Method for predicting the response to a bipolar disorder treatement

Abstract

The invention relates to the field of Bipolar Disorder. In a recent study, the inventors performed the first genome-wide analysis of DNA methylation profiles among individuals with BD type I assessed for their response to long-term treatment with Li and identified differentially methylated regions (DMRs) between GR and NR, selecting only methylation differences that were not impacted by co-prescribed treatments that are -by definition- more frequent in NR (Marie-Claire et al., 2020). They then used MS-HRM to validate 3 DMRs that discriminated GR from NR in an extended sample of 70 individuals with BD-I. They also showed, in an independent sample that combining a few clinical variables with the three DMRs improved the performance of these MS-HRM-based biomarkers to discriminate groups (Marie-Claire et al., 2023). In the present study, they improved the performance of the combination of MS-HRM- based biomarkers and clinical predictors to discriminate individuals that benefited from Li treatment (GR and PaR) from those who did not (NR) in a retrospective sample. Thus, the present invention relates to a method for predicting whether a patient achieves a response with a treatment of Bipolar Disorder (BD) comprising determining, in a biological sample from the patient the epigenetic profile of at least 4 of the differentially methylated regions (DMRs) selected in the list consisting of DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888, DMR106540 and DMR81023.

Description

[0001] METHOD FOR PREDICTING THE RESPONSE TO A BIPOLAR DISORDER

[0002] TREATMENT

[0003] FIELD OF THE INVENTION:

[0004] The invention relates to a method for predicting whether a patient achieves a response with a treatment of Bipolar Disorder (BD) comprising determining, in a biological sample from the patient the epigenetic profile of at least 4 of the differentially methylated regions (DMRs) selected in the list consisting of DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888, DMR106540 and DMR81023.

[0005] BACKGROUND OF THE INVENTION:

[0006] Bipolar disorder (BD) is a prevalent and severe psychiatric disorder characterized by the recurrence and altemance of mood episodes: (hypo)-mania and major depression (Zhou et al., 2021), interspersed by periods of normothymia. BD affects more than 1% of the general population and is one of the most burdensome disorders worldwide in terms of disability- adjusted life-year and is the 6th leading cause of disability in the world (Collins et al., 2011; Colombo et al., 2012; Grande et al., 2016).

[0007] Lithium (Li) is the first-line prophylactic treatment for BD, not only for preventing mood episode relapses and recurrences of the different polarities (Geddes et al., 2004), treating acute manic episodes, but also in order to decrease suicidal risk (Tondo et al., 2021). After two consecutive years of treatment with Li, only a fraction of individuals display significant improvement in the frequency and / or severity of mood recurrences and three subpopulations, each representing about a third of individuals have been described, the Good, the Partial and the Non-Responders (respectively GR, PaR and NR) (Manchia et al., 2013). This interindividual variability of response could be determined by genetic factors still misidentified by available genetic studies (Senner et al., 2021). Several clinical variables have been suggested to be associated with good or poor response to Li in the literature (Grillault Laroche et al., 2020; Hui et al., 2019). However, these studies are heterogeneous and only one factor was robustly associated with Li non-response, i.e. higher lifetime number of hospitalizations (Grillault Laroche et al., 2020). Clinical characteristics might therefore appear insufficient -when use alone- to reliably define eligibility criteria for a Li treatment in BD. The identification of biological markers associated with response to Li is an important first step towards a personalized medicine. Epigenetic biomarkers appear particularly promising in this context.

[0008] SUMMARY OF THE INVENTION:

[0009] In a recent study, the inventors performed the first genome-wide analysis of DNA methylation profiles among individuals with BD type I assessed for their response to long-term treatment with Li and identified differentially methylated regions (DMRs) between GR and NR, selecting only methylation differences that were not impacted by co-prescribed treatments that are -by definition- more frequent in NR (Marie-Claire et al., 2020). They then used Methylation Specific High-Resolution Melting (MS-HRM), a PCR based method that can be implemented in any medical laboratory at low cost and minimal equipment, to validate 3 DMRs that discriminated GR from NR in an extended sample of 70 individuals with BD-I (Marie- Claire et al., 2022). They also showed, in an independent sample that combining a few clinical variables with the three DMRs improved the performance of these MS-HRM-based biomarkers to discriminate groups (Marie-Claire et al., 2023).

[0010] In the present study, they improved the performance of the combination of MS-HRM- based biomarkers and clinical predictors to discriminate individuals that benefited from Li treatment (GR and PaR) from those who did not (NR) in a retrospective sample.

[0011] Thus, the present invention relates to a method for predicting whether a patient achieves a response with a treatment of Bipolar Disorder (BD) comprising determining, in a biological sample from the patient the epigenetic profile of at least 4 of the differentially methylated regions (DMRs) selected in the list consisting of DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888, DMR106540 and DMR81023.

[0012] Particularly, the invention is described by its claims.

[0013] DETAILED DESCRIPTION OF THE INVENTION:

[0014] A first aspect of the invention relates to a method for predicting whether a patient achieves a response with a treatment of Bipolar Disorder (BD) comprising determining, in a biological sample from the patient the epigenetic profile of at least 4 of the differentially methylated regions (DMRs) selected in the list consisting of DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888, DMR106540 and DMR81023.

[0015] In a particular embodiment, the response to the BD treatment is good (or total) or partial. Thus, when the response is good (or total) the patient a response or a good responder (GR) and when the response is partial, the patient is a partial-responder (PR or PaR).

[0016] Accordingly to the invention, the term “partial-responder (PR or PaR)” refers to a patient for whom the treatment will improve symptoms, prevent relapses and improve the quality of life at least partial or at least for one of these criteria.

[0017] As used herein, the term “epigenetic profile of the differentially methylated regions (DMRs) selected in the list consisting of DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888, DMR106540 and DMR81023” denotes all modifications of these DMR other than modification in the DNA sequence. Modifications which can affect the epigenetic profile of these DMR can be methylation (mono, bi or tri for example) anywhere on the sequence of the DMR. Particularly, the modification can be at least one methylation in more or in less on the DMR.

