Methods of using emotional tears for diagnosing and / or prognosing glioblastoma multiforme (GBM)

Abstract

The present invention pertains to methods for survival prognosis, stratification, and assessment of disease progression or regression in Glioblastoma multiforme (GBM) patients. These methods are based on the detection and analysis of signature patterns of six specific metabolites in emotional tears samples. Additionally, the invention encompasses a preparation technique for emotional tears samples prior to liquid chromatography-mass spectrometry (LC-MS) analysis. This approach enables more accurate and reliable evaluation of GBM patient status and treatment efficacy through the examination of emotional tears metabolite profiles.

Description

[0001] METHODS OF USING EMOTIONAL TEARS FOR DIAGNOSING AND / OR PROGNOSING GLIOBLASTOMA MULTIFORME (GBM)

[0002] FIELD OF THE INVENTION

[0003] The present invention pertains to methods for survival prognosis, stratification, and assessment of disease progression or regression in Glioblastoma multiforme (GBM) patients. These methods are based on the detection and analysis of signature patterns of six specific metabolites in emotional tears samples. Additionally, the invention encompasses a preparation technique for emotional tears samples prior to liquid chromatography-mass spectrometry (LC-MS) analysis. This approach enables more accurate and reliable evaluation of GBM patient status and treatment efficacy through the examination of emotional tears metabolite profiles.

[0004] BACKGROUND OF THE INVENTION

[0005] Glioblastoma (GBM) is the most aggressive and lethal primary brain tumor in adults, comprising roughly half of all gliomas. Its hallmark features include rapid growth, diffuse infiltration into surrounding brain tissue, and resistance to therapy. GBM is a complex and heterogeneous disease with a poor prognosis. Understanding its diagnosis, clinical presentation, pathology, and molecular characteristics of GBM is crucial for developing effective treatment strategies and improving patient outcomes.

[0006] GBM is characterized by several histopathological features, such as cellular atypia, mitotic activity, microvascular proliferation and necrosis. GBM exhibits a high degree of genetic and molecular heterogeneity. Some of the key alterations include mutations in genes like TP53, PTEN, EGFR, and IDH1 / 2, amplification of genes like EGFR and MDM2, alterations in DNA methylation patterns and histone modifications, overexpression of proteins like EGFR, VEGF, and MGMT. GBM can be classified into different subtypes based on molecular characteristics, which can have prognostic and therapeutic implications. Some of the established subtypes include IDH-wildtype GBM, IDH-mutant GBM and GBM with H3 K27M mutation.

[0007] Diagnosing GBM involves a multi-pronged approach. Patients typically present symptoms like headaches, seizures, cognitive changes, focal neurological deficits and increased intracranial pressure depending on the tumor location and size. Magnetic resonance imaging (MRI) is the gold standard for diagnosis. It reveals the tumor's location, size, and characteristics like necrosis, edema, and contrast enhancement. A surgical procedure to obtain a tissue sample is essential for confirming the diagnosis and classifying the tumor based on its histopathological features.

[0008] Ongoing researches are focused on identifying new therapeutic targets and developing personalized treatment approaches based on individual tumor profiles. Treatment for GBM typically involves a combination of surgery, radiation therapy, and chemotherapy. The current standard therapy is based on oral Temozolomide (TMZ) combination with radiotherapy. However, despite aggressive treatment, the prognosis remains poor, highlighting the need for novel therapeutic approaches. Immunotherapy, targeted therapies, and other innovative treatment modalities are being actively investigated to improve GBM patient outcomes. The tumor microenvironment, including interactions with immune cells and the blood-brain barrier, plays a significant role in GBM progression and treatment resistance.

[0009] Understanding the molecular mechanisms underlying GBM development and progression is crucial for identifying new therapeutic targets and improving patient outcomes.

[0010] Recent searches have highlighted the role of oncometabolites, small-molecule intermediates or end products of altered metabolism, as crucial players in GBM pathogenesis. These oncometabolites can act as epigenetic factors, influencing gene expression and contributing to the malignant phenotype of GBM. Some key oncometabolites and their epigenetic effects in GBM are the following:

[0011] 2-Hydroxyglutarate (2-HG): This oncometabolite is produced in excess due to mutations in isocitrate dehydrogenase (TDH) enzymes, which are present in a significant subset of GBMs. 2-HG competitively inhibits a-ketoglutarate (a-KG)-dependent dioxygenases, including histone demethylases and TET enzymes. This leads to widespread histone and DNA hypermethylation, altering gene expression and promoting tumorigenesis.

[0012] Succinate and Fumarate: Mutations in succinate dehydrogenase (SDH) and fumarate hydratase (FH) enzymes lead to the accumulation of succinate and fumarate, respectively. Similar to 2- HG, these oncometabolites can inhibit a-KG-dependent dioxygenases, causing epigenetic alterations and contributing to GBM development. Lactate: GBM cells exhibit high rates of glycolysis, even in the presence of oxygen, a phenomenon known as the Warburg effect. This leads to increased lactate production, which can promote histone acetylation and gene expression changes associated with cell proliferation and survival.

[0013] Acetyl-CoA is a key metabolite involved in various cellular processes, including histone acetylation. Increased levels of acetyl-CoA, as observed in some GBMs, can lead to global histone hyperacetylation and altered gene expression patterns.

[0014] S-adenosyl methionine (SAM) is the primary methyl donor for DNA and histone methylation. Changes in SAM levels can influence the activity of methyltransferases and impact the epigenetic landscape of GBM cells.

[0015] Adenosine, a crucial signaling molecule, actively participates in glioblastoma by facilitating immune evasion and propelling tumor progress.

[0016] Sphingolipid metabolism: In glioblastoma, the production of sphingosine- 1 -phosphate, a key signaling molecule, is significantly disrupted, fostering tumor growth and survival.

[0017] Finding reliable markers to detect cancer, such as glioblastoma multiforme (GBM) is often a great challenge. Furthermore, the method for detecting the presence of these markers in a sample must be sensitive while not being (too) invasive and the results obtained must be reliable, easily interpretable and reproducible.

[0018] Thus, there is still an unmet need for a reliable, reproducible, precise and very sensitive method of diagnosis and prognosis of glioblastoma multiforme (GBM) using biological samples, such as in tears.

[0019] SUMMARY OF THE INVENTION

[0020] An aspect of the present invention provides a method for determining survival prognosis of a cancer subject having glioblastoma multiforme (GBM), the method comprising: a) determining levels of six metabolites in a first emotional tears sample obtained from the subject before the treatment, b) determining levels of six metabolites in a second emotional tears sample obtained from the subject after the treatment, c) comparing the levels of the six metabolites in said first and second emotional tears samples, wherein the six metabolites are Glutamate or an enantiomer thereof, Guanosine triphosphate or an enantiomer thereof, Adenine or an enantiomer thereof, Lactate or an enantiomer thereof, N-Acetyl-L-Glutamine or an enantiomer thereof, and Tryptophan or an enantiomer thereof, and wherein log2 fold changes in the levels of at least three of said metabolites from the first emotional tears sample to the second emotional tears sample in the following ranges are indicative of bad survival prognosis:

[0021] • Glutamate: between -5.0 and - 7.0,

[0022] • Guanosine triphosphate: between -0.01 and - 0.50,

[0023] • Adenine: between 1.00 and 3.00,

[0024] • Lactate: between 1.00 and 2.00,

[0025] • N-Acetyl-L-Glutamine: between -2.00 and -5.00,

[0026] • Tryptophan: between -0.5 and -3.00.

