WO2023150826A1 - Prognostic biomarkers and uses therefor - Google Patents

Prognostic biomarkers and uses therefor Download PDF

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WO2023150826A1
WO2023150826A1 PCT/AU2023/050078 AU2023050078W WO2023150826A1 WO 2023150826 A1 WO2023150826 A1 WO 2023150826A1 AU 2023050078 W AU2023050078 W AU 2023050078W WO 2023150826 A1 WO2023150826 A1 WO 2023150826A1
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gbm
biomarker
protein
patient
salivary
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French (fr)
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Chamindie Punyadeera
Juliana MÜLLER BARK
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Griffith University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/573—Immunoassay; Biospecific binding assay; Materials therefor for enzymes or isoenzymes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
    • G01N33/57488—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites involving compounds identifable in body fluids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/90—Enzymes; Proenzymes
    • G01N2333/914—Hydrolases (3)
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/90—Enzymes; Proenzymes
    • G01N2333/914—Hydrolases (3)
    • G01N2333/948—Hydrolases (3) acting on peptide bonds (3.4)
    • G01N2333/95—Proteinases, i.e. endopeptidases (3.4.21-3.4.99)
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/90—Enzymes; Proenzymes
    • G01N2333/914—Hydrolases (3)
    • G01N2333/948—Hydrolases (3) acting on peptide bonds (3.4)
    • G01N2333/95—Proteinases, i.e. endopeptidases (3.4.21-3.4.99)
    • G01N2333/964—Proteinases, i.e. endopeptidases (3.4.21-3.4.99) derived from animal tissue
    • G01N2333/96425—Proteinases, i.e. endopeptidases (3.4.21-3.4.99) derived from animal tissue from mammals
    • G01N2333/96427—Proteinases, i.e. endopeptidases (3.4.21-3.4.99) derived from animal tissue from mammals in general
    • G01N2333/9643—Proteinases, i.e. endopeptidases (3.4.21-3.4.99) derived from animal tissue from mammals in general with EC number
    • G01N2333/96433—Serine endopeptidases (3.4.21)
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/90—Enzymes; Proenzymes
    • G01N2333/914—Hydrolases (3)
    • G01N2333/948—Hydrolases (3) acting on peptide bonds (3.4)
    • G01N2333/95—Proteinases, i.e. endopeptidases (3.4.21-3.4.99)
    • G01N2333/964—Proteinases, i.e. endopeptidases (3.4.21-3.4.99) derived from animal tissue
    • G01N2333/96425—Proteinases, i.e. endopeptidases (3.4.21-3.4.99) derived from animal tissue from mammals
    • G01N2333/96427—Proteinases, i.e. endopeptidases (3.4.21-3.4.99) derived from animal tissue from mammals in general
    • G01N2333/9643—Proteinases, i.e. endopeptidases (3.4.21-3.4.99) derived from animal tissue from mammals in general with EC number
    • G01N2333/96433—Serine endopeptidases (3.4.21)
    • G01N2333/96441—Serine endopeptidases (3.4.21) with definite EC number
    • G01N2333/96455—Kallikrein (3.4.21.34; 3.4.21.35)
    • G—PHYSICS
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    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/90—Enzymes; Proenzymes
    • G01N2333/988—Lyases (4.), e.g. aldolases, heparinase, enolases, fumarase
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/56—Staging of a disease; Further complications associated with the disease
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/70—Mechanisms involved in disease identification
    • G01N2800/7023—(Hyper)proliferation
    • G01N2800/7028—Cancer
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6875—Nucleoproteins

Definitions

  • This disclosure relates generally to biomarkers of cancer. More particularly, the present disclosure relates to extracellular vesicle biomarkers and their use in methods, apparatuses, compositions and kits for determining an indicator that is useful for assessing a likelihood of a decreased or poor survival prognosis or an increased or good survival prognosis in a glioblastoma patient.
  • the disclosed methods, apparatuses, compositions and kits are useful for monitoring prognosis of a glioblastoma patient before and after exposure to a treatment regimen, and for managing treatment of a glioblastoma patient.
  • GBM glioblastoma multiforme
  • BBB blood-brain barrier
  • Exosomes can be isolated from several body fluids, including blood, saliva, urine, and cerebrospinal fluid (CSF) (Raposo et al., J Cell Biol. 2013;200(4):373-83).
  • CSF cerebrospinal fluid
  • saliva composition is altered under pathological conditions, including cancer (Schulz et al., Crit Rev Biotechnol. 2013;33(3):246-59).
  • saliva is easy and cost-effective to collect and store (Pfaffe et al., Clin Chem. 2011;57(5):675-87).
  • saliva is considered a potential matrix for the discovery of cancer biomarkers for diagnosis, prognosis, and drug monitoring (Pfaffe et al., 2011; supra; Malamud et al., IntJ Oral Sci. 2016;8(3): 133-7; Zhang et al., Int J Oral Sci.
  • salivary exosomes are potential candidates to avoid this drawback and they have been explored as diagnostic or prognostic biomarkers in several cancer types, including head and neck cancer (Langevin et al., Oncotarget. 2017;8(47):82459-74; Tang K et al., Mol Diagn Ther.
  • pancreatic cancer Machida et al., Oncol Rep. 2015;36(4):2375-81; Lau et al., J Biol Chem. 2013;288(37):26888-97
  • lung cancer Sun et al., J Proteome Res. 2018;17(3): 1101-7; Sun et al., Sci Rep. 2016;6:24669.
  • salivary exosomes there are no studies in the literature exploring the role of salivary exosomes in patients with GBM.
  • the present disclosure arises from the determination that certain protein biomarkers in salivary EVs from GBM patients have strong discrimination performance for differentiating between GBM patients with favorable outcomes (e.g., no disease recurrence, no disease progression or no death from disease within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM) and GBM patients with unfavorable outcomes (e.g., disease recurrence, disease progression or death from disease within six months from diagnosis of GBM or within nine months from diagnosis of GBM).
  • favorable outcomes e.g., no disease recurrence, no disease progression or no death from disease within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM
  • unfavorable outcomes e.g., disease recurrence, disease progression or death from disease within six months from diagnosis of GBM or within nine months from diagnosis of GBM.
  • compositions and kits which take advantage of the biomarkers disclosed herein for predicting a favorable or unfavorable outcome in glioblastoma patient, for monitoring prognosis of glioblastoma patients (e.g., before and after exposure to a treatment regimen for treating glioblastoma) and for making better decisions for treating or triaging the patients.
  • methods for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis. These methods generally comprise, consist or consist essentially of:
  • determining a biomarker value for at least one protein biomarker e.g., 1, 2 or 3 protein biomarkers
  • a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample
  • the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kallikrei n- 1 (KLK1); and
  • a biomarker value is obtained for each of LKHA4, H4 and KLK1.
  • the poor prognosis is suitably disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, and the good prognosis is suitably no disease recurrence, no disease progression or no death from disease within and/or after six months from diagnosis of GBM.
  • H4 is present at a higher level in salivary EV samples obtained from GBM patients with an unfavorable outcome than in salivary EV samples obtained from GBM patients with a favorable outcome
  • LKHA4 and KLK1 are present at a lower level in salivary EV samples obtained from GBM patients with an unfavorable outcome than in salivary EV samples obtained from GBM patients with a favorable outcome
  • the unfavorable outcome is disease recurrence, disease progression or death from disease within six months from diagnosis of GBM
  • the favorable outcome is no disease recurrence, no disease progression or death from disease within and/or after six months from diagnosis of GBM.
  • the indicator indicates a likelihood of a poor prognosis if:
  • H4 is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome
  • LKHA4 is present in the salivary EV sample obtained from the GBM patient at a lower level than in control salivary EV samples obtained from a reference population of GBM patients with a favorable outcome;
  • KLK1 is present in the salivary EV sample obtained from the GBM patient at a lower level than in control salivary EV samples obtained from a reference population of GBM patients with a favorable outcome.
  • the indicator may indicate a likelihood of a good prognosis if:
  • H4 is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome
  • LKHA4 is present in the salivary EV sample obtained from the GBM patient at a higher level than in control salivary EV samples obtained from a reference population of GBM patients with an unfavorable outcome;
  • KLK1 is present in the salivary EV sample obtained from the GBM patient at a higher level than in control salivary EV samples obtained from a reference population of GBM patients with an unfavorable outcome.
  • methods for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis. These methods generally comprise, consist or consist essentially of:
  • determining a biomarker value for at least one protein biomarker e.g., 1, 2, 3 or 4 protein biomarkers
  • a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample
  • the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI); and
  • a biomarker value is obtained for each of ALDOA, 1433E, TM11B and ECHI.
  • the poor prognosis is suitably disease recurrence, disease progression or death from disease within nine months from diagnosis of GBM, and the good prognosis is suitably no disease recurrence, no disease progression or no death from disease within and/or after nine months from diagnosis of GBM.
  • each of ALDOA, 1433E, TM11B and ECHI are present at a higher level in salivary EV samples obtained from GBM patients with an unfavorable outcome than in salivary EV samples obtained from GBM patients with a favorable outcome, wherein the unfavorable outcome is disease recurrence, disease progression or death from disease within nine months from diagnosis of GBM, and wherein the favorable outcome is no disease recurrence, no disease progression or death from disease within and/or after nine months from diagnosis of GBM.
  • the indicator indicates a likelihood of a poor prognosis if:
  • ALDOA is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome
  • TM11B is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome;
  • ECHI is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome.
  • the indicator may indicate a likelihood of a good prognosis if:
  • ALDOA is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome
  • TM11B is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome
  • ECHI is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome.
  • the GBM patient may or may not have undergone a treatment regimen for treating GBM.
  • a treatment regimen for treating GBM Illustrative examples of GBM treatment regimens which include surgery, radiotherapy and/or chemotherapy.
  • EVs of the sample are suitably small EVs, typically with a diameter of less than about 200 nm. In some embodiments, the EVs have a diameter ranging from about 30 nm to about 200 nm.
  • Individual biomarker values suitably represent a measured amount, abundance or concentration of a corresponding protein biomarker in the sample.
  • the methods may further comprise applying a function to biomarker values to yield at least one functionalized biomarker value and determining the indicator using the at least one functionalized biomarker value.
  • the function includes at least one of: (a) multiplying biomarker values; (b) dividing biomarker values; (c) adding biomarker values; (d) subtracting biomarker values; (e) a weighted sum of biomarker values; (f) a log sum of biomarker values; (g) a geometric mean of biomarker values; (h) a sigmoidal function of biomarker values; and (i) normalization of biomarker values.
  • the methods may further comprise combining the biomarker values, optionally with clinical parameters, to provide a composite score and determining the indicator using the composite score.
  • the biomarker values are combined by adding, multiplying, subtracting, and/or dividing biomarker values.
  • the methods suitably further comprise analyzing the biomarker value(s) or composite score with reference to one or more reference biomarker values, biomarker value ranges, functionalized biomarker value(s), functionalized biomarker value ranges, biomarker value cut-offs or functionalized biomarker value cut offs, or reference composite scores, composite score ranges or composite score cut-offs, to determine the indicator.
  • a respective reference biomarker value, biomarker value range, functionalized biomarker value, functionalized biomarker value range, biomarker value cut-off or functionalized biomarker value cut-off, or reference composite score, composite score range or composite score cut-off may be a biomarker value, biomarker value range, functionalized biomarker value, functionalized biomarker value range, biomarker value cut-off or functionalized biomarker value cut-off, or reference composite score, composite score range or composite score cut-off corresponding to a control subject or control population of subjects.
  • the control subject or control population of subjects is suitably selected from a subject or population of subjects with an unfavorable outcome within a period (e.g., six months or nine months) from diagnosis of GBM, or a subject or population of subjects with a favorable outcome within and/or after a period (e.g., six months or nine months) from diagnosis of GBM.
  • the indicator suitably indicates a likelihood of a poor prognosis, if the biomarker value(s), functionalized biomarker value(s) or composite score is(are) indicative of the level of the biomarker(s) in the sample that correlates with an increased likelihood of a poor prognosis relative to a predetermined reference biomarker value, value range or cut-off value, or to a predetermined reference functionalized biomarker value, value range or cut-off value, or to a predetermined reference composite score, composite score range or composite score cut-off.
  • the indicator indicates a likelihood of a good prognosis, if the biomarker value(s), functionalized biomarker value(s) or composite score is(are) indicative of the level of the biomarker(s) in the sample that correlates with an increased likelihood of a good prognosis relative to a predetermined reference biomarker value, value range or cut-off value, or to a predetermined reference functionalized biomarker value, value range or cut-off value, or to a predetermined reference composite score, composite score range or composite score cut-off.
  • methods for monitoring prognostic status or treatment of a GBM patient. These methods generally comprise, consist or consist essentially of:
  • a biomarker value for at least one protein biomarker e.g., 1, 2 or 3 protein biomarkers
  • a respective biomarker value is indicative of a level of a corresponding protein biomarker in the first sample, and wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kallikrein-1 (KLK1);
  • LKHA4 leukotriene A-4 hydrolase
  • H4 histone H4
  • KLK1 kallikrein-1
  • determining a biomarker value for at least one protein biomarker e.g., 1, 2, 3 or 4 protein biomarkers
  • a respective biomarker value is indicative of a level of a corresponding protein biomarker in the first sample
  • the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI);
  • the first sample may be obtained from the patient before undergoing a therapeutic regimen for treating GBM and the second sample may be obtained from the patient after undergoing the therapeutic regimen.
  • apparatuses for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, suitably wherein the poor prognosis is disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, and suitably wherein the good prognosis is no disease recurrence, no disease progression or no death from disease within and/or after six months from diagnosis of GBM.
  • These apparatuses general comprise, consist or consist essentially of at least one electronic processing device that:
  • LKHA4 leukotriene A-4 hydrolase
  • H4 histone H4
  • KLK1 kallikrein-1
  • apparatuses for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis suitably wherein the poor prognosis is disease recurrence, disease progression or death from disease within nine months from diagnosis of GBM, and suitably wherein the good prognosis is no disease recurrence, no disease progression or no death from disease within and/or after nine months from diagnosis of GBM.
  • These apparatuses general comprise, consist or consist essentially of at least one electronic processing device that:
  • a biomarker value for at least one protein biomarker e.g., 1, 2, 3 or 4 protein biomarkers
  • a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample
  • the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI); and
  • compositions suitably for use in determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, suitably wherein the poor prognosis is disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, and suitably wherein the good prognosis is no disease recurrence, no disease progression or no death from disease within and/or after six months from diagnosis of GBM.
  • compositions generally comprise, consist or consist essentially of a mixture of a salivary EV sample obtained from a GBM patient, and for one or a plurality of protein biomarkers (e.g., 1, 2 or 3 protein biomarkers) in the sample an antibody or antigen-binding fragment that binds specifically to the protein biomarker, wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kallikrein-1 (KLK1).
  • LKHA4 leukotriene A-4 hydrolase
  • H4 histone H4
  • KLK1 kallikrein-1
  • compositions are disclosed, suitably for use in determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, suitably wherein the poor prognosis is disease recurrence, disease progression or death from disease within nine months from diagnosis of GBM, and suitably wherein the good prognosis is no disease recurrence, no disease progression or no death from disease within and/or after nine months from diagnosis of GBM.
  • compositions generally comprise, consist or consist essentially of a mixture of a salivary EV sample obtained from a GBM patient, and for one or a plurality of protein biomarkers (e.g., 1, 2, 3 or 4 protein biomarkers) in the sample an antibody or antigen-binding fragment that binds specifically to the protein biomarker, wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI).
  • ALDOA aldolase A
  • 1433E 14-3-3 protein epsilon
  • TM11B transmembrane protease serine 11B
  • ECHI enoyl CoA hydratase 1
  • the composition may comprise a plurality of antibodies or antigen-binding fragments, each of which specifically binds to a different protein biomarker and comprises the same label or a different label, as compared to the protein biomarker specificity and label of other antibodies or antigen-binding fragments of the composition.
  • the labels of different antibodies or antigen-binding fragments are detectably distinct.
  • methods for managing treatment of a GBM patient. These methods generally comprise, consist or consist essentially of:
  • the GBM patient has been administered a treatment regimen prior to undertaking the indicator-determining method. In other embodiments, the GBM patient has not undergone a treatment regimen prior to undertaking the indicator-determining method.
  • the treatment management methods further comprise: taking a sample from the patient and determining an indicator indicative of a likelihood of a disclosed prognosis using the indicator-determining method.
  • the methods further comprise: sending a sample obtained from the patient to a laboratory at which the indicator is determined according to the indicator-determining method, and optionally receiving the indicator from the laboratory.
  • kits for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis suitably wherein the poor prognosis is disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, and suitably wherein the good prognosis is no disease recurrence, no disease progression or no death from disease within and/or after six months from diagnosis of GBM.
  • kits generally comprise for one or a plurality of protein biomarkers (e.g., 1, 2 or 3 protein biomarkers) an antibody or antigen-binding fragment that binds specifically to the protein biomarker, wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kalli krein-1 (KLK1).
  • protein biomarkers e.g., 1, 2 or 3 protein biomarkers
  • an antibody or antigen-binding fragment that binds specifically to the protein biomarker
  • the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kalli krein-1 (KLK1).
  • kits for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis suitably wherein the poor prognosis is disease recurrence, disease progression or death from disease within nine months from diagnosis of GBM, and suitably wherein the good prognosis is no disease recurrence, no disease progression or no death from disease within and/or after nine months from diagnosis of GBM.
  • kits generally comprise for one or a plurality of protein biomarkers (e.g., 1, 2, 3 or 4 protein biomarkers) an antibody or antigen-binding fragment that binds specifically to the protein biomarker, wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI).
  • ALDOA aldolase A
  • 1433E 14-3-3 protein epsilon
  • TM11B transmembrane protease serine 11B
  • ECHI enoyl CoA hydratase 1
  • kits may further comprise any one or more of: at least one reagent for preparing EVs from a saliva sample; buffer(s), positive and negative controls, and reaction vessel(s).
  • the kits may further comprise instructions for performing the indicatordetermining methods as broadly described above and elsewhere herein.
  • Figure 1 is a graphical and photographic representation showing characterization of salivary small extracellular vesicles in glioblastoma patients:
  • A Size and concentration of salivary small extracellular vesicles in pre and postoperative GBM patients.
  • B Morphology of salivary small extracellular vesicles imaged by Transmission Electron Microscopy (TEM). Representative images of the cup-shaped morphology of EVs (red arrow) isolated from pre (left panel) and postoperative (right panel) samples.
  • C Immunoblotting for positive (CD9, CD63) and negative (GM130) markers of small extracellular vesicles isolated from saliva of GBM patients.
  • Fl postoperative sample.
  • FIG. 2 is a diagrammatic and graphical representation showing proteomic profiling of small extracellular vesicles in pre and postoperative saliva samples from glioblastoma patients.
  • C Venn diagram of all proteins identified in salivary small EV samples of GBM patients compared to proteins annotated in two EV databases, Exocarta and Vesiclepedia.
  • Figure 3 is a graphical representation showing the performance of partial least squares discriminant analysis (PLS-DA) to stratify patients into two groups: (1) patients with favorable outcomes (no disease recurrence, no disease progression or no death from disease within and/or after six months from diagnosis of GBM); and (2) patients with unfavorable outcomes (disease recurrence, disease progression or death from disease within six months from diagnosis of GBM).
  • PLS-DA Partial least squares-discriminant analysis
  • FIG. 4 is a graphical representation showing ROC curves for individual biomarkers. Receiver Operating Characteristics (ROCs) are shown for H4_ HUMAN, LKHA4_ HUMAN and KLK1_HUMAN proteins in preoperative salivary EVs from GBM patients with favorable and unfavorable outcomes.
  • ROCs Receiver Operating Characteristics
  • Figure 5 is a graphical representation showing a ROC curve for a 3-protein biomarker panel (H4JHUMAN + LKHA4_ HUMAN + KLK1_ HUMAN) in preoperative salivary EVs from GBM patients with favorable and unfavorable outcomes.
  • a multivariate ROC curve was generated to evaluate the prognostic performance of the 3 proteins panel.
  • Figure 6 is a graphical representation showing a box and whisker plot of normalized protein abundance of the 3-protein panel pre-operatively.
  • a logistic regression predictive model was applied to the candidate biomarkers to calculate a predictive score for each individual sample and plotted into the groups analyzed (patients with favorable outcomes and patients with unfavorable outcomes).
  • Figure 7 is a graphical representation showing A) Size of salivary small extracellular vesicles of GBM patients with favorable and unfavorable outcomes in pre and postoperative samples. B) Concentration of salivary small extracellular vesicles of GBM patients with favorable and unfavorable outcomes in pre and postoperative samples. The Mann-Whitney test (GraphPad Prism) was used to determine significance, p* ⁇ 0.05. Partial least squares- discriminant analysis (PLS-DA) score plots of proteome signatures in salivary small EVs from GBM patients with favorable prognosis (blue) and unfavorable prognosis (red) C) pre and D) postoperative.
  • PLS-DA Partial least squares- discriminant analysis
  • Figure 8 is a graphical representation showing A) Box and whisker plots of normalized protein abundance of four protein biomarker candidates preoperatively, ALDOA, 1433E, ECHI and TM11B. The Mann-Whitney test (GraphPad Prism) was used to determine significance, p* ⁇ 0.05 p** ⁇ 0.01.
  • ROC Receiver operator characteristic
  • Figure 9 is a graphical representation showing A-D) Receiver operator characteristic (ROC) curve analysis of ALDOA, ECHI. TM11B, 1433E individually in preoperative salivary EVs from GBM patients with favorable and unfavorable outcomes.
  • ROC Receiver operator characteristic
  • the term "about” as used herein refers to the usual error range for the respective value readily known to the skilled person in this technical field. Reference to “about” in connection with a value or parameter herein includes (and describes) embodiments that are directed to that value or parameter per se. In specific embodiments, the term “about” refers to a value or parameter (e.g., quantity, level, concentration, number, frequency, percentage, dimension, size, amount, weight or length) that varies by as much 15, 14, 13, 12, 11, 10, 9, 8, 7, 5, 5, 4, 3, 2 or 1 % to a reference value or parameter.
