WO2018178071A1 - Method for predicting the therapeutic response to antipsychotic drugs - Google Patents

Abstract

The present invention discloses a method for predicting, before treatment, the individual clinical response to antipsychotic medication in subjects suffering from psychosis. This method is based on a gene expression profile that is useful to predict the therapeutic response to antipsychotics in drug-naïve patients. Particularly, the gene signature proposed in the invention comprises six-genes (SLC9A3, HMOX1, SLC22A16, LOC284581, PF4V1 and GSTT1) which expression profile, preferably measured in blood samples collected from the patient, provides useful information to predict the clinical response to antipsychotics in psychotic patients, more preferably in schizophrenia patients, before treatment.

Description

METHOD FOR PREDICTING THE THERAPEUTIC RESPONSE TO

ANTIPSYCHOTIC DRUGS

This invention falls within the field of prognosis or predicting methods for identifying which patients suffering from a psychotic disorder will benefit from an antipsychotic treatment. Particularly, the invention provides genetic biomarkers or genes which expression levels allow predicting, before the treatment, which patients will show sensitivity and an adequate clinical response to an antipsychotic treatment. The invention relates thus to a method for predicting the clinical response to antipsychotic drugs before treatment in individuals diagnosed with a psychotic disorder, such as schizophrenia.

STATE OF THE ART Antipsychotic drugs, also termed as tranquilizers or neuroleptics, are the cornerstone to treat psychotic disorders such as schizophrenia. They are also majorly used to treat several other psychotic disorders including bipolar disorder, delirium, dementia and psychotic depression and they can be also used to treat severe depression, personality disorders, autism and anxiety. Therefore, antipsychotic medications are among the most common and costly prescribed drugs with significant increases in overall prescription in recent years.

An adequate treatment at initial stages of the psychotic disorder is crucial to improve the prognosis of this clinical condition. However, only 55-60% of first episode patients will significantly reduce the severity of their psychopathology with adequate doses of antipsychotic drugs (responders) and 30% of patients will fail to respond to at least two antipsychotics after adequate trials.

Despite being one of the largest types of prescribed drugs and having large inter- individual differences in efficacy, no methodology to predict the treatment response in these patients is currently available.

The research to find predictors for the clinical outcome for antipsychotic treatment in schizophrenia is an old field of psychiatry. However, despite a wealth of large studies into prognostic factors, there are no molecular tests available that allow predicting the clinical response to antipsychotic treatments. An early occurrence of the psychotic disorder and a delay in administering an effective treatment are among the main factors associated to a poor response to the medication in the subject (Marshall M et al. Arch. Gen. Psychiatry. 2005 Sep;62(9): 975-83; Crespo-Facorro B et al. J. Psychiatr. Res. 2007 Oct:41 (8): 659-66). However, characterization of the molecular factors that may be useful for predicting a positive response to the pharmacological treatment is of crucial importance. Being able to determine if the patient will have a favourable response to the antipsychotic treatment before administering the same, would be very helpful in the clinical practice to early select the appropriate treatment for each individual case. This would allow improving the prognosis of the subject. Nevertheless, biomarkers for predicting this clinical response of each single patient to the treatment have not been identified yet.

It is postulated that the variability of several genetic factors could contribute to determine the clinical response to the antipsychotic treatment. Some studies have pointed out to functional mutations present in CYP metabolic enzymes (Ingelman- Sundberg M et al. Pharmacogenetics 2000 Feb;10(1 ): 91 -3; Nebert DW et al. Pharmacology 2000 Sep;61 (3): 124-35) and in genes related to dopaminergic and serotonergic neurotransmission (Zhang JP et al. Expert Opin. Drug Metab. Toxicol. 201 1 Jan;7(1 ): 9-37).

On the other hand, in a previous study (Crespo-Facorro, et al., 2015, Int J Neuropsychopharmacol, 18) the blood transcriptome of 22 schizophrenia patients was analyzed before and after medication with atypical antipsychotics and it was found that 17 genes had significantly altered expression after medication.

In another study (Aminollah Bahaoddini, et al., 2009, Journal of Molecular Neuroscience, 38:173) the role of the polymorphism in GSTT1 gene over the QTc interval in patients diagnosed with schizophrenia was determined. It was concluded that, whereas several antipsychotics drugs lengthen the QT interval in a dose- dependent fashion, QTc was reduced in those patients with a positive GSTT1 genotype.

Paolo Gravina, et al., 201 1 , Psychiatry research, Volume 187, Issue 3, Pages 454- 456, described the role of the GSTP1 , GSTM1 , GSTT1 and GSTA1 genes in the predisposition to suffer from schizophrenia. Likewise, Milica M. Pejovic-Milovancevic, et al., 2016, Lab Med, 47 (3): 195-204, described the relationship between deletion polymorphisms in GSTM 1 y GSTT1 genes and schizophrenia. Christian Grinan-Ferre, et al., 2016, Experimental Gerontology, Volume 80, Pages 57- 69, identified the HMOX1 gene as a depression marker. US2008299125A1 mentions the LOC234581 gene within a list of sequences related to depression and WO2010036969A2 cites, among others, the SLC22A16 gene as a schizophrenia marker.

However, none of the above mentioned documents provides useful molecular markers (biomarkers) that allow predicting the clinical response to antipsychotics drugs in subjects diagnosed with psychosis in a reliable and simple manner, before treatment. In this context, currently there are no predictive methods that allow the identification of the non-responding patients, making it hard to predict the effectiveness of the therapy prior to the treatment.

The existence of non-responder patients to the antipsychotic treatments makes it necessary to develop predicting methods that can be applied in the clinical practice to identify these patients at an early stage. This approach would help to design effective treatment strategies tailored to each patient.

DESCRIPTION OF THE INVENTION

The present invention discloses a method for predicting, before treatment, the individual clinical response to antipsychotic medication in subjects suffering from (diagnosed with) psychosis. This method of the invention is based on a gene expression profile that is useful to predict the therapeutic response to antipsychotics in drug-naive patients, /. e. in patients diagnosed with a psychotic disorder who have not received antipsychotic medication yet. This method allows therefore the selection of patients for further or alternative antipsychotic therapies. This predicting method of the present invention is useful for the identification, in early stages before the treatment, of those patients that will or will not respond to the antipsychotic drugs, which allow designing specific individual therapeutic strategies for each patient. Particularly, the inventors have identified six-genes (SLC9A3, H OX1 , SLC22A16, LOC284581 , PF4V1 and GSTT1 ) which expression profile, preferably measured in blood samples collected from the patient, provides useful information to predict the clinical response to antipsychotics in psychotic patients, more preferably in schizophrenia patients, before treatment. The predicting method of the invention is therefore based on the analysis of the expression level of the gene SLC9A3, or its combination with at least one of the genes HMOX1 , SLC22A16, LOC284581 , PF4V1 and/or GSTT1 . To arrive to the invention, the inventors sequenced total mRNA from biological samples obtained from antipsychotic naive patients (Table 1 ) that, after 3 months of treatment, were in the top 40% with the best clinical response (15 patients) and in the bottom 40% with the poorer clinical response (15 patients) according to the Brief Psychiatric Rating Scale (Lukoff, et al., 1986, Schizophrenia bulletin, 12, 578-602). The transcriptome before treatment of these 30 patients was characterized using next generation sequencing and 130 genes were found with significant differential expression (Padj value <0.01 ) between the responders and the poorer responders. Then, Random Forests, an ensemble learning method for classification and regression, was used to obtain a list of predictor genes (Koo, et al., 2013, BioMed research international, 432375).

These predictor genes or new genetic markers identified in the invention showed, before treatment, significantly different expression between those patients that, after the treatment, had a positive response and those patients that had a poorer response to the treatment.

The six-gene expression profile identified with this methodology can predict the clinical response with a cross-validation estimate of accuracy of 0.83 and an area under the curve of 0.96 using a logistic regression. Thus, the method provided in the present invention allows determining the clinical response to antipsychotic drugs before the treatment in a reliable manner. This method is also simple since it can be performed by means of, for instance but without limitation, sequencing and/or immunohistochemical techniques commonly used in medicine and molecular biology. Finally, the method of the invention is non-invasive, as it can be performed on isolated biological samples obtained from the patients through non-invasive techniques, such as blood extraction. Another advantage of this method is that in the proposed six-gene signature each individual gene has high predictive power (based on the Gini value). Thus, the expression level of each single gene provides useful information to predict the clinical response. The preferred gene for the prediction with a single biomarker is the SLC9A3 gene, since it shows the highest Gini value (see Table 6) and therefore the highest predictive capacity. The combination of this SLC9A3 gene with the other five genes also identified in this invention as biomarkers, HMOX1 , SLC22A16, LOC284581 . PF4V1 and GSTT1 , improves the prediction value of the gene expression signature proposed herein.

Additionally, as mentioned before, these six predictor genes identified in this invention have, before treatment, significantly altered expression in those patients that after the treatment have a positive response compared to the expression before treatment in those patients that after the treatment have a poorer response. For this reason, this six- gene signature is useful to predict the therapeutic response to antipsychotic drugs. However, this significantly differential expression before treatment between responders and non-responders further evidences that these six genes must be direct or indirect targets for the antipsychotic drugs action. Thus, this invention also provides a method for the screening of compounds, molecules or compositions useful for the treatment of a psychotic disorder based on the measurement of the expression levels of the SLC9A3 gene or its combination with any of HMOX1 , SLC22A16, LOC284581 , PF4V1 and/or GSTT1 genes, before and after the treatment. This screening method will help, not only to discover and identify those drugs useful for the treatment of psychotic disorders but also, given that these genes are associated to the symptoms improved by the antipsychotics, could be used as targets to improve the existing drugs or to generate new drugs for the psychotic symptoms and, in turns, to select the specific antipsychotics that will produce the best clinical response for each specific patient.

Therefore, a first aspect of the invention refers to the use of the expression level value of the SLC9A3 gene in an isolated biological sample as a biomarker in an in vitro method for predicting the therapeutic response to antipsychotic drugs in a subject suffering from (who has been diagnosed with) a psychotic disorder or as a biomarker in an in vitro method for the screening or identification of compounds, molecules or compositions useful for the treatment of a psychotic disorder. "Predicting the therapeutic response", as used in this invention, refers to determining, before administering the treatment, whether the antipsychotic treatment will have a favourable, positive or adequate response in the subject once treated (after administering the treatment). A "positive response" to an antipsychotic treatment or an antipsychotic drug occurs when an improvement or a reduction in the symptoms of the psychotic disorder is observed in the patient. The expression "positive response to the treatment with antipsychotic drugs" means a favourable response to the treatment that can be recognized when a significant reduction, for instance 40% reduction, in the symptoms of the psychotic disorder is observed in the patient according to the Brief Phsychiatric Rating Scale (BPRS) (Overall, JE y Gorham, DR (1962) The Brief Psychiatric Rating Scale. Psychol Rep 10: 799-812).

The term "in vitro" means that the methods of the invention are fully performed outside of the human or animal body.

The term "subject", "individual" or "patient", as used in the present invention, refers to a human or a non-human mammal, such as rodents, ruminants, cats or dogs. In a preferred embodiment, the subject is a human. The expression levels of the genes indicated in the present invention may be previously normalized.

The "SLC9A3 gene" is the gene known by its identification number 6550 {Gene ID NCBl) (Solute carrier family 9 member A3).

In a preferred embodiment of the first aspect of the invention, the use further comprises the expression level of the HMOX1 gene.

The "HMOX1 gene" is the gene known by its identification number 3162 (Gene ID NCBl) (Heme oxygenase 1 ).

In a more preferred embodiment of the first aspect of the invention, the use further comprises the expression level of the SLC22A16 gene. The "SLC22A16 gene" is the gene known by its identification number 85413 (Gene ID NCBI) (Solute carrier family 22 member 16).

In an even more preferred embodiment of the first aspect of the invention, the use further comprises the expression level of the LOC284581 gene.

The "LOC284581 gene" is the gene known by its identification number 284581 (Gene ID NCBI).

In an even more preferred embodiment of the first aspect of the invention, the use further comprises the expression level of the PF4V1 gene.

The "PF4V1 gene" is the gene known by its identification number 5197 (Gene ID NCBI) (Platelet factor 4 variant 1 ). In an even more preferred embodiment of the first aspect of the invention, the use further comprises the expression level of the GSTT1 gene.

The "GSTT1 gene" is the gene known by its identification number 2952 (Gene ID NCBI) (Glutathione S-transferase theta 1 ).

In a particular embodiment of this aspect of the invention, the genes which expression levels are used as a biomarker in an in vitro method for predicting the therapeutic response to antipsychotic drugs in a subject suffering from a psychotic disorder or for the screening of compounds, molecules or compositions useful for the treatment of a psychotic disorder are SLC9A3, HMOX1 , SLC22A16, LOC284581 , PF4V1 and GSTT1 .

A "psychotic disorder" is a group of severe mental disorders that cause abnormal thinking and perceptions. Psychotic disorders are a psychopathological condition in which symptoms such as delusions, hallucinations, delirium and altered behaviour are present. Subjects suffering from psychotic disorders have a disturbed reality perception.

In another preferred embodiment of the first aspect of the invention, the psychotic disorder is selected from the list consisting of: schizophrenia, schizophreniform disorder, schizoaffective disorder, bipolar disorder, delusional disorder, delirium, dementia and/or behavioral disorders. More preferably, the psychotic disorder is schizophrenia or bipolar disorder. Even more preferably, the psychotic disorder is schizophrenia. "Schizophrenia" is the brain illness that course with the criteria indicated in Psychiatry American Society DSM-IV for schizophrenia, schizophreniform disorder, schizoaffective disorder or brief psychotic disorder. It is a chronic and severe mental disorder that affects how a person thinks, feels, and behaves. Symptoms of schizophrenia usually start between ages 16 and 30. The symptoms of schizophrenia fall into three categories: positive, negative, and cognitive. "Positive" symptoms are psychotic behaviors not generally seen in healthy people. People with positive symptoms may "lose touch" with some aspects of reality. Positive symptoms include: hallucinations, delusions, thought disorders (unusual or dysfunctional ways of thinking) or movement disorders (agitated body movements). "Negative" symptoms are associated with disruptions to normal emotions and behaviors. Negative symptoms include: "Flat affect" (reduced expression of emotions via facial expression or voice tone), reduced feelings of pleasure in everyday life, difficulty beginning and sustaining activities or reduced speaking. Regarding the "cognitive" symptoms, for some patients, the cognitive symptoms of schizophrenia are subtle, but for others, they are more severe and patients may notice changes in their memory or other aspects of thinking. Cognitive symptoms include: poor "executive functioning" (the ability to understand information and use it to make decisions), trouble focusing or paying attention or problems with "working memory" (the ability to use information immediately after learning it). It is considered that a subject suffers from schizophrenia when these symptoms last at least six months.