[0027] A further aspect of the present invention provides a method for assessing treatment efficacy of a drug in a cancer subject having glioblastoma multiforme (GBM), the method comprising a) determining levels of six metabolites in a first emotional tears sample obtained from the subject before the treatment with the drug, b) determining levels of six metabolites in a second emotional tears sample obtained from the subject after the treatment with the drug c) comparing the levels of the six metabolites in said first and second emotional tears samples, wherein the six metabolites are Glutamate or an enantiomer thereof, Guanosine triphosphate or an enantiomer thereof, Adenine or an enantiomer thereof, Lactate or an enantiomer thereof, N-Acetyl-L-Glutamine or an enantiomer thereof, and Tryptophan or an enantiomer thereof; and wherein log2 fold changes in the levels of at least three of said metabolites from the first emotional tears sample to the second emotional tears sample in the following ranges are indicative of a decreased therapeutic success and resistance to the drug:

[0028] • Glutamate: between -5.0 and - 7.0, • Guanosine triphosphate: between -0.01 and - 0.50,

[0029] • Adenine: between 1.00 and 3.00,

[0030] • Lactate: between 1.00 and 2.00,

[0031] • N-Acetyl-L-Glutamine: between -2.00 and -5.00,

[0032] • Tryptophan: between -0.5 and -3.00.

[0033] Another aspect of the present invention provides a method of determining the progression or the regression of glioblastoma multiforme (GBM) in a subject suffering from said disease, the method comprising determining the levels of six metabolites in consecutive emotional tears samples, obtained at designated time intervals, wherein the six metabolites are Glutamate or an enantiomer thereof, Guanosine triphosphate or an enantiomer thereof, Adenine or an enantiomer thereof, Lactate or an enantiomer thereof, N-Acetyl-L-Glutamine or an enantiomer thereof, and Tryptophan or an enantiomer thereof; and wherein log2 fold changes in the levels of at least three of said metabolites between the consecutive emotional tears samples in the following ranges are indicative of the GBM progression:

[0034] • Glutamate: between -5.0 and - 7.0,

[0035] • Guanosine triphosphate: between -0.01 and - 0.50,

[0036] • Adenine: between 1.00 and 3.00,

[0037] • Lactate: between 1.00 and 2.00,

[0038] • N-Acetyl-L-Glutamine: between -2.00 and -5.00,

[0039] • Tryptophan: between -0.5 and -3.00.

[0040] Another aspect of the present invention provides a kit for performing the methods of the present invention, the kit comprising a) means for collecting emotional tears samples of a subject, b) means and / or reagents for determining the levels of the six metabolites in the emotional tears samples of the subject, and c) instructions for use, wherein the six metabolites are Glutamate or an enantiomer thereof, Guanosine triphosphate or an enantiomer thereof, Adenine or an enantiomer thereof, Lactate or an enantiomer thereof, N-Acetyl-L-Glutamine or an enantiomer thereof, and Tryptophan or an enantiomer thereof. Another aspect of the present invention provides a method of assessing treatment efficacy of a selected drug in a cancer subject having glioblastoma multiforme (GBM), the method comprising: a) determining levels of six metabolites in a first emotional tears sample obtained from the subject before the treatment, b) determining levels of six metabolites in consecutive emotional tears samples, obtained at designated time intervals from the subject after the treatment, c) perform machine learning on the levels of six metabolites obtained in steps a) and b), d) obtaining output data generated by machine learning which indicates whether the subject is likely to benefit from treatment with the selected drug, and e) selecting either to continue the treatment with the selected drug or to find an alternative drug, different from the selected drug, as a candidate treatment for the subject based on the obtained output data, wherein the six metabolites are Glutamate or an enantiomer thereof, Guanosine triphosphate or an enantiomer thereof, Adenine or an enantiomer thereof, Lactate or an enantiomer thereof, N-Acetyl-L-Glutamine or an enantiomer thereof, and Tryptophan or an enantiomer thereof.

[0041] Another aspect of the present invention provides a method for extracting metabolites from an emotional tears sample obtained from a subject, the method comprising a) mixing the emotional tears sample with water and with an alcohol, preferably with methanol, to provide a first mixture, b) adding 0.1% to 10% v / v ammonium hydroxide in the first mixture to provide a second mixture, c) vortex the second mixture, d) incubate the second mixture at a temperature ranging from 0 °C to 8 °C, e) centrifuge the second mixture at 1'000 to 20'000 rpm at a temperature ranging from

[0042] 2 °C to 8 °C during 15 minutes to 60 minutes in desalting column, f) isolating the supernatant containing the metabolites, wherein the metabolites are Glutamate or an enantiomer thereof, Guanosine triphosphate or an enantiomer thereof, Adenine or an enantiomer thereof, Lactate or an enantiomer thereof, N-Acetyl-L-Glutamine or an enantiomer thereof, and Tryptophan or an enantiomer thereof. BRIEF DESCRIPTION OF THE FIGURES

[0043] Figure 1 shows molecular pathways of oncometabolites within emotional tears of GBM patients and illustrates the most pertinent metabolic pathways exhibiting low false discovery rates (FDR) and high total compound counts.

[0044] Figure 2 shows discriminated metabolite patterns of oncometabolites pre-therapy vs posttherapy groups (identified metabolites and non-identified metabolites together).

[0045] Figure 3 shows Log2 fold change of validated metabolites of the present invention (Pre-therapy / Post-therapy Groups).

[0046] DETAILED DESCRIPTION OF THE INVENTION

[0047] All, documents, patents, patent applications, publications, product descriptions, and protocols which are cited throughout this application are incorporated herein by reference in their entireties for all purposes. The publications and applications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. In addition, the materials, methods, and examples are illustrative only and are not intended to be limiting.

[0048] In the case of conflict, the present specification, including definitions, will control. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as is commonly understood by one of skill in the art to which the subject matter herein belongs. As used herein, the following definitions are supplied in order to facilitate the understanding of the present invention.

[0049] Reference throughout this specification to "one aspect", "an aspect", "another aspect", "a particular aspect", "combinations thereof' means that a particular feature, structure or characteristic described in connection with the invention aspect is included in at least one aspect of the present invention. Thus, the appearances of the foregoing phrases in various places throughout this specification are not necessarily all referring to the same aspect. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more aspects. The term “comprise” is generally used in the sense of include, permitting the presence of one or more features or components. Also as used in the specification and claims, the language "comprising" can include analogous embodiments described in terms of "consisting of “ and / or "consisting essentially of’.

[0050] As used in the specification and claims, the singular form "a", "an" and "the" include plural references unless the context clearly dictates otherwise.

[0051] As used in the specification and claims, the term "and / or" used in a phrase such as "A and / or B" herein is intended to include "A and B", "A or B", "A", and "B".

[0052] The term “about”, particularly in reference to a given quantity or percentage, is meant to encompass deviations of plus or minus ten (10) percent (+ / - 10%). For example, about 5 % encompasses numeral between, and including, 4.5% and 5.5%.