[0018] Particularly, the DMR7291 is located on chrl :92012425:92012845 and its nucleic acid sequence is (SEQ ID NO: 1): cgcgtggggccactttggtataggtgggaattaagccggggagggaggagtaggtcggaacagtttgcagctggcggctc gctgattgggtgttattttacagctgggcagaaatgaccgcggtagcactcctggggaggcctctaaagctgcagacacgatgtgtcgc gggaccatccagttctggccccgtgcaggaggacaggacgccgtctggtcgctgccctgggcgctcaacatgggagctgccagag gagagcgccctgtgagtgcctgacaaatggaggtatttctttccaggttttacaacccgatgtattttcagggaggaccttcctaggtacct ccacttctcagtctctcaactcagcagcctccatccatttccacgaatccgctggactcgctcctgcaaaacacg

[0019] Particularly, the DMR17107 is located on chr2:74642725:74642935 and its nucleic acid sequence is (SEQ ID NO: 2): ccggtccacgcccccagggtcctggggaaatgcactcgggcagtgagatgtcggaaaggggagagcaggagcaatgg cagaggagtccggagaggctcctgaggagtgtacttcgccttggaagttctccaggccctccgaggtggacgcgtgcagcacgggc actgaaccctgagcccaggagtccgtcgtgcatgccgaaggctcctcg

[0020] Particularly, the DMR17978 is located on chr chr2:98377790:98378036 and its nucleic acid sequence is (SEQ ID NO: 3): gcggggtggctcccccagcctcagtggttactgcctcttgggcaacggcggggaggacctgaccctaggctggccggtg acgcgatgcacagaaacccacgccacggcgaggcacacggtgagagtgccatccgtgactgtcaggcttgcggtacactgtggcag gcggacacgagtgaggctccgtgcctcggagaacagcatgcttcctgcatcctgtctgtgtggtcaggaggctgccctcccg

[0021] Particularly, the DMR24332 is located on chr2:242904658:242904829 and its nucleic acid sequence is (SEQ ID NO: 4): acgcaactttcatctctgctaaatgtttaattgtaaagcatgagtctgacctaaaaacaagtgtgcccgcagaagtgaggcgg cacgcccgttactcctcacgcaggaagcgcagcaatgaaacaaaacgccgtgcgtttaacgcttcggtttcttgattttgagatgaccg

[0022] Particularly, the DMR63769 is located on chr7: 158904946: 158905088 and its nucleic acid sequence is (SEQ ID NO: 5): ccgcacgtgagggagcagagctagacgctgtcaggatgtgaggaagcggactcgagggcgtcagggcgccgaggctg aggtagcagccggaagtgcagccgaaggcgcagccggaagcgctgccacagtgccggtcgcacacg

[0023] Particularly, the DMR79888 is located on chrlO: 124638668: 124638931 and its nucleic acid sequence is (SEQ ID NO: 6): acgagggcatccagcatagtcctggcaacgccgtaggatcagggagcatcaaatgagatggcacctggggaaggcccgt gtggcccagcgtccaccactaacgccgttcaatatctacctcaactcccctctaacgcgagaatgtgtatgagagaggcagggagagtt cggagtaaaattaacccgaatggacagcgcttccgccgcgttcgctgccacaaatgctgcagacccgacggagtggaggcagaaaa ggccctggcg

[0024] Particularly, the DMR106540 is located on chrl5:39871984:39872187 and its nucleic acid sequence is (SEQ ID NO: 7): ccgtgctgagatccttatttggtcaagcttctacctatgccctggcctcggagcgagcccgatagcgctggatcacagcaga gggagcgaggcggctgacgtcccatcccgaagagatgaatggaattccaggaagctagagtcatgctggcttgggacagtggcttg gagaccagacttcaatgacagaagcactaggcagcg

[0025] Particularly, the DMR81023 is located on chrl0:134045488: 134045630 and its nucleic acid sequence is (SEQ ID NO: 8): acggaacttggttgtggcgggaaactcgctttctccacgcctcctgtgtctctgcatccctgataacaccctgcagactctgag cacctgcggccacgcctgcaggcagcagcctcacctgcgctcacgctgcgccctcaccg

[0026] Thus, and according to the invention, the DMR methylation profile is determine on at least 4 DMRs of SEQ ID NO: 1, 2, 3, 4, 5, 6, 7 and 8. Particularly, the DMR methylation profile is determine on the SEQ ID NO: 1, 2, 3, 4, 5, 6, 7 and 8. According to the invention, the level of methylation can be determined all-over the entire sequence. According to the invention, “the level of methylation” means the percentage of methylation which affect the DMR of the invention. At least one change of methylation (in more or in less) in the sequence of a DMR means that an epigenetic modification has been realized.

[0027] Thus, in a particular embodiment, the invention relates to a method for predicting whether a patient achieves a response with a treatment of Bipolar Disorder (BD), comprising the steps of: i) determining, in a sample obtained from the patient if at least 4 of the differentially methylated regions (DMRs) selected in list consisting of DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888, DMR106540 and DMR81023 are differentially methylated compared to a predetermined reference value. ii) concluding that the patient achieves or not achieves a response to the treatment of BD according to the level of DNA methylation of the DMR.

[0028] In another particular embodiment, the invention relates to a method for predicting whether a patient achieves a response with a treatment of Bipolar Disorder (BD), comprising the steps of: i) determining, in a sample obtained from the patient if at least 4 of the differentially methylated regions (DMRs) selected in the list consisting of DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888, DMR106540 or DMR81023 are differentially methylated compared to a predetermined reference value; ii) concluding that the patient benefits from the treatment of BD when the level of DNA methylation is decreasing for the DMR24332, DMR17978 or DMR63769 or increasing for the DMR7291, DMR79888, DMR81023, DMR17107 or DMR106540 or concluding that the patient does not achieve a response to the treatment of BD when the level of DNA methylation is increasing for the DMR24332, DMR17978 or DMR63769 or decreasing for the DMR7291, DMR79888, DMR81023, DMR17107 or DMR106540.

[0029] According to the invention, the terms “level of DNA methylation” means the total percentage of methylation on the DMR. Indeed, as explained above, the DMR can be methylated in different place on its nucleic sequence. Thus, the level (percentage) of methylation on the DMR can vary in function of the response of the patient to the treatment of BD.