In another preferred embodiment of the first aspect of the invention, the subject suffering from a psychotic disorder is a first-episode psychosis patient. A "first-episode psychosis patient" is a subject who has only experienced his first episode of psychosis and who does not present affective psychosis. A first episode of psychosis is the first time a person experiences a psychotic episode.

"Antipsychotic drugs", "antipsychotic treatment" or "antipsychotic medication" is understood as the medication or actions known by the skilled in the art taken in order to palliate, reduce, cure, mitigate, eliminate or the like, a psychotic disorder or episode. Preferably, the antipsychotic drug as used in the present invention is selected from the list consisting of: aripiprazole (Ability®), risperidone (Risperdal®), olanzapine (Zyprexa®), paliperidone (Invega®), chlorpromazine (Largactil®, Thorapine®), clozapine (Clorazil®), quetiapine (Seroquel®), ziprasidone (Geodon®), asenapine, iloperidone (Zomaril®), zotepine, amisulpride (Solian@), fluphenazine (Prolixin®), haloperidol (Aldol®, Serenace®), loxapine (Loxapac®, Loxitane®), perphenazine, pimozide (Orap®), zuclopenthixol (Clopixol®), or any combination thereof. More preferably, the antipsychotic drug referred to in the present invention is selected from aripiprazole, risperidone, olanzapine, paliperidone and/or clozapine.

The term "expression level", as used in the present invention, refers to the expression level of any genetic expression product of the genes SLC9A3, HMOX1 , SLC22A16, LOC284581 , PF4V1 and GSTT1 .

In another preferred embodiment of the first aspect of the invention, the expression levels are RNA and/or protein. The RNA may be mRNA or microRNA, preferably mRNA. Another aspect of the invention refers to a method for obtaining information or data useful for predicting the therapeutic response to antipsychotic drugs in a subject suffering from a psychotic disorder, comprising measuring the expression level of the SLC9A3 gene in an isolated biological sample collected from the subject suffering from a psychotic disorder before the treatment with antipsychotic drugs, and comparing this expression level value obtained with an standard value. Preferably, this method further comprises measuring the expression level of at least one of the genes of the list consisting of HMOX1 , SLC22A16, LOC284581 , PF4V1 or GSTT1 , in the isolated biological sample. More preferably, this method comprises measuring the expression level of the SLC9A3, HMOX1 , SLC22A16, LOC284581 , PF4V1 and GSTT1 genes, in the isolated biological sample.

Another aspect of the invention refers to an in vitro method for predicting the therapeutic response to antipsychotic drugs in a subject suffering from (who has been diagnosed with) a psychotic disorder, comprising the following steps: a) measuring the expression level of the SLC9A3 gene in an isolated biological sample collected from the subject suffering from a psychotic disorder before the treatment with antipsychotic drugs,

b) comparing the expression level value obtained after step (a) with an standard value, and

c) assigning the subject of step (a) to the group of patients that will positively respond to the treatment with antipsychotic drugs when the expression level value obtained after step (a) is lower than the standard value, wherein the standard value is the mean value obtained after measuring the expression level of the SLC9A3 gene in isolated biological samples collected from a group of subjects suffering from a psychotic disorder who have not been treated with antipsychotic drugs. This method will be also referred to as "the first method of the invention".

As used herein, the expression "before the treatment with antipsychotic drugs" or "subjects who have not been treated with antipsychotic drugs" means that the subject has not received prior treatment with antipsychotic medication. These subjects will be also called "antipsychotic drug-naive patients".

The expression level of the genes indicated in this invention may, for example, be measured directly by assessing the protein levels soluble in the samples. For instance, these protein levels can be measured by immunohistochemistry, Western blot, ELISA, lateral flow devices or Luminex®. In another preferred embodiment, the mRNA expression levels can be measured instead. For example, mRNA expression levels can be measured by RT-PCR, Northern blot or array hybridization.

Thus, in a preferred embodiment of the first method of the invention, the expression levels are RNA and/or protein. The RNA may be mRNA or microRNA, preferably mRNA.

The measurement of the expression levels in the present invention refers to the measurement of the quantity or concentration. This measurement may be performed as a direct or an indirect measurement. The direct measurement refers to the measurement of the quantity or concentration of the expression levels based on a signal directly obtained from the expression of the genes to be assessed. This signal is directly correlated with the number of product molecules present in the sample. This signal, also referred to as "intensity signal", may be obtained for instance by measuring an intensity value derived from a physical or chemical property of the product. The indirect measurement refers to the measurement obtained from a secondary component (for example, a component which is different from the gene products) or a measurement derived from, for example, cellular responses, ligands, tags or enzymatic reaction products associated to these molecules or their activities. In a preferred embodiment of the first method of the invention, mRNA levels are measured. mRNA extractions protocols are well known for those skilled in the art. This measurement may be performed, but without limitation, by sequencing, amplification with the polymerase chain reaction (PGR), retrotranscription combined with ligase chain reaction (RTLCR), retrotranscription combined with PGR (RT-PCR), retrotranscription combined with quantitative PGR (qRT-PCR), SAGE or any other method for the amplification of nucleic acids; microarrays made with oligonucleotides deposited by any technique, microarrays made with in situ synthesized oligonucleotides, in situ hybridization using specific labeled probes, electrophoresis gels, membrane transfer and hybridization with a specific probe, RMN or any other image diagnosis technique using paramagnetic nanoparticles or other type of functionalized nanoparticles.

The term "microarray" (or "chip") refers to a solid support to which RNA or protein is bound being thus useful for the gene expression analysis through label and/or antibody hybridization.

The term "SAGE" refers to the detection and quantification of the gene expression by means of the RNA measurement. In the present invention SAGE variants may be used, such as SuperS AGE, MicroSAGE or LongSAGE.

Protein levels may also be measured in the present invention. This measurement may be performed, but without limitation, by incubation with a specific antibody against the protein or a fragment thereof in assays such as Western blot, electrophoresis gels, immunoprecipitation assays, protein arrays preferably antibodies based microarrays, immunofluorescence, immunohistochemistry, ELISA or any other enzymatic method, by incubation with a specific ligand, RMN or any other image diagnosis technique or by chromatographic techniques preferably combined with mass spectrometry. Protein levels measurement may be performed by the specific recognition of any protein fragment by means of probes and/or antibodies. Proteins or fragments thereof may be quantified by electrophoresis and/or immunoassays. For the immunoassay the antibodies used may be labeled with, for instance, an enzyme, radioisotopes, magnetic tags or fluorescence.

The term "isolated biological sample" refers, but without limitations, to any biological tissue and/or fluid collected from a subject. Biological samples may be obtained by means of any method known by those skilled in the art. Preferably, the biological sample referred to in the present invention is a biological fluid, for instance, blood, plasma, serum, lymph, saliva, urine, tears, synovial fluid or the like. In another preferred embodiment, the biological sample in the present invention is blood, more preferably peripheral blood. The biological sample comprises RNA and/or protein, preferably mRNA. The biological sample may be fresh, frozen, fixed or fixed and paraffin-embedded.

In another preferred embodiment, the first method of the invention further comprises: measuring in step (a) the expression level of the HMOX1 gene, comparing in step (b) this expression level value with an standard value, and assigning in step (c) the subject to the group of patients that will positively respond to the treatment with antipsychotic drugs when this expression level value obtained after step (a) is higher than the standard value, wherein this standard value is the mean value obtained after measuring the expression level of the HMOX1 gene in isolated biological samples collected from a group of subjects suffering from a psychotic disorder who have not been treated with antipsychotic drugs.

In another preferred embodiment, the first method of the invention further comprises: measuring in step (a) the expression level of the SLC22A16 gene, comparing in step (b) this expression level value with an standard value, and assigning in step (c) the subject to the group of patients that will positively respond to the treatment with antipsychotic drugs when this expression level value obtained after step (a) is higher than the standard value, wherein this standard value is the mean value obtained after measuring the expression level of the SLC22A16 gene in isolated biological samples collected from a group of subjects suffering from a psychotic disorder who have not been treated with antipsychotic drugs.

In another preferred embodiment, the first method of the invention further comprises: measuring in step (a) the expression level of the LOC284581 gene, comparing in step (b) this expression level value with an standard value, and assigning in step (c) the subject to the group of patients that will positively respond to the treatment with antipsychotic drugs when this expression level value obtained after step (a) is lower than the standard value, wherein this standard value is the mean value obtained after measuring the expression level of the LOC284581 gene in isolated biological samples collected from a group of subjects suffering from a psychotic disorder who have not been treated with antipsychotic drugs.

In another preferred embodiment, the first method of the invention further comprises: measuring in step (a) the expression level of the PF4V1 gene, comparing in step (b) this expression level value with an standard value, and assigning in step (c) the subject to the group of patients that will positively respond to the treatment with antipsychotic drugs when this expression level value obtained after step (a) is higher than the standard value, wherein this standard value is the mean value obtained after measuring the expression level of the PF4V1 gene in isolated biological samples collected from a group of subjects suffering from a psychotic disorder who have not been treated with antipsychotic drugs.

In another preferred embodiment, the first method of the invention further comprises: measuring in step (a) the expression level of the GSTT1 gene, comparing in step (b) this expression level value with an standard value, and assigning in step (c) the subject to the group of patients that will positively respond to the treatment with antipsychotic drugs when this expression level value obtained after step (a) is lower than the standard value, wherein this standard value is the mean value obtained after measuring the expression level of the GSTT1 gene in isolated biological samples collected from a group of subjects suffering from a psychotic disorder who have not been treated with antipsychotic drugs. In the most preferred embodiment of the first method of the invention, said method comprises measuring in step (a) the expression levels of the SLC9A3, HMOX1 , SLC22A16, LOC284581 , PF4V1 and GSTT1 genes. As used herein, the term "mean value" refers to the arithmetic mean of the gene expression level value considering every member of the sample population or group. The mean gene expression level of the sample population or group is equal to the sum of the gene expression levels of every individual divided by the total number of individuals in the sample group. Preferably, the "group of subjects suffering from a psychotic disorder who have not been treated with antipsychotic drugs" from which the mean value is obtained comprises at least 15, more preferably at least 30 individuals, and includes individuals of both conditions responders and non-responders to antipsychotic drugs once this medication is administered. In a more preferred embodiment, the mean value for the SLC9A3 gene is 564, the mean value for the HMOX1 gene is 2923, the mean value for the SLC22A16 gene is 101 , the mean value for the LOC284581 gene is 149, the mean value for the PF4V1 gene is 153 and the mean value for the GSTT1 gene is 158. The terms "lower" and "higher" than the standard value, as used in the present invention, means that the value obtained after step (a) of the method of the invention is significantly lower or significantly higher than the standard value. The signification level may be calculated by statistical methods well known by those skilled in the art, such as confidence intervals, p-value, Student test or Fisher discriminant analysis.

Steps (a) and (b) of the first method of the invention may be total or partially computerized. Furthermore, the first method of the invention may comprise other additional steps, for example, related to the pre-treatment of the biological samples prior to the analysis.

In another preferred embodiment of the first method of the invention, the psychotic disorder is selected from the list consisting of: schizophrenia, schizophreniform disorder, schizoaffective disorder, bipolar disorder, delusional disorder, delirium, dementia and/or behavioral disorders. More preferably, the psychotic disorder is schizophrenia or bipolar disorder. Even more preferably, the psychotic disorder is schizophrenia.

In another preferred embodiment of the first method of the invention, the subject suffering from a psychotic disorder is a first-episode psychosis patient.

In another preferred embodiment of the first method of the invention, the antipsychotic drug is selected from the list consisting of: aripiprazole (Ability®), risperidone (Risperdal®), olanzapine (Zyprexa®), paliperidone (Invega®), chlorpromazine (Largactil®, Thorapine®), clozapine (Clorazil®), quetiapine (Seroquel®), ziprasidone (Geodon®), asenapine, iloperidone (Zomaril®), zotepine, amisulpride (Solian@), fluphenazine (Prolixin®), haloperidol (Aldol®, Serenace®), loxapine (Loxapac®, Loxitane®), perphenazine, pimozide (Orap®), zuclopenthixol (Clopixol®), or any combination thereof. More preferably, the antipsychotic drug referred to in the first method of the invention is selected from aripiprazole, risperidone, olanzapine, paliperidone and/or clozapine.

Another aspect of the present invention refers to an in vitro method for the screening or identification of compounds, molecules or compositions useful for the treatment of a psychotic disorder that comprises: i) measuring the expression level of at least one gene of those included in Table 4 of the present invention or of the SLC9A3 gene in an isolated biological sample obtained from a subject suffering from a psychotic disorder before the administration of the compound, molecule or composition to be tested, ii) measuring the expression level of the same gene/s as that measured in step (i) in an isolated biological sample obtained from the same subject after the administration of the compound, molecule or composition to be tested, (iii) comparing the expression level values obtained in (i) and (ii), and (iv) classifying the compound, molecule or composition as useful for the treatment of a psychotic disorder when a significant difference in the expression level values has been detected in step (iii). This method will be also referred to as "the second method of the invention" and allows the discovery and identification of new drugs useful for the treatment of psychotic disorders as well as the improvement of the existing drugs in terms of, for instance, administering route, dosage regime, etc. This method also allows the selection of the specific antipsychotic drug that will produce the best clinical response for each specific patient. This method represents thus a tool to select the antipsychotic that is expected to provide the best response.

In a preferred embodiment of the second method of the invention, said method further comprises measuring in step (i) the expression level of at least one gene selected from the list consisting of: HMOX1 , SLC22A16, LOC284581 , PF4V1 and/or GSTT1 . More preferably, this second method comprises measuring in step (i) the expression level of the following genes: SLC9A3, HMOX1 , SLC22A16, LOC284581 , PF4V1 and GSTT1 . In another preferred embodiment of the second method of the invention, the psychotic disorder is selected from the list consisting of: schizophrenia, schizophreniform disorder, schizoaffective disorder, bipolar disorder, delusional disorder, delirium, dementia and/or behavioral disorders. More preferably, the psychotic disorder is schizophrenia.

In another preferred embodiment of the second method of the invention, the expression levels are RNA and/or protein, preferably mRNA.

In another preferred embodiment of the second method of the invention, the isolated biological sample is peripheral blood.

Another aspect of the present invention refers to a kit comprising compounds capable of specifically binding SLC9A3 gene or its expression products. This kit comprises all those necessary elements for predicting the therapeutic response to antipsychotic drugs in a subject suffering from a psychotic disorder according to the first method of the invention or for the screening of compounds, molecules or compositions useful for the treatment of a psychotic disorder according to the second method of the invention. That is, this kit comprises all the elements needed for measuring the gene expression levels as explained before. Hereinafter this kit will be referred to as the "kit of the invention".