[0053] The term “level,” when used in reference to a particular metabolite in a sample in the present application, means an absolute level, i.e. concentration of said particular metabolite in said sample (e.g. molar concentration or weight concentration) or a relative level such as a percentage or fraction compared to one or more other metabolites or compounds in said sample or total weight of the sample. In view of the above, it is understood that a level or an amount of the particular metabolite can be a molar concentration (mol / L) or weight concentration (e.g. pg / mol, ng / mol, pg / mol, etc. . .). Determining both the amount and the level of a metabolite of the invention is also within the scope of the present invention.

[0054] As used herein the terms "subject" / " subject in need thereof, or "patient" / "patient in need thereof " are well-recognized in the art and are used interchangeably herein to refer to a mammal, including dog, cat, rat, mouse, monkey, cow, horse, goat, sheep, pig, camel, and, most preferably, a human. In some cases, the subject is a subject in need of treatment or a subject with a disease or disorder. However, in other aspects, the subject can be a normal subject, i.e. a healthy subject. The term does not denote a particular age or sex. Thus, adult and newborn subjects, whether male or female, are intended to be covered. Preferably, the subject is a human, most preferably a human suffering from a disclosed described herein or a human that might be at risk of suffering from a disease disclosed herein. The term "tears" refers to basal tears, reflex tears or emotional tears. Basal tears are basic functional tears that are released continuously in tiny quantities to lubricate the cornea and keep it clear of dust. Basal tears also fight against bacterial infection as a part of the immune system. Reflex tears (or irritant tears or mechanical tears) result from irritation of the eye by foreign particles, or from the presence of irritant substances such as vapors from chopping onions, or having any kind of perfume or fragrance, tear gas, or pepper spray in the eye’s environment. These tears can also occur with bright light and hot or peppery stimuli to the tongue and mouth. They are released in much larger amounts than basal tears. Emotional or psychic tears are referred to as crying or weeping. These tears are associated with all emotions and are often brought caused by strong emotional reaction, such as emotional stress, emotional anger, or emotional suffering. While it is known that all tears contain enzymes, lipids, metabolites and electrolytes, there is still more to learn about the chemistry of emotional tears. It is known that the composition of emotional tears, caused by an emotional reaction, differs from basal tears and reflex tears. It is suggested that emotional tears contain additional proteins and hormones not found in basal or reflex tears. Higher levels of prolactin, adrenocorticotropic hormone, Leu- enkephalin, potassium and manganese have also been located in emotional tears. Preferably, the tears of the invention are emotional tears.

[0055] In an embodiment of the present invention, the emotional tears are triggered by the subject's emotional reaction to emotional questions that the subject is asked to think about and reply thereto. Such questions are carefully prepared, under the control of a psychiatrist, being respectful to the subject and complying with medical ethics. The subject's prior consent is required for answering the questions.

[0056] Tears can be obtained or collected from any part of the eye, in particular they can be obtained or collected from one or more of the following parts: lower fornix, cul-de-sac, upper punctum, plica semilunaris, lower punctum, lateral canthus, and caruncle.

[0057] Tears can be obtained or collected from a subject in need of treatment against a disease or disorder, a subject that underwent or is currently under a medical treatment against a disease or disorder, a subject with a disease or disorder, or from a healthy donor. In a preferred embodiment of the present invention, the disease is glioblastoma multiforme (GBM). Any technique and method known in the art for obtaining or collecting emotional tears from a subject are contemplated in the methods described herein as long as they are fast, non-invasive, inexpensive, easy to use, and with minimal risk of injury to the subject. Such methods also avoid mixing basal tears and reflex tears with emotional tears samples of interest. Typically, before collecting the samples of emotional tears, the subject's face is carefully cleaned with neutral face cleaning agent. Non-limiting examples of samples collection comprise direct and non-direct methods selected from microcapillary tubes (MCT) or micropipettes such glass or polyester fiber rod which will be placed in contact intermittently with the tear fluid, absorbing supports such as Schirmer test strips (STS), filter paper disks, cellulose sponges and polyester rods.

[0058] Once obtained or collected, water and alcohol, preferably methanol, as well as ammonium hydroxide are added to the emotional tears samples, and the samples will be either frozen or analyzed before degradation of the metabolites of the present invention. The alcohol is most preferably methanol, even more preferably 98-99% of methanol. Alternatively, once collected, the emotional tears samples can be stored frozen (at -20 to -80°C or in liquid nitrogen) for a long period, wherein the frozen samples being protected from degradation.

[0059] The present invention is based, in part, on surprising results showing that log2 fold change of levels of at least three of the following six metabolites, preferably log2 fold change of levels of all six metabolites, in emotional tears of a subject can correlate with the presence of the glioblastoma multiforme (GBM), the classification of the glioblastoma multiforme (GBM), the efficacy or the inefficacy of the glioblastoma multiforme (GBM) treatment, the progression or regression of glioblastoma multiforme (GBM) and good or bad survival prognosis. The six metabolites are Glutamate or an enantiomer thereof, Guanosine triphosphate or an enantiomer thereof, Adenine or an enantiomer thereof, Lactate or an enantiomer thereof, N-Acetyl-L- Glutamine or an enantiomer thereof, and Tryptophan or an enantiomer thereof.

[0060] The above-mentioned correlations are based on log2 fold changes in the following ranges in the levels of at least three, preferably all six, of said six metabolites from the first emotional tears sample obtained before the GBM treatment to the second and consecutive emotional tears samples obtained after the GBM treatment or between the consecutive emotional tears samples, that are indicative of the GBM treatment inefficacy and / or bad survival prognosis:

[0061] • Glutamate: between -5.0 and - 7.0, • Guanosine triphosphate: between -0.01 and - 0.50,

[0062] • Adenine: between 1.00 and 3.00,

[0063] • Lactate: between 1.00 and 2.00,

[0064] • N-Acetyl-L-Glutamine: between -2.00 and -5.00,

[0065] • Tryptophan: between -0.5 and -3.00.

[0066] Indeed, the inventors could not get meaningful results for five clinically well-known oncometabolites of GBM: fumarate, 2-hydroxyglutarate (2-HG), glucose-6-phosphate, acetyl- CoA, and tyrosine. A significant log2 fold change of levels of these five oncometabolites in emotional tears samples between the pre-therapy and post-therapy groups was not observed. Specifically, tyrosine levels remained unchanged when comparing pre-therapy and posttherapy patient groups, whereas 2-hydroxyglutarate (2-HG) has not been detected in emotional tears samples or was out of detection limit, thus could not be used for obtaining reproducible results.

[0067] Further, the important aspect of the present invention is that the above-mentioned correlations are only based on log2 fold changes in the specific ranges as outline above in the levels of at least three, preferably all six, of said six metabolites from the first emotional tears sample obtained before the GBM treatment to the second and consecutive emotional tears samples obtained after the GBM treatment or between the consecutive emotional tears samples. Indeed, the above-mentioned correlations is not based on the fact that "levels" (quantities) of the six metabolites of present invention in emotional tears samples are altered / modified before and after the GBM treatment; only a simple alteration / modification is not important. For example, high / low Glutamate levels, high / low N-Acetyl-L-Glutamine levels, high / low Lactate levels or high / low Tryptophan levels are not sufficient or significant to make the above-mentioned correlations; only the log2 fold changes in the specific ranges as outlined above are important and significant to make the above-mentioned correlations.