[0030] According to the invention the "reference value” (or “cut-off value) is the level (or percentage) of DNA methylation level of one of the DMR of the invention determined in a biological sample of a patient before the BD treatment. Preferably, said normal level of DNA methylation is assessed in a control sample (e.g., sample from a healthy patient, which is not afflicted by a BD or from a patient afflicted by a BD which respond or not to an anti-BD treatment). In a particular embodiment and according to the method of the invention, the level of methylation of 4, 5, 6, 7 or 8 differentially methylated regions (DMRs) selected in the list consisting of DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888, DMR106540 and DMR81023 are determined.

[0031] In a particular embodiment and according to the method of the invention, the levels of methylation of the 3 DMRs selected from the group consisting of DMR17107, DMR24332 and DMR106540 are determined with a fourth DMRs selected in the list consisting of DMR7291, DMR17978, DMR63769, DMR79888 and DMR81023 are determined..

[0032] In a particular embodiment and according to the method of the invention, the levels of methylation of the 3 DMRs selected from the group consisting of DMR17107, DMR24332 and DMR1 06540 are determined with 1, 2, 3 or 4 DMRs selected in the list consisting of DMR7291, DMR17978, DMR63769, DMR79888 and DMR81023 are determined..

[0033] In a particular embodiment and according to the method of the invention, the levels of methylation of the 7 DMRs selected in the list consisting of DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888 and DMR106540 are determined.

[0034] In a particular embodiment and according to the method of the invention, the levels of methylation of the 8 DMRs selected in the list consisting of DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888, DMR106540 and DMR81023 are determined.

[0035] In a particular embodiment and according to the method of the invention, the level of methylation of 7 DMRs selected in the list consisting of DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888, DMR106540 and DMR81023 are determined.

[0036] According to the invention, the method of the invention is an in vitro method. According to the invention, the Bipolar Disorder is a Bipolar Disorder type I.

[0037] Several clinical factors such as family history of Bipolar disorder, manic polarity, psychotic symptoms, number of hospitalizations, polarity at onset, panic disorders, lithium (Li) prescribed as the 1st mood stabilizer, Age and Sex and lifetime alcohol misuse of the patients have been associated with Li response in BD (Grillaut Laroche et al, Socio-demographic and clinical predictors of outcome to long-term treatment with lithium in bipolar disorders: a systematic review of the contemporary literature and recommendations from the ISBD / IGSLI Task Force on treatment with lithium Int J Bipolar Disord. 2020 Dec 16;8(l):40. doi: 10.1186 / s40345-020-00203-3). Thus, in particular embodiment and according to the method of the invention, at least 1 clinical markers is further determined and combined with the epigenetic profile of at least 8 of the differentially methylated regions (DMRs) selected in the list consisting of DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888, DMR106540 and DMR81023 to predicts whether a patient achieves a response (good or partial) with a treatment of Bipolar Disorder (BD).

[0038] In particular embodiment and according to the method of the invention, at least one clinical markers selected in the list consisting of family history of Bipolar disorder, manic polarity, psychotic symptoms and lifetime alcohol misuse of the patient is further determined and combined with the epigenetic profile of at least 4 of the differentially methylated regions (DMRs) DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888, DMR1 06540 and DMR81023 to predicts whether a patient benefits from a treatment of Bipolar Disorder (BD).

[0039] In preferred embodiment and according to the method of the invention, at least one clinical markers selected in the list consisting of history of Bipolar disorder, manic polarity, psychotic symptoms number of hospitalizations, polarity at onset, panic disorders, lithium (Li) prescribed as the 1st mood stabilizer, Age and Sex and lifetime alcohol misuse of the patient is further determined and combined with the epigenetic profile of the differentially methylated regions (DMRs) of the invention to predicts whether a patient achieves a response with a treatment of Bipolar Disorder (BD).

[0040] In particular embodiments, 1, 2, 3, 4, 5 or 6 clinical marker selected in the list consisting of history of Bipolar disorder, manic polarity, psychotic symptoms number of hospitalizations, polarity at onset, panic disorders, lithium (Li) prescribed as the 1st mood stabilizer, Age and Sex and lifetime alcohol misuse of the patients are determined with the epigenetic profile of the differentially methylated regions (DMRs) of the invention.

[0041] In a particular embodiment, the 6 following clinical markers are determined with the epigenetic profile of the differentially methylated regions (DMRs) of the invention: number of hospitalizations, polarity at onset, panic disorders, Li prescribed as the 1st mood stabilizer, Age and Sex.

[0042] As used herein, the term “treatment of Bipolar Disorder (BD)” has its general meaning in the art and refers to molecule use to treat BD like antiepileptic drugs such as paraldehyde, stiripentol, phenobarbital, methylphenobarbital, barbexaclone, clobazam, clonazepam, clorazepate, diazepam, midazolam, lorazepam, potassium bromide, felbamate, carbamazepine, oxcarbazepine, eslicarbazepine actetate, vigabatrin, progabide, tiagabine, pregabalin, mirogabalin, gabapentin, ethotoin, phenytoin, mephenytoin, fosphenytoin, paramethadione, trimethadione, ethadione, beclamide, primidone, brivaracetam, etiracetam, levetiracetam, seletracetam, ethosuximide, phensuximide, mesuximide acetazolamide, sultiame, methazolamide, zonisamide, lamotrigine, pheneturide and topiramate, mood stabilizers like lithium, valproic acid, divalproex sodium, carbamazepine and lamotrigine, antidepressants like fluoxetine, citalopram, escitalopram, paroxetine, duloxetine, venlafaxine, mirtazapine, amitriptyline, clomipramine, dosulepin, imipramine, lofepramine, nortriptyline, trazodone and sertraline or antipsychotics like olanzapine, risperidone, quetiapine, aripiprazole, ziprasidone, haloperidol, cariprazine, lumateperone, lurasidone, and asenapine. Particularly, the first-line treatment of BD is the lithium.

[0043] Accordingly to the invention, the term “responder” or “good responder (GR)” refers to a patient for whom the treatment will improve symptoms, prevent relapses and improve the quality of life.

[0044] Accordingly to the invention, the term “non-responder (NR)” refers to a patient for whom the treatment will not impact the course of the disease.

[0045] Accordingly to the invention, the term “partial-responder (PR or PaR)” refers to a patient for whom the treatment will improve symptoms, prevent relapses and improve the quality of life at least partial or at least for one of these criteria.