In a preferred embodiment of this aspect of the invention, the kit further comprises compounds capable of specifically binding HMOX1 , SLC22A16, LOC284581 , PF4V1 and/or GSTT1 genes or their expression products. In a more preferred embodiment of this aspect of the invention, the compounds comprised in the kit are labels, antibodies and/or primers, preferably primers capable of detecting and amplyfing the genes indicated in this invention or fragments thereof. In the most preferred embodiment, the kit of the invention consists of labels, antibodies and/or primers capable of specifically binding SLC9A3, HMOX1 , SLC22A16, LOC284581 , PF4V1 and GSTT1 genes or their expression products.

The term "primer" refers to a nucleic acid sequence capable of acting as the starting point for the DNA synthesis when it hybridizes with the template nucleic acid. Preferably, the primer is a deoxyribose primer. Primers may be designed for instance by direct chemical synthesis. Primers may be designed to hybridize with specific sequences within the genes indicated in this invention (specific primers) or they may be randomly synthetized (arbitrary primers).

Furthermore, primers comprised in the kit of the invention may be labeled with detectable tags, such as radioactive isotopes, fluorescence tags, chemiluminescent or bioluminescent tags or enzymatic tags. In another preferred embodiment, at least one of the antibodies comprised in the kit of the invention is labeled or immobilized. The antibody may be labeled with, for example, a tag selected from the list consisting of a radioisotope (such as 32P, 35S or 3H), a fluorescent or luminescent marker (such as fluorescein (FITC), rhodamine, texas red, ficoeritrine (PE), aloficocianine, 6-carboxyfluorescein (6-FAM), 2^',7'-dimetoxi-4',5'- dichlore-6-carboxyfluorescein (JOE), 6-carboxy-X-rhodamine (ROX), 6-carboxy- 2',4',7',4,7-hexachlorefluorescein (HEX), 5-carboxyfluorescein (5-FAM) o Ν,Ν,Ν',Ν'- tetramethyl-6-carboxyrhodamine (TAMRA)), a secondary antibody, an antibody fragment (such as F(ab)2), an affinity tag (such as biotin, avidin, agarose, BMP, haptens), an enzyme or a substrate of an enzyme (such as alkaline phosphatase (AP) and HRP) and the like. The antibody may be immobilized without losing its activity in, for example, a matrix (preferably a nylon or latex matrix), a microtiter plate or a similar plastic support, beads, glass support, gel, cellulosic support, adsorption resin, or the like. The kit of the invention comprises all those reactives needed to perform the methods of the invention. Thus, the kit may further comprise elements such as buffers, enzymes, such as polymerases, cofactors needed to obtain an optimal activity of said enzymes, etc. The kit may comprise all those supports and recipients needed for the implementation of the methods of the invention. Finally, the kit may comprise other molecules, genes, proteins or probes suitable as positive or negative controls. Preferably, the kit of the invention further comprises instructions explaining how to perform the methods of the invention. Another aspect of the invention refers to the use of the kit of the invention for predicting the therapeutic response to antipsychotic drugs in a subject suffering from a psychotic disorder or for the screening of compounds, molecules or compositions useful for the treatment of a psychotic disorder, preferably for performing the first or the second method of the invention.

Another aspect of the present invention refers to a method of treating a subject suffering from a psychotic disorder comprising: selecting a subject as responding or non-responding to the antipsychotic treatment in accordance with the first method of the invention; and administering an antipsychotic therapy to the patient. In a preferred embodiment of this aspect of the invention, the antipsychotic therapy is an antipsychotic drug as those defined herein, psychosocial treatment, coordinated specialty care (CSC), or any other antipsychotic treatment of those known in the art.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skilled in the art to which this invention belongs. Methods and materials similar or equivalent to those described herein can be used in the practice of the present invention. Throughout the description and claims the word "comprise" and its variations are not intended to exclude other technical features, additives, components, or steps. Additional objects, advantages and features of the invention will become apparent to those skilled in the art upon examination of the description or may be learned by practice of the invention. The following examples and drawings are provided by way of illustration and are not intended to be limiting of the present invention.

DESCRIPTION OF THE DRAWINGS FIG. 1. Variable Importance (Gini) for the top 30 predictor genes. Gini variable importance measures reflect the mean decrease in impurity by splits of a given variable in the classification tree, weighted by the proportion of samples reaching that node. A greater "mean decrease Gini" indicates that the gene plays a greater role in partitioning the data into the defined classes.

FIG. 2. Receiver operating characteristic (ROC) curves, a) Using the best two genes (SCL9A3, HMOX1 ) for training the predictor it was obtain a ROC with an area under the curve (AUC) of 0.92. b) Using the best three genes (SCL9A3, HMOX1 , SLC22A16) for training the predictor it was obtain a ROC with an AUC of 0.96. c) Using the best four genes (SCL9A3, HMOX1 , SLC22A16, LOC284581 ) for training the predictor it was obtain a ROC with an AUC of 0.97.

FIG. 3. Predicted probability of response to antipsychotics. Scatter plot which represent the predicted probability of response (y-axis) for each input sample (x-axis) in the logistic regression predictor. Grey points represent no responder patients and black points represent responder patients.

EXAMPLES

With the goal of generating an expression profile that could predict the clinical outcome of treatment, the transcriptome of a drug-na^'ive cohort (before antipsychotic medication was initiated) of first episode schizophrenia patients was characterized. Patients were then divided into two groups according to their clinical response to antipsychotics after 3 months of treatment according to the Brief Psychiatric Rating Scale (BPRS) (Lukoff, D., et al., 1986, Schizophrenia bulletin, 12, 578-602) that measure the positive, negative, and affective symptoms of individuals who have schizophrenia: one group including the top 40% patients that had the best response to treatment (highest absolute decrease of BPRS score) that were named "responders", and other group including the bottom 40% patients with the worst response (lower absolute decrease of BPRS score) that were named "no responders". The transcriptome of both groups were compared using the program Deseq (Anders, S. & Huber, W., 2010, Genome biology, 1 1 , R106) to define the genes with significant differential expression. 130 genes with significant differential expression between both groups (Padj value <0.01 ) were found (Table 2). These genes were significantly enriched for schizophrenia related genes according to the scientific literature in the Gene Reference into Function (GeneRIF) database that provides functional and morbid annotation of genes. 14 schizophrenia differential expression genes between responders and no responders were found (13.4% observed versus 6.8% expected; Fisher P value = 0.016). This data may suggest that the response to treatment could be due, at least partially, to the fact that the two groups of patients analyzed have different genetic background causing schizophrenia.

Furthermore, it was also analyzed the GeneRIF annotations for genes involved in drug processing (using the string "drug" combined with one of the strings "response", "toxicity", "resistance", "metabol" or "sensitivity") and it was found a significant enrichment of these type of genes among the 130 genes (1 1 out of the 104 annotated genes or 10,6% when we expect 4,5%; Fisher Pvalue = 0.0007). These data suggest that the response to antipsychotics could also depend of the expression profile of genes that process the drugs.

Thus, we hypothesize that the lack of response could be due to two different reasons, a different set of genes causative of schizophrenia for the responders compared to the no responders, and a different expression profile of genes related to drug metabolism and drug response. The enrichment of schizophrenia genes and in drug processing genes among the differential expression genes supports both hypotheses: the poorer clinical response might be due to different causative genes of schizophrenia and to different expression profiles in genes related to drug processing. To obtain more information, we also sequenced the transcriptome of the patients after 3 months of treatment with antipsychotics. We defined 219 differential expression genes between responders and no responders after 3 months of medication (Table 3); these genes are also enriched significantly for schizophrenia (21 genes or 1 1 .3% versus 6.8%; Fisher P value = 0.027). After 3 months of medication with antipsychotics, six out of the 14 schizophrenia-annotated genes with differential expression before medication between responders and no responders have no longer differential expression. These six genes are excellent candidates to be the targets used by the drugs to improve the symptoms of the responders: VWF, a glycoprotein with levels increased in plasma of not medicated patients and in bipolar disorder and schizophrenia compared to control individuals; UGT1 A1 , a gene with promoter variations in patients with schizophrenia that result in lower serum bilirubin levels; HMOX1 , an enzyme that has anti-inflammatory properties and mediates the first step of heme catabolism, is over-expressed in transgenic mice with schizophrenia-like features; IL8 (known also as CXCL8), a chemokine with altered expression in the dorsolateral prefrontal cortex of individuals with schizophrenia; NTNG2, a gene with haplotypes associated with schizophrenia and isoform expression significantly different between schizophrenic and control brains; and PTGDS, a prostaglandin that acts as neuromodulator as well as a trophic factor in the central nervous system and was studied as schizophrenia candidate and with reduced mRNA expression in peripheral blood of bipolar disorder patients compared with healthy control subjects. The differential expression genes between responders and non-responders after 3 months of medication indicates that 5 out the 1 1 genes involved in "drug processing" with differential expression before medication still have differential expression and similar expression profile after medication. These 5 genes are: GSTM1 , a glutathione S- transferase involved in detoxification of electrophilic compounds, such as therapeutic drugs, by conjugation with glutathione, with genetic variations that affect the toxicity and efficacy of certain drugs; THBS1 , an adhesive glycoprotein that mediates cell-to- cell and cell-to-matrix interactions and related to drug resistance; PRAME, an antigen related to cytotoxic drug sensitivity; GSTT1 , a glutathione S-transferase that functions as a drug metabolizing enzyme; and SLC22A16, a solute carrier reported to be involved in response to drugs. These genes could be good candidates to be involved in the different clinical response of both groups of patients.

We also compared the transcriptome of the responders before and after medication and characterized 176 genes with differential expression (Table 4). These genes were also enriched for schizophrenia annotations (9.4% observed versus 6.7% expected). When we defined the differential expression genes of the worst responders, before and after medication, we found only 23 genes (Table 5) and they were not enriched for schizophrenia annotations (5.2% versus 6.7%). This data indicates that the individuals that respond worst to treatment have much fewer genes (a 7.6 fold decrease) altered in their expression by antipsychotics than the good responders. This also indicates that the genes altered in the no responders are less associated to schizophrenia that the ones altered in the responders. The previous data suggest that the efficacy of antipsychotics is dependent of the expression profile of the patient before medication and that a predictor could be generated using a gene expression profile of untreated patients.

To generate a predictor test of response to antipsychotics before medication, we analyzed all 130 genes with significantly different expression between the responders and no responders using Random Forests, an ensemble learning method for classification, regression, among other tasks, that operate by constructing a multitude of decision trees (Koo, et al., 2013, BioMed research international, 432375). Functional analyses of the 30 genes with the highest predictive power (Fig. 1 ; Table 6) indicated a significant enrichment of genes related to schizophrenia (29.2% observed versus 6.9% expected; Fisher P value = 0.0009) and bipolar disorder (16.7% versus 2.6%; Fisher P value = 0.003). In the complete list of 130 genes, the enrichment for schizophrenia genes was smaller (13.4%) indicating that genes related to schizophrenia tend to have a higher predictive value. This would suggest that schizophrenia genes are involved in the response and that the schizophrenia causative genes tend to be different between responders and no responders.

Using logistic regression we defined the area under the curve (AUC), representing the combined predictive power of the genes, and a cross-validation estimate of accuracy for the prediction using the first 2, 3 and 4 genes. We obtained that the prediction using two genes (SLC9A3 and HMOX1 ) had an area under the curve of 0.92 and a cross- validation estimate of accuracy of 0.73; with three genes (adding SLC22A16) the values were 0.96 and 0.833 respectively (Fig. 2); and with 4 genes (adding LOC284581 ) were 0.97 and 0.833 respectively. This data indicates that test with the three most predictive genes seems to be an appropriated choice. We tested the predictive power of the 3-gene predictor in our patients, and we found that the test would predict accurately 100% of the non-responders and 87% of the responders (Fig. 3). The gene with the highest predictive value, SLC9A3, is a Na/H exchanger and belongs to several pathways involved in transmembrane transportation of small molecules. The second best predictor is HMOX1 , an essential enzyme in heme catabolism that is involved in the production of carbon monoxide, a putative neurotransmitter, belongs to a pathway for transmembrane transport of small molecules, and has been related to schizophrenia and to drug resistance in acute myeloid leukemia. The third predictor, SLC22A16, encodes a protein that been shown to be involved in the transport and response to anticancer drugs like bleomycin or others and is located within a schizophrenia susceptibility locus in chromosome 6q. The fourth gene, LOC284581 , is not annotated but locates within a Parkinson disease locus of 15.8 Mb. This data have allowed generating a simple test (expression level of at least 1 gene) to predict the response to antipsychotics, one of the most prescribed type of drugs worldwide, and provide a tool to select the antipsychotic that is expected to provide the best response. Methods

Study setting and subjects

The cohort analyzed in this study (Table 1 ) was obtained from an ongoing epidemiological and three-year longitudinal intervention program of first-episode psychosis (PAFIP) conducted at the outpatient clinic and the inpatient unit at the University Hospital Marques de Valdecilla, Cantabria, Spain. Conforming to international standards for research ethics, the research in this study was approved by the Cantabria Ethics Institutional Review Board (IRB). Patients meeting inclusion criteria and their families provided written informed consent to be included in the PAFIP. The biological samples of patients included in the study were provided by the Valdecilla biobank.