[0068] The detection of the six metabolites of the present invention comprises determining the presence, the amount and / or the level of said metabolites. Non-limiting detection, level determination and examination methods include one- and two-dimensional gel electrophoresis, ELISA, high performance liquid chromatography (HPLC), mass spectrometry (MS) related techniques such as MS / MS, matrix-assisted laser desorption / ionization time-of-flight MS, surface enhanced laser desorption / ionization time-of-flight MS, Liquid chromatography coupled to mass spectrometry (LC / MS), Liquid chromatography coupled to mass spectrometry and ion mobility spectrometry (LC-IMS-MS), various antibody arrays, multiplex bead analysis, NMR, Western blot analysis, etc. . . Preferably, LC-MS / MS is used to detect the metabolites of the present invention in the emotional tears samples.

[0069] An aspect of the present invention provides a method for determining survival prognosis of a cancer subject having glioblastoma multiforme (GBM), the method comprising: a) determining levels of six metabolites in a first emotional tears sample obtained from the subject before the treatment, b) determining levels of six metabolites in a second emotional tears sample obtained from the subject after the treatment, c) comparing the levels of the six metabolites in said first and second emotional tears samples, wherein the six metabolites are Glutamate or an enantiomer thereof, Guanosine triphosphate or an enantiomer thereof, Adenine or an enantiomer thereof, Lactate or an enantiomer thereof, N-Acetyl-L-Glutamine or an enantiomer thereof, and Tryptophan or an enantiomer thereof, and wherein log2 fold changes in the levels of at least three of said metabolites from the first emotional tears sample to the second emotional tears sample in the following ranges are indicative of bad survival prognosis:

[0070] • Glutamate: between -5.0 and - 7.0,

[0071] • Guanosine triphosphate: between -0.01 and - 0.50,

[0072] • Adenine: between 1.00 and 3.00,

[0073] • Lactate: between 1.00 and 2.00,

[0074] • N-Acetyl-L-Glutamine: between -2.00 and -5.00,

[0075] • Tryptophan: between -0.5 and -3.00.

[0076] As used in the present application, the survival prognosis of a cancer subject having glioblastoma multiforme (GBM) is generally divided into two types of prognosis: a bad (a short term) survival prognosis or a good (a long term) survival prognosis, whereas the good (long term) prognosis provides that the cancer subject having glioblastoma multiforme (GBM) will survive at least 5 years after the treatment, the bad (short term) prognosis predicts that the cancer subject having glioblastoma multiforme (GBM) will not survive for more than 18 months after the treatment unless further and / or different treatment is provided. The method of the present invention specifically enables the distinction between the bad (short term) and the good (long term) survival. The survival prognosis however also provides information regarding the status and type of glioblastoma multiforme (GBM) and therefore also enables to provide the subject with an appropriate and best suited treatment.

[0077] Glutamate, Guanosine triphosphate, Lactate, N-Acetyl-Glutamine and Tryptophane are chiral compounds; these compounds can exist in D form and L form.

[0078] According to an embodiment, the second emotional tears sample is obtained from the subject 2-4 weeks after the treatment.

[0079] In an embodiment, the above-mentioned log2 fold changes are in the levels of all six of said metabolites.

[0080] In another embodiment, the GBM treatment inefficacy and / or bad survival prognosis includes

[0081] • complete GBM treatment inefficacy, wherein the subject having glioblastoma multiforme (GBM) does not respond to the GBM treatment and is resistant to the GBM treatment, which is indicative of bad survival prognosis and requires further and / or different GBM treatment, and

[0082] • partial GBM treatment inefficacy, wherein the subject having glioblastoma multiforme (GBM) responds partially to the GBM treatment, which is indicative of a significant risk of recurrence and thereby suggests bad survival prognosis, and requires a rigorous periodic monitoring protocol to detect promptly any potential disease progression as well as eventual adjustment of the GBM treatment.

[0083] In some embodiments, in case of the complete GBM treatment inefficacy, the log2 fold changes in the levels of all six of said metabolites are within the above-mentioned log2 fold value ranges. In other embodiments, in case of the incomplete GBM treatment inefficacy, the log2 fold changes in the levels of three of said metabolites are within the above-mentioned log2 fold value ranges.

[0084] According to a further embodiment, the method for determining the survival prognosis of a cancer subject having glioblastoma multiforme (GBM) further comprises repeating step b) for one or more times to determine the levels of the six metabolites in consecutive emotional tears samples, obtained at designated time intervals, and comparing the levels of the six metabolites between said consecutive emotional tears samples, wherein said log2 fold changes in the levels of at least three, preferably all six, of said metabolites between said consecutive emotional tears samples are indicative of the GBM treatment inefficacy and / or bad survival prognosis.

[0085] According to an embodiment, the time intervals between the consecutive emotional tears samples are in the range of 10-14 weeks, preferably the time intervals between the consecutive emotional tears samples are 12 weeks.

[0086] According to some embodiments, the GBM treatment is selected from the group comprising chemotherapy, radiotherapy, cell therapy, immunotherapy, tumor resection and combinations thereof. According to an embodiment, chemotherapy is a therapy with a drug selected from the group consisting of Temozolomide (TMZ), Savolitinib, Ivosidenib, Veliparib, Carmustine, Bevacizumab, Lomustine, Trametinib, Dabrafenib, Abemaciclib, Terameprocol, and Vorasidenib. According to another embodiment, cell therapy is typically selected from stem cell therapies, such as pluripotent stem cells, allogenic stem cells, autologous stem cells, and CAR-T therapies. According to another embodiment, the immunotherapy is selected from PD- 1 inhibitors and PD-L1 inhibitors targeting specific antigens on brain tumour cells. According to another embodiment, radiotherapy typically consists in radiation therapy to doses of 5,000- 6,000 cGy. According to another embodiment, one combination of the above-mentioned GBM treatments includes tumor resection and radiotherapy. According to another embodiment, another combination of the above-mentioned GBM treatments includes, with or without tumor resection, concurrent use of Temozolomide and radiotherapy over 30 days followed by adjuvant Temozolomide treatment for 6 months.