  • a value or parameter e.g., quantity, level, concentration, number, frequency, percentage, dimension, size, amount, weight or length
  • the "amount”, "level” or “abundance” of a biomarker is a detectable level, amount or abundance in a sample. These can be measured by methods known to one skilled in the art and also disclosed herein. These terms encompass a quantitative amount, abundance or level (e.g., weight or moles), a semi-quantitative amount, abundance or level, a relative amount, abundance or level (e.g., weight % or mole % within class), a concentration, and the like. Thus, these terms encompass absolute or relative amounts, abundances or levels or concentrations of a biomarker in a sample.
  • the term "more aggressive” refers to a treatment regimen that may include more drugs or drugs with more severe side effects and/or it may include an increased dosage or increased frequency of drugs. It may also include radiation or a combination of therapies.
  • the therapy includes one or more chemotherapeutics and/or biologies.
  • the patient is treated with a therapy comprising an anti-angiogenic agent.
  • the therapy further comprises a chemotherapeutic agent in addition to the anti-angiogenic agent.
  • antibody means any antigen-binding molecule or molecular complex comprising at least one complementarity determining region (CDR) that binds specifically to or interacts with a particular antigen (e.g., one of LKHA4, H4, KLK1, ALDOA, 1433E, TM11B and ECHI).
  • CDR complementarity determining region
  • the term “antibody” includes immunoglobulin molecules comprising four polypeptide chains, two heavy (H) chains and two light (L) chains inter-connected by disulfide bonds, as well as multimers thereof (e.g., IgM). Each heavy chain comprises a heavy chain variable region (which may be abbreviated as HCVR or V H ) and a heavy chain constant region.
  • the heavy chain constant region comprises three domains, CHI, CH2 and CH3.
  • Each light chain comprises a light chain variable region (which may be abbreviated as LCVR or V ) and a light chain constant region.
  • the light chain constant region comprises one domain (CLI).
  • the V H and V L regions can be further subdivided into regions of hypervariability, termed complementarity determining regions (CDRs), interspersed with regions that are more conserved, termed framework regions (FR).
  • CDRs complementarity determining regions
  • FR framework regions
  • Each V H and V is composed of three CDRs and four FRs, arranged from amino-terminus to carboxy-terminus in the following order: FR1, CDR1, FR2, CDR2, FR3, CDR3, FR4.
  • the FRs of an antibody of the invention may be identical to the human germline sequences, or may be naturally or artificially modified.
  • An amino acid consensus sequence may be defined based on a side-by-side analysis of two or more CDRs.
  • An antibody includes an antibody of any class, such as IgG, IgA, or IgM (or sub-class thereof), and the antibody need not be of any particular class.
  • immunoglobulins can be assigned to different classes.
  • immunoglobulins There are five major classes of immunoglobulins: IgA, IgD, IgE, IgG, and IgM, and several of these may be further divided into subclasses (isotypes), e.g., IgGl, IgG2, IgG3, IgG4, IgAl and IgA2.
  • the heavy-chain constant regions that correspond to the different classes of immunoglobulins are called a, 6, e, y, and p, respectively.
  • the subunit structures and three-dimensional configurations of different classes of immunoglobulins are well known.
  • antigens refer to a compound, composition, or substance that may be specifically bound by the products of specific humoral or cellular immunity, such as an antibody molecule or T-cell receptor.
  • Antigens can be any type of molecule including, for example, haptens, simple intermediary metabolites, sugars (e.g., oligosaccharides), lipids, and hormones as well as macromolecules such as complex carbohydrates (e.g., polysaccharides), phospholipids, and proteins.
  • antigen-binding fragment refers to a part of an antigen-binding molecule that participates in antigen-binding. These terms include any naturally occurring, enzymatically obtainable, synthetic, or genetically engineered polypeptide or glycoprotein that specifically binds an antigen to form a complex.
  • Antigen-binding fragments of an antibody may be derived, e.g., from full antibody molecules using any suitable standard techniques such as proteolytic digestion or recombinant genetic engineering techniques involving the manipulation and expression of DNA encoding antibody variable and optionally constant domains.
  • DNA is known and/or is readily available from, e.g., commercial sources, DNA libraries (including, e.g., phage-antibody libraries), or can be synthesized.
  • the DNA may be sequenced and manipulated chemically or by using molecular biology techniques, for example, to arrange one or more variable and/or constant domains into a suitable configuration, or to introduce codons, create cysteine residues, modify, add or delete amino acids, etc.
  • Non-limiting examples of antigen-binding fragments include: (i) Fab fragments; (ii) F(ab')2 fragments; (Hi) Fd fragments; (iv) Fv fragments; (v) single-chain Fv (scFv) molecules; (vi) dAb fragments; and (vii) minimal recognition units consisting of the amino acid residues that mimic the hypervariable region of an antibody (e.g., an isolated complementarity determining region (CDR) such as a CDR3 peptide), or a constrained FR3-CDR3-FR4 peptide.
  • CDR complementarity determining region
  • engineered molecules such as domain-specific antibodies, single domain antibodies, domain-deleted antibodies, chimeric antibodies, CDR-grafted antibodies, one- armed antibodies, diabodies, triabodies, tetrabodies, minibodies, nanobodies (e.g. monovalent nanobodies, bivalent nanobodies, etc.), small modular immunopharmaceuticals (SMIPs), and shark variable IgNAR domains, are also encompassed within the expression "antigen-binding fragment," as used herein.
  • SMIPs small modular immunopharmaceuticals
  • antigen-binding molecule is meant a molecule that has binding affinity for a target antigen. It will be understood that this term extends to immunoglobulins, immunoglobulin fragments and non-immunoglobulin derived protein frameworks that exhibit antigen-binding activity.
  • Representative antigen-binding molecules that are useful in the practice of the present invention include antibodies and their antigen-binding fragments.
  • the term “antigen-binding molecule” includes antibodies and antigen-binding fragments of antibodies.
  • the term "array” refers to an arrangement of capture reagents on a substrate, in which individual capture reagents bind specifically to a particular molecule (e.g., protein or antigen).
  • the capture reagents are antibodies or antigenbinding fragments.
  • the term "biomarker” refers to a naturally occurring biological molecule present in a subject at varying concentrations useful in predicting an outcome of a disease or a condition, such as GBM.
  • the biomarker can be a protein present in higher or lower amounts in salivary EVs of a patient with GBM.
  • the biomarker is a protein selected from LKHA4, H4 and KLK1.
  • the biomarker is a protein selected from ALDOA, 1433E, TM11B and ECHI.
  • biomarker value refers to a value measured or functionalized for at least one corresponding biomarker of a subject and which is typically indicative of an abundance or concentration of a biomarker in a sample obtained from the subject.
  • the biomarker values could be measured biomarker values, which are values of biomarkers measured for the subject. These values may be quantitative or qualitative.
  • a measured biomarker value may refer to the presence or absence of a biomarker or may refer to an amount, level or abundance of a biomarker in a sample.
  • the measured biomarker values can be values relating to raw or normalized biomarker levels (e.g., a raw, non-normalized biomarker level, or a normalized biomarker levels that is determined relative to an internal or external control biomarker level) and to mathematically transformed biomarker levels.
  • the biomarker values could be functionalized biomarker values, which are values that have been functionalized from one or more measured biomarker values, for example by applying a function to the one or more measured biomarker values.
  • Biomarker values can be of any appropriate form depending on the manner in which the values are determined.
  • the biomarker values could be determined using high-throughput technologies such as mass spectrometry, sequencing platforms, array and hybridization platforms, immunoassays, flow cytometry, or any combination of such technologies and in representative examples, the biomarker values relate to a level of activity or abundance of an expression product or other measurable molecule, quantified using a nucleic acid assay such as real-time polymerase chain reaction (RT-PCR), sequencing or the like.
  • RT-PCR real-time polymerase chain reaction
  • biomarker signature refers to one or a combination of biomarkers whose expression is an indicator, e.g., predictive, diagnostic, and/or prognostic.
  • a biomarker signature may comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or more biomarkers.
  • a biomarker signature can further comprise one or more controls or internal standards.
  • a biomarker signature comprises at least one biomarker, or indication thereof, that serves as an internal standard.
  • a biomarker signature comprises an indication of one or more types of biomarkers.
  • biomarker signature is also used herein to refer to a biomarker value or combination of at least two biomarker values, wherein individual biomarker values correspond to values of biomarkers that can be measured or functionalized from one or more subjects, which combination is characteristic of a discrete condition, stage of condition, subtype of condition or a prognosis for a discrete condition, stage of condition, subtype of condition.
  • signature biomarkers is used to refer to a subset of the biomarkers that have been identified for use in a biomarker signature that can be used in performing a clinical assessment, such as to rule in or rule out a specific condition, different stages or severity of conditions, subtypes of different conditions or different prognoses.
  • the number of signature biomarkers will vary, but is typically of the order of 16 or less (e.g., 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2 or 1).
  • the term "binds”, “specifically binds to” or is “specific for” refers to measurable and reproducible interactions such as binding between a target and an antibody, which is determinative of the presence of the target in the presence of a heterogeneous population of molecules including biological molecules.
  • an antibody that binds to or specifically binds to a target is an antibody that binds this target with greater affinity, avidity, more readily, and/or with greater duration than it binds to other targets.
  • the extent of binding of an antibody to an unrelated target is less than about 10% of the binding of the antibody to the target as measured, e.g., by ELISA or radioimmunoassay (RIA).
  • an antibody that specifically binds to a target has a dissociation constant (Kd) of ⁇ 1 pM, ⁇ 100 nM, ⁇ 10 nM, ⁇ 1 nN, or ⁇ 0.1 nM.
  • Kd dissociation constant
  • an antibody specifically binds to an epitope on a protein that is conserved among the protein from different species.
  • specific binding can include, but does not require exclusive binding.
  • composite score refers to an aggregation of the obtained values for biomarkers measured in a sample from a subject, optionally in combination with one or more patient clinical parameters or signs.
  • the obtained biomarker values are normalized to provide a composite score for each subject tested.
  • the "biomarker composite score” may be used, at least in part, by a machine learning system to determine the "risk score” for each subject tested wherein the numerical value (e.g., a multiplier, a percentage, etc.) indicating increased likelihood of having a disclosed prognosis for the stratified grouping becomes the "risk score”.
  • the numerical value e.g., a multiplier, a percentage, etc.
  • the term "correlates” or “correlates with” and like terms refers to a statistical association between two or more things, such as events, characteristics, outcomes, numbers, data sets, etc., which may be referred to as "variables”. It will be understood that the things may be of different types. Often the variables are expressed as numbers (e.g., measurements, values, likelihood, risk), wherein a positive correlation means that as one variable increases, the other also increases, and a negative correlation (also called anti-correlation) means that as one variable increases, the other variable decreases.
  • numbers e.g., measurements, values, likelihood, risk
  • correlating a biomarker or biomarker signature with a prognosis comprises determining the abundance, level or amount of at least one protein biomarker in a salivary EV sample from a GBM patient, preferably a GBM patients after at therapeutic regimen for treating GBM; or in persons known to be free of that condition or prognosis.
  • a profile of biomarker levels, absences or presences is correlated to a global probability or a particular outcome, using receiver operating characteristic (ROC) curves.
  • cut-off value is an abundance, level or amount (or concentration) which may be an absolute level or a relative abundance, level or amount (or concentration), which is indicative of whether a GBM patient has a particular prognosis (e.g., an unfavorable outcome selected from disease recurrence, disease progression and death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, or a favorable outcome selected from no disease recurrence, no disease progression and no death from disease/cancer-free survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM).
  • a prognosis e.g., an unfavorable outcome selected from disease recurrence, disease progression and death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, or a favorable outcome selected from no disease recurrence, no disease progression and no death from disease/cancer-free survival within and/or after six months from diagnosis of GBM, or within
  • a GBM patient is regarded as having a particular prognosis, if either the level of the biomarker(s) detected and determined, respectively, is lower than the cut-off value, or the level of the biomarker(s) detected and determined, respectively, is higher than the cut-off value.
  • the terms “detectably distinct” and “detectably different” are used interchangeably to refer to a signal that is distinguishable or separable by a physical property either by observation or by instrumentation.
  • a fluorophore is readily distinguishable either by spectral characteristics or by fluorescence intensity, lifetime, polarization or photobleaching rate from another fluorophore in a sample, as well as from additional materials that are optionally present.
  • the terms “detectably distinct” and “detectably different” refer to a set of labels (such as dyes, suitably organic dyes) that can be detected and distinguished simultaneously.
  • the phrase "developing a classifier” refers to using input variables to generate an algorithm or classifier capable of distinguishing between two or more prognostic outcomes (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, or a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM).
  • an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM
  • a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM.
  • the term "differentially expressed” refers to differences in the quantity and/or the frequency of a biomarker present in a sample obtained from patients having, for example, a first prognosis (e.g., an unfavorable outcome selected from disease recurrence, disease progression and death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM) as compared to subjects with a second prognosis (e.g., a favorable outcome selected from no disease recurrence, no disease progression and no death from disease/cancer-free survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM).
  • a biomarker can be differentially present in terms of quantity, frequency or both.
  • discrimination performance refers to numeric representation of the index including, for example, sensitivity, specificity, positive predictability, negative predictability or accuracy.
  • discrimination performance may also refer to a value computed by the functions of the indexes. For example, sensitivity, specificity, positive predictive value, negative predictive value and accuracy may each be used as the discrimination performance, or alternatively, the sum of two or more indexes, e.g., the sum of sensitivity and specificity, the sum of sensitivity and positive predictive value, or the sum of negative predictive value and accuracy, may be used as the discrimination performance.
  • exosomes refers to vesicles of tens to hundreds of nanometers in size (suitably, less than about 200 nm, more suitably from about 30 nm to about 200 nm), which comprise a phospholipid bilayer membrane having the same structure as that of the cell membrane. Exosomes may contain proteins, nucleic acids (mRNA, miRNA, etc.) and the like which are called exosome cargo. It is known that exosome cargo includes a wide range of signaling factors, and these signaling factors are specific for cell types and regulated differently depending on secretory cells environment.
  • exosomes are intercellular signaling mediators secreted by cells, and various cellular signals transmitted through them regulate cellular behaviors, including the activation, growth, migration, differentiation, dedifferentiation, apoptosis, and necrosis of target cells.
  • extracellular vesicles refers to membranous microvesicles that may be shed by eukaryotic cells, or budded off of the plasma membrane, to the exterior of the cell.
  • EVs extracellular vesicles
  • These membrane vesicles are heterogeneous in size with diameters ranging from about 10 nm to about 5000 nm, more typically between 30 nm and 1000 nm, and most typically between about 50 nm and 750 nm.
  • the EVs have a diameter ranging from 30 nm to about 200 nm (also referred to herein as "small EVs").
  • EVs will have a size (average diameter) that is up to 5% of the size of the donor cell. Therefore, especially contemplated EVs include those that are shed from a cell.
  • EVs encompassed by the present disclosure include microvesicles, microvesicle-like particles, prostasomes, dexosomes, texosomes, ectosomes, oncosomes, apoptotic bodies, retrovirus-like particles, and human endogenous retrovirus (HERV) particles and any other terms that refer to such extracellular structures.
  • the EVs are exosomes which are purified or are otherwise obtained from saliva (/.e., "salivary exosomes").
  • Fluorophore as used herein to refer to a moiety that absorbs light energy at a defined excitation wavelength and emits light energy at a different defined wavelength.
  • fluorescence labels include, but are not limited to: Alexa Fluor dyes (Alexa Fluor 350, Alexa Fluor 488, Alexa Fluor 532, Alexa Fluor 546, Alexa Fluor 568, Alexa Fluor 594, Alexa Fluor 633, Alexa Fluor 660 and Alexa Fluor 680), AMCA, AMCA-S, BODIPY dyes (BODIPY FL, BODIPY R6G, BODIPY TMR, BODIPY TR, BODIPY 530/550, BODIPY 558/568, BODIPY 564/570, BODIPY 576/589, BODIPY 581/591, BODIPY 630/650, BODIPY 650/665), Carboxyrhodamine 6G, carboxy-X-rho
  • glioblastoma As used herein, the terms “glioblastoma”, “glioblastoma multiforme” and “GBM” are used interchangeably herein to refer to the most common and aggressive primary malignant adult tumor of the central nervous system. Glioblastoma may be located anywhere in the brain or spinal cord, but is typically found in the cerebral hemispheres of the brain.
  • the term “higher” with reference to a biomarker measurement refers to a statistically significant and measurable difference in the level of a biomarker compared to the level of another biomarker or to a control level where the biomarker measurement is greater than the level of the other biomarker or the control level. The difference is suitably at least about 10%, or at least about 20%, or of at least about 30%, or of at least about 40%, or at least about 50%.
  • the term "increase” or “increased' with reference to a biomarker level refers to a statistically significant and measurable increase in the biomarker level compared to the level of another biomarker or to a control level.
  • the increase is suitably an increase of at least about 10%, or an increase of at least about 20%, or an increase of at least about 30%, or an increase of at least about 40%, or an increase of at least about 50%.
  • the term "indicator” as used herein refers to a result or representation of a result, including any information, number (e.g., biomarker value including functionalized biomarker value and composite score), ratio, signal, sign, mark, or note by which a skilled artisan can estimate and/or determine a likelihood or risk of whether or not a subject is suffering from a given disease or condition.
  • the "indicator” may optionally be used together with other clinical characteristics, to arrive at a prognosis for a GBM patient. That such an indicator is "determined” is not meant to imply that the indicator is 100% accurate.
  • the skilled clinician may use the indicator together with other clinical parameters or signs to arrive at a diagnosis.
  • kits of the disclosure include a publication, a recording, a diagram, or any other medium of expression which can be used to communicate the usefulness of the compositions and methods of the disclosure.
  • the instructional material of the kit of the disclosure may, for example, be affixed to a container which contains the therapeutic or diagnostic agents of the disclosure or be shipped together with a container which contains the therapeutic or diagnostic and/or prognostic agents of the disclosure.
  • label is used herein in a broad sense to refer to an agent that is capable of providing a detectable signal, either directly or through interaction with one or more additional members of a signal producing system and that has been artificially added, linked or attached via chemical manipulation to a molecule.
  • Labels can be visual, optical, photonic, electronic, acoustic, optoacoustic, by mass, electro-chemical, electro-optical, spectrometry, enzymatic, or otherwise chemically, biochemically hydrodynamically, electrically or physically detectable.
  • Labels can be, for example tailed reporter, marker or adapter molecules.
  • a molecule such as a nucleic acid or proteinaceous molecule is labeled with a detectable molecule selected form the group consisting of radioisotopes, fluorescent compounds, bioluminescent compounds, chemiluminescent compounds, metal chelators or enzymes.
  • labels include, but are not limited to, the following radioisotopes (e.g., 3 H, 14 C, 35 S, 125 I, 131 I), fluorescent labels (e.g., FITC, rhodamine, lanthanide phosphors), luminescent labels such as luminol; enzymatic labels (e.g., horseradish peroxidase, beta-galactosidase, luciferase, alkaline phosphatase, acetylcholinesterase), biotinyl groups (which can be detected by marked avidin, e.g., streptavidin containing a fluorescent marker or enzymatic activity that can be detected by optical or calorimetric methods), predetermined polypeptide epitopes recognized by a secondary reporter (e.g., leucine zipper pair sequences, binding sites for secondary antibodies, metal binding domains, epitope tags).
  • radioisotopes e.g., 3 H, 14 C, 35 S,
  • the term "lower" with reference to a biomarker measurement refers to a statistically significant and measurable difference in the level of a biomarker compared to the level of another biomarker or to a control level where the biomarker measurement is less than the level of the other biomarker or the control level.
  • the difference is suitably at least about 10%, or at least about 20%, or of at least about 30%, or of at least about 40%, or at least about 50%.
  • normalization when used in conjunction with measurement of biomarkers across samples and time, refer to mathematical methods, including but not limited to multiple of the median (MoM), standard deviation normalization, sigmoidal normalization, etc., where the intention is that these normalized values allow the comparison of corresponding normalized values from different datasets in a way that eliminates or minimizes differences and gross influences.
  • MoM median
  • standard deviation normalization standard deviation normalization
  • sigmoidal normalization sigmoidal normalization
  • samples so obtained refers to come into possession.
  • Samples so obtained include, for example, protein extracts isolated or derived from a particular source (e.g., EVs).
  • predictive and grammatical forms thereof, generally refer to a biomarker or biomarker signature that provides a means of identifying, directly or indirectly, a likelihood of a patient responding to a therapy or obtaining a clinical outcome in response to therapy.
  • prognosis refers to a prediction of the probable course and outcome of a clinical condition or disease.
  • a prognosis is usually made by evaluating factors or symptoms of a disease that are indicative of a favorable or unfavorable course or outcome of the disease.
  • prognosis refers to an increased probability that a certain course or outcome (e.g., disease recurrence, no disease recurrence, disease progression, no disease progression, death, survival, etc.) will occur; that is, that a course or outcome is more likely to occur in a subject exhibiting a given condition, when compared to those individuals not exhibiting the condition.
  • prognosis also refers to the ability to demonstrate a positive or negative response to therapy or other treatment regimens, for the disease or condition in the subject. In some embodiments, prognosis refers to the ability to predict the presence or diminishment of disease/condition associated symptoms.
  • a prognostic agent or method may comprise classifying a subject or sample obtained from a subject into one of multiple categories, wherein the categories correlate with different likelihoods that a subject will experience a particular outcome.
  • categories can be low risk and high risk, wherein subjects in the low risk category have a lower likelihood of experiencing a poor outcome (e.g., within a given time period such as 6 months, 9 months, 12 months or 18 months, or 2, 3, 4, 5, 5, 7, 8, 9 or 10 years) than do subjects in the high risk category.
  • a poor outcome could be, for example, disease progression, disease recurrence, or death attributable to the disease.
  • Protein Polypeptide and “peptide” are used interchangeably herein to refer to a polymer of amino acid residues and to variants or synthetic analogues of the same.
  • the term “reduce” or “reduced” with reference to a biomarker level refers to a statistically significant and measurable reduction in the biomarker level compared to the level of another biomarker or to a control level.
  • the reduction is suitably a reduction of at least about 10%, or a reduction of at least about 20%, or a reduction of at least about 30%, or a reduction of at least about 40%, or a reduction of at least about 50%.
  • a cancer patient who has been treated with a therapy is considered to "respond”, have a “response”, have “a positive response” or be “responsive” to the therapy if the subject shows evidence of an anti-cancer effect according to an art-accepted set of objective criteria or reasonable modification thereof, including a clinically significant benefit, such as the prevention, or reduction of severity, of symptoms, or a slowing of the progression of the cancer.