All referrals to PAFIP were screened for patients who met the following criteria: 1 ) 15- 60 years; 2) living in the catchment area (Cantabria); 3) experiencing their first episode of psychosis; 4) no prior treatment with antipsychotic medication; 5) DSM-IV criteria for schizophrenia, schizophreniform disorder, schizoaffective disorder, or brief psychotic disorder. Patients were excluded for any of the following reasons: 1 ) meeting DSM-IV criteria for drug dependence, 2) meeting DSM-IV criteria for mental retardation, 3) having a history of neurological disease or head injury. The diagnoses were confirmed using the Structured Clinical Interview for DSM-IV (SCID -I) carried out by an experienced psychiatrist 6 months on from the baseline visit. Our operational definition for a "first episode of psychosis" included individuals with a non-affective psychosis (meeting the inclusion criteria defined above) who have not received previously antipsychotic treatment regardless the duration of psychosis. 37 individuals who gave written consent to their participation in the program, fulfilled inclusion criteria at 6 months and had mRNA samples at baseline and at 3 months were included in the analyses. Premorbid and sociodemographic variables

Premorbid and sociodemographic information was recorded from patients (Table 1 ), relatives and medical records. Age of onset of psychosis was defined as the age when the emergence of the first continuous (present most of the time) psychotic symptom occurred. Duration of untreated illness (DUI) was defined as the time from the first unspecific symptoms related to psychosis (for such a symptom to be considered, there should be no return to previous stable level of functioning) to initiation of adequate antipsychotic drug treatment. Sample and study design

Premorbid and sociodemographic information was recorded from patients (Table 1 ), relatives and medical records. After informed consent was signed, patients were included in a prospective, randomized, flexible-dose, open-label study. We used a simple randomization procedure. At study intake, all patients were antipsychotic na^'ive and were randomized to aripiprazole (N=17), risperidone (N=20). Initial dose ranges were 5-10 mg/day of aripiprazole and 1 -2 mg/day of risperidone. Rapid titration schedule (5-day), until optimal dose was reached, was as a rule used unless severe side effects occur. At the treating physician^'s discretion, the dose and type of antipsychotic medication could be changed based on clinical efficacy and the profile of side effects during the follow-up period. Antimuscarinic medication, lormetazepam and clonazepam were permitted for clinical reasons. No antimuscarinic agents were administered prophylactically. At 3-month follow-up patients were on: Aripiprazole (N=12), Risperidone (N=16), Olanzapine (N=8), Paliperidona (N=1 ). Mean daily dose of antipsychotics at baseline was 189.19 (51 .55) mg/day chlorpormazine (CPZ) equivalent doses. Mean daily dose of antipsychotics at 3 months was 361 .43 (179.50) mg/day CPZ equivalent doses.

Clinical assessment. The severity scale of the Clinical Global Impression (CGI) scale, the Brief Psychiatric Rating Scale (BPRS) (expanded version of 24 items), the Scale for the Assessment of Positive symptoms (SAPS), the Scale for the Assessment of Negative symptoms (SANS), the Calgary Depression Scale for Schizophrenia (CDSS) and the Young Mania Rating Scale (YMRS) were used to evaluate clinical symptomatology. The same trained psychiatrist (BC-F) completed all clinical assessments. The analysis of clinical efficacy reveals that there was a significant improvement of general psychopathology (assessed by BPRS total score changes at 3 months) and positive symptoms (hallucinations, delusions, positive formal thought disorders, bizarre behavior) (assessed by SAPS total score changes at 3 months).

Laboratory assessments

Blood samples were assessed for biochemical and hematological parameters. To minimize the effects of diet and technique, blood samples were obtained from fasting subjects from 8:00 to 10:00 a.m. by the same personnel, in the same setting. No patient had a chronic inflammation or infection, or was taking medication that may apparently influence the results of blood tests. RNA extraction

Total RNA was extracted from blood using the Tempus™ Blood RNA Tube and Tempus™ Spin RNA Isolation Kit (Applied Biosystems, Foster City, CA, USA) using the manufacturer protocols. To define expression profiles, a key factor is that the RNA is intact. To select only RNA with good quality, the RNA Integrity Number (RIN) was characterized with a Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) and samples with a RIN of at least 7.2 were selected. The selected samples have RINs that range from 8 to 10 with an average of 9.1 1 . RNA Next Generation Sequencing. Total RNA was extracted from peripheral blood of each individual. The mRNA obtained from blood was sequenced at the Centra Nacional de Analisis Genomico (CNAG) using lllumina HiSeq instruments (San Diego, CA, USA). The mRNA was isolated from the total RNA and was fragmented once transformed in cDNA. Fragments of 300 bp on average were selected to construct the libraries for sequencing. Pair-end sequences of 70 nucleotides for each end were produced. Alignment of reads to the human genome reference

Alignment of the reads was performed in an SLURM HPC server running Tophat 2.0.6 with default options. Tophat aligns RNA-Seq reads to genomes using the Bowtie 2.0.2 alignment program, and then analyzes the mapping results to identify splice junctions between exons.

Differential Expression Statistical Analyses

Bedtools 2.17.0 (multicov option) was used to count the amount of reads mapped to each gene. The Reference Sequence (RefSeq) gene coordinates were defined using the RefFlat file from the UCSC Genome Bioinformatics Site (as February 28th, 2014). DESeq 1 .4 package, setting up fit-only as fitting method, was used to test for differential expression using gene-count data.

Prediction method

Gene selection was performed with the implementation of Random Forest method in the the RandomForest 4.6-12 package (Breiman, L, 2001 , Mach Learn, 45, 5-32) of R. Expression values of the 130 genes with significantly different expression between the responders and no responders was used as input with default parameters. Genes with the best Gini were selected for the predictor. It was trained using Logistic regression (Cox, D. R., 1958, J Roy Stat Soc B, 20, 215-242) with the glm function of R 3.2.3 and the calculation of the estimated cross-validation was performed with the cv.glm function of boot package which implements bootstrapping methods (Hinkley, D. V., 1988, J Roy Stat Soc B Met, 50, 321 -337).

Table 1 . Characteristics of the patients assessed.

(years)

Age at

psychosis 10, 10,

onset (years) 28,6 10,4 27,1 4 30,8 5 28,7 1 1 ,7 28,5 9,4

Duration of

untreated

psychosis 17, 30,

(months) 16,4 23,8 15,9 2 17,1 6 14,9 25,5 18,0 22,9

Duration of

untreated

illness 15, 21 ,

(months) 12,0 17,8 13,3 5 10,1 3 7,9 10,9 16, 1 22,3

BPRS at 14, 16,

admission 74,1 15,4 76,3 5 70,8 7 83,5 8,9 64,8 15,0

BPRS at 3

months of 1 1 , 1 1 ,

treatment 34,6 1 1 ,8 35,9 9 32,6 7 29,7 5,2 39,5 14,4

Table 2. Differential expression between responders and non-responders before antipsychotic medication. "GenelD" Gene Identification. "Gene Symbol" Official Symbol. "Base Mean" Mean normalized counts, averaged over all samples from both conditions. "Base Mean Responders" Mean normalized counts from condition A. "Base Mean No Responders" Mean normalized counts from condition B. "P value" P value for the statistical significance of this change. "Padj" P value adjusted for multiple testing with the Benjamini-Hochberg procedure, which controls false discovery rate.