[0087] According to a further embodiment, the method for determining the survival prognosis of a cancer subject having glioblastoma multiforme (GBM), the GBM treatment regime is adjusted or changed based on the determined survival prognosis. Typically, chemotherapeutic drug is changed and / or can be used in combination with other therapies, such as a cell therapy or an immune therapy, for a better prognosis. Further, the GBM treatment adjustment can also involve dose adjustment, administrate regimen adjustment, and combinations with other GBM therapies (such as other chemotherapies, cell therapies, radiotherapies and immunotherapies). Another aspect of the present invention provides a method for assessing treatment efficacy of a drug in a cancer subject having glioblastoma multiforme (GBM), the method comprising a) determining levels of six metabolites in a first emotional tears sample obtained from the subject before the treatment with the drug, b) determining levels of six metabolites in a second emotional tears sample obtained from the subject after the treatment with the drug c) comparing the levels of the six metabolites in said first and second emotional tears samples, wherein the six metabolites are Glutamate or an enantiomer thereof, Guanosine triphosphate or an enantiomer thereof, Adenine or an enantiomer thereof, Lactate or an enantiomer thereof, N-Acetyl-L-Glutamine or an enantiomer thereof, and Tryptophan or an enantiomer thereof; and wherein log2 fold changes in the levels of at least three, preferably all six, of said metabolites from the first emotional tears sample to the second emotional tears sample in the following ranges are indicative of the decreased therapeutic success and resistance to the drug:

[0088] • Glutamate: between -5.0 and - 7.0,

[0089] • Guanosine triphosphate: between -0.01 and - 0.50,

[0090] • Adenine: between 1.00 and 3.00,

[0091] • Lactate: between 1.00 and 2.00,

[0092] • N-Acetyl-L-Glutamine: between -2.00 and -5.00,

[0093] • Tryptophan: between -0.5 and -3.00.

[0094] According to an embodiment, the second emotional tears sample is obtained from the subject 2-4 weeks after the treatment with the drug.

[0095] According to a further embodiment, the levels of the six metabolites are determined at one or more time points after initiation of treatment with the drug on consecutive emotional tears samples.

[0096] According to another embodiment, the method for assessing treatment efficacy of a drug in a cancer subject having glioblastoma multiforme (GBM) further comprises comparing the levels of the six metabolites between the consecutive emotional tears samples, wherein said log2 fold changes in the levels of at least three, preferably all six, of said metabolites between consecutive emotional tears samples is indicative of decreased therapeutic success and resistance to the drug.

[0097] According to an embodiment, the consecutive emotional tears samples are obtained at time intervals in the range of 10-14 weeks, preferably the time intervals between the consecutive emotional tears samples are 12 weeks.

[0098] Another aspect of the present invention provides a method of determining the progression or the regression of glioblastoma multiforme (GBM) in a subject suffering from said disease, the method comprising determining the levels of six metabolites in consecutive emotional tears samples, obtained at designated time intervals, wherein the six metabolites are Glutamate or an enantiomer thereof, Guanosine triphosphate or an enantiomer thereof, Adenine or an enantiomer thereof, Lactate or an enantiomer thereof, N-Acetyl-L-Glutamine or an enantiomer thereof, and Tryptophan or an enantiomer thereof; and wherein log2 fold changes in the levels of at least three, preferably all six, of said metabolites between the consecutive emotional tears samples in the following ranges are indicative of the GBM progression:

[0099] • Glutamate: between -5.0 and - 7.0,

[0100] • Guanosine triphosphate: between -0.01 and - 0.50,

[0101] • Adenine: between 1.00 and 3.00,

[0102] • Lactate: between 1.00 and 2.00,

[0103] • N-Acetyl-L-Glutamine: between -2.00 and -5.00,

[0104] • Tryptophan: between -0.5 and -3.00.

[0105] According to an embodiment, the time intervals between the consecutive emotional tears samples are in the range of 10-14 weeks, preferably the time intervals between the consecutive emotional tears samples are 12 weeks.

[0106] Another aspect of the present invention provides a kit for performing the methods of the present invention, the kit comprising a) means for collecting emotional tears samples of a subject, b) means and / or reagents for determining the levels of the six metabolites in the emotional tears samples of the subject, and c) instructions for use, wherein the six metabolites are Glutamate or an enantiomer thereof, Guanosine triphosphate or an enantiomer thereof, Adenine or an enantiomer thereof, Lactate or an enantiomer thereof, N-Acetyl-L-Glutamine or an enantiomer thereof, and Tryptophan or an enantiomer thereof.

[0107] Another aspect of the present invention provides a method of assessing treatment efficacy of a selected drug in a cancer subject having glioblastoma multiforme (GBM), the method comprising: a) determining levels of six metabolites in a first emotional tears sample obtained from the subject before the treatment, b) determining levels of six metabolites in consecutive emotional tears samples, obtained at designated time intervals from the subject after the treatment, c) perform machine learning on the levels of six metabolites obtained in steps a) and b), d) obtaining output data generated by machine learning which indicates whether the subject is likely to benefit from treatment with the drug, and e) selecting either to continue the treatment with the selected drug or to find an alternative drug, different from the selected drug, as a candidate treatment for the subject based on the obtained output data, wherein the six metabolites are Glutamate or an enantiomer thereof, Guanosine triphosphate or an enantiomer thereof, Adenine or an enantiomer thereof, Lactate or an enantiomer thereof, N-Acetyl-L-Glutamine or an enantiomer thereof, and Tryptophan or an enantiomer thereof.

[0108] The present invention provides the use of a machine learning approach to analyze levels of six metabolites of the present invention in emotional tears samples obtained from a cancer subject having glioblastoma multiforme (GBM) before the treatment and after the treatment. The emotional tears samples are first obtained 2-4 weeks after the treatment and then consecutive emotional tears samples are obtained at designed time intervals. The time intervals between the consecutive emotional tears samples are in the range of 10-14 weeks, preferably the time intervals between the consecutive emotional tears samples are 12 weeks. The machine learning approach is used to predict benefit or lack of benefit from a selected drug used in the treatment of cancer subject having glioblastoma multiforme (GBM). The machine learning classification models were trained with different emotional tears samples obtained from a cancer subject having glioblastoma multiforme (GBM) before the treatment and after the treatment with different drugs.

[0109] Training should be done based on data obtained with the emotional tears of GBM patients and of the healthy subjects (i.e. the control group). Training data comprise profiles of the six metabolites of the invention in the emotional teras of GBM patients pretherapy and posttherapy, as well as profiles of the six metabolites of the invention in the emotional tears of GBM patients posttherapy vs relapse GBM patients (as a subgroup of post-therapy). Relapse GBM patients are useful to identify the risk of relapse and assessment of the therapeutic success. If the profiles of the six metabolites of the invention in the emotional tears of GBM patients posttherapy are similar the profiles of the six metabolites in the emotional tears of the control group (healthy group), this is indicative of the success of the therapy and good prognosis.

[0110] The classification was based on log2 fold changes in the following ranges in the levels of at least three, preferably all six, of said metabolites from the first emotional tears sample obtained before the treatment to the second and consecutive emotional tears samples obtained after the treatment or between the consecutive emotional tears samples, that are indicative of the GBM treatment inefficacy and / or bad survival prognosis:

[0111] • Glutamate: between -5.0 and - 7.0,

[0112] • Guanosine triphosphate: between -0.01 and - 0.50,

[0113] • Adenine: between 1.00 and 3.00,

[0114] • Lactate: between 1.00 and 2.00,

[0115] • N-Acetyl-L-Glutamine: between -2.00 and -5.00,

[0116] • Tryptophan: between -0.5 and -3.00.