[0046] To determine the status of a patient (GR, NR or PR), the "Alda scale" was used (see Grof et al., 2002). This scale is based on a retrospective assessment of prophylactic response to Li, considering overall response to the introduction of Li on a scale from 0 to 10, with accounting for confounders of (non-)response such as poor adherence and required comedications. Individuals with a total score > 7 were characterized as good responders (GR) and those with a total score < 3 were characterized as non-responders (NR), as previously described (Manchia et al., 2013). Patients with a total score between 4 and 6 were characterized as partial responders (PaR).

[0047] According to the invention, when a patient achieves a response with a treatment of Bipolar Disorder (BD) it means that the patient has a good or a partial response to the treatment.

[0048] According to the invention, “ achieving a response” or “benefiting from a treatment” means that the patient responses to the treatment and that he will be a good or partial responder. As used herein, the term “patient” refers to any mammals, such as a rodent, a feline, a canine, and a primate. Particularly, in the present invention, the term “patient” refers to a human suffering from a BD.

[0049] As used herein, the term "sample" refers to any substance of biological origin. Examples of samples includes, but are not limited to blood, saliva, urine, cerebrospinal fluids, or any of other biological fluids or tissues. As used herein "blood" includes whole blood, plasma, peripheral-blood, peripheral blood mononuclear cell (PBMC), lymph sample, serum, circulating cells, constituents, or any derivative of blood. In a particular embodiment, the sample includes nucleic acids. In another particular embodiment, the sample is a DNA sample.

[0050] In particular embodiments, the sample is previously obtained from the patient.

[0051] Methods for determining the DMR epigenetic profile (or methylation profile) are well known in the art bisulfite treatment-based methods followed either by enzymatic digestion, sequencing, next generation sequencing, polymerase chain or real-time polymerase chain (for a review of available methods see Halabian R. et al, 2021). Particularly, the methods for determining the DMR epigenetic profile can the SeqCapEpi or MS-HRM methods.

[0052] When the MS-HRM is used, the primers for the different DMR can be the primers of the Table A.

[0053] Table A: example of primers used for MS-HRM method.

[0054] In another aspect of the invention, a method for training a model for predicting whether a patient achieves a response with a treatment of Bipolar Disorder (BD), the method being implemented by a training device comprising a computer and a memory storing a training dataset, wherein the training dataset comprises data inputs obtained from a plurality of BD patients, each data input comprising an evaluation of the response of the patient to the treatment of BD by the Alda method, each data input being further associated to an indication of the level of DNA methylation of the DMR of the invention and the method comprises performing supervised training, over the training dataset, of a nonlinear binary classification model configured to receive as input the evaluation of the level of DNA methylation of the DMR of the invention, and to output a classification of said patient as achieving or not a response to a BD treatment.

[0055] Particularly, the DMRs are the DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888, DMR106540 and DMR81023. More particularly, at least 4 DMR are selected in the list consisting of DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888, DMR106540 and DMR81023.

[0056] In particular embodiment, each data input is further associated to clinical markers and the method comprises performing supervised training, over the training dataset, of a nonlinear binary classification model configured to receive as input the evaluation of the level of DNA methylation of the DMRs of the invention in combination with clinical markers of the patient, and to output a classification of said patient as benefiting or not from a BD treatment.

[0057] In particular embodiment, 1, 2, 3, 4, 5 or 6 clinical markers selected in the list consisting of history of Bipolar disorder, manic polarity, psychotic symptoms number of hospitalizations, polarity at onset, panic disorders, lithium (Li) prescribed as the 1st mood stabilizer, Age and Sex and lifetime alcohol misuse of the patients are further associated to each data input.

[0058] As used herein, the Alda method denotes the most used method in research studies to determine if a patient will achieve or not a response to BD treatment (see reference [5]).

[0059] According to the invention, the training device may comprise a computer, for instance a processor or microprocessor, including for instance a Computer Processing Unit CPU or a Graphical Processing Unit GPU. The training device may further include a memory, storing code instructions executed by the computer for implementing the training method, and also storing a training dataset. The memory also stores the parameters of the prediction model and their updates during training of the model.

[0060] The training dataset may comprise data inputs obtained from a plurality of BD patients, where each data input corresponds to the evaluation, from a patient, of the response of the patient to the treatment of BD by the Alda method associated to an indication of the level of DNA methylation of the DMR of the invention. The pre-defined set of evaluation of the response of the patient is thus the same for all patients and all evaluations of a given patient.

[0061] The method of the invention allows training a prediction model which is configured to determine whether a patient will achieve or not a response to a BD treatment.

[0062] The prediction model may be a non-linear binary classification model, for instance a Support- Vector Machine (SVM) model with a non-linear kernel, adapted to project the input data into a higher dimensional space where classification of data between the responder status can be performed. SVM models can be chosen because of their robustness for modelling complex data without any prior assumption under the underlying distribution. Moreover, SVM models do not require huge amounts of training data. In an embodiment, the classification model is a SVM model with a Gaussian kernel.

[0063] The methods of the present invention can also be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The algorithm can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device. Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry. To provide for interaction with a user, embodiments of the invention can be implemented on a computer having a display device, e.g., in non-limiting examples, a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. Accordingly, in some embodiments, the algorithm can be implemented in a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the invention, or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), e.g., the Internet. The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0064] Another object of the present invention is a computer-program product comprising code instructions for executing the method described above, when it is implemented by a computer.

[0065] Method for treating Bipolar Disorder

[0066] Thus, in a second aspect, the invention relates to a method for personalizing treatment of Bipolar Disorder in a patient in need thereof comprising administering a therapeutically effective amount of an anti-Bipolar Disorder treatment when the patient is identified as responder according to the invention or deciding not to administer this treatment to the patient if he is identified as a non-responder.

[0067] Indeed, the inventors, thanks to the method of the invention using the specific DMRs of the invention, are capable of evaluate if a patient will respond or not to (or benefit or not from) a specific anti-Bipolar Disorder treatment. Thus, if the patient is predicted as a non-responder, the specific anti-Bipolar Disorder treatment will not work and another treatment should be administered to the patient.