1582,027 1289,4034 1874,65238 2,80499 5,4319E-

6614 SIGLEC1 93 79 1 E-29 26

20,82508 38,403432 3,24673692 1 ,42432 2,5074E-

56163 RNF17 48 74 3 E-28 25

2952,086 2469,2971 3434,87492 2,19718 3,5457E-

10964 IFI44L 06 94 6 E-28 25

160,7443 108,75464 4,4581 1 6,6409E-

722 C4BPA 83 91 212,7341 16 E-26 23

1273,055 574,63892 1971 ,47303 1 .368E- 1 .8922E-

991 1 TMCC2 98 56 2 24 21

1005061 LOC1005061 138,0722 79,954079 196,190447 7,40859 9,5645E- 59 59 63 37 3 E-22 19

37,62247 6,9819286 68,2630266 8,90692

144453 BEST3 77 96 8 E-22 1 ,078E-18

99,88463 32,683132 167,086146 5,36898 6,1 159E-

55553 SOX6 93 08 5 E-21 18

176,0578 232,46913 1 19,646525 2, 1 1802 2,2786E-

10529 NEBL 32 9 7 E-18 15

13,48445 3,5458247 23,4230775 4,071 19 4,1494E-

124912 SPACA3 1 1 37 6 E-18 15

492, 1792 670,47517 313,883350 5,81509 5,6305E-

26807 SNORD43 64 81 6 E-18 15

20,33129 2,6365923 38,0259970 7, 10397 6.5509E-

85495 RPPH1 47 65 1 E-18 15

149,2520 91 ,994440 206,509710 3,84513 3,3846E-

284581 LOC284581 75 4 3 E-16 13

63,89937 23,655831 104,142912 7,9894E- 6,7267E-

2993 GYPA 18 47 2 16 13

4047, 138 3499,4475 4594,82964 1 ,53753 1 ,2406E-

10561 IFI44 62 96 8 E-15 12

142,7318 60,158758 2,31251 1 .7913E-

219539 YPEL4 83 58 225,305008 E-15 12

27,70918 5,4627763 49,9555906 3,36928 2,5095E-

1 1227 GALNT5 35 55 7 E-15 12

378,7347 167,93822 589,531340 5,2141 1 3,7397E-

85462 FHDC1 81 1 1 3 E-14 1 1

266,7400 350,451 17 183,028909 6,98884 4,8335E-

2944 GSTM1 43 66 2 E-14 1 1

3834,175 2379,3961 5288,95421 1 ,51698

6521 SLC4A1 19 65 2 E-13 1 ,013E-10

5,859496 1 1 ,568822 0,15017121 2,48687 1 ,6053E-

389396 GLYATL3 93 65 4 E-13 10

340,8463 253,32874 428,363984 1 , 1 1927 6,9918E-

50509 COL5A3 63 12 3 E-12 10

381 ,9751 489,60679 274,343572 3,81373 2,3079E-

55225 RAVER2 83 34 7 E-12 09

136,7020 71 ,270147 202,133882 7,58663

10107 TRIM10 15 05 2 E-12 4,452E-09

54,1 1 164 37,553383 70,6699049 1 ,05032 5,9822E-

64478 CSMD1 43 65 4 E-1 1 09

773,4469 467,37409 1079,51977 1 , 16262 6,4326E-

55363 HEMGN 34 66 2 E-1 1 09

475,6133 349,63315 601 ,593584 5,06478 1 .5819E-

383 ARG1 71 84 5 E-08 05

490,8433 351 ,23737 630,449326 7,3477E- 2,2585E-

2038 EPB42 51 49 5 08 05

520,3897 435,74462 8,73219 2,6422E-

266727 MDGA1 53 49 605,034882 E-08 05

272,8759 31 1 ,44079 234,31 1 176 1 , 12882

7450 VWF 84 17 5 E-07 3,363E-05

71 ,69307 36,553660 106,832484 1 ,46567 4,3004E-

6423 SFRP2 24 32 5 E-07 05

8072,7234 1 1065,2605 1 ,72186 4,9767E-

160364 CLEC12A 9568,992 7 4 E-07 05

3180, 194 3193,8800 3166,50892 1 ,94424 5,5368E-

3434 IFIT1 49 63 7 E-07 05

176,4973 225,04356 127,951058 2,08067 5,8394E-

165530 CLEC4F 13 8 3 E-07 05

41 ,53994 60,095041 22,9848400 2,27046 6,281 1 E-

10395 DLC1 08 58 8 E-07 05

514,5964 404,44707 624,745910 2,88967 7,8815E-

140807 KRT72 92 36 3 E-07 05

2155,780 1848,8895 2462,67240 3,23149 8,6914E-

7057 THBS1 97 41 8 E-07 05

8,902951 2,8474145 14,9584876 3,73704 9,9134E-

64284 RAB17 12 89 6 E-07 05

97,36781 122,92275 5,24045 0,000137

51 162 EGFL7 46 18 71 ,8128775 E-07 14

564,9768 398,15144 731 ,802171 5,68418 0,000146

6550 SLC9A3 07 15 5 E-07 77

69,67986 50,135090 89,2246450 5,82013 0,000148

4070 TACSTD2 8 82 9 E-07 3

107,4942 67,180183 147,808372 6,44659 0,000162

148534 TMEM56 78 77 8 E-07 13

718,8573 489,901 16 947,813485 8,17945 0,000203

6563 SLC14A1 26 56 9 E-07 07

19,79408 30,381070 9,20709316 8,98878 0,000220

6368 CCL23 2 77 8 E-07 34

77,43971 60,768800 94,1 106338 9,36669 0,000226

1 17854 TRIM6 74 86 8 E-07 73

46,43596 57,644518 35,2274013 9,8741 E- 0,000233

3816 KLK1 01 86 1 07 19

136,3721 95,71 1091 9,84562 0,000233

7138 TNNT1 16 59 177,03314 E-07 19

75,99648 56,200679 95,7922834 1 ,03753 0,000242

1305 COL13A1 15 61 3 E-06 07

4,837106 8,8358097 0,83840391 1 ,14603 0,000264

1281 COL3A1 85 94 6 E-06 2

1 1 ,45652 19,765653 3,14740422 1 ,28858 0,000293

219970 GLYATL2 89 57 6 E-06 57

21 ,22497 12,082944 30,3669972 1 ,47543 0,000332

253559 CADM2 1 84 5 E-06 23

196,3169 239,49813 153,135773 1 ,72268 0,000383

287 ANK2 54 49 4 E-06 45 2004,315 2151 .7251 1856,90574 1 ,87197 0,00041 1

91543 RSAD2 44 4 2 E-06 94

428,8082 500,74882 356,867734 2,04675 0,000445

31 18 HLA-DQA2 8 56 3 E-06 34

769,3277 889,77867 2,22955 0,000479

10398 MYL9 49 57 648,876823 E-06 73

10,87129 17,522302 4,22027835 2,49216 0,000530

645843 TMEM14E 04 37 1 E-06 34

1 ,649092 3,29818528 3,25906

54659 UGT1 A3 64 0 6 E-06 0,000686

158,0135 1 15,52481 200,502298 3,53764 0,000736

2952 GSTT1 56 3 4 E-06 63

1017,391 791 ,32547 4,21249 0,000866

3042 HBM 97 38 1243,45847 E-06 09

1475,322 1642,3267 1308,31812 4,24883 0,000866

7849 PAX8 42 07 8 E-06 09

21 ,52343 32,144077 10.9028019 4,72879 0,000953

728577 CNTNAP3B 99 87 3 E-06 89

190,6901 164,86167 216,518582 4,89019 0,000976

57596 BEGAIN 28 43 5 E-06 27

1 ,579371 3,15874219 5,27759 0,001041

54658 UGT1 A1 1 0 8 E-06 87

153,1975 193,79368 1 12,601492 5,32637 0,001041

5197 PF4V1 9 7 2 E-06 87

89,39294 105,53547 73,2504198 6,2304E- 0,001206

954 ENTPD2 57 15 7 06 52

269,6364 312,79801 226,474864 7,61071 0,001459

140733 MACROD2 39 37 1 E-06 22

21 17,509 2050,9235 2184,09538 8,22731 0,001546

2537 IF16 48 69 4 E-06 81

1 197,053 1329,0616 1065,04529 8, 19658 0,001546

654433 PAX8-AS1 47 47 1 E-06 81

RP1 1 - 1533,191 1351 ,1617 1715,22169 9,12169 0,001698

284751 290F20.1 74 81 7 E-06 48

617,2615 546,53149 687,991518 1 .1326E- 0,002088

643418 LIPN 05 07 8 05 83

880,0183 967,64367 792,393017 1 ,20407 0,002199

928 CD9 49 98 9 E-05 71

1 1 ,20386 6,0722601 16,3354743 1 ,46566 0,002628

55228 PNMAL1 73 69 9 E-05 01

1 13,4837 91 ,425687 135,541906 1 ,46186 0,002628

202134 FAM153B 97 58 6 E-05 01

49,28080 60,1 1521 1 38,4464051 1 ,76629 0,003138

346171 ZFP57 82 15 7 E-05 01

717,3638 623,84094 810,886812 2,04821 0,003605

64105 CENPK 8 79 5 E-05 78

254,0530 209,61760 298,488477 2,37359 0,004140

642846 LOC642846 4 28 5 E-05 96

274,6158 207,21691 342,014758 2,501 17 0,004324

439996 IFIT1 B 35 1 1 3 E-05 57

1713, 150 1898,0410 1528,25981 2,56269 0,004354

2213 FCGR2B 42 18 5 E-05 42 709,0658 628,53140 789,600227 2,56341 0,004354

387837 CLEC12B 16 42 4 E-05 42

478,5214 375,84009 581 ,202756 2,82138 0,004731

1397 CRIP2 26 45 5 E-05 15

1019275 RP1 1 - 714,2930 636,15882 792,427272 2,83405 0,004731 86 290F20.2 49 63 2 E-05 15

80, 18984 98,549984 61 ,8297145 2,93224 0,004853

89872 AQP10 93 16 3 E-05 23

2923,469 3437,5231 2409,4161 1 3,42533 0,005621

3162 HMOX1 62 24 5 E-05 31

159,6787 135,81246 183,545009 3,49447 0,005686

440068 CARD17 38 69 5 E-05 6

1002722 LOC1002722 457,5360 396,95578 518,1 16292 4,22406 0,006816 16 16 36 01 7 E-05 58

3,744182 1 ,0680014 6,42036384 4,3976E- 0,007037

54600 UGT1 A9 63 18 1 05 98

7,005503 1 ,4254971 12,5855101 4,49777 0,007070

420 ART4 65 34 8 E-05 8

3554,043 4097,6534 3010,43334 4,52765 0,007070

5473 PPBP 38 12 4 E-05 8

4548,389 3892, 1354 5204,64329 4,4883E- 0,007070

84628 NTNG2 36 14 8 05 8

1892,183 1723,2221 2061 ,14392 4,58289 0,007099

440836 ODF3B 05 7 8 E-05 81

14,08760 8,7009935 19,4742260 5,32618 0,008185

2740 GLP1 R 98 45 6 E-05 82

489,2380 387,59363 590,882444 5,37871 0,008201

3576 IL8 41 7 8 E-05 48

7,038951 1 ,9296872 12, 1482147 5,57307 0,008431

6517 SLC2A4 02 99 4 E-05 44

RP1 1 - 24,94888 33,002620 16,8951445 5,72036 0,008587

339975 138B4.1 24 23 3 E-05 2

79,56556 64,445209 94,6859270 5,97407 0,008899

284194 LGALS9B 85 93 8 E-05 07

Table 3. Differential expression between responders and non-responders after 3 months of antipsychotic medication. "GenelD" gene identification. "Gene symbol" official symbol. "Base mean" mean normalized counts, averaged over all samples from both conditions. "Base mean responders" mean normalized counts from condition A. "Base mean no responders" mean normalized counts from condition B. "P value" p value for the statistical significance of this change. "Padj" P value adjusted for multiple testing with the Benjamini-Hochberg procedure, which controls false discovery rate. Base Base Mean