[0117] All models were combined to develop a machine-learning approach to predict GBM subjects as responders or non-responders to the selected drug (chemotherapeutic) treatment regimen. Benefit is a relative term and indicates that treatment has a positive influence in treating a cancer subject having glioblastoma multiforme (GBM), but does not require complete remission. A subject that receives a benefit may be referred to as a benefiter, responder, or the like. Likewise, a subject unlikely to receive a benefit or that does not benefit may be referred to herein as a non-benefiter, non-responder, or similar. If a GMB subject is predicted to be a responder to the selected drug (chemotherapeutic) treatment regimen, then the GMB subject continues the treatment with the selected drug treatment regimen. If a GMB subject is predicted to be a non-responder to the selected drug / chemotherapeutic regimen), then an alternative drug, different from the selected drug, has to be taken as a candidate drug treatment regimen.

[0118] In some embodiments of the present invention, the selected drug and the alternative drug are independently selected from the group consisting of chemotherapy drugs, cell therapy drugs, and immunotherapy drugs. In an embodiment, chemotherapy drugs are selected from the group consisting of Temozolomide (TMZ), Savolitinib, Ivosidenib, Veliparib, Carmustine, Bevacizumab, Lomustine, Trametinib, Dabrafenib, Abemaciclib, Terameprocol, and Vorasidenib. According to another embodiment, cell therapy is typically selected from stem cell therapies, such as pluripotent stem cells, allogenic stem cells, autologous stem cells, and CAR-T therapies. According to another embodiment, the immunotherapy is selected from PD- 1 inhibitors and PD-L1 inhibitors targeting specific antigens on brain tumour cells.

[0119] The method of assessing treatment efficacy of a selected drug in a cancer subject having glioblastoma multiforme (GBM) according to the present invention can be used as a clinical and / or translational medicine tool during clinical drug development against GBM.

[0120] The present invention provides the use of a machine learning approach to analyze levels of six metabolites of the present invention in emotional tears samples obtained from a cancer subject having glioblastoma multiforme (GBM) before the treatment and after the treatment. The emotional tears samples are first obtained 2-4 weeks after the treatment and then consecutive emotional tears samples are obtained at designed time intervals. The time intervals between the consecutive emotional tears samples are in the range of 10-14 weeks, preferably the time intervals between the consecutive emotional tears samples are 12 weeks. The machine learning approach is used to predict benefit or lack of benefit from a selected drug used in the treatment of cancer subject having glioblastoma multiforme (GBM). The machine learning classification models were trained with different emotional tears samples obtained from a cancer subject having glioblastoma multiforme (GBM) before the treatment and after the treatment with different drugs. The classification was based on log2 fold changes in the following ranges in the levels of at least three, preferably all six, of said metabolites from the first emotional tears sample obtained before the treatment to the second and consecutive emotional tears samples obtained after the tretament or between the consecutive emotional tears samples, that are indicative of the GBM treatment inefficacy and / or bad survival prognosis:

[0121] • Glutamate: between -5.0 and - 7.0,

[0122] • Guanosine triphosphate: between -0.01 and - 0.50,

[0123] • Adenine: between 1.00 and 3.00,

[0124] • Lactate: between 1.00 and 2.00,

[0125] • N-Acetyl-L-Glutamine: between -2.00 and -5.00,

[0126] • Tryptophan: between -0.5 and -3.00.

[0127] The metabolic signature described herein serves as a powerful prognostic tool for evaluating the efficacy of therapeutic interventions in glioblastoma multiforme (GBM) patients. This distinctive pattern is strongly associated with improved overall survival time in GBM patients. Of particular significance is the log2 fold change of metabolites between pretherapy and posttherapy patients. A critical threshold has been established: when the log2 fold change of at least three metabolites within this signature increases by a minimum of 20% above the experimentally determined baseline, it serves as a robust predictor of disease relapse in posttherapy patients. This increase is concomitant with a higher risk of reduced overall survival time.

[0128] The signature comprises six specific metabolites, carefully selected from comprehensive metabolome profiling of emotional tears samples obtained from GBM patients. These metabolites were identified through rigorous hierarchical clustering analysis (data not shown) and represent the most clinically relevant and thoroughly validated biomarkers. The log2 fold change of these metabolites provides crucial insights into the metabolic reprogramming occurring in response to therapy and offers a valuable window into the likelihood of disease recurrence.

[0129] This metabolic signature not only enhances the understanding of GBM progression but also presents a non-invasive and highly sensitive method for monitoring treatment efficacy and predicting patient outcomes. The emphasis on log2 fold change as a predictive metric underscores its potential as a clinically actionable tool in personalized GBM management strategies.

[0130] The innovative methodology employed in the present study has yielded groundbreaking insights into the molecular underpinnings of Glioblastoma multiforme (GBM) pathology. Through rigorous pathway analysis of LC-MS raw data (Figure 1), it was possible to successfully identified and elucidated several critical molecular pathways intricately involved in GBM progression. Figure 1 illustrates the most pertinent metabolic pathways exhibiting low false discovery rates (FDR) and high total compound counts. The visualization provides a comprehensive overview of the key metabolic alterations associated with GBM, facilitating a deeper understanding of the disease's metabolic landscape and potential therapeutic targets. Of particular significance are the citrate and glutamate pathways, which have been found to produce metabolites that serve as clinically relevant and highly specific biomarkers for GBM progression and relapse. This represents a substantial advancement in understanding of GBM biology and offers unprecedented potential for improved patient care. Moreover, the present study allowed identification and precise quantification of six key metabolites within the emotional tears of GBM patients, both before and after therapy. The log2 fold change in these metabolites presents a novel and powerful tool for prognostic evaluation of GBM and assessment of therapeutic efficacy. This approach provides a non-invasive, highly sensitive means of monitoring disease progression and treatment response, potentially revolutionizing clinical management strategies for GBM patients. The robustness and sensitivity of methodology in identifying these molecular pathways and associated biomarkers underscore its potential to significantly impact GBM research and clinical practice. By offering a more nuanced understanding of GBM pathology at the molecular level, the present disclosure paves the way for the development of more targeted and effective therapeutic interventions, ultimately improving patient outcomes in this challenging disease.

[0131] Another aspect of the present invention provides a method for extracting metabolites from an emotional tears sample obtained from a subject, the method comprising a) mixing the emotional tears sample with water and with an alcohol, preferably with methanol, to provide a first mixture, b) adding 0.1% to 10% v / v ammonium hydroxide in the first mixture to provide a second mixture, c) vortex the second mixture, d) incubate the second mixture at a temperature ranging from 0 °C to 8 °C, e) centrifuge the second mixture at 1'000 to 20'000 rpm at a temperature ranging from 2 °C to 8 °C during 15 minutes to 60 minutes in desalting column, f) isolating the supernatant containing the metabolites, wherein the metabolites are Glutamate or an enantiomer thereof, Guanosine triphosphate or an enantiomer thereof, Adenine or an enantiomer thereof, Lactate or an enantiomer thereof, N-Acetyl-L-Glutamine or an enantiomer thereof, and Tryptophan or an enantiomer thereof.

[0132] Those skilled in the art will appreciate that the invention described herein is susceptible to variations and modifications other than those specifically described. It is to be understood that the invention includes all such variations and modifications without departing from the spirit or essential characteristics thereof. The invention also includes all of the steps, features, compositions and compounds referred to or indicated in this specification, individually or collectively, and any and all combinations or any two or more of said steps or features. The present disclosure is therefore to be considered as in all aspects illustrated and not restrictive, the scope of the invention being indicated by the appended claims, and all changes which come within the meaning and range of equivalency are intended to be embraced therein.