  • a cancer patient who has been treated with a therapy is considered “not to respond”, “to lack a response”, to have “a negative response” or be “non-responsive” to the therapy if the therapy provides no clinically significant benefit, such as the prevention, or reduction of severity, of symptoms, or increases the rate of progression of the cancer.
  • saliva sample includes any biological specimen that may be extracted, untreated, treated, diluted or concentrated from a sample of saliva obtained from a subject.
  • saliva sample includes saliva obtained from within the mouth, saliva obtained as spit, and saliva obtained from an oral rinse with a sampling fluid, such as sterile water.
  • solid support refers to a solid inert surface or body to which a molecular species, such as a nucleic acid and polypeptides can be immobilized.
  • solid supports include glass surfaces, plastic surfaces, latex, dextran, polystyrene surfaces, polypropylene surfaces, polyacrylamide gels, gold surfaces, and silicon wafers.
  • the solid supports are in the form of membranes, chips or particles.
  • the solid support may be a glass surface (e.g., a planar surface of a flow cell channel).
  • the solid support may comprise an inert substrate or matrix which has been "functionalized", such as by applying a layer or coating of an intermediate material comprising reactive groups which permit covalent attachment to molecules such as polynucleotides.
  • such supports can include polyacrylamide hydrogels supported on an inert substrate such as glass.
  • the molecules e.g., polynucleotides
  • the intermediate material e.g., a hydrogel
  • the intermediate material can itself be non-covalently attached to the substrate or matrix (e.g., a glass substrate).
  • the support can include a plurality of particles or beads each having a different attached molecular species.
  • treatment and “treating” is meant the medical management of a subject with the intent to cure, ameliorate, stabilize, or prevent a disease, pathological condition, or disorder.
  • This term includes active treatment, that is, treatment directed specifically toward the improvement of a disease, pathological condition, or disorder, and also includes causal treatment, that is, treatment directed toward removal of the cause of the associated disease, pathological condition, or disorder.
  • this term includes palliative treatment, that is, treatment designed for the relief of symptoms rather than the curing of the disease, pathological condition, or disorder; preventative treatment, that is, treatment directed to minimizing or partially or completely inhibiting the development of the associated disease, pathological condition, or disorder; and supportive treatment, that is, treatment employed to supplement another specific therapy directed toward the improvement of the associated disease, pathological condition, or disorder.
  • palliative treatment that is, treatment designed for the relief of symptoms rather than the curing of the disease, pathological condition, or disorder
  • preventative treatment that is, treatment directed to minimizing or partially or completely inhibiting the development of the associated disease, pathological condition, or disorder
  • supportive treatment that is, treatment employed to supplement another specific therapy directed toward the improvement of the associated disease, pathological condition, or disorder.
  • treatment while intended to cure, ameliorate, stabilize, or prevent a disease, pathological condition, or disorder, need not actually result in the cure, amelioration, stabilization or prevention.
  • the effects of treatment can be measured or assessed as described herein and as known in the art
  • treatment regimen refers to prophylactic and/or therapeutic (/.e., after onset of a specified condition) treatments, unless the context specifically indicates otherwise.
  • treatment regimen encompasses natural substances and pharmaceutical agents (i.e., "drugs") as well as any other treatment regimen including but not limited to dietary treatments, physical therapy or exercise regimens, surgical interventions, radiotherapy, chemotherapy, immunotherapy and combinations thereof. Desirable effects of treatment include decreasing the rate of disease progression, ameliorating or palliating the disease state, and remission or improved prognosis.
  • an individual is successfully "treated” if one or more symptoms associated with a cancer are mitigated or eliminated, including, but are not limited to, reducing the proliferation of (or destroying) cancerous cells, reducing pathogen infection, decreasing symptoms resulting from the disease, increasing the quality of life of those suffering from the disease, decreasing the dose of other medications required to treat the disease, and/or prolonging survival of individuals.
  • treatment with a therapy refers to the administration of an effective amount of a therapy or agent, including a cancer therapy or agent, (e.g., a cytotoxic agent or an immunotherapeutic agent) to a patient, or the concurrent administration of two or more therapies or agents, including cancer therapies or agents, (e.g., two or more agents selected from cytotoxic agents and immunotherapeutic agents) in effective amounts to a patient.
  • a cancer therapy or agent e.g., a cytotoxic agent or an immunotherapeutic agent
  • Salivary EV biomarkers for predicting poor outcomes and good outcomes in GBM patients
  • the present inventors have determined that certain protein biomarkers are commonly, specifically and differentially expressed in salivary EV samples obtained from GBM patients with favorable outcomes (e.g., no disease recurrence, no disease progression, no death from disease within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM) and GBM patients with unfavorable outcomes (e.g., disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM).
  • favorable outcomes e.g., no disease recurrence, no disease progression, no death from disease within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM
  • unfavorable outcomes e.g., disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM.
  • the protein biomarkers that can be used in the practice of the methods, apparatuses and treatment management methods disclosed herein include: LKHA4, H4 and KLK1 for differentiating between GBM patients with favorable outcomes within and/or after six months from diagnosis of GBM and GBM patients with unfavorable outcomes within six months from diagnosis of GBM (also referred to herein as the "six-month signature biomarkers”); and ALDOA, 1433E, TM11B and ECHI for differentiating between GBM patients with favorable outcomes within and/or after nine months from diagnosis of GBM and GBM patients with unfavorable outcomes within nine months from diagnosis of GBM (also referred to herein as the "nine-month signature biomarkers").
  • methods are disclosed herein for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, suitably wherein the poor prognosis is disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, and suitably wherein the good prognosis is no disease recurrence, no disease progression or no death from disease within and/or after six months from diagnosis of GBM.
  • These methods generally comprise, consist or consist essentially of: (1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2 or 3 protein biomarkers) in a salivary extracellular vesicle (EV) sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample, and wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (also referred to herein as "LKHA4" or ”LKHA4_HUMAN”), histone H4 (also referred to herein as "H4" or ''H4—HUMAN”) and kallikrein-1 (also referred to herein as "KLK1” or “KLK1_ HUMAN”); and (2) determining the indicator using the biomarker value(s).
  • leukotriene A-4 hydrolase also referred to herein as "LKHA4" or ”LKHA4_HUMAN”
  • H4 histone H4
  • H4
  • Biomarker panels disclosed herein for use in indicator-determining methods according to this aspect typically comprise at least 1, 2 or 3 protein biomarkers.
  • the biomarker panel comprises each of LKHA4, H4 and KLK1.
  • Disclosed herein in another aspect are methods for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, suitably wherein the poor prognosis is disease recurrence, disease progression or death from disease within nine months from diagnosis of GBM, and suitably wherein the good prognosis is no disease recurrence, no disease progression or no death from disease within and/or after nine months from diagnosis of GBM.
  • These methods generally comprise, consist or consist essentially of: (1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2, 3 or 4 protein biomarkers) in a salivary extracellular vesicle (EV) sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample, and wherein the at least one protein biomarker is selected from aldolase A (also referred to herein as "ALDOA” or “ALDOA_ HUMAN”)), 14-3-3 protein epsilon (also referred to herein as "1433E” or “1433EJHUMAN”), transmembrane protease serine 11B (also referred to herein as "TM11B” or “TM11B_HUMAN”) and enoyl CoA hydratase 1 (also referred to herein as "ECHI” or “ECH1_HUMAN”); and (2) determining the indicator using the biomarker value(
  • Biomarker panels disclosed herein for use in indicator-determining methods according to this aspect typically comprise at least 1, 2, 3 or 4 protein biomarkers.
  • the biomarker panel comprises each of ALDOA, 1433E, TM11B and ECHI.
  • Biomarker values that are indicative of the levels of protein biomarkers in EVs obtained from a saliva sample may be obtained by any suitable means known in the art.
  • a saliva sample can be saliva obtained from within the mouth, or obtained as spit.
  • a saliva sample can also be a sample comprising saliva, as obtained by oral rinsing with a sampling rinse fluid, typically, e.g., sterile water, and then collecting the rinse, which then comprises saliva diluted with the rinse fluid.
  • Methods of obtaining saliva samples may include but are not limited to forcible ejection from the subject's mouth (e.g., spitting), aspiration, or removal by a swab or other collection tool.
  • the saliva may be separated into cellular and non-cellular fractions by suitable methods (e.g., centrifugation).
  • a saliva sample may be enriched for EVs using standard methods.
  • EVs may be concentrated or isolated from a saliva sample using size exclusion chromatography, density gradient centrifugation, differential centrifugation, nanomembrane ultrafiltration, immunoabsorbent capture, affinity purification, microfluidic separation, or combinations thereof.
  • Methods for isolating or enriching EVs can be performed with microfluidic devices, including optionally conducting protein analysis of the isolated exosomes.
  • Microfluidic devices which may also be referred to as "lab-on-a-chip” systems, biomedical micro-electro- mechanical systems (bioMEMs), or multicomponent integrated systems, can be used for isolating, and analyzing EVs.
  • Such systems miniaturize and compartmentalize processes that allow for isolation (e.g., binding) of EVs, detection of EV protein biomarkers, and/or other processes.
  • a microfluidic device can also be used for isolation of EVs through size differential or affinity selection.
  • a microfluidic device can use one more channels for isolating an EV from a saliva sample based on size, or by using one or more binding agents for isolating a EV from a saliva sample.
  • a saliva sample can be introduced into one or more microfluidic channels, which selectively allows the passage of EVs. The selection can be based on a property of the EVs, for example, size, shape, deformability, biomarker profile, or bio-signature.
  • a heterogeneous population of EVs can be introduced into a microfluidic device, and one or more different homogeneous populations of EVs can be obtained.
  • a microfluidic device can isolate a plurality of EVs, wherein at least a subset of the plurality of EVs comprises a different bio-signature from another subset of the plurality of EVs.
  • the microfluidic device can comprise one or more channels that permit further enrichment or selection of EVs.
  • a population of EVs that has been enriched after passage through a first channel can be introduced into a second channel, which allows the passage of the desired EV population to be further enriched, such as through binding agents present in the second channel.
  • Isolation or enrichment of EVs from saliva samples can also be enhanced by use of sonication (e.g., by applying ultrasound), or the use of detergents, other membrane-active agents, or any combination thereof.
  • sonication e.g., by applying ultrasound
  • detergents, other membrane-active agents, or any combination thereof can be used.
  • ultrasonic energy can be applied to a sample, and without being bound by theory, release of EVs from the sample or tissue can be increased, allowing an enriched population of EVs that can be analyzed or assessed from a saliva sample using one or more methods disclosed herein or known in the field.
  • Variability in salivary EV sample preparation can be corrected by normalizing the data by, for example, protein content or EV number.
  • the sample may be normalized relative to the total protein content in the sample. Total protein content in the sample can be determined using standard procedures, including, without limitation, Bradford assay and the Lowry method.
  • the sample may be normalized relative to EV number.
  • the level of the one or more protein biomarkers may be measured or assessed using any appropriate technique or means known to those of skill in the art.
  • the level of a protein biomarker such as LKHA4, H4, KLK1, ALDOA, 1433E, TM11B or ECHI
  • an antibody-based technique non-limiting examples of which include immunoassays, such as the enzyme-linked immunosorbent assay (ELISA) and the radioimmunoassay (RIA).
  • ELISA enzyme-linked immunosorbent assay
  • RIA radioimmunoassay
  • LKHA4, H4, KLK1, ALDOA, 1433E, TM11B or ECHI include both singlesite and two-site or “sandwich” assays of the non-competitive types, as well as in the traditional competitive binding assays. These assays also include direct binding of a labeled antibody to a target biomarker.
  • ELISAs for measuring the levels of LKHA4, H4, KLK1, ALDOA, 1433E, TM11B or ECHI are available commercially and/or can be readily developed by those skilled in the art using known antibodies specific for LKHA4, H4, KLK1, ALDOA, 1433E, TM11B or ECHI.
  • a multiplex assay such as a multiplex immunoassay (e.g., multiplex ELISA)
  • Multiplex assays include arrays comprising spatially addressed antigen-binding molecules, commonly referred to as antibody arrays, which can facilitate extensive parallel analysis of multiple proteins.
  • Antibody arrays have been shown to have the required properties of specificity and acceptable background.
  • Various methods for the preparation of antibody arrays have been reported (see, e.g., Lopez et al., J. Chromatogr. 2003; 787: 19-27; Cahill, Trends Biotechnol. 2000;7:47-51; U.S. Pat. App. Pub.
  • Individual spatially distinct protein-capture agents are typically attached to a support surface, which is generally planar or contoured.
  • Common physical supports include glass slides, silicon, microwells, nitrocellulose or PVDF membranes, and magnetic and other microbeads.
  • Particles in suspension can also be used as the basis of multiplex assays and arrays, providing they are coded for identification; systems include color coding for microbeads (e.g., available from Luminex, Bio-Rad and Nanomics Biosystems) and semiconductor nanocrystals (e.g., QDotsTM, available from Quantum Dots), and barcoding for beads (UltraPlexTM, available from Smartbeads) and multimetal microrods (NanobarcodesTM particles, available from Surromed). Beads can also be assembled into planar arrays on semiconductor chips (e.g., available from LEAPS technology and BioArray Solutions).
  • color coding for microbeads e.g., available from Luminex, Bio-Rad and Nanomics Biosystems
  • semiconductor nanocrystals e.g., QDotsTM, available from Quantum Dots
  • barcoding for beads UltraPlexTM, available from Smartbeads
  • individual protein-capture agents are typically attached to an individual particle to provide the spatial definition or separation of the array.
  • the particles may then be assayed separately, but in parallel, in a compartmentalized way, for example in the wells of a microtiter plate or in separate test tubes.
  • LuminexTM-based multiplex assay which is a bead-based multiplexing assay, where beads are internally dyed with fluorescent dyes to produce a specific spectral address.
  • Biomolecules such as an antibody
  • Flow cytometric or other suitable imaging technologies known to persons skilled in the art can then be used for characterization of the beads and detection and quantitation of the biomarkers.
  • multiplex assays use detectably distinct antibodies to distinctly label individual protein biomarkers.
  • MS mass spectrometry
  • LC-MS Liquid Chromatography-Mass Spectrometry
  • DART MS Direct Analysis in Real Time Mass Spectrometry
  • SELDI-TOF SELDI-TOF
  • MALDI-TOF MALDI-TOF
  • GC-MS gas chromatography-mass spectrometry
  • HPLC-MS high performance liquid chromatography-mass spectrometry
  • capillary electrophoresis-mass spectrometry e.g., MS/MS, MS/MS/MS, ESI-MS/MS, etc.
  • tandem mass spectrometry e.g., MS/MS, MS/MS/MS, ESI-MS/MS, etc.
  • Biomarker data may be analyzed by a variety of methods to identify salivary EV protein biomarkers and determine the statistical significance of differences in observed levels of protein biomarkers between test and reference salivary EV samples in order to evaluate whether a GBM patient has a likelihood of a favorable outcome (e.g., no disease recurrence, no disease progression, no death from disease within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM) or an unfavorable outcome (e.g., disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM).
  • a favorable outcome e.g., no disease recurrence, no disease progression, no death from disease within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM
  • an unfavorable outcome e.g., disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or
  • a threshold is selected, above which (or below which, depending on how protein biomarker changes with a specified prognosis) the test is considered to be "positive” and below which the test is considered to be “negative.”
  • the area under the ROC curve (AUC) provides the C-statistic, which is a measure of the probability that the perceived measurement will allow correct identification of a condition (see, e.g., Hanley et al., Radiology 143: 29-36 (1982)).
  • thresholds may be established by obtaining an earlier protein biomarker result from the same patient, to which later results may be compared.
  • the individual in effect acts as their own "control group.”
  • a decrease over time in the same patient can indicate a worsening of the condition or a failure of a treatment regimen or poor outcome, while an increase over time can indicate remission of the condition or success of a treatment regimen or good outcome.
  • a positive likelihood ratio, negative likelihood ratio, odds ratio, and/or AUC or receiver operating characteristic (ROC) values are used as a measure of a method's ability to prognose patient outcome.
  • the term "likelihood ratio" is the probability that a given test result would be observed in a subject with a particular prognostic outcome divided by the probability that that same result would be observed in a patient without the prognostic outcome.
  • a positive likelihood ratio is the probability of a positive result observed in subjects with the specified prognostic outcome, divided by the probability of a positive results in subjects without the specified prognostic outcome.
  • a negative likelihood ratio is the probability of a negative result in subjects without the specified prognostic outcome divided by the probability of a negative result in subjects with specified prognostic outcome.
  • the term "odds ratio,” as used herein, refers to the ratio of the odds of an event occurring in one group (e.g., one of the prognostic outcomes discloses herein) to the odds of it occurring in another group (e.g., another of the disclosed prognostic outcomes), or to a data-based estimate of that ratio.
  • area under the curve or "AUC” refers to the area under the curve of a receiver operating characteristic (ROC) curve, both of which are well known in the art. AUC measures are useful for comparing the accuracy of a classifier across the complete data range.
  • Classifiers with a greater AUC have a greater capacity to classify unknowns correctly between two groups of interest (e.g., a first disclosed prognostic outcome such as a poor prognosis (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM), and a second disclosed prognostic outcome such as good prognosis (e.g., a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM).
  • a first disclosed prognostic outcome such as a poor prognosis (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM)
  • a second disclosed prognostic outcome such as good prognosis (e.g
  • ROC curves are useful for plotting the performance of a particular feature e.g., any of the salivary EV protein biomarkers disclosed herein and/or any item clinical parameter or symptom information) in distinguishing or discriminating between two populations (e.g., a first disclosed prognostic outcome and a second disclosed prognostic outcome).
  • the feature data across the entire population e.g., subjects with a first disclosed prognostic outcome and subjects with a second disclosed prognostic outcome
  • the true positive and false positive rates for the data are calculated.
  • the sensitivity is determined by counting the number of cases above the value for that feature and then dividing by the total number of cases.
  • the specificity is determined by counting the number of controls below the value for that feature and then dividing by the total number of controls. Alternatively, specificity may be calculated by ROC curve and threshold value.
  • this definition refers to scenarios in which a feature is elevated in one patient group compared to another patient group, this definition also applies to scenarios in which a feature is lower in one patient group compared to the other patient group (in such a scenario, samples below the value for that feature would be counted).
  • ROC curves can be generated for a single feature as well as for other single outputs, for example, a combination of two or more features (e.g., a combination of two or more biomarker values) can be mathematically combined (e.g., added, subtracted, multiplied, etc.) to produce a single value, and this single value can be plotted in a ROC curve. Additionally, any combination of multiple features (e.g., a combination of multiple biomarker values), in which the combination derives a single output value, can be plotted in a ROC curve. These combinations of features may comprise a test.
  • the ROC curve is the plot of the sensitivity of a test against the specificity of the test, where sensitivity is traditionally presented on the vertical axis and specificity is traditionally presented on the horizontal axis.
  • AUC ROC values are equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one.
  • An AUC ROC value may be thought of as equivalent to the Mann-Whitney U test, which tests for the median difference between scores obtained in the two groups considered if the groups are of continuous data, or to the Wilcoxon test of ranks.
  • a protein biomarker or a panel of protein biomarkers is selected to discriminate between subjects with a first disclosed prognostic outcome and subjects with a second a first disclosed prognostic outcome and a second disclosed prognostic outcome, with at least about 50%, 55% 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% accuracy or having a C- statistic of at least about 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95.
  • first condition group is meant to refer to a group having one characteristic (e.g., a first disclosed prognostic outcome) and "second condition” group (e.g., a second disclosed prognostic outcome) lacking the same characteristic.
  • a value of 1 indicates that a negative result is equally likely among subjects in both the "first condition” and “second condition” groups; a value greater than 1 indicates that a negative result is more likely in the "first condition” group; and a value less than 1 indicates that a negative result is more likely in the "second condition” group.
  • an odds ratio a value of 1 indicates that a positive result is equally likely among subjects in both the "first condition” and “second condition” groups; a value greater than 1 indicates that a positive result is more likely in the "first condition” group; and a value less than 1 indicates that a positive result is more likely in the "second condition” group.
  • AUC ROC value this is computed by numerical integration of the ROC curve.
  • the range of this value can be 0.5 to 1.0.
  • a value of 0.5 indicates that a classifier (e.g., a protein biomarker signature) is no better than a 50% chance to classify unknowns correctly between two groups of interest (e.g., a first disclosed prognostic outcome and a second disclosed prognostic outcome disclosed herein), while 1.0 indicates the relatively best diagnostic accuracy.
  • individual protein biomarkers and/or protein biomarker panels are selected to exhibit a positive or negative likelihood ratio of at least about 1.5 or more or about 0.67 or less, at least about 2 or more or about 0.5 or less, at least about 5 or more or about 0.2 or less, at least about 10 or more or about 0.1 or less, or at least about 20 or more or about 0.05 or less.
  • individual protein biomarkers and/or protein biomarker panels are selected to exhibit an odds ratio of at least about 2 or more or about 0.5 or less, at least about 3 or more or about 0.33 or less, at least about 4 or more or about 0.25 or less, at least about 5 or more or about 0.2 or less, or at least about 10 or more or about 0.1 or less.
  • individual protein biomarkers and/or protein biomarker panels are selected to exhibit an AUC ROC value of greater than 0.5, preferably at least 0.6, more preferably at least 0.7, still more preferably at least 0.8, even more preferably at least 0.9, and most preferably at least 0.95.
  • thresholds may be determined in so-called “tertile,” “quartile,” or “quintile” analyses.
  • the “diseased” and “control groups” (or “high risk” and “low risk”) groups are considered together as a single population, and are divided into 3, 4, or 5 (or more) "bins” having equal numbers of individuals. The boundary between two of these "bins” may be considered “thresholds.”
  • a risk (of a particular diagnosis or prognosis for example) can be assigned based on which "bin” a test subject falls into.
  • particular thresholds for the protein biomarker(s) measured are not relied upon to determine if the biomarker level(s) obtained from a subject are correlated to a particular prognosis.
  • a temporal change in the protein biomarker(s) can be used to rule in or out one or more particular prognoses.
  • protein biomarker(s) may be correlated to a prognosis by the presence or absence of one or more protein biomarkers in a particular assay format.
  • the detection methods disclosed herein may utilize an evaluation of the entire population or subset of protein biomarkers disclosed herein to provide a single result value (e.g., a "panel response" value expressed either as a numeric score or as a percentage risk).