Base Mean Non- genelD Gene Symbol Pval Padj

Mean Responde Responder

rs s

10046298 552,17489 123,8910 6,636E- 1 .281 E-

1 MTRNR2L2 1 61 980,45872 138 133

129,21914 28,23670 230,20158 1 ,0615E 1 ,0243E-

8120 AP3B2 5 73 2 -87 83

RP1 1 - 109,64487 24,55259 194,73715 2.7763E 1 .786E-

283692 752G15.3 2 1 1 4 -79 75

474,08023 750,3438 197,81665 3,3552E 1 .6188E-

10562 OLFM4 8 18 8 -59 55

19,651856 38,68053 8,0864E 3,1212E-

84873 GPR128 8 91 0,6231745 -51 47

1029,6142 539,0070 1520,2213 3,6889E 1 .1865E-

3045 HBD 1 43 8 -43 39

19,415530 37,26160 1 ,5694540 2,0635E 5,689E-

56163 RNF17 5 7 4 -41 38

320,45983 168,8642 472,05537 7,7401 E 1 .8672E-

10917 BTNL3 6 92 9 -40 36

470,83716 703,0304 6,9975E 1 .5005E-

4317 MMP8 3 55 238,64387 -36 32

18,619172 35,31758 1 ,9207572 9,298E- 1 ,7944E-

23532 PRAME 3 74 7 35 31

3926,5159 5762,890 2090,1416 2,5228E 4,4261 E-

4057 LTF 2 19 4 -34 31

1543,3001 885,8479 1 ,7445E 2,8056E-

91543 RSAD2 9 71 2200,7524 -33 30

97,75275 331 ,07284 8,4691 E 1 .2573E-

722 C4BPA 214,4128 32 6 -33 29

5103,1645 6871 ,256 3335,0728 4,0073E 5,5241 E-

212 ALAS2 5 22 7 -28 25

274,44085 163,8935 384,98818 4,4797E 5,7636E-

85462 FHDC1 1 15 8 -28 25

1532,406 613,221 15 1 ,4512E 1 .7505E-

3934 LCN2 1072,8138 45 7 -27 24

2221 ,9309 1379,960 3063,9013 5,3086E 6.0266E-

10964 IFI44L 2 46 7 -25 22

625,61843 883,9483 367,28854 3,21 1 E 3,4428E-

1088 CEACAM8 2 21 3 -24 21

75,425525 1 13,1756 37,675430 1 ,5421 E 1 .5663E-

221687 RNF182 8 21 6 -23 20

162,60296 93,72638 231 ,47953 1 ,3283E 1 .2817E-

284581 LOC284581 2 42 9 -22 19

32,49880 95,029621 5.7727E 5,3051 E-

55553 SOX6 63,764215 86 4 -22 19

1676,534 3421 ,0507 1 ,2962E 1 ,1371 E-

3434 IFIT1 2548,7926 48 2 -21 18

299,25781 421 ,0737 177,44191 1 ,8144E 1 ,5224E-

10321 CRISP3 8 25 1 -21 18

3516,2495 2318,881 4713,6173 1 ,9203E 1 ,5442E-

10561 IFI44 3 71 5 -21 18

631,33919 466,8233 795,85508 6,7497E 2,5542E-

710 SERPING1 5 01 8 -13 10

5,1946117 10,16110 0,2281158 9,3048E 3,4533E-

389396 GLYATL3 5 77 4 -13 10

136,3350 64,640835 9,8535E 3,588E-

7280 TUBB2A 100,48793 25 9 -13 10

899,33552 661,5974 1137,0735 1,1283E 4,0324E-

129607 CMPK2 8 74 8 -12 10

5724,8357 4210,131 7239,5396 1 ,4732E 5.1695E-

4599 MX1 2 8 4 -12 10

2993,8033 2295,020 3692,5857 2,5335E 8,7311E-

54855 FAM46C 1 82 9 -12 10

2274,5906 1762,402 2786,7790 2,6787E 8.9131E-

6513 SLC2A1 3 21 5 -12 10

23,628429 12,77357 34,483280 2,6479E 8.9131E-

56603 CYP26B1 1 78 4 -12 10

166,84194 210,7197 122,96413 2,8318E 9,2627E-

165530 CLEC4F 9 65 4 -12 10

RP11- 1461,1093 1140,121 1782,0976 3,2868E 1.0572E-

284751 290F20.1 5 02 8 -12 09192758 RP11- 659,10951 510,3167 807,90230 3,3448E 1.0582E- 6 290F20.2 9 34 4 -12 09

73,352548 103,5862 43,118890 3,6493E 1.1359E-

5657 PRTN3 8 07 8 -12 09

349,04061 453,3201 244,76103 4,5018E 1.3791E-

55225 RAVER2 4 94 4 -12 09

18,096246 8,328078 27,864413 4.9875E 1.504E-

144453 BEST3 4 89 9 -12 09

261,51970 336,3083 186,73107 5,2581 E 1.5612E-

6285 S100B 2 29 6 -12 09

229,32678 291,4022 167,25136 6,2267E 1.8207E-

343171 OR2W3 6 11 1 -12 09

1417,4144 1053,578 1781,2508 6,3749E 1.8362E-

51191 HERC5 6 11 1 -12 09

4833,0955 3637,696 6028,4947 6,6098E 1.8759E-

4940 OAS3 8 39 7 -12 09

84,506972 111,4716 57,542301 2,8784E 8,0506E-

400566 C17orf97 7 43 9 -11 09

13,227464 5,149652 21,305276 4,402E- 1.2136E-

11227 GALNT5 5 59 4 11 08

6784,8452 8247,688 5322,0016 5,2199E 1.4189E-

6231 RPS26 5 88 1 -11 08

36,592492 21,91621 51,268766 5,6118E 1 ,5042E-

389337 ARHGEF37 5 81 9 -11 08

5971,7391 4517,000 7426,4780 6,0802E 1 ,6074E-

81788 NUAK2 4 21 7 -11 08

443,12757 565,5653 320,68976 8,3947E 2,1893E-

1669 DEFA4 7 93 2 -11 08

RP11- 188,9415 331,41372 1,0041E 2,5838E-

731424 701P16.5 260,17764 54 5 -10 08

48,303915 29,56465 67,043180 2,8707E 7,2896E-

2731 GLDC 9 1 7 -10 08 384,34074 453,9712 314,71024 3,1 103E 7.7957E-

3848 KRT1 8 48 9 -10 08

44,387728 28,28516 60,490288 4,8447E 1 .1987E-

2993 GYPA 9 95 3 -10 07

680,87549 818,8303 542,92061 8,0173E 1 .9586E-

8991 SELENBP1 6 78 3 -10 07

10039,942 7870,088 12209,797 1 ,21 18E 2,9233E-

3433 IFIT2 9 44 4 -09 07

34,204017 21 ,34137 1 ,8598E 4,4312E-

8444 DYRK3 8 57 47,06666 -09 07

1 13,84055 145,6100 82,071 103 2,3947E 5,636E-

57094 CPA6 7 12 3 -09 07

551 ,54289 417,3817 685,70408 2,6796E 6,2305E-

6550 SLC9A3 9 15 3 -09 07

101 ,25241 125,9285 76,576229 3.5735E 8,1 136E-

55384 MEG3 4 98 9 -09 07

129,48251 92,00690 166,95813 3,5653E 8,1 136E-

608 TNFRSF17 9 24 6 -09 07

5191 ,3863 6346,065 4036,7076 4,1213E 9,2485E-

1740 DLG2 5 07 3 -09 07

215,95612 272,3165 159,59565 5,6657E 1 .2568E-

3240 HP 3 89 8 -09 06

10831 ,461 8493,895 13169,027 9,0434E 1 .9678E-

3772 KCNJ15 4 43 5 -09 06

RP1 1 - 23,798751 33,78333 13,814164 9,0747E 1 .9678E-

339975 138B4.1 5 89 1 -09 06

5051 ,1859 3974,999 6127,3720 9,3935E 2.0143E-

10410 IFITM3 7 89 6 -09 06

129,66570 165,9687 1 ,149 E 2,437E-

5197 PF4V1 3 51 93,362654 -08 06

19248,313 23465,55 15031 ,073 1 ,2057E 2,5292E-

81796 SLC05A1 3 32 4 -08 06

71 1 ,29959 541 ,9058 880,69329 1 .235E- 2,5629E-

1 16369 SLC26A8 6 99 3 08 06

1358,7227 1065,049 1652,3955 1 ,7685E 3,6309E-

91947 ARRDC4 2 92 2 -08 06

33832,514 41213,36 26451 ,667 1 ,7892E 3,6346E-

164045 HFM1 2 09 5 -08 06013088 28,313067 17,25501 39,371 1 18 1 ,9705E 3,9614E- 9 PSORS1 C3 2 59 5 -08 06

612, 18640 490,0683 734,30449 2,0936E 4,1654E-

55363 HEMGN 7 23 1 -08 06

847,57148 691 ,8448 1003,2981 3,0378E 5,9823E-

5266 PI3 6 39 3 -08 06

383,73644 308,6000 458,87286 3,3054E 6,4435E-

6097 RORC 4 21 8 -08 06

3,1270045 5,927302 0,3267067 5,9604E 1 .1503E-

138255 C9orf135 8 4 6 -08 05

836,06048 1025,053 7,6186E 1 ,4558E-

4353 MPO 4 18 647,06779 -08 05

939,24407 1 149,998 728,48982 8,3453E 1 .579E-

671 BP! 1 32 3 -08 05 1 17,0650 192,29351 8,4695E 1 .5869E-

2952 GSTT1 154,6793 85 5 -08 05

184,80300 231 ,6972 137,90876 1 ,1543E 2.1421 E-

10529 NEBL 9 56 1 -07 05

107,61653 79,76686 135,46619 1 ,3618E 2,5031 E-

5013 OTX1 2 64 8 -07 05

4622,3814 3693,528 5551 ,2344 1 ,5613E 2,8427E-

81567 TXNDC5 3 45 1 -07 05

3575,8351 4203,386 2948,2840 1 ,9443E 3,5068E-

25893 TRIM58 7 29 5 -07 05

304,08527 370,2312 237,93929 2,0438E 3,6186E-

6947 TCN1 6 59 4 -07 05

19,218465 1 1 ,21223 27,224699 2,033E- 3,6186E-

253012 HEPACAM2 6 2 3 07 05

3233,7616 2626,244 3841 ,2792 2,1 184E 3.7166E-

7850 IL1 R2 9 14 5 -07 05

38,257428 51 ,08702 25,427832 2,1445E 3,7285E-

9623 TCL1 B 1 33 9 -07 05

549,74051 440,9527 658,52823 3,0357E 5,231 E-

51237 MZB1 1 86 5 -07 05

131 ,56903 166,4797 96,658339 3,0852E 5,2691 E-

151 1 CTSG 4 28 4 -07 05

1703,833 3,1 563E 5,3433E-

6286 S100P 2083,7966 39 2463,7598 -07 05

606,97450 472,6278 3,3974E 5,701 5E-

342184 FMN1 3 26 741 ,321 18 -07 05

10,900134 5,217429 16,582839 3,4489E 5,7379E-

7173 TPO 7 55 9 -07 05013394 1465,5169 1746,775 1 184,2579 4,258E- 7,0236E-

1 CD24 3 96 1 07 05

7,2378760 2,423442 12,052309 4,6787E 7,652E-

64284 RAB17 8 25 9 -07 05

179,35356 217,2415 141 ,46561 5,1 19E- 8,3018E-

56729 RETN 6 13 9 07 05

193,43979 239,7560 5,4828E 8,8178E-

6037 RNASE3 9 79 147,12352 -07 05

56,81 1513 43,35490 70,268124 6,0376E 9,6298E-

64478 CSMD1 1 19 3 -07 05

9225,0184 7419,867 1 1030, 169 6,2345E 9,8363E-

160364 CLEC12A 9 41 6 -07 05

1493,6550 1775,873 121 1 ,4367 6,2691 E 9,8363E-

83869 I I I Y14 5 36 5 -07 05

85,620231 66,01601 105,22444 7,6135E 0,0001 18

3627 CXCL10 4 87 4 -07 49

1566,4926 1266,544 1866,4410 7,7246E 0,0001 19

83999 KREMEN1 1 14 9 -07 26

51 1 ,82771 407,4217 616,23367 7,9043E 0,000121

383 ARG 1 6 6 2 -07 07

8,8082549 13,97671 3,6397916 8,2599E 0,000125

3624 INHBA 8 83 8 -07 52

90,229904 1 14,4775 8,7146E 0,000131

85413 SLC22A16 3 37 65,982272 -07 39 60,302819 74,06843 46,537200 9,394E- 0,000140

5909 RAP1 GAP 6 86 6 07 54

25,31081 9,4857052 9,8762E 0,000146

26577 PCOLCE2 17,398262 87 5 -07 62

32,962215 44,01069 21 ,913737 1 ,0624E 0,000156

283120 H 19 9 43 6 -06 52

58,197148 46,76420 69,630092 1 ,0809E 0,0001 58

1091 1 UTS2 5 45 6 -06 03

120,98183 91 ,32555 150,63810 1 ,1 144E 0,000161

9828 ARHGEF17 3 88 8 -06 7

94,700076 74,07615 1 15,32399 1 .134E- 0,000163

419 ART3 3 41 8 06 32

835,5558 555,30759 1 ,244E- 0,000177

3823 KLRC3 695,43173 62 8 06 84

2403,362 1743,9804 1 ,4079E 0,000199

6622 SNCA 2073,6717 93 7 -06 79

53,503099 69,91 124 37,094958 1 ,5065E 0,000212

1308 COL17A1 2 03 1 -06 22

76,992832 97,88085 56,104807 1 ,5312E 0,000214

2078 ERG 2 7 4 -06 14

2580,017 1 ,8283E 0,000253

2999 GZMH 2177,1582 1 1774,2993 -06 84

80,873034 58,68455 103,06151 2,3218E 0,000320

387755 INSC 5 75 1 -06 06

516,93614 434,4376 599,43463 3,0175E 0,000413

57282 SLC4A10 8 63 3 -06 01

177,59459 210,1256 145,06354 3,3433E 0,000454

287 ANK2 2 36 9 -06 38

70,788613 88,87549 52,701730 3,4501 E 0,000465

1 14132 SIGLEC1 1 9 69 9 -06 62

34,352608 46,48898 22,216226 3,7217E 0,000498

10395 DLC1 2 99 5 -06 78

17,595484 1 1 ,00683 24,184130 3,8097E 0,000507

6783 SULT1 E1 8 92 4 -06 06

237,94466 279,1078 196,78148 4,036E- 0,000533

140733 MACROD2 8 5 7 06 5

3756,300 5542,0187 4,6812E 0,000614

4318 MMP9 4649, 1595 28 1 -06 57

484,67031 402,8319 566,50869 5,0573E 0,000659

54892 NCAPG2 3 31 6 -06 47

312,18452 373,3812 250,98780 5,3559E 0,000693

5159 PDGFRB 2 35 8 -06 71

1 1 137,403 9193,608 13081 ,197 5,7017E 0,000733

9586 CREB5 1 31 8 -06 58

20,815291 1 1 ,53386 30,096717 6,0106E 0,000763

253559 CADM2 7 6 4 -06 15

3686,9714 3053,352 4320,5901 6,0003E 0,000763

3092 HIP1 2 7 4 -06 15

10,098568 4,832927 15,364210 6,3086E 0,000795

55228 PNMAL1 8 38 2 -06 75

396,62981 473,9503 6,743E- 0,000845

26807 SNORD43 9 18 319,30932 06 03

305,5983 420,79549 2,5625E 0,002732

1397 CRIP2 363,19694 85 6 -05 22

240,83171 196,0846 285,57874 2,5875E 0,002743

125058 TBC1 D16 4 83 5 -05 71

1 162,4867 1296,904 1028,0692 3,2201 E 0,003395

654433 PAX8-AS1 4 26 1 -05 86

351 ,56899 410,7252 292,41271 3,4904E 0,003660

80310 PDGFD 9 87 2 -05 89

7,5225055 10,78813 4,2568800 3,5513E 0,003704

29065 ASAP1 -IT1 4 1 5 -05 72

60,547297 76,28159 44,812999 3,6582E 0,003795

399697 CTXN2 9 64 3 -05 66

6632,1 100 5649,438 7614,7820 3.714E- 0,003832

20031 5 APOBEC3A 7 08 6 05 96

150,08441 178,9598 121 ,20901 4,0065E 0,0041 12

6320 CLEC1 1 A 4 1 8 -05 83

142,30692 1 13,5735 171 ,04033 4,0528E 0,004138

440068 CARD17 7 16 7 -05 31

278,65322 222,151 15 4,3906E 0,004459

566 AZU1 9 335,1553 7 -05 72

566,80921 498,6298 634,98855 4,8453E 0,004895

619207 SCART1 9 86 3 -05 76

2614,6208 2206,751 3022,4906 4,9708E 0,004996

8843 HCAR3 4 04 3 -05 44

646,08618 547,2759 744,89641 5,3836E 0,005358

6563 SLC14A1 7 56 7 -05 89

179,63778 147,6379 21 1 ,63762 5,3869E 0,005358

875 CBS 7 5 5 -05 89

746,87023 625,7794 867,96104 5.4452E 0,005389

84418 CYSTM1 5 25 5 -05 1

1496,0030 1231 ,458 1760,5474 5,5257E 0,005440

83416 FCRL5 1 61 1 -05 79091318 APOBEC3A 71 14,9352 6076,242 8153,6275 5,5721 E 0,005458 7 B 1 83 8 -05 69

606,41 1 10 519,3373 693,48488 5,9728E 0,005792

1870 E2F2 8 27 9 -05 43

190,3771 262,76614 5,9443E 0,005792

3084 NRG1 226,57166 7 9 -05 43

575, 16442 657,2394 493,08935 6,0213E 0,005810

6535 SLC6A8 3 96 1 -05 26

22,135681 14,49321 29,778147 6,3553E 0,006102

1 17156 SCGB3A2 1 44 8 -05 03

5,5703250 9,020504 2,1201456 6,8558E 0,006550

79776 ZFHX4 8 5 6 -05 02

33718,681 28281 ,45 39155,910 6,9079E 0,006567

2180 ACSL1 3 17 8 -05 24

22,939816 15,01 158 30,868045 7,3044E 0,006910

7367 UGT2B17 9 83 5 -05 14

4,01 17517 1 ,551216 6,4722872 7,7728E 0,007317

420 ART4 9 33 4 -05 38

1000,6430 853,0528 1 148,2333 7,9805E 0,007476

8638 OASL 7 42 1 -05 45 187,98174 156,0240 219,93944 8,3333E 0,007760

51733 UPB1 5 49 1 -05 38

22,702671 31 ,78053 13,62481 1 8,364E- 0,007760

9509 ADAMTS2 5 15 6 05 38

25361 ,486 21542,95 29180,019 8,8121 E 0,008137

366 AQP9 2 27 7 -05 1 1

1764,2694 1992,999 1535,5398 9,0142E 0,008284

22846 VASH1 8 12 4 -05 02

97,870328 1 15,1778 9,1252E 0,008346

7380 UPK3A 6 06 80,562851 -05 32

81915,570 70230,05 93601 ,087 9,293E- 0,008459

2215 FCGR3B 7 4 5 05 03

435,46651 502,5057 368,42726 9,3361 E 0,008459

356 FASLG 4 63 5 -05 03

510,27924 447,4345 573, 12392 0,00010 0,009076

266727 MDGA1 6 71 2 065 72

279,1 1 1 13 234,2439 0,00010 0,009155

1 193 CLIC2 4 17 323,97835 199 33

12859,828 10910,61 14809,045 0,00010 0,009257

8972 MGAM 6 13 8 361 41

1700,5905 1435,762 0,00010 0,00931 1

8876 VNN1 1 31 1965,4187 47 67

181 ,28218 144,9142 217,65008 0,00010 0,009670

7053 TGM3 4 87 2 924 51

67,988989 54,61554 81 ,362431 0,0001 1 0,009749

84073 MYCBPAP 8 82 4 064 98

Table 4. Differential expression between responders before and after medication. "GenelD" gene identification. "Gene symbol" official symbol. "Base mean" mean normalized counts, averaged over all samples from both conditions. "Base mean before medication" mean normalized counts from condition A. "Base mean after medication" mean normalized counts from condition B. "P value" p value for the statistical significance of this change. "Padj" P value adjusted for multiple testing with the Benjamini-Hochberg procedure, which controls false discovery rate.