[0133] The foregoing description will be more fully understood with reference to the following Examples. Such Examples, are, however, exemplary of methods of practicing the present invention and are not intended to limit the application and the scope of the invention.

[0134] EXAMPLES

[0135] Metabolite extraction from emotional tears sample

[0136] 100 pL of tears were mixed with 400 pl of methanol (HPLC grade) and 50 pL water (as cosolvent) in a 1.5ml Eppendorf tube. For maximizing metabolite extraction efficiency, 2% ammonium hydroxide (NH40H, pH = 9) was added to the tube. The mixture was vortexed for 10 s, and then incubated for 20 min on ice. The mixture centrifuged at 16,000 x g, 4 °C, 15 min in desalting column (ZebaTM Spin Desalting Columns) then 200 pl of the supernatant containing the extracted metabolites was transferred to a 1 ml glass vial and stored at -20 °C until analysis. Metabolomic Analysis

[0137] • LC-MS / MS sample preparation

[0138] 20 different processed emotional tears samples (10 pre-therapy patients, 10 post-therapy patients) have been centrifuged for 10 min at 4°C / 10000g and the clear supernatant was transferred to clean test tubes (discarding the pellet). One aliquot of 380 ul of each supernatant was dried under N2, reconstitute in 100 ul of 90% ACN (lOmin incubation on ice, 30sec vortexing, lOmin on shaker at 4°C / 1000rpm, lOmin centrifugation at 10000g / 4°C), and the supernatant was recovered to glass vials (Total Recovery Glass Vial, Waters) prior to analysis.

[0139] • LC-MS / MS data acquisition

[0140] LC-MS analysis of metabolomics samples was performed on a Exploris 480 (Thermo Fisher Scientific) mass spectrometer coupled to a Thermo Vanquish LC (Thermo Scientific). Samples were separated using a 12 min gradient with constant flow of 0.4 ml / min on a HILIC analytical column (ACQUITY Premier BEH amide Column, 1.7 pm, 2.1 x 150 mm) kept at 40°C (LC gradient: initial conditions = 99% B, 30% B at 6 min, 30% B at 7 min, 99% B at 7.5 min, 99% B at 12 min. A: 5% ACN, lOmM ammonium bicarbonate, pH9 adjusted with ammonium hydroxide, B: 95%ACN, lOmM ammonium bicarbonate). Mass spectrometry data have been collected following un untargeted approach, MS spectra were acquired in Full MS - DDA Top5 mode in the time intervals 0 - 12 min (negative mode, scan range: 70 - 1050 m / z, 70000 resolution, AGC target le6, max IT 100ms). The mass spectrometry data were handled and stored using the local laboratory information management system B-fabric [C. Turker, F. Akai, D. Joho, C. Panse, B. Oesterreicher, H. Rehrauer, R. Schlapbach. « B-Fabric: The Swiss Army Knife for Life Sciences”. EDBT 2010, March 22-26, 2010, Lausanne, Switzerland (DOI 10.1145 / 1739041.1739135)].

[0141] Data processing and metabolites identification

[0142] The MS data generated with the untargeted approach were processed by means of the commercial software Compound Discoverer 3.3 (Thermo Fisher Scientific), following an untargeted metabolomics data processing. The workflow includes spectra selection, retention times alignment, compound detection and grouping, gap filling, background filtering and normalization (data are normalized on sample mass). mzCloud and mzVault have been used to score fragmentation patterns and assign MS2-based identities to the features. Filtering parameters used were the following: Signal / noise > 3, mzCloud or mzVault match >50, ppm mass error within + / - 5ppm., match with an in house developed MS1_RT library within + / - lOsec., chromatographic peak and MS2 spectra quality.

[0143] Results

[0144] Metabolomics analysis of tears of GBM patients (GBM patients phenotyping)

[0145] Emotional tears from GBM patients might be used to discriminate between pre-therapy GBM patients and post-therapy patients (Figure 2). Furthermore, signature pattern of 6 specific metabolites might be used as a prediction of relapse of GBM with high confidence.

[0146] A comprehensive metabolomic analysis has been performed to elucidate the differential metabolite profiles between treatment-naive and post-therapeutic cohorts of GBM patients. Utilizing high resolution mass spectrometry and advanced bioinformatics algorithms, it was possible to quantify and to compare the concentrations of key metabolites, including adenine, lactate, glutamate. Additionally, a volcano plot analysis was performed to visualize the statistical significance and fold change of clinically relevant metabolites. This approach allowed to identify metabolites with both high statistical significance (p < 0.05) and substantial fold change, potentially serving as biomarkers for treatment response or indicators of tumor progression. The integration of these metabolomic data with genomic and transcriptomic profiles may provide insights into the metabolic reprogramming associated with glioma pathogenesis and therapeutic interventions (Figure 3).

[0147] Specific Signature Pattern of 6 metabolites

[0148] Fold Change / 6 signature metabolites pre-therapy / post-therapy

[0149] Pre-therapy: Before tumor surgery or resection, chemotherapy and radiation therapy.

[0150] Post-therapy: After tumor surgery or resection, chemotherapy, immunotherapy and radiation therapy.

[0151] To ensure accurate metabolomic analysis, tear samples should be collected within a window of 14 to 30 days following the completion of radiation therapy. This timing is crucial because radiation treatment induces an inflammatory response in cerebral tissues, potentially altering the metabolome profile. Collecting samples during or immediately after radiation therapy may lead to misinterpretation of metabolomic data due to the transient effects of treatment-induced inflammation. By allowing a minimum of two weeks post-treatment, the acute inflammatory response is likely to have subsided, providing a more representative metabolomic profile for analysis. However, collection should occur within 30 days to minimize the impact of other potential confounding factors that may arise over time.

[0152] • Glutamate level log2 fold change between -5.0 / - 7.0 (experimentally observed mean value -6.2)

[0153] • Guanosine triphosphate level log2 fold change between -0.01 / - 0.50 (experimentally observed mean value -0.07)

[0154] • Adenine level log2 fold change between 1.00 / 3.00 (experimentally observed mean value 1.99)

[0155] • Lactate level log2 fold change between 1.00 / 2.00 (experimentally observed mean value 1.12)

[0156] • N-Acetyl-L-Glutamine level log2 fold change between -2.00 / -5.00 (experimentally observed mean value -3.53)

[0157] • Tryptophan level log2 fold change between -0.5 / -3.00 (experimentally observed mean value -1.42)

[0158] As evidenced in Table 1, while the 3 -metabolites sub-pattern demonstrates excellent predictive capabilities for both survival outcomes and therapeutic efficacy, the complete 6-metabolites signature provides better prognostic accuracy. Although the reduced 3-metabolites panel remains a valuable analytical tool for survival prognosis prediction and therapeutic efficacy, the comprehensive 6-metabolites signature seems to provide optimal clinical utility.