  • a panel of protein biomarkers is selected to assist in distinguishing a pair of groups (/.e., assist in assessing whether a subject has an increased likelihood of being in one group or the other group of the pair) selected from a "favorable outcome group” (having outcomes selected from no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM) and an "unfavorable outcome group” (having outcomes selected from disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM), or "low risk” and "high risk” with at least about 70%, 80%, 85%, 90% or 95% sensitivity, suitably in combination with at least about 70% 80%, 85%, 90% or 95% specificity. In some embodiments, both the sensitivity and specificity are at least about 75%, 80%, 85%, 90% or 95%.
  • the probability that an individual predicted to have a specified prognosis may be expressed as a "positive predictive value" or "PPV.”
  • Positive predictive value can be calculated as the number of true positives divided by the sum of the true positives and false positives. PPV is determined by the characteristics of the predictive methods disclosed herein as well as the prevalence of the condition in the population analyzed.
  • the statistical algorithms can be selected such that the positive predictive value in a population having a condition prevalence is in the range of 70% to 99% and can be, for example, at least 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 85%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
  • the probability that an individual predicted as not having a specified prognosis actually does not have that prognosis may be expressed as a "negative predictive value" or "NPV.”
  • Negative predictive value can be calculated as the number of true negatives divided by the sum of the true negatives and false negatives. Negative predictive value is determined by the characteristics of the prognostic method as well as the prevalence of the disease in the population analyzed.
  • the statistical methods and models can be selected such that the negative predictive value in a population having a condition prevalence is in the range of about 70% to about 99% and can be, for example, at least about 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
  • a subject is determined as having a significant likelihood of having or not having a specified prognosis (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, or a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM).
  • a specified prognosis e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM.
  • a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM.
  • the protein biomarker analysis disclosed herein permits the generation of high- density data sets that can be evaluated using informatics approaches.
  • High data density informatics analytical methods are known and software is available to those in the art, e.g., cluster analysis (Pirouette, Informetrix), class prediction (SIMCA-P, Umetrics), principal components analysis of a computationally modeled dataset (SIMCA-P, Umetrics), 2D cluster analysis (GeneLinker Platinum, Improved Outcomes Software), and metabolic pathway analysis (biotech.icmb.utexas.edu).
  • the choice of software packages offers specific tools for questions of interest (Kennedy et al., Solving Data Mining Problems Through Pattern Recognition.
  • any suitable mathematic analyses can be used to evaluate at least one (e.g., 1, 2, 3, 4, etc.) protein biomarker in a population disclosed herein with respect to a disclosed prognosis (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, or a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM).
  • a disclosed prognosis e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, or a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM).
  • methods such as multivariate analysis of variance, multivariate regression, and/or multiple regression can be used to determine relationships between dependent variables (e.g., clinical measures) and independent variables (e.g., levels of protein biomarkers).
  • Clustering including both hierarchical and non-hierarchical methods, as well as non-metric Dimensional Scaling can be used to determine associations or relationships among variables and among changes in those variables.
  • principal component analysis is a common way of reducing the dimension of studies, and can be used to interpret the variance-covariance structure of a data set.
  • Principal components may be used in such applications as multiple regression and cluster analysis.
  • Factor analysis is used to describe the covariance by constructing "hidden" variables from the observed variables.
  • Factor analysis may be considered an extension of principal component analysis, where principal component analysis is used as parameter estimation along with the maximum likelihood method.
  • simple hypothesis such as equality of two vectors of means can be tested using Hotelling's T squared statistic.
  • the data sets corresponding to protein biomarker panels disclosed herein are used to create a predictive rule or model based on the application of a statistical and machine learning algorithm.
  • a protein biomarker panel uses relationships between a protein biomarker panel and a disclosed prognosis (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, or a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM), observed in control subjects or typically cohorts of control subjects (sometimes referred to as training data), which provides combined control or reference protein biomarker panels for comparison with protein biomarker panels of a subject.
  • the data are used to infer relationships that are then used to predict the status of a subject, including the presence or absence of one of the conditions referred to herein.
  • the protein biomarkers disclosed herein provide illustrative lists of protein biomarkers ranked according to their p value. Illustrative models comprising 1, 2, 3 or 4 protein biomarkers were able to develop a classifier or generative algorithm for discriminating between two control groups as defined above with significantly improved positive predictive values compared to conventional methodologies. This algorithm can be advantageously applied to determine presence or probability of one of the conditions or prognoses disclosed herein in a patient, and thus diagnose the patient as having or as likely to have the condition, or prognose the patient as having decreased or poor survival prognosis, or as having increased or good survival prognosis.
  • evaluation of protein biomarkers includes determining the levels of individual protein biomarkers, which correlate with a prognosis, as defined above.
  • the techniques used for detection of protein biomarkers may include internal or external standards to permit quantitative or semi-quantitative determination of those biomarkers, to thereby enable a valid comparison of the level of the protein biomarkers in a salivary EV sample with the corresponding protein biomarkers in a reference sample or samples.
  • standards can be determined by the skilled practitioner using standard protocols.
  • absolute values for the level or functional activity of individual expression products are determined.
  • a threshold or cut-off value is suitably determined, and is optionally a predetermined value.
  • the threshold value is predetermined in the sense that it is fixed, for example, based on previous experience with the assay and/or a population of affected and/or unaffected subjects.
  • the predetermined value can also indicate that the method of arriving at the threshold is predetermined or fixed even if the particular value varies among assays or may even be determined for every assay run.
  • the level of a protein biomarker is normalized.
  • the methodology used to normalize the values of the measured biomarkers provided that the same methodology is used for testing a human subject sample as was used to generate a risk categorization table or threshold value.
  • Many methods for data normalization exist and are familiar to those skilled in the art. These include methods such as background subtraction, scaling, MoM analysis, linear transformation, least squares fitting, etc.
  • the goal of normalization is to equate the varying measurement scales for the separate biomarkers such that the resulting values may be combined according to a weighting scale as determined and designed by the user or by the machine learning system and are not influenced by the absolute or relative values of the protein biomarker found within nature.
  • Composite scores may be calculated using standard statistical analysis well known to one of skill in the art wherein the measurements of each protein biomarker in the panel are combined, optionally with clinical parameters, to provide a probability value.
  • generalized or multivariate logistic regression analysis may be used to derive a mathematical function with a set of variables corresponding to each protein biomarker and optional clinical parameter, which provides a weighting factor for each variable.
  • the weighting factors are derived to optimize the agency of the function to predict the dependent variable, which is the dichotomy of a first prognostic outcome (e.g., unfavorable outcome ) as compared to a second prognostic outcome (e.g., favorable outcome) disclosed herein.
  • the weighting factors are specific to the particular variable combination (e.g., biomarker panel analyzed).
  • the function can then be applied to the original samples to predict a probability of a disclosed condition.
  • a retrospective data set may be used to provide weighting factors for a particular panel of salivary protein biomarkers, optionally in combination with clinical parameters, which is then used to calculate the probability of a disclosed condition in a patient where the outcome of the condition is unknown or indeterminate prior to screening using the present methods.
  • Composite scores may be calculated for example using the statistical methodology disclosed in US Publ. No. 2008/013314 for handling and interpreting data from a multiplex assay.
  • the amount of any one biomarker is compared to a predetermined cut-off distinguishing positive from negative for that biomarker as determined from a control population study of patients with a prognostic outcome (e.g., unfavorable outcome) and suitably matched controls (e.g., patients with an unfavorable outcome) to yield a score for each biomarker based on that comparison; and then combining the scores for each biomarker to obtain a composite score for the biomarker(s) in the sample.
  • a prognostic outcome e.g., unfavorable outcome
  • suitably matched controls e.g., patients with an unfavorable outcome
  • a predetermined cut-off can be based on ROC curves and the score for each biomarker can be calculated based on the specificity of the biomarker. Then, the total score can be compared to a predetermined total score to transform that total score to a qualitative determination of the likelihood or risk of having a condition as disclosed herein.
  • the protein biomarkers disclosed herein are measured and those resulting values normalized and then summed to obtain a composite score.
  • normalizing the measured biomarker values comprises determining the multiple of median (MoM) score.
  • the present method further comprises weighting the normalized values before summing to obtain a composite score.
  • the median value of each biomarker is used to normalize all measurements of that specific biomarker, for example, as provided in Kutteh et a/. (Obstet. Gynecol. 1994;84:811-815) and Palomaki et a/. Clin. Chem. Lab. Med 2001;39: 1137-1145).
  • any measured biomarker level is divided by the median value of a disclosed prognosis group (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, and a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM), resulting in a MoM value.
  • the MoM values can be combined (namely, summed or added) for each biomarker in the panel resulting in a panel MoM value or aggregate MoM score for each sample.
  • a machine learning system may be utilized to determine weighting of the normalized values as well as how to aggregate the values (e.g., determine which protein biomarkers are most predictive, and assign a greater weight to these biomarkers).
  • a composite score for determining an indicator used in assessing a likelihood of having a disclosed prognostic outcome is determined by a statistical model based on analyzing protein significance by applying a linear mixed-effects model using MSstats, as described previously (Zhang et al., Theranostics. 2017;7(18):4350-8).
  • This analysis consists of quantitative measurements for a targeted protein based on peptides, charge states, transitions, samples, and conditions.
  • the method identifies protein alterations in abundance between conditions more systematically than random chance (Zhang et al., 2017; supra).
  • the protein abundance levels between patients with unfavorable and favorable outcomes were compared using the Mann-Whitney test (GraphPad Prism). A p value ⁇ 0.05 was defined as statistically significant.
  • composite scores include one or more clinical parameters or signs of the patient.
  • Representative clinical parameters or signs include age, ethnicity, gender, tumor burden, pain, edema of the brain, frequency or severity of seizures, frequency or severity of vomiting, frequency or severity of headache, memory deficit, neurological deficit, and occurrence of tumor spread or metastasis.
  • the detection methods utilize a risk categorization table to generate a risk score for a patient based on a composite score by comparing the composite score with a reference set derived from a cohort of patients with one of the prognostic outcomes disclosed herein.
  • the detection methods may further comprise quantifying the increased risk for the presence of a disclosed prognostic outcome in the patient as a risk score, wherein the composite score (combined obtained biomarker value and optionally obtained clinical parameter values) is matched to a risk category of a grouping of stratified patient populations wherein each risk category comprises a multiplier (or percentage) indicating an increased likelihood of having the prognostic outcome correlated to a range of composite scores.
  • This quantification is based on the pre-determined grouping of a stratified cohort of subjects.
  • the grouping of a stratified population of subjects, or stratification of a prognosis cohort is in the form of a risk categorization table.
  • the selection of the prognosis cohort, the cohort of subjects that share disclosed prognostic outcome risk factors, are well understood by those skilled in the art of cancer research.
  • the skilled person would also recognize that the resulting stratification, may be more multidimensional and take into account further environmental, occupational, genetic, or biological factors (e.g., epidemiological factors).
  • a disclosed prognostic outcome e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, or a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM
  • this score may be provided in a form amenable to understanding by a physician.
  • the risk score is provided in a report.
  • the report may comprise one or more of the following: patient information, a risk categorization table, a risk score relative to a cohort population, one or more biomarker test scores, a biomarker composite score, a master composite score, identification of the risk category for the patient, an explanation of the risk categorization table, and the resulting test score, a list of biomarkers tested, a description of the disease cohort, environmental and/or occupational factors, cohort size, biomarker velocity, genetic mutations, family history, margin of error, and so on.
  • kits comprising a reagent that permits quantification of at least one protein biomarker or each protein biomarker of a biomarker panel disclosed herein.
  • kit is understood to mean a product containing the different reagents necessary for carrying out the methods of the disclosure packed so as to allow their transport and storage. Additionally, the kits of the present disclosure can contain instructions for the simultaneous, sequential or separate use of the different components contained in the kit.
  • the instructions can be in the form of printed material or in the form of an electronic support capable of storing instructions such that they can be read by a subject, such as electronic storage media (magnetic disks, tapes and the like), optical media (CD-ROM, DVD) and the like.
  • the media can contain internet addresses that provide the instructions.
  • the kits may contain software for interpreting assay data to determine the likelihood of a GBM patient having a poor prognosis or a good prognosis.
  • the kits may provide a means to access a machine learning system provided, for example, as a software as a service (SaaS) deployment.
  • SaaS software as a service
  • Reagents that allow quantification of a protein biomarker include compounds or materials, or sets of compounds or materials, which allow quantification of the protein biomarker.
  • the compounds, materials or sets of compounds or materials permit determining the level or abundance of a protein biomarker (e.g., a salivary EV protein biomarker disclosed herein) include without limitation the isolation or preparation of EVs from a saliva sample, the determination of the level of a corresponding protein biomarker, etc., antibodies for specifically binding to disclosed protein biomarkers, etc.
  • Kit reagents can be in liquid form or can be lyophilized. Suitable containers for the reagents include, for example, bottles, vials, syringes, and test tubes. Containers can be formed from a variety of materials, including glass or plastic. The kit can also comprise a package insert containing written instructions for methods of diagnosing a condition disclosed herein or prognosis patient survival.
  • kits may also optionally include appropriate reagents for detection of labels, positive and negative controls, washing solutions, blotting membranes, microtiter plates, dilution buffers and the like.
  • the kit can also feature various devices (e.g., one or more) and reagents (e.g., one or more) for performing one of the assays described herein; and/or printed instructions for using the kit to quantify at least one protein biomarker disclosed herein and/or carry out an indicator-determining method, as broadly described above and elsewhere herein.
  • reagents described herein which may be optionally associated with detectable labels, can be presented in the format of a microfluidics card, a reaction vessel, a microarray or a kit adapted for use with the assays described in the examples.
  • the indicator-determining methods, apparatuses, composition and kits of the present disclosure are useful for managing treatment decision for GBM, including managing the development or progression GBM, in a human subject.
  • a subject is positively identified as having a poor prognosis (e.g., an unfavorable outcome selected from disease recurrence, disease progression and death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM), for instance after being administered a cancer therapy (e.g., surgery), the patient may be administered an alternative cancer therapy including combination therapy, or with an increased dosage of a cancer therapy agent, or may be placed into palliative care.
  • a patient identified as having a poor prognosis may be exposed to a more aggressive treatment regimen for treating GBM (also referred to herein as "GBM therapy").
  • Representative treatments include: surgery, radiotherapy, chemotherapy and other cancer therapies.
  • Radiotherapies include radiation and waves that induce DNA damage for example, -/-irradiation, X-rays, UV irradiation, microwaves, electronic emissions, radioisotopes, and the like. Therapy may be achieved by irradiating the localized tumor site with the above described forms of radiations. It is most likely that all of these factors effect a broad range of damage DNA, on the precursors of DNA, the replication and repair of DNA, and the assembly and maintenance of chromosomes. Dosage ranges for X-rays range from daily doses of 50 to 200 roentgens for prolonged periods of time (3 to 4 weeks), to single doses of 2000 to 6000 roentgens.
  • Radioisotopes vary widely, and depend on the half-life of the isotope, the strength and type of radiation emitted, and the uptake by the neoplastic cells.
  • Non-limiting examples of radiotherapies include conformal external beam radiotherapy (50-100 Grey given as fractions over 4-8 weeks), either single shot or fractionated, high dose rate brachytherapy, permanent interstitial brachytherapy, systemic radio-isotopes (e.g., Strontium 89).
  • the radiotherapy may be administered in combination with a radiosensitizing agent.
  • radiosensitizing agents include but are not limited to efaproxiral, etanidazole, fluosol, misonidazole, nimorazole, temoporfin and tirapazamine.
  • Chemotherapeutic agents which may be cytostatic or cytotoxic, non-limiting examples of which include:
  • antiproliferative/antineoplastic drugs and combinations thereof, as used in medical oncology such as alkylating agents (for example cis-platin, carboplatin, cyclophosphamide, nitrogen mustard, melphalan, chlorambucil, busulphan and nitrosoureas); antimetabolites (for example antifolates such as fluoropyridines like 5-fluorouracil and tegafur, raltitrexed, methotrexate, cytosine arabinoside and hydroxyurea; anti-tumor antibiotics (for example anthracyclines like adriamycin, bleomycin, doxorubicin, daunomycin, epirubicin, idarubicin, mitomycin-C, dactinomycin and mithramycin); antimitotic agents (for example vinca alkaloids like vincristine, vinblastine, vindesine and vinorelbine and taxoids like
  • alkylating agents
  • cytostatic agents such as antiestrogens (for example tamoxifen, toremifene, raloxifene, droloxifene and idoxifene), oestrogen receptor down regulators (for example fulvestrant), antiandrogens (for example bicalutamide, flutamide, nilutamide and cyproterone acetate), UH antagonists or LHRH agonists (for example goserelin, leuprorelin and buserelin), progestagens (for example megestrol acetate), aromatase inhibitors (for example as anastrozole, letrozole, vorozole and exemestane) and inhibitors of 5a-reductase such as finasteride; and
  • antiestrogens for example tamoxifen, toremifene, raloxifene, droloxifene and idoxifene
  • agents which inhibit cancer cell invasion for example metalloproteinase inhibitors like marimastat and inhibitors of urokinase plasminogen activator receptor function;
  • inhibitors of growth factor function for example growth factor antibodies, growth factor receptor antibodies (for example the anti-erbb2 antibody trastuzumab [HerceptinTM] and the anti-erbbl antibody cetuximab [C225]), farnesyl transferase inhibitors, MEK inhibitors, tyrosine kinase inhibitors and serine/threonine kinase inhibitors, for example other inhibitors of the epidermal growth factor family (for example other EGFR family tyrosine kinase inhibitors such as N-(3-chloro-4-fluorophenyl)-7-methoxy-6-(3-morpholinopropoxy)quinazolin-4-amine (gefitinib, AZD1839), N-(3-ethynylphenyl)-5,7-bis(2-methoxyethoxy)quinazolin-4-amine (erlotinib, OSI-774) and 6-acrylamido-N-
  • growth factor antibodies
  • anti-angiogenic agents such as those which inhibit the effects of vascular endothelial growth factor, (for example the anti-vascular endothelial cell growth factor antibody bevacizumab [AvastinTM], compounds such as those disclosed in International Patent Applications WO 97/22595, WO 97/30035, WO 97/32855 and WO 98/13354) and compounds that work by other mechanisms (for example linomide, inhibitors of integrin av
  • vascular endothelial growth factor for example the anti-vascular endothelial cell growth factor antibody bevacizumab [AvastinTM]
  • compounds that work by other mechanisms for example linomide, inhibitors of integrin av
  • vascular damaging agents such as Co mbreta statin A4 and compounds disclosed in International Patent Applications WO 99/02155, WO00/40529, WO 00/41569, WO01/92224, W002/04434 and W002/08213;
  • antisense therapies for example those which are directed to the targets listed above, such as ISIS 2503, an anti-ras antisense; and
  • gene therapy approaches including for example approaches to replace aberrant genes such as aberrant p53 or aberrant GDEPT (gene-directed enzyme pro-drug therapy) approaches such as those using cytosine deaminase, thymidine kinase or a bacterial nitroreductase enzyme and approaches to increase patient tolerance to chemotherapy or radiotherapy such as multi-drug resistance gene therapy.
  • aberrant genes such as aberrant p53 or aberrant GDEPT (gene-directed enzyme pro-drug therapy) approaches
  • cytosine deaminase such as those using cytosine deaminase, thymidine kinase or a bacterial nitroreductase enzyme
  • approaches to increase patient tolerance to chemotherapy or radiotherapy such as multi-drug resistance gene therapy.
  • immunotherapy approaches including for example immune checkpoint such as: those that target CTLA-4 and thus block or inhibit the interaction between CTLA-4 and CD80/CD86 (i.e. CTLA-4 inhibitors, such as ipilimumab or tremelimumab); those that target PD-1 and thus block or inhibit the interaction between PD-1 and PD-L1 (i.e.
  • immune checkpoint such as: those that target CTLA-4 and thus block or inhibit the interaction between CTLA-4 and CD80/CD86 (i.e. CTLA-4 inhibitors, such as ipilimumab or tremelimumab); those that target PD-1 and thus block or inhibit the interaction between PD-1 and PD-L1 (i.e.
  • PD-1 inhibitors representative examples of which include pembrolizumab, pidilizumab, nivolumab, REGN2810, CT- 001, AMP-224, BMS-936558, MK-3475, MEDI0680 and PDR001); and those that target PD-L1 and thus block or inhibit the interaction between PD-1 and PD-L1 (i.e. PD-L1 inhibitors such as atezolizumab, durvalumab, avelumab, BMS-935559 and MEDI4735).
  • PD-L1 inhibitors such as atezolizumab, durvalumab, avelumab, BMS-935559 and MEDI4735.
  • ex vivo and in vivo approaches may be used to increase the immunogenicity of patient tumor cells, such as transfection with cytokines such as interleukin 2, interleukin 4 or granulocyte-macrophage colony stimulating factor, approaches to decrease T-cell anergy, approaches using transfected immune cells such as cytokine-transfected dendritic cells, approaches using cytokine-transfected tumor cell lines and approaches using anti-idiotypic antibodies.
  • cytokines such as interleukin 2, interleukin 4 or granulocyte-macrophage colony stimulating factor
  • approaches to decrease T-cell anergy approaches using transfected immune cells such as cytokine-transfected dendritic cells
  • approaches using cytokine-transfected tumor cell lines approaches using anti-idiotypic antibodies.
  • the immune effector may be, for example, an antibody specific for some marker on the surface of a malignant cell.
  • the antibody alone may serve as an effector of therapy or it may recruit other cells to actually facilitate cell killing.
  • the antibody also may be conjugated to a drug or toxin (chemotherapeutic, radionuclide, ricin A chain, cholera toxin, pertussis toxin, etc.) and serve merely as a targeting agent.
  • the effector may be a lymphocyte carrying a surface molecule that interacts, either directly or indirectly, with a malignant cell target.
  • Various effector cells include cytotoxic T cells and NK cells.
  • cancer therapy agents are administered in pharmaceutical (or veterinary) compositions together with a pharmaceutically acceptable carrier and in an effective amount to achieve their intended purpose.
  • the dose of active compounds administered to a subject should be sufficient to achieve a beneficial response in the subject over time, such as a reduction in tumor burden and the like.
  • the quantity of the pharmaceutically active compounds(s) to be administered may depend on the subject to be treated inclusive of the age, sex, weight and general health condition thereof. In this regard, precise amounts of the active compound(s) for administration will depend on the judgment of the practitioner.