Base

Base

Base Mean

genelD Gene Symbol Mean After Pval Padj

Mean Before

Medication

Medication

527,25290 238,81619 815,68960 5,9818E

10562 OLFM4 1 7 5 -57 1 ,158E-52

644,61072 328,22668 3,8657E 3,7416E-

1088 CEACAM8 6 2 960,99477 -51 47

2376,6686 6266,8451 2.14E- 1 .3809E-

4057 LTF 4321 ,7569 3 6 47 43

310,04765 162,331 17 457,76412 9,847E- 4,7654E-

10321 CRISP3 3 9 8 40 36 314,92960 162,85824 467,00096 3,51 16E 1 .3595E-

154664 ABCA13 4 5 3 -38 34

1 192,7173 719,06809 1666,3666 1 ,7475E

3934 LCN2 7 6 4 -37 5,638E-34

309,43516 764,31615 4,751 1 E 1 .3139E-

4317 MMP8 536,87566 3 8 -37 33

82,001410 129,41863 34,584183 1 ,0569E 2,5575E-

9509 ADAMTS2 3 7 9 -34 31

10019098 LOC10019098 7,9754786 66,769539 1 ,8272E

6 6 37,372509 7 4 -33 3,93E-30

2240,0878 1549,9964 2930,1792 6,925E- 1 ,1 171 E-

1667 DEFA1 5 9 1 33 29

2240,0878 1 549,9964 2930,1792 6,925E- 1 , 1 171 E-

728358 DEFA1 B 5 9 1 33 29

2240,0878 1549,9964 2930, 1792 6,925E- 1 , 1 171 E-

1668 DEFA3 5 9 1 33 29

465,64719 283, 10914 648, 18523 1 ,7492E 2,6048E-

4680 CEACAM6 1 6 5 -30 27

10046298 80,751659 26,812259 5,7677E

1 MTRNR2L2 7 5 134,69106 -28 7,975E-25

305,46734 615,07877 5,9021 E 7,6169E-

1669 DEFA4 460,27306 9 2 -27 24

10042306 2936.1530 4033,3834 1838,9225 4,3234E 5,2307E- 2 IGLL5 2 8 5 -26 23

943,02965 635,53983 1250,5194 9,4636E 1 ,0776E-

671 BPI 6 8 7 -25 21

681 ,78150 451 ,33373 912,22927 5,4121 E 5,8205E-

820 CAMP 8 8 7 -23 20

102,46433 56,125069 148,80360 1 ,6712E 1 ,7027E-

4973 OLR1 8 2 7 -22 19

4839,5916 6549,0446 3130, 1386 4,251 E- 4.1 146E-

3512 IGJ 1 1 1 22 19

90,675899 58,063648 123,28815 1 ,0538E

221687 RNF182 4 1 1 -20 9,714E-18

869,30563 624,26322 1 1 14,3480 4,2754E 3,7619E-

4353 MPO 3 6 4 -18 15

166, 12560 232,16430 100,08689 3,0278E 2,5484E-

608 TNFRSF17 1 8 3 -17 14

280,90044 197,35655 364,44433 4,6227E 3,7286E-

566 AZU1 2 2 1 -17 14

7419,8570 4018,0397 3,4318E 2,6573E-

81567 TXNDC5 5718,9484 7 3 -16 13

136,39585 91 ,819820 180,97189 1 ,4061 E 1 ,0469E-

151 1 CTSG 8 6 6 -15 12

496,16566 587,35379 5,7961 E 4,1556E-

3045 HBD 4 404,97753 8 -15 12

1559,2212 963,65695 1 ,91 12E 1 .3213E-

91543 RSAD2 3 2154,7855 8 -14 1 1

438,40156 524,97532 6,9296E 4,6256E-

2038 EPB42 7 351 ,82781 5 -14 1 1

854,59064 479,66387 8,2765E 5,3406E-

51237 MZB1 667,12726 2 9 -14 1 1 926,37498 825,36546 1027,3844 1,5513E 9,6873E-

9172 MYOM2 1 7 9 -13 11

555,24372 437,31347 673,17397 1.854E- 1,1215E-

27181 SIGLEC8 2 1 4 13 10

6731,3943 5977,2105 7485,5782 2,4208E 1.4201E-

212 ALAS2 7 1 3 -13 10

227,67768 168,48743 286,86792 2,6937E 1.5336E-

1991 ELANE 3 8 7 -13 10

2780,5732 2383,7242 3177,4222 3,4142E 1 ,8884E-

6521 SLC4A1 7 8 7 -13 10

728,60470 547,98058 909,22882 1,2056E 6,4828E-

10720 UGT2B11 4 1 6 -12 10

3988,8797 3400,5925 4577,1668 2,4243E 1 ,2684E-

25893 TRIM58 1 8 4 -12 09

1850,3182 2292,5776 1408,0588 3,8297E 1.9509E-

5166 PDK4 5 6 4 -12 09

602,60921 729,68516 5,0796E 2,5213E-

932 MS4A3 3 475,53326 6 -12 0952683 BLOC1S5- 6727,0593 8446,5168 5007,6019 5,2383E 2,5351 E-

6 TXNDC5 8 1 5 -12 09

782,34076 891,61588 1,0179E

8991 SELENBP1 1 673,06564 2 -11 4,806E-09

398,72018 501,97841 295,46195 1,1795E 5,4364E-

6948 TCN2 5 6 4 -11 09

42,495348 57,713837 27,276858 2,5059E 1 ,1281 E-

3816 KLK1 4 8 9 -11 08

491 ,48037 407,31921 575,64152 2,7773E 1.2219E-

10900 RUNDC3A 1 9 3 -11 08

79,982925 53,531363 106,43448 5,4569E 2,2964E-

2078 ERG 9 9 8 -11 08

55,475458 78,792734 32,158181 5,4387E 2,2964E-

2731 GLDC 1 7 6 -11 08

325,81804 249,02897 402,60711 5,8124E

6947 TCN1 4 4 4 -11 2,394E-08

139,08894 96,333072 6,3419E 2,5576E-

712 C1QA 1 181,84481 6 -11 08

57,091519 38,153971 76,029067 7,8452E 3,0993E-

1308 COL17A1 3 2 5 -11 08

217,83487 174,86314 260,80660 8,2043E 3,1764E-

6037 RNASE3 3 3 3 -11 08

6051,5212 7778,3313 4324,7111 1,0115E 3,8393E-

10410 IFITM3 4 1 7 -10 08

1512,4814 1228,3738 1796,5890 1,4618E 5,4417E-

246 ALOX15 3 2 3 -10 08

428,76119 363,11388 494,40851 1,6598E 6,0624E-

3848 KRT1 6 1 2 -10 08

1044,5651 874,51142 1214,6189 2,1807E 7,8175E-

7145 TNS1 7 2 2 -10 08

85,108990 57,656510 112,56146 6,1899E 2,1786E-

5657 PRTN3 1 7 9 -10 07

7,9702067 2,0096040 13,930809 1,0291 E 3,5573E-

7180 CRISP2 7 3 5 -09 07 17,971533 8,43551 1 1 27,507556 1 ,6018E 5,4398E-

26577 PCOLCE2 7 6 2 -09 07

238,44199 303,56239 1 ,6995E 5,6722E-

4481 MSR1 6 2 173,3216 -09 07

2356,5001 2095,7550 2617,2452 2,4261 E

6622 SNCA 5 9 1 -09 7,96E-07

1223,3031 1482,7038 963,90253 5,0691 E 1 ,6354E-

6768 ST14 7 1 8 -09 06

135,25242 96,316006 174, 18884 5,8869E 1 .8682E-

1 1 18 CHIT1 7 9 7 -09 06

319,63500 419,10331 6,3053E 1 .9687E-

644248 RP1 1 -67M1 .1 369,36916 5 6 -09 06

208,35808 239,74361 176,97255 9, 1 132E 2,8002E-

146439 CCDC64B 2 4 1 -09 06

5089, 1873 6312,5998 3865,7748 1 ,3406E

3437 IFIT3 5 7 2 -08 4,055E-06

170,57981 138,98668 202, 17293 2,4124E 7, 1844E-

10215 OLIG2 2 8 5 -08 06

839,50729 507,84254 2,4835E 7,2842E-

710 SERPING 1 673,67492 3 6 -08 06

231 ,66735 283,86947 179,46524 2,7402E 7,9172E-

9672 SDC3 8 2 4 -08 06

106,25918 55,151220 3,8784E 1 ,1041 E-

713 C1 QB 80,705202 4 5 -08 05

269,94391 335,40461 204,48321 4,4551 E 1 ,2499E-

1 16071 BATF2 5 4 6 -08 05

2046,1708 2357,5361 5,2622E 1 ,4552E-

55512 SMPD3 1 1734,8055 1 -08 05

707,77765 572,48998 843,06532 7,0593E 1 ,9247E-

765 CA6 6 6 6 -08 05

163,73739 204,29849 7,4754E 2,0099E-

1958 EGR1 4 7 123,17629 -08 05

393,56176 343,93084 443,19269 7,782E- 2,0636E-

3620 ID01 6 1 1 08 05

4657,1085 4072,0153 5242,2016 1 ,0335E 2,7035E-

2039 DMTN 3 8 9 -07 05

6,9532929 12,373145 1 ,5334408 1 ,0526E 2,7169E-

1278 COL1 A2 4 1 1 -07 05

944,93045 820,37103 1069,4898 1 ,0906E 2,7779E-

3568 IL5RA 4 8 7 -07 05

251 1 ,1264 3198,4148 1823,8380 1 ,3244E 3,3296E-

3434 IFIT1 5 8 2 -07 05

18,604795 42,027064 1 ,5974E 3,9645E-

94031 HTRA3 30,31593 5 5 -07 05

248,50710 317,23869 1 ,7029E 4,1726E-

343171 OR2W3 282,8729 5 4 -07 05

520,17885 634,24814 406,10956 2,2124E 5,3534E-

8685 MARCO 5 1 9 -07 05

1318,6232 1489,4132 2,4771 E 5,9199E-

54674 LRRN3 2 1 147,8332 3 -07 05

181 ,05543 215,47061 146,64024 2,7143E 6,4078E-

23495 TNFRSF13B 1 5 8 -07 05 34,357404 24,180279 44,534528 5,2304E 0,0001191

200132 TCTEX1D1 1 4 8 -07 2

231,11696 189,40996 272,82397 5,1899E 0,0001191

116285 ACSM1 7 1 3 -07 2

726,66178 624,71190 828,61167 5.218E- 0,0001191

64105 CENPK 9 5 3 07 2

19,922285 29,733597 5,5165E 0,0001241

6382 SDC1 1 2 10,110973 -07 7

25,341033 15,773885 34.908182 5,8904E 0,0001310

51208 CLDN18 7 1 2 -07 6

4130,9406 4783,0639 3478,8172 6,3985E 0,0001407

1374 CPT1A 2 4 9 -07 5

46177,577 54173,117 8,1121 E 0,0001764

6279 S100A8 9 8 38182,038 -07 4

41,096638 18,419451 8,5678E 0,0001842

714 C1QC 29,758045 3 6 -07 8

147,05236 115,17257 178,93214 8,9316E

139189 DGKK 3 7 9 -07 0,00019

9,6437905 4,0929946 15,194586 9,1112E 0,0001917

3624 INHBA 7 4 5 -07 1

207,56547 255,25820 1,0668E

439996 IFIT1B 231,41184 4 6 -06 0,0002212

315,33307 337,98137 292,68477 1,0741 E

57156 TMEM63C 5 7 2 -06 0,0002212

273,93619 222,83010 1,1512E 0,0002345

54094 C21orf15 3 7 325,04228 -06 7

456,03278 548,91703 363,14854 1,1878E 0,0002395

26790 SNORD58B 9 2 6 -06 2

1315,9804 1608,4542 1,2575E 0,0002509

9636 ISG15 3 7 1023,5066 -06 6

330,21547 284,09893 376,33202 1.336E-

1053 CEBPE 8 4 2 06 0,0002639

305,74427 244,11484 1,4178E 0,0002772

717 C2 3 367,3737 7 -06 3

1564,5544 1854,1663 1274,9426 1,4655E 0,0002836

26509 MYOF 9 7 1 -06 9

65,539051 47,164879 83,913222 1,6751 E 0,0003182

401399 PRRT4 1 3 8 -06 2

183,57783 217,94657 149,20909 1,6768E 0,0003182

11326 VSIG4 7 4 9 -06 2

2790,4910 2292,5901 1,8283E

51348 KLRF1 4 9 3288,3919 -06 0,0003403

370,80014 335,82408 405,77619 1,819E-

759 CA1 3 8 8 06 0,0003403

2323,0342 2888,0735 1.936E- 0,0003569

1232 CCR3 2605,5539 7 3 06 2087426 33,763351 46,101387 21,425315 2,119E- 0,0003869 4 AOAH-IT1 2 2 2 06 8

35,741644 23,614804 47,868484 2,5693E 0,0004648

283120 H19 7 7 7 -06 3

20962,762 17383,851 2,6782E 0,0004800

10437 IFI30 3 24541,673 7 -06 4 66,594714 50,4581 19 82,731309 3,0531 E 0,0005422

5273 SERPINB10 2 1 2 -06 2

1 1 .061831 17,531277 4,5923855 3,091 1 E 0,0005439

645843 TMEM14E 7 9 5 -06 7

43532,975 34680,122 3,7802E 0,0006592

10581 IFITM2 39106,549 7 3 -06 6

7030,7768 6357,0200 3,9293E 0,0006791

598 BCL2L1 6 1 7704,5337 -06 3

67,219256 53,767598 80,670913 4.0162E 0,0006880

5909 RAP1 GAP 2 8 5 -06 1

135,40090 109,62470 161 ,17710 4,0866E 0,0006939

28 ABO 4 6 2 -06 4

1668,8289 1473,0720 1864,5859 4,2799E 0,0007204

83943 IMMP2L 8 1 6 -06 4

214,98776 255,83413 174,14139 4,341 1 E 0,0007244

3047 HBG1 8 9 6 -06 4

776,40176 681 ,83138 870,97214 4,3832E 0,0007252

260429 PRSS33 8 7 9 -06 2

791 ,68047 928,64439 654,71655 4,4581 E 0,0007313

2209 FCGR1 A 5 2 8 -06 6

19,655291 1 1 ,259905 28,050677 5,9465E 0,0009673

441864 TARM1 8 7 9 -06 3

139,02023 1 15,21825 162,82221 6,0743E 0,0009798

23251 KIAA1024 4 5 4 -06 8

65,240208 83,259775 47,220641 6,2286E 0,0009964

991 CDC20 8 7 9 -06 7

31 ,917821 51 ,428976 6,3478E 0,0010072

4883 NPR3 41 ,673399 6 4 -06 2

288,45818 346,93578 6,8231 E 0,0010738

125875 CLDND2 8 229,98059 7 -06 3

148,36532 174,65158 122,07906 7,0404E 0,0010990

79365 BHLHE41 2 3 1 -06 9

56,101737 42,455613 69,747861 7,7457E 0,001 1995

54490 UGT2B28 1 1 1 -06 3

139,73573 1 1 1 ,54576 167,92570 8,1438E 0,001251 1

149345 SHISA4 3 1 6 -06 7

237,36190 268,00486 206,71894 8,6306E 0,0013155

8347 HIST1 H2BC 5 2 9 -06 2

380,83654 331 ,20031 430,47278 8,8577E 0,0013395

59340 HRH4 8 2 4 -06 8

1 15029,06 132140,14 97917,981 8,9709E 0,0013461

6280 S100A9 2 2 3 -06 9

671 ,74789 515,49350 1 ,0551 E 0,001571 1

26807 SNORD43 593,6207 2 7 -05 7

439,20902 509,41489 369,00315 1 ,0852E 0,0016036

6038 RNASE4 6 7 6 -05 2

160,50627 185,71360 1 ,1885E 0,0017298

51339 DACT1 4 135,29894 8 -05 6

3001 ,4479 3442,3179 2560,5778 1 ,1821 E 0,0017298

3162 HMOX1 3 7 8 -05 6

135,32305 1 10,72894 159,91715 1 ,2551 E

5097 PCDH1 4 9 9 -05 0,0018131 1487,7056 1320,4653 1654,9460 1 ,3353E 0,0019146

201305 SPNS3 9 5 3 -05 7

2327,4466 2718,6623 1936,2309 1 ,3534E 0,0019263

9935 MAFB 9 9 9 -05 3

512,95451 423,45898 602,45004 1 ,3785E 0,0019464

59352 LGR6 4 3 5 -05 3

767,80705 885,47722 650, 13688 1 ,3876E 0,0019464

199675 C19orf59 4 2 7 -05 3

1572,5268 1767,5493 1377,5043 1 ,4985E 0,0020869

83999 KREMEN1 6 6 5 -05 4

1029,9393 1291 ,1 142 1 ,6083E

6614 SIGLEC1 2 8 768,76437 -05 0,0022238

4,9419042 8,8446621 1 ,0391463 1 ,9949E 0,0027361

1281 COL3A1 4 3 5 -05 8

1 174,6190 1050,4769 1298,761 1 2,0071 E 0,0027361

2030 SLC29A1 5 6 3 -05 8

103,77104 125,30590 2,2044E 0,0029840

440603 BCL2L15 4 82,236179 9 -05 7

794,82316 917,56716 2,4464E 0,0032886

83742 MARVELD1 7 3 672,07917 -05 4

639,27856 752,72197 525,83515 2,6253E 0,0035048

2322 FLT3 6 5 6 -05 1

68,216770 53, 132006 83,301534 2,6885E 0,0035646

2900 GRIK4 3 3 3 -05 7

10,784746 4,9158554 2,8343E 0,0037323

1277 COL1 A1 7 16,653638 6 -05 4

1 15,03644 95,286215 134,78667 2,9681 E 0,0038822

360226 PRSS41 8 8 9 -05 5

256,93192 226,09643 287,76741 3,3702E 0,0043785

59283 CACNG8 2 2 1 -05 1

451 ,70557 518,16678 385,24436 3,6605E 0,0047239

432 ASGR1 3 2 3 -05 4

13521 ,955 12353,973 14689,937 3,9206E

51629 SLC25A39 3 1 4 -05 0,0050261

69,000239 56,795139 4,1858E 0,0053307

151473 SLC16A14 6 1 81 ,20534 -05 9

1839,2261 1440,2080 4,3092E 0,0054521

440689 HIST2H2BF 1639,7171 4 6 -05 8

1 152,6634 1009,0198 1296,3069 4,3536E 0,0054725

401258 RAB44 1 8 4 -05 6

3580,0410 4236,2371 2923,8449 5,6053E 0,0069956

4938 OAS1 5 2 9 -05 9

1712,4089 1493,3260 5,6376E 0,0069956

83869 I I I Y14 2 4 1931 ,4918 -05 9

60,464260 89,528671 5,8272E 0,0071849

7225 TRPC6 74,996466 5 5 -05 1

18,800023 25,863530 1 1 ,736516 5,8831 E 0,0072079

29065 ASAP1 -IT1 8 8 8 -05 6

84,732794 71 ,368685 98,096903 6,0218E 0,0073314

10107 TRIM10 5 1 9 -05 7

78,756566 68, 169744 89,343389 6.123E- 0,0074080

57801 HES4 9 8 1 05 8 1858,3442 2144,701 1 1571 ,9874 6,2004E 0,0074550

719 C3AR1 7 1 3 -05 6

1 13,09035 89,096947 137,08375 6,2674E 0,0074891

1 14990 VASN 1 6 5 -05 9

181 ,81099 159,70717 203,91482 6,41 15E 0,0076143

83483 PLVAP 9 4 4 -05 6

705,00332 903,0891 1 6,51 1 E 0,0076854

6322 SCML1 804,04622 3 7 -05 5

1723,9896 1958,3756 1489,6036 6,6584E 0,00781 16

59286 UBL5 6 5 7 -05 7

60,746688 45,361807 76,131568 6,843E- 0,0079799

10501 SEMA6B 1 4 7 05 8

218,51841 266,63515 170,40167 7,0092E 0,0080764

2359 FPR3 2 3 1 -05 3

10013394 1685,6891 1471 ,5416 6,9765E 0,0080764

1 CD24 7 3 1899,8367 -05 3

1866,7584 1610,5321 2122,9846 7,4594E 0,0085443

146857 SLFN13 3 9 6 -05 7

4919,3735 4142,8152 5695,9318 7,8465E 0,0089348

10417 SPON2 5 6 5 -05 2

456,48029 533,16053 379,80005 7,9726E 0,0090253

91319 DERL3 4 3 5 -05 7

8417,7798 9266,4719 7569,0876 8,1337E 0,0091541

10409 BASP1 1 7 5 -05 5

1715,2260 1524,6689 1905,7831 8,2413E 0,0092217

22807 IKZF2 4 7 1 -05 2

252,66031 293,84424 21 1 ,47639 8,4701 E

6241 RRM2 9 7 1 -05 0,0094232

727,95230 613,30494 842,59967 8,9808E 0,0099343

51314 NME8 6 1 1 -05 4

54,692306 94,967867 9,0329E 0,0099351

8510 MMP23B 74,830087 8 2 -05 3

Table 5. Differential expression between non-responders before and after medication. "GenelD" gene identification. "Gene symbol" official symbol. "Base mean" mean normalized counts, averaged over all samples from both conditions. "Base mean before medication" mean normalized counts from condition A. "Base mean after medication" mean normalized counts from condition B. "P value" p value for the statistical significance of this change. "Padj" P value adjusted for multiple testing with the Benjamini-Hochberg procedure, which controls false discovery rate.