[0159] Table 1 - Predicted survival time vs Clinically observed survival time

Claims

27CLAIMS1. A method for determining survival prognosis of a cancer subject having glioblastoma multiforme (GBM), the method comprising: a) determining levels of six metabolites in a first emotional tears sample obtained from the subject before the treatment, b) determining levels of six metabolites in a second emotional tears sample obtained from the subject after the treatment, c) comparing the levels of the six metabolites in said first and second emotional tears samples, wherein the six metabolites are Glutamate or an enantiomer thereof, Guanosine triphosphate or an enantiomer thereof, Adenine or an enantiomer thereof, Lactate or an enantiomer thereof, N-Acetyl-L-Glutamine or an enantiomer thereof, and Tryptophan or an enantiomer thereof, and wherein log2 fold changes in the levels of at least three of said metabolites from the first emotional tears sample to the second emotional tears sample in the following ranges are indicative of bad survival prognosis:• Glutamate: between -5.0 and - 7.0,• Guanosine triphosphate: between -0.01 and - 0.50,• Adenine: between 1.00 and 3.00,• Lactate: between 1.00 and 2.00,• N-Acetyl-L-Glutamine: between -2.00 and -5.00,• Tryptophan: between -0.5 and -3.00.

2. The method of claim 1 further comprising repeating step b) for one or more times to determine the levels of the six metabolites in consecutive emotional tears samples, obtained at designated time intervals, and comparing the levels of the six metabolites between said consecutive emotional tears samples, wherein said log2 fold changes in the levels of at least three of said metabolites between said consecutive emotional tears samples are indicative of bad survival prognosis.

3. The method of claim 2, wherein the time intervals between the consecutive emotional tears samples are in the range of 10-14 weeks, preferably the time intervals between the consecutive emotional tears samples are 12 weeks.

4. The method of any one of claims 1-3, wherein the treatment is selected from the group comprising chemotherapy, radiotherapy, cell therapy, immunotherapy, tumor resection and combinations thereof.

5. A method for assessing treatment efficacy of a drug in a cancer subject having glioblastoma multiforme (GBM), the method comprising a) determining levels of six metabolites in a first emotional tears sample obtained from the subject before the treatment with the drug, b) determining levels of six metabolites in a second emotional tears sample obtained from the subject after the treatment with the drug c) comparing the levels of the six metabolites in said first and second emotional tears samples, wherein the six metabolites are Glutamate or an enantiomer thereof, Guanosine triphosphate or an enantiomer thereof, Adenine or an enantiomer thereof, Lactate or an enantiomer thereof, N-Acetyl-L-Glutamine or an enantiomer thereof, and Tryptophan or an enantiomer thereof; and wherein log2 fold changes in the levels of at least three of said metabolites from the first emotional tears sample to the second emotional tears sample in the following ranges are indicative of a decreased therapeutic success and resistance to the drug:• Glutamate: between -5.0 and - 7.0,• Guanosine triphosphate: between -0.01 and - 0.50,• Adenine: between 1.00 and 3.00,• Lactate: between 1.00 and 2.00,• N-Acetyl-L-Glutamine: between -2.00 and -5.00,• Tryptophan: between -0.5 and -3.00.

6. The method of claim 5, wherein the levels of the six metabolites are determined at one or more time points after initiation of treatment with the drug on consecutive emotional tears samples.

7. The method of claim 6, further comprising comparing the levels of the six metabolites between the consecutive emotional tears samples, wherein said log2 fold changes in the levels of at least three of said metabolites between consecutive emotional tears samples are indicative of the decreased therapeutic success and resistance to the drug.

8. The method of claim 6 or 7, wherein the consecutive emotional tears samples are obtained at time intervals in the range of 10-14 weeks, preferably the time intervals between the consecutive emotional tears samples are 12 weeks.

9. A method of determining the progression or the regression of glioblastoma multiforme (GBM) in a subject suffering from said disease, the method comprising determining the levels of six metabolites in consecutive emotional tears samples, obtained at designated time intervals, wherein the six metabolites are Glutamate or an enantiomer thereof, Guanosine triphosphate or an enantiomer thereof, Adenine or an enantiomer thereof, Lactate or an enantiomer thereof, N-Acetyl-L-Glutamine or an enantiomer thereof, and Tryptophan or an enantiomer thereof; and wherein log2 fold changes in the levels of at least three of said metabolites between the consecutive emotional tears samples in the following ranges are indicative of the GBM progression:• Glutamate: between -5.0 and - 7.0,• Guanosine triphosphate: between -0.01 and - 0.50,• Adenine: between 1.00 and 3.00,• Lactate: between 1.00 and 2.00,• N-Acetyl-L-Glutamine: between -2.00 and -5.00,• Tryptophan: between -0.5 and -3.00.

10. The method of claim 9, wherein the time intervals between the consecutive emotional tears samples are in the range of 10-14 weeks, preferably the time intervals between the consecutive emotional tears samples are 12 weeks.

11. A kit for performing the methods according to any one of claims 1 to 10, the kit comprising a) means for collecting emotional tears samples of a subject,b) means and / or reagents for determining the levels of the six metabolites in the emotional tears samples of the subject, and c) instructions for use, wherein the six metabolites are Glutamate or an enantiomer thereof, Guanosine triphosphate or an enantiomer thereof, Adenine or an enantiomer thereof, Lactate or an enantiomer thereof, N-Acetyl-L-Glutamine or an enantiomer thereof, and Tryptophan or an enantiomer thereof.

12. A method of assessing treatment efficacy of a selected drug in a cancer subject having glioblastoma multiforme (GBM), the method comprising: a) determining levels of six metabolites in a first emotional tears sample obtained from the subject before the treatment, b) determining levels of six metabolites in consecutive emotional tears samples, obtained at designated time intervals from the subject after the treatment, c) perform machine learning on the levels of six metabolites obtained in steps a) and b), d) obtaining output data generated by machine learning which indicates whether the subject is likely to benefit from treatment with the selected drug, and e) selecting either to continue the treatment with the selected drug or to find an alternative drug, different from the selected drug, as a candidate treatment for the subject based on the obtained output data, wherein the six metabolites are Glutamate or an enantiomer thereof, Guanosine triphosphate or an enantiomer thereof, Adenine or an enantiomer thereof, Lactate or an enantiomer thereof, N-Acetyl-L-Glutamine or an enantiomer thereof, and Tryptophan or an enantiomer thereof.

13. The method of claim 12, wherein the selected drug and the alternative drug are independently selected from the group consisting of chemotherapy drugs, cell therapy drugs, and immunotherapy drugs.

14. A method for extracting metabolites from an emotional tears sample obtained from a subject, the method comprising a) mixing the emotional tears sample with water and with an alcohol, preferably with methanol, to provide a first mixture,b) adding 0.1% to 10% v / v ammonium hydroxide in the first mixture to provide a second mixture, c) vortex the second mixture, d) incubate the second mixture at a temperature ranging from 0 °C to 8 °C, e) centrifuge the second mixture at 1'000 to 20'000 rpm at a temperature ranging from2 °C to 8 °C during 15 minutes to 60 minutes in desalting column, f) isolating the supernatant containing the metabolites, wherein the metabolites are Glutamate or an enantiomer thereof, Guanosine triphosphate or an enantiomer thereof, Adenine or an enantiomer thereof, Lactate or an enantiomer thereof, N-Acetyl-L-Glutamine or an enantiomer thereof, and Tryptophan or an enantiomer thereof.

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