moment correlation (PPMC) was used to compare different clusters. Gene expression patterns within motifs are considered common for p-values ​​< 0.05.

[0173] Weighted Gene Co-expression Network Analysis (WGCNA) was performed by applying the R package "WGCNA" to RNAseq data. The inventors first constructed a Topological Overlap Matrix (TOM) of all investigated transcripts (~160,000) using a soft thresholding method. The inventors calculated the eigenvalues ​​of the transcripts and evaluated adjacency based on distance. The inventors applied hierarchical clustering (mean linkage) to the transcripts and assigned the transcripts to groups using a dynamic hybrid method. The inventors identified interaction partners ( k Connectivity was calculated based on ) and the significance of genes representing the result module membership was evaluated.

[0174] Extended gene expression analysis and validation in 37 patients

[0175] Peripheral blood (PB) whole-genome expression and circulating endothelial progenitor cell (EPC) analysis was Phase 3 PERFECT (CABG and CD133) in responders (△LVEF +16% day 180 / 0) and non-responders (NR, n = 9; △LVEF -1.1% day 180 / 0) and an independent center biomarker patient cohort (n = 14). + BMSC or placebo) was studied in the study biomarker subgroup (n=23) on days 0, 1, 3, and 10.

[0176] Left ventricular function recovery was accompanied by increased myocardial perfusion (R vs NR, p<0.05). Twenty biomarkers were identified for the preoperative prediction of cardiac regenerative capacity in PB (R / NR 95.6% accuracy, 91% AUC) (Fig. 3). These were identified based on ML clustering analysis, which revealed three distinct gene profile subgroups in R that differed from NR across a total of 700 genes in differential expression (n = 23, q < 0.05). Similarly, the identified SH2B3-associated co-expressed hub genes NOTCH2, MTOR, and KIT contained variants associated with their respective NR / R subgroups, primarily related to myocardial perfusion, △LVEF, and PB-CD133. + It was associated with EPC and △CT SH2B3 (p<0,01).

[0177] The invention described herein by way of example may be appropriately practiced without any configurations or configurations, limitations or restrictions that are not specifically disclosed herein. Accordingly, for example, in each example of this specification, any terms “comprising,” “essentially comprising,” and “comprising” may be replaced with one of the other two terms. The terms and expressions included are used for the purposes of description and are not limited thereto, and there is no intention to exclude any equivalents of the features or parts thereof illustrated and described in the use of such terms and expressions, but it has been recognized that various modifications are possible within the scope of the claimed invention. Accordingly, the invention is specifically disclosed by preferred embodiments, and optional features, modifications, and variations of the concepts disclosed herein may be made by those skilled in the art, and such modifications and variations are considered to be within the scope of the invention as defined by the appended claims.

[0178] All references cited in this specification are incorporated herein by reference in connection with their entire disclosure and the disclosures specifically mentioned in this specification.

[0179] Abbreviation Code

[0180] ACE = Angiotensin Converting Enzyme

[0181] AE = Adverse Event

[0182] AESI = Adverse Event of Special Interest

[0183] AFAP1 = Actin Filament Associated Protein 1

[0184] AHA = American Heart Association

[0185] ANCOVA = Analysis of Covariance

[0186] ANOVA = Analysis of Variances

[0187] Ang-1 = Angiopoietin 1

[0188] AP1B1 = Adapter Related Protein Complex 1 Subunit Beta 1

[0189] ASS = Acetylsalicylic Acid

[0190] ATII = Angiotensin II

[0191] AUC = Area Under the Curve

[0192] BAZ1A = Bromodomain adjacent to zinc finger domain protein 1A

[0193] BCIP = 5-Bromo-4chloro-3-indolyl-phosphate

[0194] BEX3 = Brain Expressed X-Linked 3

[0195] BM = Bone Marrow

[0196] BMSC = Bone Marrow Stem Cells

[0197] CABG = Coronary Artery Bypass Graft

[0198] CAP-EPC = Concentrated Ambient Particles - Endothelial Progenitor Cells

[0199] CBA = Cytometric Bead Array

[0200] CCS = Canadian Cardiovascular Society

[0201] CCTRN = Cardiovascular Cell Therapy Research Network

[0202] CD = Cluster of Differentiation

[0203] CEC = Circulating endothelial cells, CEC panel, CDs measured in PB

[0204] CFU = Colony-forming unit

[0205] CI = Confidence interval

[0206] CMV = Cytomegalovirus

[0207] CXCL12 = CXC Motif Chemokine 12

[0208] DAB = Diamino Benzidine

[0209] DELFIA = Dissociation Enhanced Lanthanide Fluorescent Immunoassay

[0210] Delta_CT_SH2B3 (CT SH2B3) = Normalized value of SH2B3 gene expression (Normalized vs. Housekeeping gene glycerin aldehyde-3-phosphate dehydrogenase (GAPDH))