  • the medical practitioner may evaluate one or more clinical signs associated with the presence of GBM, including the severity of clinical signs. In any event, those of skill in the art may readily determine suitable dosages of the therapeutic agents and suitable treatment regimens without undue experimentation.
  • the GBM therapy may be administered in concert with an adjunctive cancer therapy, representative examples of which include agents to reduce pain, hair loss, vomiting, immune suppression, nausea, diarrhea, rash, sensory disturbance, anemia and fatigue.
  • a subject in cases where a subject is positively identified as having a good prognosis (e.g., a favorable outcome selected from no disease recurrence, no disease progression and no death from disease within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM), for instance after being administered a cancer therapy (e.g., surgery), the patient may be continued to be administered the cancer therapy or cease to be administered the cancer therapy.
  • a good prognosis e.g., a favorable outcome selected from no disease recurrence, no disease progression and no death from disease within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM
  • a first sample is obtained from a GBM patient at an earlier time point to determine a first indicator and a second sample is obtained from the GBM patient at a later time point to determine a second indicator.
  • the first and second indicators are then compared, wherein a difference between the first and second indicators is indicative of a change in prognostic status of the GBM patient, and wherein a similarity between the first and second indicators is indicative of no or negligible change in prognostic status of the GBM patient.
  • the first indicator may be determined before administering a GBM therapy
  • the second indicator may be determined after administration of the GBM therapy, to the patient.
  • a change in indicator from a poor prognosis (/.e., first indicator) to a good prognosis (/.e., second indicator) indicates a likelihood that the GBM therapy was effective in treating the GBM and that the cancer is not progressing.
  • the time difference between the early time point and the later time point is at least 1 week, or at least 2 weeks, or at least 3 weeks, or at least 4 weeks, or at least 5 weeks, or at least 6 weeks, or at least 7 weeks, or at least 8 weeks, or at least 9 weeks, or at least 10 weeks, or at least 11 weeks, or at least 12 weeks, or at least 1 month, or at least 2 months, or at least 3 months, or at least 4 months, or at least 5 months, or at least 6 months, or at least 7 months, or at least 8 months, or at least 7 months, or at least 8 months, or at least 9 months, or at least 10 months, or at least 11 months, or at least 12 months, or at least 1 year, or at least 2 years, or at least 3 years, or at least 4 years, or at least 5 years.
  • the time difference could also be determined by the number of treatment cycles.
  • the time difference between the early time point and the later time point is 1 treatment cycle, or 2 treatment cycles, or 3 treatment cycles, or 4 treatment cycles, or 5 treatment cycles, or 6 treatment cycles, or 7 treatment cycles, or 8 treatment cycles, or 9 treatment cycles, or 10 treatment cycles, or 11 treatment cycles, or 12 treatment cycles.
  • the indicator-determining method of the invention is implemented using one or more processing devices.
  • the method that is implemented by the processing device(s) determines an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, as disclosed herein (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM from diagnosis of GBM, or a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM), wherein the method comprises: (1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2 or 3 protein biomarkers) in a salivary extracellular vesicle (EV) sample obtained from the patient, wherein a respective biomarker value for at least one protein biomarker (e.g., 1, 2 or
  • the method that is implemented by the processing device(s) determines an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, as disclosed herein (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM from diagnosis of GBM, or a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after nine months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM), wherein the method comprises: (1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2, 3 or 4 protein biomarkers) in a salivary extracellular vesicle (EV) sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample, and wherein the at least one protein
  • an apparatus for determining a likelihood of a human GBM patient having a poor prognosis or a good prognosis, as disclosed herein (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, or a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM).
  • the apparatus typically includes at least one electronic processing device that:
  • biomarker value for at least one protein biomarker (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more biomarkers) disclosed herein in a saliva sample obtained from the subject; and
  • the apparatus may further include any one or more of:
  • a protein biomarker e.g., 1, 2, 3, or more biomarkers
  • (C) at least one processing device that: o (i) receives the biomarker value(s) from the measuring device; o (ii) determines an indicator that is indicative of a disclosed prognosis using the biomarker values optionally in combination with one or more clinical parameters or signs of the subject; o (iii) compares the indicator to at least one indicator reference; o (iv) determines a likelihood of the subject having or not having the disclosed prognosis using the results of the comparison; and o (v) generates a representation of the indicator and the likelihood for display to a user.
  • the apparatus comprises a processor configured to execute computer readable media instructions (e.g., a computer program or software application, e.g., a machine learning system, to receive the biomarker values from the evaluation of EV protein biomarkers in a saliva sample and, in combination with other risk factors (e.g., medical history of the patient, publically available sources of information pertaining to a risk of GBM) may determine a master composite score and compare it to a grouping of stratified cohort population comprising multiple risk categories (e.g., a risk categorization table) and provide a risk score.
  • risk categories e.g., a risk categorization table
  • the apparatus can take any of a variety of forms, for example, a handheld device, a tablet, or any other type of computer or electronic device.
  • the apparatus may also comprise a processor configured to execute instructions (e.g., a computer software product, an application for a handheld device, a handheld device configured to perform the method, a world- wide-web (WWW) page or other cloud or network accessible location, or any computing device.
  • the apparatus may include a handheld device, a tablet, or any other type of computer or electronic device for accessing a machine learning system provided as a software as a service (SaaS) deployment.
  • SaaS software as a service
  • the correlation may be displayed as a graphical representation, which, in some embodiments, is stored in a database or memory, such as a random access memory, read-only memory, disk, virtual memory, etc.
  • a database or memory such as a random access memory, read-only memory, disk, virtual memory, etc.
  • Other suitable representations, or exemplifications known in the art may also be used.
  • the apparatus may further comprise a storage means for storing the correlation, an input means, and a display means for displaying the status of the subject in terms of the particular prognosis disclosed herein (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, or a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM).
  • the storage means can be, for example, random access memory, read-only memory, a cache, a buffer, a disk, virtual memory, or a database.
  • the input means can be, for example, a keypad, a keyboard, stored data, a touch screen, a voice-activated system, a downloadable program, downloadable data, a digital interface, a hand-held device, or an infrared signal device.
  • the display means can be, for example, a computer monitor, a cathode ray tube (CRT), a digital screen, a light-emitting diode (LED), a liquid crystal display (LCD), an X-ray, a compressed digitized image, a video image, or a hand-held device.
  • the apparatus can further comprise or communicate with a database, wherein the database stores the correlation of factors and is accessible to the user.
  • the apparatus is a computing device, for example, in the form of a computer or hand-held device that includes a processing unit, memory, and storage.
  • the computing device can include, or have access to a computing environment that comprises a variety of computer-readable media, such as volatile memory and non-volatile memory, removable storage and/or non-removable storage.
  • Computer storage includes, for example, RAM, ROM, EPROM & EEPROM, flash memory or other memory technologies, CD ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other medium known in the art to be capable of storing computer-readable instructions.
  • the computing device can also include or have access to a computing environment that comprises input, output, and/or a communication connection.
  • the input can be one or several devices, such as a keyboard, mouse, touch screen, or stylus.
  • the output can also be one or several devices, such as a video display, a printer, an audio output device, a touch stimulation output device, or a screen reading output device.
  • the computing device can be configured to operate in a networked environment using a communication connection to connect to one or more remote computers.
  • the communication connection can be, for example, a Local Area Network (LAN), a Wide Area Network (WAN) or other networks and can operate over the cloud, a wired network, wireless radio frequency network, and/or an infrared network.
  • LAN Local Area Network
  • WAN Wide Area Network
  • a method for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis comprising, consisting or consisting essentially of:
  • determining a biomarker value for at least one protein biomarker e.g., 1, 2 or 3 protein biomarkers
  • a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample
  • the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kallikrein-1 (KLK1); and
  • biomarker values are determined for at least two protein biomarkers.
  • H4 is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome
  • LKHA4 is present in the salivary EV sample obtained from the GBM patient at a lower level than in control salivary EV samples obtained from a reference population of GBM patients with a favorable outcome;
  • KLK1 is present in the salivary EV sample obtained from the GBM patient at a lower level than in control salivary EV samples obtained from a reference population of GBM patients with a favorable outcome.
  • H4 is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome
  • LKHA4 is present in the salivary EV sample obtained from the GBM patient at a higher level than in control salivary EV samples obtained from a reference population of GBM patients with an unfavorable outcome;
  • KLK1 is present in the salivary EV sample obtained from the GBM patient at a higher level than in control salivary EV samples obtained from a reference population of GBM patients with an unfavorable outcome.
  • a method for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis comprising, consisting or consisting essentially of: (1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2, 3 or 4 protein biomarkers) in a salivary extracellular vesicle (EV) sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample, and wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI); and
  • ALDOA aldolase A
  • 1433E 14-3-3 protein epsilon
  • TM11B transmembrane protease serine 11B
  • biomarker values are determined for at least two protein biomarkers.
  • biomarker values are determined for at least three protein biomarkers.
  • biomarker values are determined for each of ALDOA, 1433E, TM11B and ECHI.
  • ALDOA is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome
  • TM11B is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome;
  • ECHI is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome.
  • ALDOA is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome
  • TM11B is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome
  • ECHI is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome.
  • the function includes at least one of: (a) multiplying biomarker values; (b) dividing biomarker values; (c) adding biomarker values; (d) subtracting biomarker values; (e) a weighted sum of biomarker values; (f) a log sum of biomarker values; (g) a geometric mean of biomarker values; (h) a sigmoidal function of biomarker values; and (i) normalization of biomarker values.
  • a respective reference biomarker value, biomarker value range, functionalized biomarker value, functionalized biomarker value range, biomarker value cut-off or functionalized biomarker value cut-off, or reference composite score, composite score range or composite score cut-off may be a biomarker value, biomarker value range, functionalized biomarker value, functionalized biomarker value range, biomarker value cutoff or functionalized biomarker value cut-off, or reference composite score, composite score range or composite score cut-off corresponding to a control subject or control population of subjects.
  • control subject or control population of subjects is selected from a subject or population of subjects with an unfavorable outcome within a period (e.g., six months or nine months) from diagnosis of GBM, or a subject or population of subjects with a favorable outcome within and/or after a period (e.g., six months or nine months) from diagnosis of GBM.
  • any one of embodiments 25 to 27, wherein the indicator indicates a likelihood of a poor prognosis if the biomarker value(s), functionalized biomarker value(s) or composite score is(are) indicative of the level of the bioma rker(s) in the sample that correlates with an increased likelihood of a poor prognosis relative to a predetermined reference biomarker value, value range or cut-off value, or to a predetermined reference functionalized biomarker value, value range or cut-off value, or to a predetermined reference composite score, composite score range or composite score cut-off.
  • any one of embodiments 25 to 27, wherein the indicator indicates a likelihood of a good prognosis if the biomarker value(s), functionalized biomarker value(s) or composite score is(are) indicative of the level of the bioma rker(s) in the sample that correlates with an increased likelihood of a good prognosis relative to a predetermined reference biomarker value, value range or cut-off value, or to a predetermined reference functionalized biomarker value, value range or cut-off value, or to a predetermined reference composite score, composite score range or composite score cut-off.
  • a method for monitoring prognostic status or treatment of a GBM patient comprising, consisting or consisting essentially of:
  • a biomarker value for at least one protein biomarker e.g., 1, 2 or 3 protein biomarkers
  • a respective biomarker value is indicative of a level of a corresponding protein biomarker in the first sample, and wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kallikrein-1 (KLK1);
  • LKHA4 leukotriene A-4 hydrolase
  • H4 histone H4
  • KLK1 kallikrein-1
  • a method for monitoring prognostic status or treatment of a GBM patient comprising, consisting or consisting essentially of: (1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2, 3 or 4 protein biomarkers) in a first salivary EV sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the first sample, and wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI);
  • ALDOA aldolase A
  • 1433E 14-3-3 protein epsilon
  • TM11B transmembrane protease serine 11B
  • ECHI enoyl CoA hydratase 1
  • An apparatus for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis comprising at least one electronic processing device that:
  • LKHA4 leukotriene A-4 hydrolase
  • H4 histone H4
  • KLK1 kallikrein-1
  • An apparatus for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis comprising at least one electronic processing device that:
  • a biomarker value for at least one protein biomarker e.g., 1, 2, 3 or 4 protein biomarkers
  • a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample
  • the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI); and
  • a composition comprising a mixture of a salivary EV sample obtained from a GBM patient, and for one or a plurality of protein biomarkers (e.g., 1, 2 or 3 protein biomarkers) in the sample an antibody or antigen-binding fragment that binds specifically to the protein biomarker, wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kallikrein-1 (KLK1).
  • LKHA4 leukotriene A-4 hydrolase
  • H4 histone H4
  • KLK1 kallikrein-1
  • a composition comprising a mixture of a salivary EV sample obtained from a GBM patient, and for one or a plurality of protein biomarkers (e.g., 1, 2, 3 or 4 protein biomarkers) in the sample an antibody or antigen-binding fragment that binds specifically to the protein biomarker, wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI).
  • ALDOA aldolase A
  • 1433E 14-3-3 protein epsilon
  • TM11B transmembrane protease serine 11B
  • ECHI enoyl CoA hydratase 1
  • composition of embodiment 39 wherein the composition comprises a plurality of antibodies or antigen-binding fragments, each of which specifically binds to a different protein biomarker and comprises the same label or a different label, as compared to the protein biomarker specificity and label of other antibodies or antigen-binding fragments of the composition.
  • a method managing treatment of a GBM patient comprising:
  • kits for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis comprising : for one or a plurality of protein biomarkers (e.g., 1, 2 or 3 protein biomarkers) an antibody or antigen-binding fragment that binds specifically to the protein biomarker, wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kail ikrei n-1 (KLK1).
  • LKHA4 leukotriene A-4 hydrolase
  • H4 histone H4
  • KLK1 kail ikrei n-1
  • kits for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis comprising: for one or a plurality of protein biomarkers (e.g., 1, 2, 3 or 4 protein biomarkers) an antibody or antigen-binding fragment that binds specifically to the protein biomarker, wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI).
  • ALDOA aldolase A
  • 1433E 14-3-3 protein epsilon
  • TM11B transmembrane protease serine 11B
  • ECHI enoyl CoA hydratase 1
  • kit of embodiment 48 or embodiment 49 further comprising at least one reagent for preparing EVs from a saliva sample.
  • kit of any one of embodiments 48 to 50 further comprising one or more of deoxynucleotides, buffer(s), positive and negative controls, and reaction vessel(s).
  • kit of any one of embodiments 48 to 51 further comprising instructions for performing the indicator-determining method of any one of embodiments 1 to 19.
  • DAPs differentially abundant proteins
  • FIG. 3B The analysis of the protein content of exosomes from patients with good or poor outcomes is shown in Figure 3B.
  • the volcano plot identifies proteins by their Iog2 fold changes against their corresponding p-value in patients before surgery ( Figure 3B).
  • the present inventors observed 1 abundant protein (H4_ HUMAN) and 6 less abundant proteins (KV116_HUMAN, GCFC2JHUMAN, KLK1_ HUMAN, KV230_HUMAN, ACTZ_HUMAN, LKHA4JHUMAN) in EVs of patients with poor outcomes compared to good outcomes (TABLE 2).
  • the detected immunoglobulins were excluded from further analyses.
  • a receiver operating characteristics analysis was performed for each protein individually and combined panels to evaluate the prognostic performance of a 3 proteins panel.
  • the present inventors chose H4_HUMAN, LKHA4_HUMAN and KLK1_HUMAN and a panel combining these 3 protein markers ( Figure 4 and 5).
  • a logistic regression predictive model was applied to the biomarkers, calculating a predictive score for samples individually and plotted into the groups analyzed ( Figure 5). Comparing both groups (good and poor outcomes) the present inventors were able to identify a 3- protein panel that can distinguish patients who had a good outcome versus patients with a poor outcome, with an area under the curve of 0.903.
  • the demographics and clinical information from GBM patients included in this study are shown in TABLE 3.
  • the average age of GBM patients was 60 years (ranging from 37 to 82 years) and for the control group was 63.5 years (ranging from 58 to 71 years).
  • n 17
  • IDH1R132H mutation At the time of diagnosis, which was prior to the new 2021 WHO classification of central nervous system tumors, patients presenting IDH1R132H mutation had their tumors still classified as GBM.
  • SAP Salivary EVs after surgery with poor outcomes.
  • DAPs were identified in each group pre (Figure 7E) and postoperatively (Figure 7F). Before surgery, a total of 65 DAPs were detected, among them, 54 were more abundant in patients with unfavorable outcomes, while two were less abundant. After surgery, 15 DAPs were identified, five more abundant and ten less abundant in patients with unfavorable outcomes. A list of all DAPs is presented in Table 4.
  • a receiver operating characteristics analysis was performed for each of ALDOA, 1433E, TM11B and ECHI individually and a combined panel to evaluate prognostic performance Figure 9).
  • a logistic regression predictive model was applied to the biomarkers, calculating a predictive score for samples individually and plotted into the groups analyzed ( Figure 9A-D). Comparing both groups (good and poor outcomes) the present inventors were able to identify a 4- protein panel that can distinguish patients who had a good outcome versus patients with a poor outcome, with an area under the curve of 0.803 (Figure 9E).
  • EVs were diluted (1:200) with filtered (0.22 pm) PBS and analyzed using the NanoSight NS300 with a 405-nm laser (NanoSight Ltd., Malvern, UK). This instrument explores the Brownian motion resulting from the light of the equipped laser being scattered by the particles, measuring their size and concentration. Three videos of 30 seconds were recorded for each sample, and a report was generated on the size distribution and concentration of particles.
  • EV morphology was assessed using Transmission Electron Microscopy (TEM). Samples were mixed by vortexing vigorously. Five-microliter drops of resuspended samples were placed onto a parafilm, and the mounting grid was placed over the droplet. The mount was then incubated with 2% uranyl acetate (negative staining). EVs were imaged on a JEOL JEM-1400 TEM at lOOkV mounted with a 2K TVIPS CCD camera at the Central Analytical Research Facility (CARF) - QUT.
  • CARF Central Analytical Research Facility
  • Total protein concentration was quantified using PierceTM BCA Protein Assay Kit (Thermo Fisher Scientific). For western blotting, equal amounts of protein (5 pg) were loaded onto 10% SDS-PAGE gels and ran at 100 V for 90 min. An equal amount of protein from a GBM cell line (U251MG) was also loaded onto each gel as a positive control. U251MG cell line was gifted by Prof. Bryan W. Day (QIMR, Brisbane, Australia). Proteins were then transferred onto a polyvinylidene difluoride (PVDF) membrane at 100 V for 90 min at 4°C.
  • PVDF polyvinylidene difluoride
  • the membranes were blocked for 1 h at room temperature (RT) with 5% bovine serum albumin (BSA) in tris-buffered saline, 0.1% Tween 20 (TBS-T). After blocking, membranes were washed (3 times) with TBS-T and incubated overnight at 4°C with the following primary antibodies: CD53 (Santa Cruz - #15363), CD9 (Cell signaling - #13174), GM-130 (Cell signaling - #12480), Aldolase A (C-10) (Santa Cruz - #390733), ECHI (B- 3) (Santa Cruz #515270), 14-3-3 E (8C3) (Santa Cruz #23957).
  • BSA bovine serum albumin
  • membranes were incubated with anti-rabbit IgG-HRP secondary antibody (Cell signaling - #7074) for 1 h at RT. All primary antibodies were diluted 1 : 1000 and secondary 1:2000. The membranes were incubated with Pierce ECL Western Blotting substrate (Thermo Fisher Scientific) and imaged using ChemiDoc XRS+ System (Bio-Rad Laboratories).
  • samples were mixed with 15 pL of SDS-Tris buffer (4% sodium dodecyl sulfate (SDS), 100 mM Tris-HCI pH 8.5, 100 mM Dithiothreitol (DTT)) and 200 pL of DTT-Urea buffer (25 mM DTT, 8 M urea in 100 mM Tris-HCI pH 8.5) within a 30 kDa Microcon YM-30 centrifugal filter device (Merck Millipore, MA, USA) and incubated at RT for 60 minutes under agitation.
  • SDS-Tris buffer 4% sodium dodecyl sulfate (SDS), 100 mM Tris-HCI pH 8.5, 100 mM Dithiothreitol (DTT)
  • DTT-Urea buffer 25 mM DTT, 8 M urea in 100 mM Tris-HCI pH 8.5
  • the mass spectrometry system utilized was a nanoflow liquid chromatographytandem mass spectrometry (LC-MS/MS) with a Prominence nanoLC system (Shimadzu) coupled with a TripleTOF 5600+ mass spectrometer system with a Nanospray III interface (AB SCIEX) as previously described (34). Briefly, approximately 2 pg of peptides were injected and separated using an analytical column packed with ChromXP C18 (150 mm x 75 pm, Eksigent Technologies, Dublin, CA).
  • Trapping was performed at a flow rate of 5 pL/min for 5 min using mobile phase C (2% acetonitrile and 0.1% formic acid), followed by elution for 40 min using mobile phases A (1% acetonitrile and 0.1% formic acid) and B (80% acetonitrile and 0.1% formic acid) at a conserved flowrate of 300 niymin. Gas and voltage settings were adjusted as required.
  • a TripleTOF® 5600+ (SCIEX) was used to analyze peptide ions in data-dependent acquisition (DDA) mode, obtaining high resolution (30,000) TOF-MS scans over a range of 350 - 1350 m/z, followed by up to 40 high sensitivity MS/MS scans of the most abundant peptide ions per cycle over the range of 100- 2000 m/z. Peptide ions meeting the criteria of intensity greater than 150 cps and charge state of 2-5 were included. Each survey (TOF-MS) scan lasted 250 ms and the product ion (MS/MS) scan was acquired for 50 ms resulting in a total cycle time of 2.3 s. The ions were fragmented in the collision cell and the collected peptide ion fragmentation spectra were stored in .wiff format (SCIEX).
  • Peptides were subjected to data-independent acquisition (DIA/SWATH-MSTM acquisition) using cycling isolation windows of equal mass ranges across a 65 min gradient method. LC conditions were the same as described above.
  • DIA/SWATH-MSTM acquisition For peptide detection, a survey scan data (MS) was performed for 80 ms, followed by MS/MS on all precursors in a cyclic manner using an accumulation time of 80 ms per individual SWATH-MS window. A total of 36 overlapping windows, each 26 m/z units wide, were used to cover the peptide ions in a range of 350 - 1500 m/z which resulted in a cycle time of 3 s. Fragment ions were recorded in a high sensitivity mode and a range of 100 - 1800 m/z. The collected data were saved in .wiff format.