[0068] Accordingly, the anti-Bipolar Disorder treatment is antiepileptic drugs such as paraldehyde, stiripentol, phenobarbital, methylphenobarbital, barbexaclone, clobazam, clonazepam, clorazepate, diazepam, midazolam, lorazepam, potassium bromide, felbamate, carbamazepine, oxcarbazepine, eslicarbazepine actetate, vigabatrin, progabide, tiagabine, pregabalin, mirogabalin, gabapentin, ethotoin, phenytoin, mephenytoin, fosphenytoin, paramethadione, trimethadione, ethadione, beclamide, primidone, brivaracetam, etiracetam, levetiracetam, seletracetam, ethosuximide, phensuximide, mesuximide acetazolamide, sultiame, methazolamide, zonisamide, lamotrigine, pheneturide and topiramate, mood stabilizes like lithium, valproic acid and derivates, carbamazepine and lamotrigine, or antidepressants like fluoxetine, citalopram, escitalopram, paroxetine, duloxetine, venlafaxine, mirtazapine, amitriptyline, clomipramine, dosulepin, imipramine, lofepramine, nortriptyline, trazodone and sertraline or atypical antipsychotics like olanzapine, risperidone, quetiapine, aripiprazole, ziprasidone, lurasidone, haloperidol, cariprazine, lumateperone and asenapine. Particularly, the first-line treatment of BD is lithium.

[0069] According to the invention, when the patient will be identified as non-responder, another treatment than the first one given to the patient will be administrated.

[0070] As used herein, the term "treatment" or "treat" refer to both prophylactic or preventive treatment as well as curative or disease modifying treatment, including treatment of patients who are ill or have been diagnosed as suffering from a disease or medical condition. The treatment may be administered to a patient having a medical disorder in order to prevent the recurrences and other severe outcomes (here suicides, suicide attempts, and hospitalizations), reduce the severity of, or ameliorate one or more symptoms of a disorder or recurring disorder, or in order to prolong the survival of a patient beyond that expected in the absence of such treatment. By "therapeutic regimen" is meant the pattern of treatment of an illness, e.g., the pattern of dosing used during therapy. A therapeutic regimen may include an induction regimen and a maintenance regimen. The phrase "maintenance regimen" or "maintenance period" refers to a therapeutic regimen (or the portion of a therapeutic regimen) that is used for the maintenance of a patient during treatment of an illness, e.g., to keep the patient in remission for long periods of time (months or years). A maintenance regimen may employ continuous therapy (e.g., administering a drug at a regular intervals, e.g., daily, weekly, monthly, yearly, etc.) or intermittent therapy (e.g., interrupted treatment, intermittent treatment, treatment at relapse, or treatment upon achievement of a particular predetermined criteria [e.g., disease manifestation, etc.]).

[0071] As used herein, the term "therapeutically effective amount" refers to an amount effective, at dosages and for periods of time necessary, to achieve a desired therapeutic result. A therapeutically effective amount of the anti-bipolar treatment of the present invention may vary according to factors such as the disease state, age, sex, and weight of the individual, and the ability of the treatment of the present invention to elicit a desired response in the individual. A therapeutically effective amount is also one in which any toxic or detrimental effects of the treatment are outweighed by the therapeutically beneficial effects. A physician having ordinary skill in the art may readily determine and prescribe the effective amount of the pharmaceutical composition required. For example, the physician could start doses of Bipolar Disorder treatment at levels lower than that required achieving the desired therapeutic effect and gradually increasing the dosage until the desired effect is achieved or the plasmatic level reached a target level (therapeutical zone). In general, a suitable dose of a treatment will be that amount of the compound, which is the lowest dose effective to produce a therapeutic effect according to a particular dosage regimen. Such an effective dose will generally depend upon the factors described above. For example, a therapeutically effective amount for therapeutic use may be measured by its ability to prevent recurrences of mood episodes. A therapeutically effective amount of a therapeutic compound may decrease the risk of a mood recurrence or otherwise ameliorate symptoms in a patient. One of ordinary skill in the art would be able to determine such amounts based on such factors as the patient’s clinical history, the severity of the patient’s symptoms, and the particular composition or route of administration selected. Dosage regimens in the above methods of treatment and uses are adjusted to provide the optimum desired response (e.g., a therapeutic response). For example, extended release or immediate release may be administered, several divided doses may be administered over time or the dose may be proportionally reduced or increased as indicated by the exigencies of the therapeutic situation. In some embodiments, the efficacy of the treatment is monitored during the therapy, e.g. at predefined points in time.

[0072] A third aspect of the invention relates to a therapeutic composition comprising an anti- Bipolar Disorder treatment for use in the treatment of a Bipolar Disorder in a patient identified as responder (good or partial) to said anti-Bipolar Disorder treatment as described above.

[0073] Any therapeutic agent of the invention may be combined with pharmaceutically acceptable excipients, and optionally sustained-release matrices, such as biodegradable polymers, to form therapeutic compositions.

[0074] "Pharmaceutically" or "pharmaceutically acceptable" refers to molecular entities and compositions that do not produce an adverse, allergic or other untoward reaction when administered to a mammal, especially a human, as appropriate. A pharmaceutically acceptable carrier or excipient refers to a non-toxic solid, semi-solid or liquid filler, diluent, encapsulating material or formulation auxiliary of any type.

[0075] The form of the pharmaceutical compositions, the route of administration, the dosage and the regimen naturally depend upon the condition to be treated, the severity of the illness, the age, weight, and sex of the patient, etc.

[0076] The pharmaceutical compositions of the invention can be formulated for an oral, administration and the like.

[0077] Particularly, the pharmaceutical compositions contain vehicles which are pharmaceutically acceptable for a formulation capable of being injected. These may be in particular isotonic, sterile, saline solutions (monosodium or disodium phosphate, sodium, potassium, calcium or magnesium chloride and the like or mixtures of such salts), or dry, especially freeze-dried compositions which upon addition, depending on the case, of sterilized water or physiological saline, permit the constitution of injectable solutions.

[0078] The doses used for the administration can be adapted as a function of various parameters, and in particular as a function of the mode of administration used, of the relevant pathology, or alternatively of the desired duration of treatment.

[0079] In addition, other pharmaceutically acceptable forms include, e.g. tablets or other solids for oral administration; time release capsules; and any other form currently can be used.

[0080] The invention will be further illustrated by the following figures and examples. However, these examples and figures should not be interpreted in any way as limiting the scope of the present invention.

[0081] FIGURES:

[0082] Figure 1: Flowchart of the selection process of new MS-HRM assays of DMRs.