Base Mean Base Mean

Base

gene!D Gene Symbol Before After Pval Padj

Mean

Medication Medication

502,52625 5.5928E-

100462981 MTRNR2L2 9 24,1282146 980,924303 240 1 .0X1· -235 140,04297 1,67837E 1.620 E-

9381 OTOF 6 218,398634 61,6873175 -52 48

77. 1 297 1.103631· 7.10 7E-

3429 II 127 I 116,330085 38,8965087 -37 34

LOC10019098 35,884687 5, 7640 IE 2J826E-

100190986 6 1 9,85915988 61,9102142 -30 26

1128,9904 5,58372E 2.1 64E-

1719 1)111 R 1 651,122435 1606,85839 -23 19

34,399576 6.7234E- 2.I638E-

9509 ADAMTS2 5 55,2185707 13,5805824 20 16

15,841337 6,37861E 1.7596E-

29065 ASAP1-IT1 9 27,4265372 4,25613857 -19 1

158,49617 8.79429E 2.12271·-

100463486 MT NR2L8 6 88,8018169 228,190535 -18 14

7.224091 ;

5139 PDE3A 856,12931 1108,7521 603,50652 -15 1.55E-11

320,68094 3.6894 IE 7, 1243 E-

554226 ANKRD30BL 4 421,9 1446 219,410442 -14 11

8,6019496 1, 47432 E 2.588 lE-

3488 IGFBP5 6 15,1012774 2,1026219 -12 09

19740,039 2,52259E 4.0593E-

81796 SLC05A1 6 24475,0927 15004,9865 -10 07

4829,2444 3.69686E 5,4913E-

212 ALAS2 9 6329,92618 3328,56281 -10 07

34344,306 8,20303E 1.1 14E-

164045 III Mi 5 42282,9209 26405,6922 -10 06

346,92370 7,23624E 9. 155E-

23500 DAAM2 6 407,583301 286,26411 -09 06

14,796190 7,95253E 9.5977E-

124912 SPACA3 4 21,5754544 8,01692647 -09 06

1596,2983 6,84918E 7.7799E-

6614 SIGLECl 1 1727,70644 1464,89018 -08 05

1071,3974 2,85356E 0,0003061

246 ALOX15 6 898,952345 1243,84257 -07 2

5040,8701 3,83212E 0,0003894

1740 DLG2 8 6052,12644 4029,61392 -07 6

9,83454E 0,0009495

221687 RNF182 59,990631 82,3998153 37,5814466 -07 2

260,26675 1.24997E 0,0011493

3620 IDOl 2 209,830713 310,70279 -06 8

284,18984 1.9071 E 0,0016739

3047 HBG1 2 366,594583 201,785101 -06 4

31,752459 2,38158E 0,0019994

714 C1QC 8 40,2568902 23,2480295 -06 9

Table 6. The 30 genes with the highest predictive power. "Gene Symbol" official symbol. "Chr" chromosome. "Start" Gene start coordinate. "End" Gene end coordinate. "Gini" Gini variable importance measures reflect the mean decrease in impurity by splits of a given variable in the classification tree, weighted by the proportion of samples reaching that node.

Gene symbol Chr Start End Gini

SLC9A3 chr5 473333 524549 0,90692249

HMOX1 chi-22 35777059 35790207 0,895315136

SLC22A16 chr6 110745891 110797844 0,505405963

LOC284581 chrl 205831206 205865215 0,501917214

PF4V1 chr4 74719012 74720198 0,392855982

GSTT1 chr22 24376138 24384284 0,380503482

DLC1 chr8 12940871 13372429 0,327980015

AQP10 chrl 154293591 154297801 0,303985142

NL P2 chrl 9 55476651 55512510 0,287145296

C17orf97 chrl 7 260117 264457 0,260801575

CRIP2 chrl 4 105939274 105946507 0,258219847

EGFL7 chr9 139553307 139567130 0,23700338

LOC642846 chrl2 9436252 9466684 0,235587821

ATOH8 chr2 85980908 86018506 0,231554415

FMN1 chrl 5 33057744 33486934 0,229543432

BTNL3 chr5 180415844 180433727 0,228373919

DGKK chrX 50108405 50213737 0,210864272

FAM153B chi-5 175490711 175541801 0,199406607

C4BPA chrl 207277606 207318317 0,195035924

ΓΚΙΜ6 chrl l 5617330 5634188 0,193156146

KRT72 chrl 2 52979372 52995322 0, 189711245

IL8 chr4 74606222 74609433 0, 18594549

PTGDS chr9 139871955 139876194 0, 184917648

NTNG2 chr9 135037333 135118220 0,182725824

TMEM63C chr l 4 77648101 77725838 0,176103025

LIPN chrlO 90521162 90537999 0,170802146

VWF chrl 2 6058039 6233836 0,168301614

ANK2 chr4 113739238 114304896 0,157175476

CENPK chr5 64813592 64858995 0,154019124

SIGLECl chr20 3667616 3687775 0, 15289179

Claims

1 . Use of the expression level of the SLC9A3 gene as a biomarker in an in vitro method for predicting the therapeutic response to antipsychotic drugs in a subject suffering from a psychotic disorder or for the screening of compounds, molecules or compositions useful for the treatment of a psychotic disorder.

2. Use according to claim 1 , further comprising the use of the expression level of the HMOX1 gene.

3. Use according to any of claims 1 or 2, further comprising the use of the expression level of the SLC22A16 gene.

4. Use according to any one of claims 1 to 3, further comprising the use of the expression level of the LOC284581 gene.

5. Use according to any one of claims 1 to 4, further comprising the use of the expression level of the PF4V1 gene. 6. Use according to any one of claims 1 to 5, further comprising the use of the expression level of the GSTT1 gene.

7. Use according to any one of claims 1 to 6, wherein the genes are SLC9A3, HMOX1 , SLC22A16, LOC284581 , PF4V1 and GSTT1 .

8. Use according to any one of claims 1 to 7, wherein the psychotic disorder is selected from the list consisting of: schizophrenia, schizophreniform disorder, schizoaffective disorder, bipolar disorder, delusional disorder, delirium, dementia and/or behavioral disorders.

9. Use according to claim 8, wherein the psychotic disorder is schizophrenia.

10. Use according to any one of claims 1 to 9, wherein the antipsychotic drug is selected from the list consisting of: aripiprazole, risperidone, olanzapine, paliperidone, chlorpromazine, clozapine, quetiapine, ziprasidone, asenapine, iloperidone, zotepine, amisulpride, fluphenazine, haloperidol, loxapine, perphenazine, pimozide, zuclopenthixol, or any combination thereof.

1 1 . Use according to any one of claims 1 to 10, wherein the expression levels are RNA and/or protein, preferably mRNA.

12. An in vitro method for predicting the therapeutic response to antipsychotic drugs in a subject suffering from a psychotic disorder, comprising the following steps: a) measuring the expression level of the SLC9A3 gene in an isolated biological sample collected from the subject suffering from a psychotic disorder before the treatment with antipsychotic drugs,

b) comparing the expression level value obtained after step (a) with an standard value, and

c) assigning the subject of step (a) to the group of patients that will positively respond to the treatment with antipsychotic drugs when the expression level value obtained after step (a) is lower than the standard value, wherein the standard value is the mean value obtained after measuring the expression level of the SLC9A3 gene in isolated biological samples collected from a group of subjects suffering from a psychotic disorder who have not been treated with antipsychotic drugs.

13. The method according to claim 12, further comprising: measuring in step (a) the expression level of the HMOX1 gene, comparing in step (b) this expression level value with an standard value, and assigning in step (c) the subject to the group of patients that will positively respond to the treatment with antipsychotic drugs when this expression level value obtained after step (a) is higher than the standard value, wherein this standard value is the mean value obtained after measuring the expression level of the HMOX1 gene in isolated biological samples collected from a group of subjects suffering from a psychotic disorder who have not been treated with antipsychotic drugs.

14. The method according to any of claims 12 or 13, further comprising: measuring in step (a) the expression level of the SLC22A16 gene, comparing in step (b) this expression level value with an standard value, and assigning in step (c) the subject to the group of patients that will positively respond to the treatment with antipsychotic drugs when this expression level value obtained after step (a) is higher than the standard value, wherein this standard value is the mean value obtained after measuring the expression level of the SLC22A16 gene in isolated biological samples collected from a group of subjects suffering from a psychotic disorder who have not been treated with antipsychotic drugs.

15. The method according to any one of claims 12 to 14, further comprising: measuring in step (a) the expression level of the LOC284581 gene, comparing in step (b) this expression level value with an standard value, and assigning in step (c) the subject to the group of patients that will positively respond to the treatment with antipsychotic drugs when this expression level value obtained after step (a) is lower than the standard value, wherein this standard value is the mean value obtained after measuring the expression level of the LOC284581 gene in isolated biological samples collected from a group of subjects suffering from a psychotic disorder who have not been treated with antipsychotic drugs.

16. The method according to any one of claims 12 to 15, further comprising: measuring in step (a) the expression level of the PF4V1 gene, comparing in step (b) this expression level value with an standard value, and assigning in step (c) the subject to the group of patients that will positively respond to the treatment with antipsychotic drugs when this expression level value obtained after step (a) is higher than the standard value, wherein this standard value is the mean value obtained after measuring the expression level of the PF4V1 gene in isolated biological samples collected from a group of subjects suffering from a psychotic disorder who have not been treated with antipsychotic drugs.

17. The method according to any one of claims 12 to 16, further comprising: measuring in step (a) the expression level of the GSTT1 gene, comparing in step (b) this expression level value with an standard value, and assigning in step (c) the subject to the group of patients that will positively respond to the treatment with antipsychotic drugs when this expression level value obtained after step (a) is lower than the standard value, wherein this standard value is the mean value obtained after measuring the expression level of the GSTT1 gene in isolated biological samples collected from a group of subjects suffering from a psychotic disorder who have not been treated with antipsychotic drugs.

The method according to any one of claims 12 to 17, wherein the psychotic disorder is selected from the list consisting of: schizophrenia, schizophreniform disorder, schizoaffective disorder, bipolar disorder, delusional disorder, delirium, dementia and/or behavioral disorders.

19. The method according to claim 18, wherein the psychotic disorder is schizophrenia.

10 20. The method according to any one of claims 12 to 19, wherein the antipsychotic drug is selected from the list consisting of: aripiprazole, risperidone, olanzapine, paliperidone, chlorpromazine, clozapine, quetiapine, ziprasidone, asenapine, iloperidone, zotepine, amisulpride, fluphenazine, haloperidol, loxapine, perphenazine, pimozide, zuclopenthixol, or any combination thereof.

1 5

21 . The method according to any one of claims 12 to 20, wherein the expression levels are RNA and/or protein, preferably mRNA.

22. The method according to any one of claims 12 to 21 , wherein the isolated biological 20 sample is peripheral blood.

An in vitro method for the screening of compounds, molecules or compositions useful for the treatment of a psychotic disorder that comprises: i) measuring the expression level of the SLC9A3 gene in an isolated biological sample obtained from a subject suffering from a psychotic disorder before the administration of the compound, molecule or composition to be tested, ii) measuring the expression level of the same gene as that measured in step (i) in an isolated biological sample obtained from the same subject after the administration of the compound, molecule or composition to be tested, iii) comparing the expression level values obtained in (i) and (ii), and

iv) classifying the compound, molecule or composition as useful for the treatment of a psychotic disorder when a significant difference in the expression level values has been detected in step (iii).

35

24. The method according to claim 23, wherein said method further comprises measuring in step (i) the expression level of at least one gene selected from the list consisting of: HMOX1 , SLC22A16, LOC284581 , PF4V1 and/or GSTT1 .

25. The method according to claim 24, wherein said method comprises measuring in 5 step (i) the expression level of the following genes: SLC9A3, HMOX1 , SLC22A16,

LOC284581 , PF4V1 and GSTT1 .

26. The method according to any one of claims 23 to 25, wherein the psychotic disorder is selected from the list consisting of: schizophrenia, schizophreniform disorder,

10 schizoaffective disorder, bipolar disorder, delusional disorder, delirium, dementia and/or behavioral disorders.

27. The method according to claim 26, wherein the psychotic disorder is schizophrenia.

15 28. The method according to any one of claims 23 to 27, wherein the expression levels are RNA and/or protein, preferably mRNA.

29. The method according to any one of claims 23 to 28, wherein the isolated biological sample is peripheral blood.

20

30. A kit for predicting the therapeutic response to antipsychotic drugs in a subject suffering from a psychotic disorder or for the screening of compounds, molecules or compositions useful for the treatment of a psychotic disorder, comprising compounds capable of specifically binding SLC9A3 gene or its expression products.

25

31 . The kit according to claim 30, further comprising compounds capable of specifically binding HMOX1 , SLC22A16, LOC284581 , PF4V1 and/or GSTT1 genes or their expression products.

30 32. The kit according to any of claims 30 or 31 , wherein the compounds are labels, antibodies and/or primers, preferably primers.

33. Use of the kit according to any one of claims 30 to 32 for performing the method according to any one of claims 12 to 22 or the method according to any one of 35 claims 23 to 29.