  • Protein Pilot software version 5.0.2 was used for peptide identification.
  • Selected DDA files included seven EV samples obtained from saliva of GBM patients representing the unfavorable and favorable group (.wiff format).
  • DDA files were searched using ProteinPilot with the following parameters: iodoacetamide for cysteine alkylation, digestion with trypsin, and no special factors using the human SwissProt database (March 2021 release).
  • Peptide identification was performed as previously described (34). Briefly, Protein Pilot 5.0.2 software was used to perform a false discovery rate (FDR) analysis for all searches, and further analyses were performed in peptides identified with a greater than 99% confidence and an FDR of less than 1%.
  • FDR false discovery rate
  • the abundance of peptides was determined using PeakView Software (version 2.2) with standard settings as previously described (34, 38). Peptide abundance was determined by the sum of the integrated are of six fragment ion, and up to six peptides per protein were used to determine protein abundance.

Abstract

The present disclosure relates generally to biomarkers of cancer. More particularly, the present disclosure relates to extracellular vesicle biomarkers and their use in methods, apparatuses, compositions and kits for determining an indicator that is useful for assessing a likelihood of a decreased or poor survival prognosis or an increased or good survival prognosis in a glioblastoma patient. The disclosed methods, apparatuses, compositions and kits are useful for monitoring prognosis of a glioblastoma patient before and after exposure to a treatment regimen, and for managing treatment of a glioblastoma patient.

Description

TITLE
PROGNOSTIC BIOMARKERS AND USES THEREFOR1
RELATED APPLICATIONS
[0001] This application claims priority to Australian Provisional Patent Application No. 2022900253 entitled "Prognostic biomarkers and uses therefor" filed 8 February 2022, the contents of which are incorporated herein by reference in their entirety.
FIELD
[0002] This disclosure relates generally to biomarkers of cancer. More particularly, the present disclosure relates to extracellular vesicle biomarkers and their use in methods, apparatuses, compositions and kits for determining an indicator that is useful for assessing a likelihood of a decreased or poor survival prognosis or an increased or good survival prognosis in a glioblastoma patient. The disclosed methods, apparatuses, compositions and kits are useful for monitoring prognosis of a glioblastoma patient before and after exposure to a treatment regimen, and for managing treatment of a glioblastoma patient.
BACKGROUND
[0003] Glioblastoma, also known as glioblastoma multiforme (GBM), is the most common type of brain tumor in adults. Despite aggressive treatment (typically surgery followed by either radio and/or chemotherapy), patient prognosis remains poor (Stupp et al., N Engl J Med. 2005;352(10):987-96; Jeffree RL. Aust J Gen Pract. 2020;49(4): 194-9). The overall survival of GBM patients is approximately 15 months, and the majority of patients (>90%) recur within 6 to 9 months from diagnosis (Twelves et al., BrJ Cancer. 2021;124(8): 1379-87; Weller et al., Neuro Oncol. 2013;15(l):4-27; Shergalis et al., Front Neurol. 2019; 10:460). At the recurrent setting, the median overall survival reduces to 6.2 months (Shergalis et al., 2019; supra). Response to treatment of GBM patients is assessed mainly by imaging techniques (Arevalo et al., Front Neurol. 2019; 10:460) and a magnetic resonance imaging (MRI) test is recommended every 3-4 months (Stupp et al., Ann Oncol. 2014;25 Suppl 3: HI93-101). However, imaging techniques sometimes fail to reliably confirm tumor progression which causes delays in critical clinical interventions (Ellingson et al., J Neurooncol. 2017;134(3):495-504). Due to limitations of the current prognosis procedure, a liquid biopsy approach has been suggested in the field (Muller Bark et al., BrJ Cancer. 2019; 122(3):295-305; Shankar et al., Expert Rev Mo! Diagn. 2017; 17(10):943-7; Best et al., Acta Neuropathol. 2015;129(6):849-65). The use of liquid biopsy allows the detection of tumor material non-invasively which can help the detection of progression before the appearance of clinical symptoms or subsequent MRI assessment.
[0004] In GBM, to obtain tumor information via body fluids like CSF, blood, or saliva, the putative biomarker needs to cross the blood-brain barrier (BBB). In this context, since extracellular vesicles (EVs) such as exosomes cross even intact BBBs, they present with a unique opportunity in comparison to other studied biomarkers, like circulating tumor cells (Garcia- Villaescusa et al., PLoS One. 2018;13(2):e0188710). Exosomes are small extracellular vesicles that play a key role in cell communication. Exosomes can be isolated from several body fluids, including blood, saliva, urine, and cerebrospinal fluid (CSF) (Raposo et al., J Cell Biol. 2013;200(4):373-83). Currently, the most frequent body fluid tested in the clinical setting is blood. Nevertheless, the use of saliva has been gaining attention as the diagnostic medium of the future. Notably, saliva composition is altered under pathological conditions, including cancer (Schulz et al., Crit Rev Biotechnol. 2013;33(3):246-59). In addition, saliva is easy and cost-effective to collect and store (Pfaffe et al., Clin Chem. 2011;57(5):675-87). Thus, saliva is considered a potential matrix for the discovery of cancer biomarkers for diagnosis, prognosis, and drug monitoring (Pfaffe et al., 2011; supra; Malamud et al., IntJ Oral Sci. 2016;8(3): 133-7; Zhang et al., Int J Oral Sci.
2016;8(3): 133-7; Nonaka et al., Enzymes. 2017;42: 125-51). However, when using the whole saliva for a proteomics approach, some less abundant proteins may be masked due to the presence of highly abundant proteins such as amylase (Han et al., Int J Biol Sci. 2018;14(6):633-43). Accordingly, salivary exosomes are potential candidates to avoid this drawback and they have been explored as diagnostic or prognostic biomarkers in several cancer types, including head and neck cancer (Langevin et al., Oncotarget. 2017;8(47):82459-74; Tang K et al., Mol Diagn Ther. 2021), pancreatic cancer (Machida et al., Oncol Rep. 2015;36(4):2375-81; Lau et al., J Biol Chem. 2013;288(37):26888-97), and lung cancer (Sun et al., J Proteome Res. 2018;17(3): 1101-7; Sun et al., Sci Rep. 2016;6:24669). To the inventors knowledge, there are no studies in the literature exploring the role of salivary exosomes in patients with GBM.
SUMMARY
[0005] The present disclosure arises from the determination that certain protein biomarkers in salivary EVs from GBM patients have strong discrimination performance for differentiating between GBM patients with favorable outcomes (e.g., no disease recurrence, no disease progression or no death from disease within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM) and GBM patients with unfavorable outcomes (e.g., disease recurrence, disease progression or death from disease within six months from diagnosis of GBM or within nine months from diagnosis of GBM). Based on this determination, methods, apparatuses, compositions and kits are disclosed, which take advantage of the biomarkers disclosed herein for predicting a favorable or unfavorable outcome in glioblastoma patient, for monitoring prognosis of glioblastoma patients (e.g., before and after exposure to a treatment regimen for treating glioblastoma) and for making better decisions for treating or triaging the patients.
[0006] Accordingly, in one aspect, methods are disclosed for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis. These methods generally comprise, consist or consist essentially of:
(1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2 or 3 protein biomarkers) in a salivary extracellular vesicle (EV) sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample, and wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kallikrei n- 1 (KLK1); and
(2) determining the indicator using the biomarker value(s).
[0007] In certain embodiments, a biomarker value is obtained for each of LKHA4, H4 and KLK1.
[0008] In this aspect, the poor prognosis is suitably disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, and the good prognosis is suitably no disease recurrence, no disease progression or no death from disease within and/or after six months from diagnosis of GBM. [0009] In accordance with the present disclosure, H4 is present at a higher level in salivary EV samples obtained from GBM patients with an unfavorable outcome than in salivary EV samples obtained from GBM patients with a favorable outcome; and LKHA4 and KLK1 are present at a lower level in salivary EV samples obtained from GBM patients with an unfavorable outcome than in salivary EV samples obtained from GBM patients with a favorable outcome, wherein the unfavorable outcome is disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, and wherein the favorable outcome is no disease recurrence, no disease progression or death from disease within and/or after six months from diagnosis of GBM.
[0010] Thus, in some embodiments, the indicator indicates a likelihood of a poor prognosis if:
• H4 is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome; and/or
• LKHA4 is present in the salivary EV sample obtained from the GBM patient at a lower level than in control salivary EV samples obtained from a reference population of GBM patients with a favorable outcome; and/or
• KLK1 is present in the salivary EV sample obtained from the GBM patient at a lower level than in control salivary EV samples obtained from a reference population of GBM patients with a favorable outcome.
[0011] Alternatively, the indicator may indicate a likelihood of a good prognosis if:
• H4 is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome; and/or
• LKHA4 is present in the salivary EV sample obtained from the GBM patient at a higher level than in control salivary EV samples obtained from a reference population of GBM patients with an unfavorable outcome; and/or
• KLK1 is present in the salivary EV sample obtained from the GBM patient at a higher level than in control salivary EV samples obtained from a reference population of GBM patients with an unfavorable outcome.
[0012] In another aspect, methods are disclosed for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis. These methods generally comprise, consist or consist essentially of:
(1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2, 3 or 4 protein biomarkers) in a salivary extracellular vesicle (EV) sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample, and wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI); and
(2) determining the indicator using the biomarker value(s).
[0013] In certain embodiments, a biomarker value is obtained for each of ALDOA, 1433E, TM11B and ECHI.
[0014] In accordance with this aspect, the poor prognosis is suitably disease recurrence, disease progression or death from disease within nine months from diagnosis of GBM, and the good prognosis is suitably no disease recurrence, no disease progression or no death from disease within and/or after nine months from diagnosis of GBM. [0015] As disclosed herein, each of ALDOA, 1433E, TM11B and ECHI are present at a higher level in salivary EV samples obtained from GBM patients with an unfavorable outcome than in salivary EV samples obtained from GBM patients with a favorable outcome, wherein the unfavorable outcome is disease recurrence, disease progression or death from disease within nine months from diagnosis of GBM, and wherein the favorable outcome is no disease recurrence, no disease progression or death from disease within and/or after nine months from diagnosis of GBM.
[0016] Thus, in some embodiments, the indicator indicates a likelihood of a poor prognosis if:
• ALDOA is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome; and/or
• 1433E is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome; and/or
• TM11B is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome; and/or
• ECHI is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome.
[0017] Alternatively, the indicator may indicate a likelihood of a good prognosis if:
• ALDOA is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome; and/or
• 1433E is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome; and/or
• TM11B is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome; and/or
• ECHI is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome.
[0018] In any of the aspects disclosed herein:
[0019] The GBM patient may or may not have undergone a treatment regimen for treating GBM. Illustrative examples of GBM treatment regimens which include surgery, radiotherapy and/or chemotherapy.
[0020] EVs of the sample are suitably small EVs, typically with a diameter of less than about 200 nm. In some embodiments, the EVs have a diameter ranging from about 30 nm to about 200 nm.
[0021] Individual biomarker values suitably represent a measured amount, abundance or concentration of a corresponding protein biomarker in the sample.
[0022] The methods may further comprise applying a function to biomarker values to yield at least one functionalized biomarker value and determining the indicator using the at least one functionalized biomarker value. In representative examples, the function includes at least one of: (a) multiplying biomarker values; (b) dividing biomarker values; (c) adding biomarker values; (d) subtracting biomarker values; (e) a weighted sum of biomarker values; (f) a log sum of biomarker values; (g) a geometric mean of biomarker values; (h) a sigmoidal function of biomarker values; and (i) normalization of biomarker values.
[0023] The methods may further comprise combining the biomarker values, optionally with clinical parameters, to provide a composite score and determining the indicator using the composite score. In non-limiting examples of this type, the biomarker values are combined by adding, multiplying, subtracting, and/or dividing biomarker values.
[0024] The methods suitably further comprise analyzing the biomarker value(s) or composite score with reference to one or more reference biomarker values, biomarker value ranges, functionalized biomarker value(s), functionalized biomarker value ranges, biomarker value cut-offs or functionalized biomarker value cut offs, or reference composite scores, composite score ranges or composite score cut-offs, to determine the indicator. A respective reference biomarker value, biomarker value range, functionalized biomarker value, functionalized biomarker value range, biomarker value cut-off or functionalized biomarker value cut-off, or reference composite score, composite score range or composite score cut-off may be a biomarker value, biomarker value range, functionalized biomarker value, functionalized biomarker value range, biomarker value cut-off or functionalized biomarker value cut-off, or reference composite score, composite score range or composite score cut-off corresponding to a control subject or control population of subjects. The control subject or control population of subjects is suitably selected from a subject or population of subjects with an unfavorable outcome within a period (e.g., six months or nine months) from diagnosis of GBM, or a subject or population of subjects with a favorable outcome within and/or after a period (e.g., six months or nine months) from diagnosis of GBM.
[0025] The indicator suitably indicates a likelihood of a poor prognosis, if the biomarker value(s), functionalized biomarker value(s) or composite score is(are) indicative of the level of the biomarker(s) in the sample that correlates with an increased likelihood of a poor prognosis relative to a predetermined reference biomarker value, value range or cut-off value, or to a predetermined reference functionalized biomarker value, value range or cut-off value, or to a predetermined reference composite score, composite score range or composite score cut-off. Alternatively, the indicator indicates a likelihood of a good prognosis, if the biomarker value(s), functionalized biomarker value(s) or composite score is(are) indicative of the level of the biomarker(s) in the sample that correlates with an increased likelihood of a good prognosis relative to a predetermined reference biomarker value, value range or cut-off value, or to a predetermined reference functionalized biomarker value, value range or cut-off value, or to a predetermined reference composite score, composite score range or composite score cut-off.
[0026] In another aspect, methods are disclosed for monitoring prognostic status or treatment of a GBM patient. These methods generally comprise, consist or consist essentially of:
(1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2 or 3 protein biomarkers) in a first salivary EV sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the first sample, and wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kallikrein-1 (KLK1);
(2) determining a first indicator using the biomarker value(s);
(3) determining a biomarker value for the at least one protein biomarker in a second salivary EV sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the second sample; and
(4) determining a second indicator using the biomarker value(s); and
(5) comparing the first indicator with the second indicator, thereby monitoring the prognostic status or treatment of a GBM patient. [0027] Disclosed herein in a further aspect are methods for monitoring prognostic status or treatment of a GBM patient. These methods generally comprise, consist or consist essentially of:
(1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2, 3 or 4 protein biomarkers) in a first salivary EV sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the first sample, and wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI);
(2) determining a first indicator using the biomarker value(s);
(3) determining a biomarker value for the at least one protein biomarker in a second salivary EV sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the second sample; and
(4) determining a second indicator using the biomarker value(s); and
(5) comparing the first indicator with the second indicator, thereby monitoring the prognostic status or treatment of a GBM patient.
[0028] In any of the aspects disclosed herein, the first sample may be obtained from the patient before undergoing a therapeutic regimen for treating GBM and the second sample may be obtained from the patient after undergoing the therapeutic regimen.
[0029] In still another aspect, apparatuses are disclosed for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, suitably wherein the poor prognosis is disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, and suitably wherein the good prognosis is no disease recurrence, no disease progression or no death from disease within and/or after six months from diagnosis of GBM. These apparatuses general comprise, consist or consist essentially of at least one electronic processing device that:
• determines a biomarker value for at least one protein biomarker (e.g., 1, 2 or 3 protein biomarkers) in a salivary EV sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample, and wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kallikrein-1 (KLK1); and
• determines the indicator using the biomarker value(s).
[0030] Disclosed herein in another aspect are apparatuses for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, suitably wherein the poor prognosis is disease recurrence, disease progression or death from disease within nine months from diagnosis of GBM, and suitably wherein the good prognosis is no disease recurrence, no disease progression or no death from disease within and/or after nine months from diagnosis of GBM. These apparatuses general comprise, consist or consist essentially of at least one electronic processing device that:
• determines a biomarker value for at least one protein biomarker (e.g., 1, 2, 3 or 4 protein biomarkers) in a salivary EV sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample, and wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI); and
• determines the indicator using the biomarker value(s).
[0031] Another aspect of the present disclosure provides compositions, suitably for use in determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, suitably wherein the poor prognosis is disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, and suitably wherein the good prognosis is no disease recurrence, no disease progression or no death from disease within and/or after six months from diagnosis of GBM. These compositions generally comprise, consist or consist essentially of a mixture of a salivary EV sample obtained from a GBM patient, and for one or a plurality of protein biomarkers (e.g., 1, 2 or 3 protein biomarkers) in the sample an antibody or antigen-binding fragment that binds specifically to the protein biomarker, wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kallikrein-1 (KLK1).
[0032] In yet another aspect, compositions are disclosed, suitably for use in determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, suitably wherein the poor prognosis is disease recurrence, disease progression or death from disease within nine months from diagnosis of GBM, and suitably wherein the good prognosis is no disease recurrence, no disease progression or no death from disease within and/or after nine months from diagnosis of GBM. These compositions generally comprise, consist or consist essentially of a mixture of a salivary EV sample obtained from a GBM patient, and for one or a plurality of protein biomarkers (e.g., 1, 2, 3 or 4 protein biomarkers) in the sample an antibody or antigen-binding fragment that binds specifically to the protein biomarker, wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI).
[0033] In any of the aspects disclosed herein:
[0034] Individual antibodies or antigen-binding fragments may be labeled.
[0035] The composition may comprise a plurality of antibodies or antigen-binding fragments, each of which specifically binds to a different protein biomarker and comprises the same label or a different label, as compared to the protein biomarker specificity and label of other antibodies or antigen-binding fragments of the composition. In illustrative examples of this type, the labels of different antibodies or antigen-binding fragments are detectably distinct.
[0036] In a further aspect, methods are disclosed for managing treatment of a GBM patient. These methods generally comprise, consist or consist essentially of:
• not exposing the patient to a treatment regimen or exposing the subject to a standard care treatment regimen at least in part on the basis that the patient is determined by the indicator-determining method as broadly described above and elsewhere herein as having a likelihood of a good prognosis; or
• exposing the patient to a more aggressive treatment regimen than standard care at least in part on the basis that the patient is determined by the indicator-determining method as broadly described above and elsewhere herein as having a likelihood of a poor prognosis. [0037] In some embodiments, the GBM patient has been administered a treatment regimen prior to undertaking the indicator-determining method. In other embodiments, the GBM patient has not undergone a treatment regimen prior to undertaking the indicator-determining method.
[0038] In some embodiments, the treatment management methods further comprise: taking a sample from the patient and determining an indicator indicative of a likelihood of a disclosed prognosis using the indicator-determining method. In some of the same or other embodiments, the methods further comprise: sending a sample obtained from the patient to a laboratory at which the indicator is determined according to the indicator-determining method, and optionally receiving the indicator from the laboratory.
[0039] A further aspect of the present disclosure provides kits for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, suitably wherein the poor prognosis is disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, and suitably wherein the good prognosis is no disease recurrence, no disease progression or no death from disease within and/or after six months from diagnosis of GBM. These kits generally comprise for one or a plurality of protein biomarkers (e.g., 1, 2 or 3 protein biomarkers) an antibody or antigen-binding fragment that binds specifically to the protein biomarker, wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kalli krein-1 (KLK1).
[0040] Disclosed herein in yet another aspect are kits for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, suitably wherein the poor prognosis is disease recurrence, disease progression or death from disease within nine months from diagnosis of GBM, and suitably wherein the good prognosis is no disease recurrence, no disease progression or no death from disease within and/or after nine months from diagnosis of GBM. These kits generally comprise for one or a plurality of protein biomarkers (e.g., 1, 2, 3 or 4 protein biomarkers) an antibody or antigen-binding fragment that binds specifically to the protein biomarker, wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI).
[0041] The kits may further comprise any one or more of: at least one reagent for preparing EVs from a saliva sample; buffer(s), positive and negative controls, and reaction vessel(s). Suitably, the kits may further comprise instructions for performing the indicatordetermining methods as broadly described above and elsewhere herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0042] Figure 1 is a graphical and photographic representation showing characterization of salivary small extracellular vesicles in glioblastoma patients: (A) Size and concentration of salivary small extracellular vesicles in pre and postoperative GBM patients. (B) Morphology of salivary small extracellular vesicles imaged by Transmission Electron Microscopy (TEM). Representative images of the cup-shaped morphology of EVs (red arrow) isolated from pre (left panel) and postoperative (right panel) samples. (C) Immunoblotting for positive (CD9, CD63) and negative (GM130) markers of small extracellular vesicles isolated from saliva of GBM patients. L = Molecular weight ladder and U251MG = commercial GBM cell line. Fl = postoperative sample. [0043] Figure 2 is a diagrammatic and graphical representation showing proteomic profiling of small extracellular vesicles in pre and postoperative saliva samples from glioblastoma patients. A) The study workflow. Following isolation of small EVs, proteins were aliquoted and protein digestion was performed. The peptides were concentrated, and mass spectrometry was performed for data-dependent and data-independent acquisition. Protein Pilot software was used for peptide identification. Bioinformatics analyses were performed and correlated with clinical information from patients. B) Venn diagram of identified proteins in salivary EVs pre and postoperative C) Venn diagram of all proteins identified in salivary small EV samples of GBM patients compared to proteins annotated in two EV databases, Exocarta and Vesiclepedia. D) Volcano plot identifying proteins from salivary small EVs by their Iog2 fold changes (log2FC) against their corresponding adjusted p-value in patients before and after surgery. Red and blue dots represent proteins with significantly higher and lower abundance, respectively.
[0044] Figure 3 is a graphical representation showing the performance of partial least squares discriminant analysis (PLS-DA) to stratify patients into two groups: (1) patients with favorable outcomes (no disease recurrence, no disease progression or no death from disease within and/or after six months from diagnosis of GBM); and (2) patients with unfavorable outcomes (disease recurrence, disease progression or death from disease within six months from diagnosis of GBM). (A) Partial least squares-discriminant analysis (PLS-DA) score plot of proteome signatures in salivary exosomes from GBM patients with good prognosis (Blue) and poor prognosis (Red) before surgery. (B) Volcano plot identifying proteins from salivary exosomes by their Iog2 fold changes against their corresponding p-value in patients before surgery. Red dots represent more abundant proteins while blue dots correspond to the less abundant proteins, and the grey dots represent the unaltered proteins.