[0083] Figure 2: Raw melt curves (in triplicates) for 100% methylated (M) and 0% methylated (UM) bisulfite treated DNA standards. (A) DMR7291, (B) DMR17978, (C) DMR79888, (D) DMR63769, (E) DMR81023.

[0084] Figure 3: Receiver operating characteristics (ROC) curves of the previously published MS-HRM assay with 3 DMRs and the optimized MS-HRM assay with 7 DMRs to identify GR+PaR vs NR individuals.

[0085] Figure 4: Receiver operating characteristics (ROC) curves of the identification of GR+PaR vs NR individuals using 6 clinical variables alone or combined with the percentage of methylation of 7 DMRs.

[0086] 5 Table 1: Socio-demographic and clinical characteristics of the sample (N = 61).

[0087] Table 2: Backward stepwise regression analysis on clinical variables

[0088] Table 3: selection criteria of the DMRs.

[0089] EXAMPLE:

[0090] Material & Methods Samples.

[0091] Participants in this study is an extended sample of the previous publication (REF). The samples consisted of 61 euthymic individuals with a diagnosis of BD-I, according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM IV) criteria. Participants were recruited in a university-affiliated psychiatric department in France. In the context of this study, the inclusion criteria were as follow: Caucasian origin, age between 18 and 55 years, scores below 10 at the Montgomery -Asberg depression rating scale (Montgomery and Asberg, 1979) et below 12 at the Young Mania Rating Scale (Young et al., 1978) at inclusion. The SCID (Structured Clinical Interview for DSM Disorders) was used to confirm the diagnosis of BD. Written informed consent was obtained from all participants. This research protocol

[0092] (Clinical Trials Number NCT02627404) was approved by the French medical ethics committee (Comite de Protection des Personnes (CPP) — IDRCB2008 A01465 50 VI - Pitie-Salpetriere 118-08) and carried out according to the approved guidelines.

[0093] Phenotype of response to Lithium (Alda scale). Response to Li was rated using the "Alda scale": the "Retrospective Criteria of Long- Term Treatment Response in Research Subjects with Bipolar Disorder" (Grof et al., 2002). This scale is based on a retrospective assessment of prophylactic response to Li, considering overall response to the introduction of Li on a scale from 0 to 10, with accounting for confounders of (non-)response such as poor adherence and required comedications. Individuals with a total score > 7 were characterized as good responders (GR) and those with a total score < 3 were characterized as non-responders (NR), as previously described (Manchia et al., 2013). Patients with a total score between 3 and 7 were characterized as partial responders (PaR).

[0094] DNA isolation.

[0095] A blood sample was collected at inclusion and whole blood DNA was extracted using the automated Maxwell 16 DNA Purification Instrument and the dedicated Maxwell® RSC Blood DNA Kit (Promega). All DNA samples were stored at -80°C.

[0096] Targeted bisulfite sequencing and selection of DMRs of interest.

[0097] Targeted bisulfite sequencing was performed by the Diagenode DNA methylation service (Diagenode). Briefly, DNA concentration of the samples was measured using Qubit® dsDNA BR Assay Kit (Thermo Fisher Scientific). DNA quality of the samples was assessed with the Fragment AnalyzerTM and the DNF-488 High Sensitivity genomic DNA Analysis Kit (Agilent). Based on the previously identified 66 DMRs associated with the response to Li in BD-I individuals, independently of the co-medication status (atypical antipsychotics, antidepressants, anticonvulsants and number of psychotropic drugs) but also current Li treatment (Marie-Claire et al., 2020), a custom panel was created (Qiagen). The 161 custom primers covered 82.2% of the requested CpGs. The EpiTect Fast Bisulfite Conversion kit (Qiagen) was used for DNA Bisulfite Conversion. Bisulfite converted DNA was used for subsequent end repair, adapter ligation, target PCR enrichment for 8 cycles and amplification of 19 cycles using the QIA-seq Targeted Methyl Panel. Libraries were purified with Qiaseq beads after ligation, target enrichment and amplification. The quality and quantity of the libraries was assessed with the Fragment AnalyzerTM and the DNF-488 High Sensitivity genomic DNA Analysis Kit (Agilent). Libraries were pooled for sequencing in an equimolar way. The pooled libraries were sequenced on a Miseq platform (Illumina) using 150bp paired- end sequencing with the MiSeq Reagent kit v2 (300-cycles). In order to select DMRs predicting Li response status, the Alda score divided in 3 groups were used: patients were divided in GR, PaR or NR. Then, a Kruskall-Wallis statistical test was performed in order to determine the significant associations (p<0.05) between, the assigned groups and the methylation percentage for each (Table 3). Methylation Sensitive High-Resolution Melting (MS-HRM).

[0098] Primers were designed for each of the DMRs selected after targeted bisulfite sequencing and statistical comparisons between groups. Due to technical issues, notably during the primers design, some DMRs had to be removed. A list of 7 DMRs were then obtained: 4 DMRs added to the 3 ones previously described and tested in our laboratory (Marie-Claire et al., 2023, 2022). Whole blood DNA samples were treated with sodium bisulfite, using the EZ DNA methylation kit (Zymo Research, CA, USA). Human methylated and unmethylated DNA standards (Milipore) were diluted before bisulfite conversion to obtain 0, 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100% methylated and unmethylated ratios. Bisulfite modified DNA was quantified using a NanoDrop spectrophotometer. The primer design was performed using the Bisearch online tool (http: / / bisearch.enzim.hu / ), as previously described (Marie-Claire et al., 2022). Methylation Sensitive High-Resolution Melting (MS-HRM) (Sestakova et al., 2019) PCR reactions were performed on a CFX384 Touch Real-Time PCR Detection System (Biorad Laboratories) in a final volume of 10 pL: 200nM of each primer, 5pL of Precision Melt Supermix (Biorad Laboratories) and 10 ng of bisulfite treated DNA. The first step was the initial denaturation (95°C during 3 min), followed by 45 cycles of 10 seconds at 95°C, 30 seconds at 50°C, 30 seconds at 72°C. The HRM step consisted of a denaturation of all the products at 95°C for 30 seconds, with an annealing at 60°C for 1 min. Samples were then warmed, slowly, to 95°C at 0.2°C per second, holding for 10 seconds after each stepwise increment and fluorescence data were collected. Each sample was analyzed in triplicate. Peak-heights were calculated with the CFX Maestro Software (Version 2.2, Bio-Rad Laboratories, Inc., Hercules, CA, USA). Linear curves of the peak-heights of the temperature melting (Tm) first derivative of HRM curves against the methylation percentage of the standard were plotted (Tse et al., 2011). The individuals performing the experiments were blinded to response status of the participants.