[0211] EA = Early Antigen

[0212] EC = Endothelial Cells

[0213] ECG = Electrocardiography

[0214] ECLIA = Electrochemiluminescence Sandwich Immunoassay

[0215] EDTA = Ethylenediaminetetraacetic Acid

[0216] ELISA = Enzyme-Linked Immunosorbent Assay

[0217] EMG1 = EMG1 N1-Specific Pseudouridine Methyltransferase

[0218] EPC = Endothelial Progenitor Cells, EPC panel, CDs measured in PB

[0219] EPO = Erythropoietin

[0220] FGF = Fibroblast Growth Factor

[0221] GFP = Green Fluorescence Protein

[0222] GRB2 = Growth factor receptor-bound protein 2

[0223] GMP = Good Manufacturing Practice

[0224] HA = Haemagglutinin

[0225] HGF = Hepatocyte Growth Factor

[0226] HR = Hazard ratio

[0227] HIF = Hypoxia-Inducible Factor, Transcription Factor

[0228] ICH GCP = Tripartite Guidelines for Good Clinical Practice

[0229] IGF-1 = Insulin-like Growth Factor 1

[0230] IHG = Analysis performed in accordance with ISHAGE guidelines

[0231] IL = Interleukin

[0232] KLF8 = Kruppel Like Factor 8

[0233] LMCA = Left Main Coronary Artery

[0234] LPCAT2 = Lysophosphatidylcholine Acyltransferase 2

[0235] LTB = Lymphotoxin-beta

[0236] LVEDV = Left Ventricular End Diastolic Volume

[0237] LVEF = Left Ventricular Ejection Fraction

[0238] LVESD = Left Ventricular End Systolic Dimension

[0239] MACE = Major Adverse Cardiovascular Events

[0240] MARK3 = Microtubule Affinity Regulating Kinase 3

[0241] MIBI SPECT = Methoxy Iso Butyl Isonitrile SPECT

[0242] ML = Machine learning

[0243] MNC = Mononuclear cells

[0244] MRI = Magnetic Resonance Imaging

[0245] MS = Mass Spectrometry

[0246] 6MWT = 6-Minute Walk Test

[0247] NBT = 4 Nitro Blue Tetrazolium

[0248] NGS = Next Generation Sequencing

[0249] NMR = Nuclear Magnetic Resonance

[0250] NYHA = New York Heart Association

[0251] PB = Peripheral blood

[0252] PBMNC = mononuclear cells isolated from peripheral blood

[0253] PCI = Percutaneous Coronary Intervention

[0254] PDGFRB = Platelet Derived Growth Factor Receptor Beta

[0255] PEI = Paul-Ehrlich Institute

[0256] PLCG1 = Phospholipase C Gamma 1

[0257] PPS = Group of patients for per-protocol set

[0258] REX1BD = Required For Excision 1-B Domain

[0259] ROC = Receiver Operating Characteristic

[0260] RT-RCR = Reverse Transcription Polymerase Chain Reaction

[0261] SACM1L = Staphylococcus aureus Cowan 1 phosphatidylinositide phosphatase

[0262] SAE = Serious adverse event

[0263] SAS = Group of patients for safety set

[0264] SDF-1 = Stromal Cell-derived Factor 1

[0265] SDS = Sodium Dodecyl Sulphate

[0266] SF = Scatter Factor

[0267] SH2B3 = SH2 Adapter Protein 3

[0268] SCF = Stem Cell Factor

[0269] SNP = Single Nucleotide Polymorphism

[0270] SPA = Scintillation Proximity Assay

[0271] STEMI = ST-segment Elevation Infarction

[0272] SUM = Support Vector Machines

[0273] SUSAR = Suspected Unexpected Serious Adverse Reaction

[0274] TNF = Tumor Necrosis Factor

[0275] t-SNE = t-distributed stochastic neighbor embedding

[0276] VAD = Ventricular Assist Device

[0277] VCA = Virus-Capsid-Antigen

[0278] VEGF = Vascular Endothelial Growth Factor

[0279] VEGF rec = Vascular Endothelial Growth Factor Receptor, ZNF205 = Zinc finger protein 205

[0280]

[0281]

[0282]

[0283]

[0284]

[0285]

[0286]

[0287]