[0045] Figure 4 is a graphical representation showing ROC curves for individual biomarkers. Receiver Operating Characteristics (ROCs) are shown for H4_ HUMAN, LKHA4_ HUMAN and KLK1_HUMAN proteins in preoperative salivary EVs from GBM patients with favorable and unfavorable outcomes.
[0046] Figure 5 is a graphical representation showing a ROC curve for a 3-protein biomarker panel (H4JHUMAN + LKHA4_ HUMAN + KLK1_ HUMAN) in preoperative salivary EVs from GBM patients with favorable and unfavorable outcomes. A multivariate ROC curve was generated to evaluate the prognostic performance of the 3 proteins panel.
[0047] Figure 6 is a graphical representation showing a box and whisker plot of normalized protein abundance of the 3-protein panel pre-operatively. A logistic regression predictive model was applied to the candidate biomarkers to calculate a predictive score for each individual sample and plotted into the groups analyzed (patients with favorable outcomes and patients with unfavorable outcomes).
[0048] Figure 7 is a graphical representation showing A) Size of salivary small extracellular vesicles of GBM patients with favorable and unfavorable outcomes in pre and postoperative samples. B) Concentration of salivary small extracellular vesicles of GBM patients with favorable and unfavorable outcomes in pre and postoperative samples. The Mann-Whitney test (GraphPad Prism) was used to determine significance, p*<0.05. Partial least squares- discriminant analysis (PLS-DA) score plots of proteome signatures in salivary small EVs from GBM patients with favorable prognosis (blue) and unfavorable prognosis (red) C) pre and D) postoperative. Volcano plots of all proteins from salivary small EVs of patients with unfavorable and favorable prognoses E) pre and F) postoperative. Plots correspond to proteins' Iog2 fold changes against their corresponding adjusted p-value. Red dots represent more abundant proteins, while blue dots correspond to less abundant proteins.
[0049] Figure 8 is a graphical representation showing A) Box and whisker plots of normalized protein abundance of four protein biomarker candidates preoperatively, ALDOA, 1433E, ECHI and TM11B. The Mann-Whitney test (GraphPad Prism) was used to determine significance, p*<0.05 p**<0.01. B) Verification of ALDOA using an independent method, western blotting, in patients with favorable (numbers colored in blue) and unfavorable (numbers colored in red) prognoses. C) Receiver operator characteristic (ROC) curve analysis of ALDOA in preoperative salivary EVs from GBM patients with favorable and unfavorable outcomes.
[0050] Figure 9 is a graphical representation showing A-D) Receiver operator characteristic (ROC) curve analysis of ALDOA, ECHI. TM11B, 1433E individually in preoperative salivary EVs from GBM patients with favorable and unfavorable outcomes. E) Receiver operator characteristic (ROC) curve analysis of ALDOA, ECHI. TM11B, 1433E in combination in preoperative salivary EVs from GBM patients with favorable and unfavorable outcomes.
[0051] Some figures and text contain color representations or entities. Color illustrations are available from the Applicant upon request or from an appropriate Patent Office. A fee may be imposed if obtained from a Patent Office.
DETAILED DESCRIPTION
1. Definitions
[0052] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the disclosure belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present disclosure, preferred methods and materials are described. For the purposes of the present disclosure, the following terms are defined below.
[0053] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, preferred methods and materials are described. For the purposes of the present invention, the following terms are defined below.
[0054] The articles "a" and "an" are used herein to refer to one or to more than one (/.e., to at least one) of the grammatical object of the article. By way of example, "an element" means one element or more than one element.
[0055] The term "about" as used herein refers to the usual error range for the respective value readily known to the skilled person in this technical field. Reference to "about" in connection with a value or parameter herein includes (and describes) embodiments that are directed to that value or parameter per se. In specific embodiments, the term "about" refers to a value or parameter (e.g., quantity, level, concentration, number, frequency, percentage, dimension, size, amount, weight or length) that varies by as much 15, 14, 13, 12, 11, 10, 9, 8, 7, 5, 5, 4, 3, 2 or 1 % to a reference value or parameter.
[0056] The "amount", "level" or "abundance" of a biomarker is a detectable level, amount or abundance in a sample. These can be measured by methods known to one skilled in the art and also disclosed herein. These terms encompass a quantitative amount, abundance or level (e.g., weight or moles), a semi-quantitative amount, abundance or level, a relative amount, abundance or level (e.g., weight % or mole % within class), a concentration, and the like. Thus, these terms encompass absolute or relative amounts, abundances or levels or concentrations of a biomarker in a sample.
[0057] The term "more aggressive" refers to a treatment regimen that may include more drugs or drugs with more severe side effects and/or it may include an increased dosage or increased frequency of drugs. It may also include radiation or a combination of therapies. In some cases, the therapy includes one or more chemotherapeutics and/or biologies. In some embodiments, the patient is treated with a therapy comprising an anti-angiogenic agent. In additional embodiments, the therapy further comprises a chemotherapeutic agent in addition to the anti-angiogenic agent.
[0058] As used herein, "and/or" refers to and encompasses any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations when interpreted in the alternative (or).
[0059] The term "antibody", as used herein, means any antigen-binding molecule or molecular complex comprising at least one complementarity determining region (CDR) that binds specifically to or interacts with a particular antigen (e.g., one of LKHA4, H4, KLK1, ALDOA, 1433E, TM11B and ECHI). The term "antibody" includes immunoglobulin molecules comprising four polypeptide chains, two heavy (H) chains and two light (L) chains inter-connected by disulfide bonds, as well as multimers thereof (e.g., IgM). Each heavy chain comprises a heavy chain variable region (which may be abbreviated as HCVR or VH) and a heavy chain constant region. The heavy chain constant region comprises three domains, CHI, CH2 and CH3. Each light chain comprises a light chain variable region (which may be abbreviated as LCVR or V ) and a light chain constant region. The light chain constant region comprises one domain (CLI). The VH and VL regions can be further subdivided into regions of hypervariability, termed complementarity determining regions (CDRs), interspersed with regions that are more conserved, termed framework regions (FR). Each VH and V is composed of three CDRs and four FRs, arranged from amino-terminus to carboxy-terminus in the following order: FR1, CDR1, FR2, CDR2, FR3, CDR3, FR4. In different embodiments of the invention, the FRs of an antibody of the invention (or antigen-binding portion thereof) may be identical to the human germline sequences, or may be naturally or artificially modified. An amino acid consensus sequence may be defined based on a side-by-side analysis of two or more CDRs. An antibody includes an antibody of any class, such as IgG, IgA, or IgM (or sub-class thereof), and the antibody need not be of any particular class. Depending on the antibody amino acid sequence of the constant region of its heavy chains, immunoglobulins can be assigned to different classes. There are five major classes of immunoglobulins: IgA, IgD, IgE, IgG, and IgM, and several of these may be further divided into subclasses (isotypes), e.g., IgGl, IgG2, IgG3, IgG4, IgAl and IgA2. The heavy-chain constant regions that correspond to the different classes of immunoglobulins are called a, 6, e, y, and p, respectively. The subunit structures and three-dimensional configurations of different classes of immunoglobulins are well known.
[0060] As used herein, the term "antigen" and its grammatically equivalents expressions (e.g., "antigenic") refer to a compound, composition, or substance that may be specifically bound by the products of specific humoral or cellular immunity, such as an antibody molecule or T-cell receptor. Antigens can be any type of molecule including, for example, haptens, simple intermediary metabolites, sugars (e.g., oligosaccharides), lipids, and hormones as well as macromolecules such as complex carbohydrates (e.g., polysaccharides), phospholipids, and proteins.
[0061] The terms "antigen-binding fragment", "antigen-binding portion", "antigenbinding domain" and "antigen-binding site" are used interchangeably herein to refer to a part of an antigen-binding molecule that participates in antigen-binding. These terms include any naturally occurring, enzymatically obtainable, synthetic, or genetically engineered polypeptide or glycoprotein that specifically binds an antigen to form a complex. Antigen-binding fragments of an antibody may be derived, e.g., from full antibody molecules using any suitable standard techniques such as proteolytic digestion or recombinant genetic engineering techniques involving the manipulation and expression of DNA encoding antibody variable and optionally constant domains. Such DNA is known and/or is readily available from, e.g., commercial sources, DNA libraries (including, e.g., phage-antibody libraries), or can be synthesized. The DNA may be sequenced and manipulated chemically or by using molecular biology techniques, for example, to arrange one or more variable and/or constant domains into a suitable configuration, or to introduce codons, create cysteine residues, modify, add or delete amino acids, etc. Non-limiting examples of antigen-binding fragments include: (i) Fab fragments; (ii) F(ab')2 fragments; (Hi) Fd fragments; (iv) Fv fragments; (v) single-chain Fv (scFv) molecules; (vi) dAb fragments; and (vii) minimal recognition units consisting of the amino acid residues that mimic the hypervariable region of an antibody (e.g., an isolated complementarity determining region (CDR) such as a CDR3 peptide), or a constrained FR3-CDR3-FR4 peptide. Other engineered molecules, such as domain-specific antibodies, single domain antibodies, domain-deleted antibodies, chimeric antibodies, CDR-grafted antibodies, one- armed antibodies, diabodies, triabodies, tetrabodies, minibodies, nanobodies (e.g. monovalent nanobodies, bivalent nanobodies, etc.), small modular immunopharmaceuticals (SMIPs), and shark variable IgNAR domains, are also encompassed within the expression "antigen-binding fragment," as used herein.
[0062] By "antigen-binding molecule" is meant a molecule that has binding affinity for a target antigen. It will be understood that this term extends to immunoglobulins, immunoglobulin fragments and non-immunoglobulin derived protein frameworks that exhibit antigen-binding activity. Representative antigen-binding molecules that are useful in the practice of the present invention include antibodies and their antigen-binding fragments. The term "antigen-binding molecule" includes antibodies and antigen-binding fragments of antibodies.
[0063] As used herein, the term "array" refers to an arrangement of capture reagents on a substrate, in which individual capture reagents bind specifically to a particular molecule (e.g., protein or antigen). In preferred embodiments, the capture reagents are antibodies or antigenbinding fragments.
[0064] As used herein, the term "biomarker" refers to a naturally occurring biological molecule present in a subject at varying concentrations useful in predicting an outcome of a disease or a condition, such as GBM. For example, the biomarker can be a protein present in higher or lower amounts in salivary EVs of a patient with GBM. In certain embodiments, the biomarker is a protein selected from LKHA4, H4 and KLK1. In other embodiments, the biomarker is a protein selected from ALDOA, 1433E, TM11B and ECHI.
[0065] The term "biomarker value" refers to a value measured or functionalized for at least one corresponding biomarker of a subject and which is typically indicative of an abundance or concentration of a biomarker in a sample obtained from the subject. Thus, the biomarker values could be measured biomarker values, which are values of biomarkers measured for the subject. These values may be quantitative or qualitative. For example, a measured biomarker value may refer to the presence or absence of a biomarker or may refer to an amount, level or abundance of a biomarker in a sample. The measured biomarker values can be values relating to raw or normalized biomarker levels (e.g., a raw, non-normalized biomarker level, or a normalized biomarker levels that is determined relative to an internal or external control biomarker level) and to mathematically transformed biomarker levels. Alternatively, the biomarker values could be functionalized biomarker values, which are values that have been functionalized from one or more measured biomarker values, for example by applying a function to the one or more measured biomarker values. Biomarker values can be of any appropriate form depending on the manner in which the values are determined. For example, the biomarker values could be determined using high-throughput technologies such as mass spectrometry, sequencing platforms, array and hybridization platforms, immunoassays, flow cytometry, or any combination of such technologies and in representative examples, the biomarker values relate to a level of activity or abundance of an expression product or other measurable molecule, quantified using a nucleic acid assay such as real-time polymerase chain reaction (RT-PCR), sequencing or the like.
[0066] The terms "biomarker signature", "signature", "biomarker panel", "panel" and the like are used interchangeably herein and refer to one or a combination of biomarkers whose expression is an indicator, e.g., predictive, diagnostic, and/or prognostic. A biomarker signature may comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or more biomarkers. A biomarker signature can further comprise one or more controls or internal standards. In certain embodiments, a biomarker signature comprises at least one biomarker, or indication thereof, that serves as an internal standard. In other embodiments, a biomarker signature comprises an indication of one or more types of biomarkers. The term "indication" as used herein in this context merely refers to a situation where the biomarker signature contains symbols, data, abbreviations or other similar indicia for a biomarker, rather than the biomarker molecular entity itself. The term "biomarker signature" is also used herein to refer to a biomarker value or combination of at least two biomarker values, wherein individual biomarker values correspond to values of biomarkers that can be measured or functionalized from one or more subjects, which combination is characteristic of a discrete condition, stage of condition, subtype of condition or a prognosis for a discrete condition, stage of condition, subtype of condition. The term "signature biomarkers" is used to refer to a subset of the biomarkers that have been identified for use in a biomarker signature that can be used in performing a clinical assessment, such as to rule in or rule out a specific condition, different stages or severity of conditions, subtypes of different conditions or different prognoses. The number of signature biomarkers will vary, but is typically of the order of 16 or less (e.g., 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2 or 1).
[0067] As use herein, the term "binds", "specifically binds to" or is "specific for" refers to measurable and reproducible interactions such as binding between a target and an antibody, which is determinative of the presence of the target in the presence of a heterogeneous population of molecules including biological molecules. For example, an antibody that binds to or specifically binds to a target (which can be an epitope) is an antibody that binds this target with greater affinity, avidity, more readily, and/or with greater duration than it binds to other targets. In one embodiment, the extent of binding of an antibody to an unrelated target is less than about 10% of the binding of the antibody to the target as measured, e.g., by ELISA or radioimmunoassay (RIA). In certain embodiments, an antibody that specifically binds to a target has a dissociation constant (Kd) of <1 pM, <100 nM, <10 nM, <1 nN, or <0.1 nM. In certain embodiments, an antibody specifically binds to an epitope on a protein that is conserved among the protein from different species. In another embodiment, specific binding can include, but does not require exclusive binding.
[0068] Throughout this specification, unless the context requires otherwise, the words "comprise," "comprises" and "comprising" will be understood to imply the inclusion of a stated step or element or group of steps or elements but not the exclusion of any other step or element or group of steps or elements. Thus, use of the term "comprising" and the like indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present. By "consisting of" is meant including, and limited to, whatever follows the phrase "consisting of". Thus, the phrase "consisting of" indicates that the listed elements are required or mandatory, and that no other elements may be present. By "consisting essentially of" is meant including any elements listed after the phrase, and limited to other elements that do not interfere with or contribute to the activity or action specified in the disclosure for the listed elements. Thus, the phrase "consisting essentially of" indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present depending upon whether or not they affect the activity or action of the listed elements.
[0069] As used herein, the term "composite score" refers to an aggregation of the obtained values for biomarkers measured in a sample from a subject, optionally in combination with one or more patient clinical parameters or signs. In some embodiments, the obtained biomarker values are normalized to provide a composite score for each subject tested. When used in the context of a risk categorization table and correlated to a stratified population grouping or cohort population grouping based on a range of composite scores in a risk categorization table, the "biomarker composite score" may be used, at least in part, by a machine learning system to determine the "risk score" for each subject tested wherein the numerical value (e.g., a multiplier, a percentage, etc.) indicating increased likelihood of having a disclosed prognosis for the stratified grouping becomes the "risk score".
[0070] As used herein, the term "correlates" or "correlates with" and like terms, refers to a statistical association between two or more things, such as events, characteristics, outcomes, numbers, data sets, etc., which may be referred to as "variables". It will be understood that the things may be of different types. Often the variables are expressed as numbers (e.g., measurements, values, likelihood, risk), wherein a positive correlation means that as one variable increases, the other also increases, and a negative correlation (also called anti-correlation) means that as one variable increases, the other variable decreases. In various embodiments, correlating a biomarker or biomarker signature with a prognosis (e.g., an unfavorable outcome selected from disease recurrence, disease progression and death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, or a favorable outcome selected from no disease recurrence, no disease progression, no death from disease/cancer-free survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM) comprises determining the abundance, level or amount of at least one protein biomarker in a salivary EV sample from a GBM patient, preferably a GBM patients after at therapeutic regimen for treating GBM; or in persons known to be free of that condition or prognosis. In specific embodiments, a profile of biomarker levels, absences or presences is correlated to a global probability or a particular outcome, using receiver operating characteristic (ROC) curves.
[0071] The term "cut-off value" as used herein is an abundance, level or amount (or concentration) which may be an absolute level or a relative abundance, level or amount (or concentration), which is indicative of whether a GBM patient has a particular prognosis (e.g., an unfavorable outcome selected from disease recurrence, disease progression and death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, or a favorable outcome selected from no disease recurrence, no disease progression and no death from disease/cancer-free survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM). Depending on the biomarker or combination of biomarkers, a GBM patient is regarded as having a particular prognosis, if either the level of the biomarker(s) detected and determined, respectively, is lower than the cut-off value, or the level of the biomarker(s) detected and determined, respectively, is higher than the cut-off value.
[0072] As used herein, the terms "detectably distinct" and "detectably different" are used interchangeably to refer to a signal that is distinguishable or separable by a physical property either by observation or by instrumentation. For example, a fluorophore is readily distinguishable either by spectral characteristics or by fluorescence intensity, lifetime, polarization or photobleaching rate from another fluorophore in a sample, as well as from additional materials that are optionally present. In certain embodiments, the terms "detectably distinct" and "detectably different" refer to a set of labels (such as dyes, suitably organic dyes) that can be detected and distinguished simultaneously.
[0073] As used herein, the phrase "developing a classifier" refers to using input variables to generate an algorithm or classifier capable of distinguishing between two or more prognostic outcomes (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, or a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM).
[0074] The term "differentially expressed" refers to differences in the quantity and/or the frequency of a biomarker present in a sample obtained from patients having, for example, a first prognosis (e.g., an unfavorable outcome selected from disease recurrence, disease progression and death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM) as compared to subjects with a second prognosis (e.g., a favorable outcome selected from no disease recurrence, no disease progression and no death from disease/cancer-free survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM). A biomarker can be differentially present in terms of quantity, frequency or both.
[0075] The term "discrimination performance" refers to numeric representation of the index including, for example, sensitivity, specificity, positive predictability, negative predictability or accuracy. The term "discrimination performance" may also refer to a value computed by the functions of the indexes. For example, sensitivity, specificity, positive predictive value, negative predictive value and accuracy may each be used as the discrimination performance, or alternatively, the sum of two or more indexes, e.g., the sum of sensitivity and specificity, the sum of sensitivity and positive predictive value, or the sum of negative predictive value and accuracy, may be used as the discrimination performance.
[0076] As used herein, the term "exosomes" refers to vesicles of tens to hundreds of nanometers in size (suitably, less than about 200 nm, more suitably from about 30 nm to about 200 nm), which comprise a phospholipid bilayer membrane having the same structure as that of the cell membrane. Exosomes may contain proteins, nucleic acids (mRNA, miRNA, etc.) and the like which are called exosome cargo. It is known that exosome cargo includes a wide range of signaling factors, and these signaling factors are specific for cell types and regulated differently depending on secretory cells environment. It is known that exosomes are intercellular signaling mediators secreted by cells, and various cellular signals transmitted through them regulate cellular behaviors, including the activation, growth, migration, differentiation, dedifferentiation, apoptosis, and necrosis of target cells.
[0077] As used herein the term "extracellular vesicles" ("EVs") refers to membranous microvesicles that may be shed by eukaryotic cells, or budded off of the plasma membrane, to the exterior of the cell. These membrane vesicles are heterogeneous in size with diameters ranging from about 10 nm to about 5000 nm, more typically between 30 nm and 1000 nm, and most typically between about 50 nm and 750 nm. In certain embodiments, the EVs have a diameter ranging from 30 nm to about 200 nm (also referred to herein as "small EVs"). Most commonly, EVs will have a size (average diameter) that is up to 5% of the size of the donor cell. Therefore, especially contemplated EVs include those that are shed from a cell. EVs encompassed by the present disclosure include microvesicles, microvesicle-like particles, prostasomes, dexosomes, texosomes, ectosomes, oncosomes, apoptotic bodies, retrovirus-like particles, and human endogenous retrovirus (HERV) particles and any other terms that refer to such extracellular structures. In specific embodiments, the EVs are exosomes which are purified or are otherwise obtained from saliva (/.e., "salivary exosomes").
[0078] "Fluorophore" as used herein to refer to a moiety that absorbs light energy at a defined excitation wavelength and emits light energy at a different defined wavelength. Examples of fluorescence labels include, but are not limited to: Alexa Fluor dyes (Alexa Fluor 350, Alexa Fluor 488, Alexa Fluor 532, Alexa Fluor 546, Alexa Fluor 568, Alexa Fluor 594, Alexa Fluor 633, Alexa Fluor 660 and Alexa Fluor 680), AMCA, AMCA-S, BODIPY dyes (BODIPY FL, BODIPY R6G, BODIPY TMR, BODIPY TR, BODIPY 530/550, BODIPY 558/568, BODIPY 564/570, BODIPY 576/589, BODIPY 581/591, BODIPY 630/650, BODIPY 650/665), Carboxyrhodamine 6G, carboxy-X-rhodamine (ROX), Cascade Blue, Cascade Yellow, Cyanine dyes (Cy3, Cy5, Cy3.5, Cy5.5), Dansyl, Dapoxyl, Dialkylaminocoumarin, 4l,5'-Dichloro-2l,7'-dimethoxy-fluorescein, DM-NERF, Eosin, Erythrosin, Fluorescein, FAM, Hydroxycoumarin, IRDyes (IRD40, IRD 700, IRD 800), JOE, Lissamine rhodamine B, Marina Blue, Methoxycoumarin, Naphthofluorescein, Oregon Green 488, Oregon Green 500, Oregon Green 514, Pacific Blue, PyMPO, Pyrene, Rhodamine 6G, Rhodamine Green, Rhodamine Red, Rhodol Green, 2',4',5',7'-Tetra-bromosulfone-fluorescein, Tetramethyl-rhodamine (TMR), Carboxytetramethylrhodamine (TAMRA), Texas Red and Texas Red-X.