[0099] Statistical analysis.

[0100] All statistical analyses were performed using R version 4.2.1 (www.r-project.org), and the JASP software. Non-parametric Kruskall-Wallis test were used to analyze the percentage of methylation between NR, PaR and GR to select the DMRs to be tested using MS-HRM. We used logistic regression analyses with a comparison of GR+PaR versus NR for the DMRs, the clinical variables, alone or in combination. The pROC and ROCR R package were used in order to create and compare ROC curves (Robin et al., 2011; Sing et al., 2005). We performed Receiver Operating Characteristic (ROC) curve analysis with findings reported as the estimated Area Under the Curve (AUC). The following available variables were included in the analyses: age, sex, cigarette smoking status, lifetime number of hospitalizations, age at onset of BD, polarity at onset, psychotic symptoms at onset, family history of BD, lifetime alcohol misuse, lifetime cannabis misuse, lifetime panic disorders, Li prescribed as the first mood stabilizer (vs 2nd or 3rd choice, etc.). The following clinical variables were not considered due to the redundancy with some items of the Alda scale: number and frequency of episodes. Analyses were performed among individuals with BD-I first, for the 3 and then 7 DMRs, then in combination with clinical variables. A p-value < 0.05 was considered statistically significant. We applied no correction for multiple testing.

[0101] Results

[0102] Sample description.

[0103] The sample consists in 61 individuals with BD-I. Eighteen individuals are NR, 24 are GR and 19 are PaR. Only the number of hospitalizations in the lifetime is statistically significant between the GR+PR and the NR groups (p=0.038). All the other clinical variables are not statistically different between the two response groups. The clinical variables are presented in Table 1.

[0104] MS-HRM assay.

[0105] The selection process of the DMRs is described in f igure 1. Among the 63 DMRs not previously validated using MS-HRM, 57 could be sequenced using bisulfite targeted sequencing. Among these 57 DMRs, 13 DMRs displayed differences using Kruskall-Wallis tests. Among these DMRs, primers were not possible to design due to GC rich sequences or too short length. Primers were designable and satisfactory results were obtained for 4 DMRs (DMR 7291, DMR 17987, DMR 63769 and DMR 79888). The raw first derivative of the HRM curves obtained for the 0% (unmethylated) and 100% (fully methylated) DNA standards are presented in Figure 2. Peak-heights were used to generate the standard curves for determination of percentage of methylation for the seven DMR analyzed. These 4 DMRs were added to the 3 previously validated DMR (DMR 17107, DMR 24332 and DMR 106540) in order to analyze all the patients using the 7 DMRs MS-HRM assays.

[0106] The selected DMRs were tested using MS-HRM. Technically satisfactory results were obtained for seven of the tested DMR. The raw first derivative of the HRM curves obtained for the 0% (unmethylated) and 100% (fully methylated) DNA standards are presented in f igure 2. Peak-heights were used to generate the standard curves for determination of percentage of methylation for the seven DMR analyzed. Performance of the MS-HRM assays in individuals with BD-I,

[0107] Logistic regression analyses were performed to estimate AUC and percentage of GR+PR and NR correctly classified with the DMRs using MS-HRM. First, we tested the performance of the 3 previously identified DMR: AUC=0.678, with 97.67% of the GR+PR and 16.67% of the NR individuals correctly classified, the optimized 7 DMRs MS-HRM assays led to an AUC = 0.797 (Figure 3), with a correct classification as GR+PR or NR of 75.41% . The AUC obtained with 7 DMRs was not significantly different from that obtained with 3 DMRs (DeLong test p-value: 0.1104), but show an improvement in NR classification.

[0108] Selection of clinical variables using Backward Stepwise Logistic Regression models for MS-HRM assays optimization We previously showed that combining clinical variables with epigenetic biomarkers improves their performance. In order to select the minimal clinical variables to classify the GR+PR vs NR in our sample, we performed Backward stepwise regression analysis on clinical variables. As shown in Table 2 in the final model, 4 clinical variables remained: number of hospitalizations, polarity at onset, panic disorders and Li prescribed as the 1st mood stabilizer. In addition, we forced the model with the addition of the Age and Sex variables, that impact respectively 1 and 3 DMRs. The final model therefore included the 7 DMRs and 6 clinical variables. We then performed Receiver operating characteristics analyses for classification of GR+PaR vs NR using 7 DMRs and 6 clinical variables alone or in combination. As shown in Figure 4, the AUC of the 6 clinical variables alone is 0.810 while the AUC of the 7 DMRs alone is 0.797. The combination of the 7 DMRs and 6 clinical variables led to a significant improvement with an AUC = 0.950 significantly different from the clinical variables alone (p-value = 0.0128) and the 7 DMRs alone (p- value=0.0065). Moreover, 90.16% of the individuals were correctly classified (93.02% of the GR+PR and 83.33% of the NR).

[0109] Conclusion:

[0110] In the present study, the inventors described the selection of 4 additional DMRs from the 66 DMRs previously identified in 2020 (Marie-Claire et al., 2020). The resulting 7 MS- HRM assays were tested in BD-I patients. The results of the analysis with 7 DMRs alone were better than with 3 DMRs. However, the combination with clinical variables was decisive to significantly improve the performance. The biomarkers could be reduced to the 7 DMRs and 6 clinical variables to classify 90.16% of the individuals: 93.02% of the GR+PR and 83.33% of the NR with an AUC=0.950. The improved identification of NR is crucial to avoid subjecting these individuals to a trial of 18-24 months of Li during which they are exposed to the risk of side effects.

[0111] Thus, the results of the inventors show the efficient identification of individuals with BD who beneficiated from Li treatment (GR+PR) vs those who did not (NR) using the prophylactic response phenotype defined using the “Alda” scale with a minimal number of DMRs and clinical variables. This cost-effective method could be easily implemented into clinical laboratories due to its minimal equipment requirements. The combination of these epigenetic biomarkers of Li response with clinical variables represent an important step toward a personalized medicine in the context of BD.

[0112] REFERENCES:

[0113] Throughout this application, various references describe the state of the art to which this invention pertains. The disclosures of these references are hereby incorporated by reference into the present disclosure.