Claims

Claim 1 A method for predicting the response to cardiovascular regeneration following stem cell therapy, wherein the method comprises the step of determining the SH2B3 gene or SH2B3 gene expression as a biomarker in a sample of an individual, wherein the SH2B3 gene includes a mutant variant, and the SH2B3 gene includes an SH2B3 gene variant 1 comprising mutations consisting of 127bp C>T(Arg43Cys), 232bp G>A(Glu78Lys), 557bp G>T(Ser186Ile), 784bp T>C(Trp262Arg), 1,180bp C>T, 1,236+24 / 28del TGGGG, 1,454bp-1,477 del(Asp485_Trp492 del), 1,553bp A>G, 1,628bp del T, and 1,643bp del T. A method for predicting a response to cardiovascular regeneration, comprising: (ii) a mutation selected from SH2B3 gene variant 2 comprising a mutation consisting of 17bp T>C(Leu6Pro); (iii) comparing the determined SH2B3 gene or SH2B3 gene expression with a reference value, a reference, or both; and (iii) predicting whether a response to cardiac recovery is expected, not expected, or ambiguous in the individual based on the comparison result. Claim 2 In claim 1, the method wherein the SH2B3-gene is an SH2B3 knockout-gene variant. Claim 3 The method of claim 1, wherein the method further utilizes the analysis of LNK protein expression or function of the SH2B3-gene. Claim 4 The method of claim 1, wherein the method further uses one or more biomarkers, said additional biomarkers are selected from the group consisting of angiogenesis factors, survival factors, chemokines, circulating endothelial progenitor cells (EPCs), circulating endothelial cells (CECs), circulating platelets, circulating mononuclear cells and their subpopulations, receptor / ligand expression in MNC subpopulations, and whole-genome sequence RNA. Claim 5 A method according to claim 4, wherein the additional biomarker is selected from the group consisting of PLCG1, EPO, LPCAT2, GRB2, AP1B1, AFAP1, KLF8, MARK3, REX1BD, SACM1L, PDGFRB, VEGF, BEX3, Delta_CT_SH2B3, ZNF205, LTB, EMG1, CD34+ cells / ml PB, BAZ1A, and CD133+ cells / ml PB. Claim 6 A method according to claim 1, wherein the sensitivity and specificity of the prediction accuracy are greater than 90%. Claim 7 A method according to claim 5, wherein the biomarker selected from the group consisting of PLCG1, LPCAT2, GRB2, AP1B1, AFAP1, KLF8, MARK3, REX1BD, SACM1L, PDGFRB, BEX3, ZNF205, EMG1, BAZ1A, and LTB is determined at the RNA level, and the biomarker selected from the group consisting of EPO and VEGF is determined at the protein level. Claim 8 The method of claim 1, wherein the method is used for preoperative prediction of one or more of the response to stem cell therapy; induction of angiogenesis response; and tissue recovery of a cardiovascular disease selected from the group consisting of myocardial infarction, stroke, peripheral ischemic vascular disease, heart disease and ischemic preconditioning. Claim 9 The method according to claim 1, wherein the sample is taken from an individual suffering from one or more of heart disease and arteriosclerosis. Claim 10 The method of claim 1, wherein the method comprises the step of profiling comparison results at least 2, 3, 4, 5, and 6 time points. Claim 11 The method of claim 1, wherein the method further comprises the use of clinical diagnostic parameters. Claim 12 In claim 1, the method is used for profiling angiogenic reactions. Claim 13 The method of claim 1 further comprises the step of analyzing one or more selected from a group consisting of RNA sequences, mRNA sequences, functional RNA, and SNPs including diagnostic signatures. Claim 14 The method of claim 1, wherein the method further comprises the analysis of pharmacokinetic and pharmacogenetic data using one or more selected from the group consisting of RNA sequence analysis, DNA sequence analysis, and network path analysis. Claim 15 In claim 1, the method further comprises phenotyping analysis. Claim 16 A method according to claim 1, wherein the stem cell therapy comprises the transplantation of CD133-positive stem cells. Claim 17 In paragraph 1, the above entity is a human, method. Claim 18 The method according to claim 1, wherein the sample is one or more selected from the group consisting of a blood sample, a serum sample, a plasma sample, a tissue biopsy sample, and a circulating stem cell sample. Claim 19 delete Claim 20 delete Claim 21 delete Claim 22 delete Claim 23 delete Claim 24 A method according to any one of claims 1 to 18, wherein the method is used for preoperative prediction of the response to stem cell therapy, and said stem cell therapy is accompanied by one or more selected from coronary artery bypass graft (CABG) surgery and ischemia-reperfusion intervention. Claim 25 delete Claim 26 A kit for performing the method of any one of claims 1 to 18, comprising a detector for determining the amount of each of the biomarkers in a sample of the subject. Claim 27 A computer device comprising a processor and a memory encoding one or more machine learning (ML) models connected to said processor, wherein a program causes said processor to perform the following methods: (i) comparing a determined amount of a biomarker according to the method of any one of claims 1 to 7 with a reference value, a reference, or both; (ii) predicting, based on the result of the comparison, whether a response to cardiovascular regeneration following stem cell therapy in said individual is expected, not expected, or ambiguous. Claim 28 An apparatus for performing a method according to any one of claims 1 to 18, comprising: (i) an analysis unit for determining the amount of each biomarker determined by the method in a sample of an individual; and (ii) a memory encoding a processor and one or more machine learning (ML) models connected to the processor, wherein a program enables the processor to perform a method comprising: (a) comparing the amount of the determined biomarker with a reference value, a reference, or both; and (b) predicting, based on the result of the comparison, whether a response to cardiovascular regeneration in the individual is expected, not expected, or ambiguous. Claim 29 delete Claim 30 delete

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