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Claims

CLAIMS:

1. A method for predicting whether a patient achieves a response with a treatment of Bipolar Disorder (BD) comprising determining, in a biological sample from the patient the epigenetic profile of at least 4 of the differentially methylated regions (DMRs) selected in the list consisting of DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888, DMR106540 and DMR81023.

2. A method for predicting according to the claim 1 wherein the DMR7291 has an nucleic acid sequence as set for in the SEQ ID NO: 1, the DMR17107 has an nucleic acid sequence as set for in the SEQ ID NO: 2, the has an nucleic acid sequence as set for in the SEQ ID NO: 3, the DMR24332 has an nucleic acid sequence as set for in the SEQ ID NO: 4, the DMR63769 has an nucleic acid sequence as set for in the SEQ ID NO: 5, DMR79888 has an nucleic acid sequence as set for in the SEQ ID NO: 6, the DMR106540 has an nucleic acid sequence as set for in the SEQ ID NO: 7 and DMR81023 has an nucleic acid sequence as set for in the SEQ ID NO: 8.

3. A method for predicting whether a patient achieves a response with a treatment of Bipolar Disorder (BD) according to claim 1 or 2, comprising the steps of: i. determining, in a sample obtained from the patient if at least 4 of the differentially methylated regions (DMRs) selected in the list consisting of DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888, DMR106540 and DMR81023 are differentially methylated compared to a predetermined reference value ii. concluding that the patient achieves or not achieves a response to the treatment of BD according to the level of DNA methylation of the DMR.

4. A method for predicting whether a patient achieves a response with a treatment of Bipolar Disorder (BD) according to claim 3, comprising the steps of: i. determining, in a sample obtained from the patient if at least 4 of the differentially methylated regions (DMRs) selected in the list consisting of DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888, DMR106540 and DMR81023 are differentially methylated compared to a predetermined reference value.ii. concluding that the patient benefits from the treatment of BD when the level of DNA methylation is decreasing for the DMR24332, DMR17978 or DMR63769 or increasing for the DMR7291, DMR79888, DMR81023, DMR17107 or DMR106540 or concluding that the patient does not achieve a response to the treatment of BD when the level of DNA methylation is increasing for the DMR24332, DMR17978 or DMR63769 or decreasing for the DMR7291, DMR79888, DMR81023, DMR17107 or DMR106540.

5. A method for predicting whether a patient achieves a response with a treatment of Bipolar Disorder (BD) according to claims 1 to 4 wherein the Bipolar Disorder is a Bipolar Disorder type I.

6. A method for predicting whether a patient achieves a response with a treatment of Bipolar Disorder (BD) according to claims 1 to 5 wherein the sample is blood like whole blood, plasma, peripheral-blood, peripheral blood mononuclear cell (PBMC), lymph sample, serum, circulating cells, constituents, or any derivative of blood, saliva, urine, cerebrospinal fluids, or any of other biological fluids or tissues.

7. A method for predicting whether a patient achieves a response with a treatment of Bipolar Disorder (BD) according to claims 1 to 5 wherein the levels of methylation of the 3 DMRs selected in the list consisting of DMR17107, DMR24332 and DMR106540 are determined with a fourth DMRs selected in the list consisting of DMR7291, DMR17978, DMR63769, DMR79888 and DMR81023.

8. A method for predicting whether a patient achieves a response with a treatment of Bipolar Disorder (BD) according to claims 1 to 5 wherein the levels of methylation of the 7 DMRs selected in the list consisting of DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888 and DMR106540 are determined.

9. A method for predicting whether a patient achieves a response with a treatment of Bipolar Disorder (BD) according to claims 1 to 5 wherein the levels of methylation of the 8 selected in the list consisting of DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888, DMR106540 and DMR81023 are determined.

10. A for predicting whether a patient achieves a response with a treatment of Bipolar Disorder (BD) according to claims 1 to 6 wherein 1, 2, 3, 4, 5 or 6 clinical markerselected in the list consisting of family history of Bipolar disorder, manic polarity, psychotic symptoms, number of hospitalizations, polarity at onset, panic disorders, lithium (Li) prescribed as the 1st mood stabilizer, Age and Sex and lifetime alcohol misuse are further associated to the determination of the epigenetic profile of the DMRs according to the claims 1.

11. A method for personalizing treatment of Bipolar Disorder in a patient in need thereof comprising administering a therapeutically effective amount of an anti-Bipolar Disorder treatment when the patient is identified as responder according to claims 1 to 10 or deciding not to administer this treatment to the patient if he is identified as a nonresponder.

12. A method for training a model for predicting whether a patient achieves a response with a treatment of Bipolar Disorder (BD), the method being implemented by a training device comprising a computer and a memory storing a training dataset, wherein the training dataset comprises data inputs obtained from a plurality of BD patients, each data input comprising an evaluation of the response of the patient to the treatment of BD by the Alda method, each data input being further associated to an indication of the level of DNA methylation of at least 4 of the differentially methylated regions (DMRs) selected in the list consisting of DMR7291, DMR17107, DMR17978, DMR24332, DMR63769, DMR79888, DMR106540 and DMR81023 and the method comprises performing supervised training, over the training dataset, of a nonlinear binary classification model configured to receive as input the evaluation of the level of DNA methylation of the DMR of the invention, and to output a classification of said patient as achieving or not a response to a BD treatment.

13. A method for training a model according to claim 12, wherein each data input is further associated to clinical markers of the patient and the method comprises performing supervised training, over the training dataset, of a nonlinear binary classification model configured to receive as input the evaluation of the level of DNA methylation of the DMR of the invention in combination with clinical markers of the patient.

14. A method for training a model according to claim 13, wherein 1, 2, 3, 4, 5 or 6 clinical markers selected in the list consisting of family history of Bipolar disorder, manic polarity, psychotic symptoms, number of hospitalizations, polarity at onset, panicdisorders, lithium (Li) prescribed as the 1st mood stabilizer, Age and Sex and lifetime alcohol misuse are further associated to each data input.

15. A method for predicting according to the claim 1 to 10 or a method for training accord to the claims 12 to 14 wherein the clinical markers are: number of hospitalizations, polarity at onset, panic disorders, Li prescribed as the 1st mood stabilizer, Age and Sex.

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