[0079] As used herein, the terms "glioblastoma", "glioblastoma multiforme" and "GBM" are used interchangeably herein to refer to the most common and aggressive primary malignant adult tumor of the central nervous system. Glioblastoma may be located anywhere in the brain or spinal cord, but is typically found in the cerebral hemispheres of the brain. [0080] As used herein, the term "higher" with reference to a biomarker measurement refers to a statistically significant and measurable difference in the level of a biomarker compared to the level of another biomarker or to a control level where the biomarker measurement is greater than the level of the other biomarker or the control level. The difference is suitably at least about 10%, or at least about 20%, or of at least about 30%, or of at least about 40%, or at least about 50%.
[0081] As used herein, the term "increase" or "increased' with reference to a biomarker level refers to a statistically significant and measurable increase in the biomarker level compared to the level of another biomarker or to a control level. The increase is suitably an increase of at least about 10%, or an increase of at least about 20%, or an increase of at least about 30%, or an increase of at least about 40%, or an increase of at least about 50%.
[0082] The term "indicator" as used herein refers to a result or representation of a result, including any information, number (e.g., biomarker value including functionalized biomarker value and composite score), ratio, signal, sign, mark, or note by which a skilled artisan can estimate and/or determine a likelihood or risk of whether or not a subject is suffering from a given disease or condition. In the case of the present invention, the "indicator" may optionally be used together with other clinical characteristics, to arrive at a prognosis for a GBM patient. That such an indicator is "determined" is not meant to imply that the indicator is 100% accurate. The skilled clinician may use the indicator together with other clinical parameters or signs to arrive at a diagnosis.
[0083] As used herein, "instructional material" includes a publication, a recording, a diagram, or any other medium of expression which can be used to communicate the usefulness of the compositions and methods of the disclosure. The instructional material of the kit of the disclosure may, for example, be affixed to a container which contains the therapeutic or diagnostic agents of the disclosure or be shipped together with a container which contains the therapeutic or diagnostic and/or prognostic agents of the disclosure.
[0084] The term "label" is used herein in a broad sense to refer to an agent that is capable of providing a detectable signal, either directly or through interaction with one or more additional members of a signal producing system and that has been artificially added, linked or attached via chemical manipulation to a molecule. Labels can be visual, optical, photonic, electronic, acoustic, optoacoustic, by mass, electro-chemical, electro-optical, spectrometry, enzymatic, or otherwise chemically, biochemically hydrodynamically, electrically or physically detectable. Labels can be, for example tailed reporter, marker or adapter molecules. In specific embodiments, a molecule such as a nucleic acid or proteinaceous molecule is labeled with a detectable molecule selected form the group consisting of radioisotopes, fluorescent compounds, bioluminescent compounds, chemiluminescent compounds, metal chelators or enzymes. Examples of labels include, but are not limited to, the following radioisotopes (e.g., 3H, 14C, 35S, 125I, 131I), fluorescent labels (e.g., FITC, rhodamine, lanthanide phosphors), luminescent labels such as luminol; enzymatic labels (e.g., horseradish peroxidase, beta-galactosidase, luciferase, alkaline phosphatase, acetylcholinesterase), biotinyl groups (which can be detected by marked avidin, e.g., streptavidin containing a fluorescent marker or enzymatic activity that can be detected by optical or calorimetric methods), predetermined polypeptide epitopes recognized by a secondary reporter (e.g., leucine zipper pair sequences, binding sites for secondary antibodies, metal binding domains, epitope tags). [0085] As used herein, the term "lower" with reference to a biomarker measurement refers to a statistically significant and measurable difference in the level of a biomarker compared to the level of another biomarker or to a control level where the biomarker measurement is less than the level of the other biomarker or the control level. The difference is suitably at least about 10%, or at least about 20%, or of at least about 30%, or of at least about 40%, or at least about 50%.
[0086] As used herein, the term "normalization" and its derivatives, when used in conjunction with measurement of biomarkers across samples and time, refer to mathematical methods, including but not limited to multiple of the median (MoM), standard deviation normalization, sigmoidal normalization, etc., where the intention is that these normalized values allow the comparison of corresponding normalized values from different datasets in a way that eliminates or minimizes differences and gross influences.
[0087] As used herein, the term "obtained" refers to come into possession. Samples so obtained include, for example, protein extracts isolated or derived from a particular source (e.g., EVs).
[0088] The term "predictive" and grammatical forms thereof, generally refer to a biomarker or biomarker signature that provides a means of identifying, directly or indirectly, a likelihood of a patient responding to a therapy or obtaining a clinical outcome in response to therapy.
[0089] The term "prognosis" as used herein refers to a prediction of the probable course and outcome of a clinical condition or disease. A prognosis is usually made by evaluating factors or symptoms of a disease that are indicative of a favorable or unfavorable course or outcome of the disease. The skilled artisan will understand that the term "prognosis" refers to an increased probability that a certain course or outcome (e.g., disease recurrence, no disease recurrence, disease progression, no disease progression, death, survival, etc.) will occur; that is, that a course or outcome is more likely to occur in a subject exhibiting a given condition, when compared to those individuals not exhibiting the condition. In some embodiments, prognosis also refers to the ability to demonstrate a positive or negative response to therapy or other treatment regimens, for the disease or condition in the subject. In some embodiments, prognosis refers to the ability to predict the presence or diminishment of disease/condition associated symptoms. A prognostic agent or method may comprise classifying a subject or sample obtained from a subject into one of multiple categories, wherein the categories correlate with different likelihoods that a subject will experience a particular outcome. For example, categories can be low risk and high risk, wherein subjects in the low risk category have a lower likelihood of experiencing a poor outcome (e.g., within a given time period such as 6 months, 9 months, 12 months or 18 months, or 2, 3, 4, 5, 5, 7, 8, 9 or 10 years) than do subjects in the high risk category. A poor outcome could be, for example, disease progression, disease recurrence, or death attributable to the disease.
[0090] "Protein", "polypeptide" and "peptide" are used interchangeably herein to refer to a polymer of amino acid residues and to variants or synthetic analogues of the same.
[0091] As used herein, the term "reduce" or "reduced" with reference to a biomarker level refers to a statistically significant and measurable reduction in the biomarker level compared to the level of another biomarker or to a control level. The reduction is suitably a reduction of at least about 10%, or a reduction of at least about 20%, or a reduction of at least about 30%, or a reduction of at least about 40%, or a reduction of at least about 50%. [0092] As used herein, a cancer patient who has been treated with a therapy is considered to "respond", have a "response", have "a positive response" or be "responsive" to the therapy if the subject shows evidence of an anti-cancer effect according to an art-accepted set of objective criteria or reasonable modification thereof, including a clinically significant benefit, such as the prevention, or reduction of severity, of symptoms, or a slowing of the progression of the cancer. By contrast, a cancer patient who has been treated with a therapy is considered "not to respond", "to lack a response", to have "a negative response" or be "non-responsive" to the therapy if the therapy provides no clinically significant benefit, such as the prevention, or reduction of severity, of symptoms, or increases the rate of progression of the cancer.
[0093] The term "saliva sample" as used herein includes any biological specimen that may be extracted, untreated, treated, diluted or concentrated from a sample of saliva obtained from a subject. The term "saliva sample" includes saliva obtained from within the mouth, saliva obtained as spit, and saliva obtained from an oral rinse with a sampling fluid, such as sterile water.
[0094] The term "solid support" as used herein refers to a solid inert surface or body to which a molecular species, such as a nucleic acid and polypeptides can be immobilized. Nonlimiting examples of solid supports include glass surfaces, plastic surfaces, latex, dextran, polystyrene surfaces, polypropylene surfaces, polyacrylamide gels, gold surfaces, and silicon wafers. In some embodiments, the solid supports are in the form of membranes, chips or particles. For example, the solid support may be a glass surface (e.g., a planar surface of a flow cell channel). In some embodiments, the solid support may comprise an inert substrate or matrix which has been "functionalized", such as by applying a layer or coating of an intermediate material comprising reactive groups which permit covalent attachment to molecules such as polynucleotides. By way of non-limiting example, such supports can include polyacrylamide hydrogels supported on an inert substrate such as glass. The molecules (e.g., polynucleotides) can be directly covalently attached to the intermediate material (e.g., a hydrogel) but the intermediate material can itself be non-covalently attached to the substrate or matrix (e.g., a glass substrate). The support can include a plurality of particles or beads each having a different attached molecular species.
[0095] By "treatment" and "treating" is meant the medical management of a subject with the intent to cure, ameliorate, stabilize, or prevent a disease, pathological condition, or disorder. This term includes active treatment, that is, treatment directed specifically toward the improvement of a disease, pathological condition, or disorder, and also includes causal treatment, that is, treatment directed toward removal of the cause of the associated disease, pathological condition, or disorder. In addition, this term includes palliative treatment, that is, treatment designed for the relief of symptoms rather than the curing of the disease, pathological condition, or disorder; preventative treatment, that is, treatment directed to minimizing or partially or completely inhibiting the development of the associated disease, pathological condition, or disorder; and supportive treatment, that is, treatment employed to supplement another specific therapy directed toward the improvement of the associated disease, pathological condition, or disorder. It is understood that treatment, while intended to cure, ameliorate, stabilize, or prevent a disease, pathological condition, or disorder, need not actually result in the cure, amelioration, stabilization or prevention. The effects of treatment can be measured or assessed as described herein and as known in the art as is suitable for the disease, pathological condition, or disorder involved. Such measurements and assessments can be made in qualitative and/or quantitative terms. Thus, for example, characteristics or features of a disease, pathological condition, or disorder and/or symptoms of a disease, pathological condition, or disorder can be reduced to any effect or to any amount.
[0096] As used herein, the term "treatment regimen" refers to prophylactic and/or therapeutic (/.e., after onset of a specified condition) treatments, unless the context specifically indicates otherwise. The term "treatment regimen" encompasses natural substances and pharmaceutical agents (i.e., "drugs") as well as any other treatment regimen including but not limited to dietary treatments, physical therapy or exercise regimens, surgical interventions, radiotherapy, chemotherapy, immunotherapy and combinations thereof. Desirable effects of treatment include decreasing the rate of disease progression, ameliorating or palliating the disease state, and remission or improved prognosis. For example, an individual is successfully "treated" if one or more symptoms associated with a cancer are mitigated or eliminated, including, but are not limited to, reducing the proliferation of (or destroying) cancerous cells, reducing pathogen infection, decreasing symptoms resulting from the disease, increasing the quality of life of those suffering from the disease, decreasing the dose of other medications required to treat the disease, and/or prolonging survival of individuals. The phrase "treatment with a therapy", "treating with a therapy", "treatment with an agent", "treating with an agent" and the like refers to the administration of an effective amount of a therapy or agent, including a cancer therapy or agent, (e.g., a cytotoxic agent or an immunotherapeutic agent) to a patient, or the concurrent administration of two or more therapies or agents, including cancer therapies or agents, (e.g., two or more agents selected from cytotoxic agents and immunotherapeutic agents) in effective amounts to a patient.
[0097] It will be appreciated that the terms used herein and associated definitions are used for the purpose of explanation only and are not intended to be limiting.
2. Salivary EV biomarkers for predicting poor outcomes and good outcomes in GBM patients
[0098] Disclosed herein are methods, apparatuses, compositions and kits for aiding prediction of clinical outcomes in GBM patients, which are useful for determining and monitoring prognosis of glioblastoma patients (e.g., before and after exposure to a treatment regimen for treating glioblastoma) and for better managing treatment of those patients.
[0099] The present inventors have determined that certain protein biomarkers are commonly, specifically and differentially expressed in salivary EV samples obtained from GBM patients with favorable outcomes (e.g., no disease recurrence, no disease progression, no death from disease within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM) and GBM patients with unfavorable outcomes (e.g., disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM). The results presented herein provide clear evidence that specific protein biomarkers can be used to differentiate between GBM patients with poor outcomes and those with good outcomes. The protein biomarkers that can be used in the practice of the methods, apparatuses and treatment management methods disclosed herein include: LKHA4, H4 and KLK1 for differentiating between GBM patients with favorable outcomes within and/or after six months from diagnosis of GBM and GBM patients with unfavorable outcomes within six months from diagnosis of GBM (also referred to herein as the "six-month signature biomarkers"); and ALDOA, 1433E, TM11B and ECHI for differentiating between GBM patients with favorable outcomes within and/or after nine months from diagnosis of GBM and GBM patients with unfavorable outcomes within nine months from diagnosis of GBM (also referred to herein as the "nine-month signature biomarkers").
[O1OO] In one aspect, methods are disclosed herein for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, suitably wherein the poor prognosis is disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, and suitably wherein the good prognosis is no disease recurrence, no disease progression or no death from disease within and/or after six months from diagnosis of GBM. These methods generally comprise, consist or consist essentially of: (1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2 or 3 protein biomarkers) in a salivary extracellular vesicle (EV) sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample, and wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (also referred to herein as "LKHA4" or ”LKHA4_HUMAN"), histone H4 (also referred to herein as "H4" or ''H4—HUMAN") and kallikrein-1 (also referred to herein as "KLK1" or "KLK1_ HUMAN"); and (2) determining the indicator using the biomarker value(s).
[0101] Biomarker panels disclosed herein for use in indicator-determining methods according to this aspect typically comprise at least 1, 2 or 3 protein biomarkers. In preferred embodiments, the biomarker panel comprises each of LKHA4, H4 and KLK1.
[0102] Disclosed herein in another aspect are methods for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, suitably wherein the poor prognosis is disease recurrence, disease progression or death from disease within nine months from diagnosis of GBM, and suitably wherein the good prognosis is no disease recurrence, no disease progression or no death from disease within and/or after nine months from diagnosis of GBM. These methods generally comprise, consist or consist essentially of: (1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2, 3 or 4 protein biomarkers) in a salivary extracellular vesicle (EV) sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample, and wherein the at least one protein biomarker is selected from aldolase A (also referred to herein as "ALDOA" or "ALDOA_ HUMAN")), 14-3-3 protein epsilon (also referred to herein as "1433E" or "1433EJHUMAN"), transmembrane protease serine 11B (also referred to herein as "TM11B" or "TM11B_HUMAN") and enoyl CoA hydratase 1 (also referred to herein as "ECHI" or "ECH1_HUMAN"); and (2) determining the indicator using the biomarker value(s).
[0103] Biomarker panels disclosed herein for use in indicator-determining methods according to this aspect typically comprise at least 1, 2, 3 or 4 protein biomarkers. In preferred embodiments, the biomarker panel comprises each of ALDOA, 1433E, TM11B and ECHI.
[0104] Biomarker values that are indicative of the levels of protein biomarkers in EVs obtained from a saliva sample (also referred to herein as a "salivary EV sample") may be obtained by any suitable means known in the art. A saliva sample can be saliva obtained from within the mouth, or obtained as spit. A saliva sample can also be a sample comprising saliva, as obtained by oral rinsing with a sampling rinse fluid, typically, e.g., sterile water, and then collecting the rinse, which then comprises saliva diluted with the rinse fluid.
[0105] Methods of obtaining saliva samples may include but are not limited to forcible ejection from the subject's mouth (e.g., spitting), aspiration, or removal by a swab or other collection tool. In some embodiments, the saliva may be separated into cellular and non-cellular fractions by suitable methods (e.g., centrifugation).
[0106] A saliva sample may be enriched for EVs using standard methods. For example, EVs may be concentrated or isolated from a saliva sample using size exclusion chromatography, density gradient centrifugation, differential centrifugation, nanomembrane ultrafiltration, immunoabsorbent capture, affinity purification, microfluidic separation, or combinations thereof.
[0107] Methods for isolating or enriching EVs can be performed with microfluidic devices, including optionally conducting protein analysis of the isolated exosomes. Microfluidic devices, which may also be referred to as "lab-on-a-chip" systems, biomedical micro-electro- mechanical systems (bioMEMs), or multicomponent integrated systems, can be used for isolating, and analyzing EVs. Such systems miniaturize and compartmentalize processes that allow for isolation (e.g., binding) of EVs, detection of EV protein biomarkers, and/or other processes.
[0108] A microfluidic device can also be used for isolation of EVs through size differential or affinity selection. For example, a microfluidic device can use one more channels for isolating an EV from a saliva sample based on size, or by using one or more binding agents for isolating a EV from a saliva sample. A saliva sample can be introduced into one or more microfluidic channels, which selectively allows the passage of EVs. The selection can be based on a property of the EVs, for example, size, shape, deformability, biomarker profile, or bio-signature. Alternatively, a heterogeneous population of EVs can be introduced into a microfluidic device, and one or more different homogeneous populations of EVs can be obtained. For example, different channels can have different size selections or binding agents to select for different EV populations. Thus, a microfluidic device can isolate a plurality of EVs, wherein at least a subset of the plurality of EVs comprises a different bio-signature from another subset of the plurality of EVs. In some embodiments, the microfluidic device can comprise one or more channels that permit further enrichment or selection of EVs. A population of EVs that has been enriched after passage through a first channel can be introduced into a second channel, which allows the passage of the desired EV population to be further enriched, such as through binding agents present in the second channel.
[0109] Isolation or enrichment of EVs from saliva samples can also be enhanced by use of sonication (e.g., by applying ultrasound), or the use of detergents, other membrane-active agents, or any combination thereof. For example, ultrasonic energy can be applied to a sample, and without being bound by theory, release of EVs from the sample or tissue can be increased, allowing an enriched population of EVs that can be analyzed or assessed from a saliva sample using one or more methods disclosed herein or known in the field.
[0110] Variability in salivary EV sample preparation can be corrected by normalizing the data by, for example, protein content or EV number. In certain embodiments, the sample may be normalized relative to the total protein content in the sample. Total protein content in the sample can be determined using standard procedures, including, without limitation, Bradford assay and the Lowry method. In some embodiments, the sample may be normalized relative to EV number.
[0111] The level of the one or more protein biomarkers may be measured or assessed using any appropriate technique or means known to those of skill in the art. In particular embodiments, the level of a protein biomarker, such as LKHA4, H4, KLK1, ALDOA, 1433E, TM11B or ECHI, is assessed using an antibody-based technique, non-limiting examples of which include immunoassays, such as the enzyme-linked immunosorbent assay (ELISA) and the radioimmunoassay (RIA). A wide range of immunoassay techniques using such an assay format are available, see, e.g., U.S. Pat. Nos. 4,016,043, 4,424,279 and 4,018,653. These include both singlesite and two-site or "sandwich" assays of the non-competitive types, as well as in the traditional competitive binding assays. These assays also include direct binding of a labeled antibody to a target biomarker. ELISAs for measuring the levels of LKHA4, H4, KLK1, ALDOA, 1433E, TM11B or ECHI are available commercially and/or can be readily developed by those skilled in the art using known antibodies specific for LKHA4, H4, KLK1, ALDOA, 1433E, TM11B or ECHI.
[0112] In specific embodiments, where the levels of two or more protein biomarkers are assessed, a multiplex assay, such as a multiplex immunoassay (e.g., multiplex ELISA), can be employed. Multiplex assays include arrays comprising spatially addressed antigen-binding molecules, commonly referred to as antibody arrays, which can facilitate extensive parallel analysis of multiple proteins. Antibody arrays have been shown to have the required properties of specificity and acceptable background. Various methods for the preparation of antibody arrays have been reported (see, e.g., Lopez et al., J. Chromatogr. 2003; 787: 19-27; Cahill, Trends Biotechnol. 2000;7:47-51; U.S. Pat. App. Pub. 2002/0055186; U.S. Pat. App. Pub. 2003/0003599; PCT publication WO 03/062444; PCT publication WO 03/077851; PCT publication WO 02/59601; PCT publication WO 02/39120; PCT publication WO 01/79849; PCT publication WO 99/39210).
[0113] Individual spatially distinct protein-capture agents are typically attached to a support surface, which is generally planar or contoured. Common physical supports include glass slides, silicon, microwells, nitrocellulose or PVDF membranes, and magnetic and other microbeads.
[0114] Particles in suspension can also be used as the basis of multiplex assays and arrays, providing they are coded for identification; systems include color coding for microbeads (e.g., available from Luminex, Bio-Rad and Nanomics Biosystems) and semiconductor nanocrystals (e.g., QDots™, available from Quantum Dots), and barcoding for beads (UltraPlex™, available from Smartbeads) and multimetal microrods (Nanobarcodes™ particles, available from Surromed). Beads can also be assembled into planar arrays on semiconductor chips (e.g., available from LEAPS technology and BioArray Solutions). Where particles are used, individual protein-capture agents are typically attached to an individual particle to provide the spatial definition or separation of the array. The particles may then be assayed separately, but in parallel, in a compartmentalized way, for example in the wells of a microtiter plate or in separate test tubes.
[0115] One illustrative example of a protein-capture array is Luminex™-based multiplex assay, which is a bead-based multiplexing assay, where beads are internally dyed with fluorescent dyes to produce a specific spectral address. Biomolecules (such as an antibody) can be conjugated to the surface of beads to capture biomarkers of interest. Flow cytometric or other suitable imaging technologies known to persons skilled in the art can then be used for characterization of the beads and detection and quantitation of the biomarkers.
[0116] In specific embodiments, multiplex assays use detectably distinct antibodies to distinctly label individual protein biomarkers.
[0117] Other methods for detecting and quantitating protein biomarkers include, but are not limited to, mass spectrometry (MS) methods, including Liquid Chromatography-Mass Spectrometry (LC-MS), Direct Analysis in Real Time Mass Spectrometry (DART MS), SELDI-TOF and MALDI-TOF, gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography-mass spectrometry (HPLC-MS), capillary electrophoresis-mass spectrometry, nuclear magnetic resonance spectrometry, or tandem mass spectrometry (e.g., MS/MS, MS/MS/MS, ESI-MS/MS, etc.). 2.1 Analysis of biomarker data
[0118] Biomarker data may be analyzed by a variety of methods to identify salivary EV protein biomarkers and determine the statistical significance of differences in observed levels of protein biomarkers between test and reference salivary EV samples in order to evaluate whether a GBM patient has a likelihood of a favorable outcome (e.g., no disease recurrence, no disease progression, no death from disease within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM) or an unfavorable outcome (e.g., disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM). For any particular protein biomarker, a distribution of protein biomarker levels or abundances for poor outcome patients and good outcome patients will likely overlap. Under such conditions, a test does not absolutely distinguish the different outcomes with 100% accuracy, and the area of overlap indicates where the test cannot distinguish the poor outcome and the good outcome. A threshold is selected, above which (or below which, depending on how protein biomarker changes with a specified prognosis) the test is considered to be "positive" and below which the test is considered to be "negative." The area under the ROC curve (AUC) provides the C-statistic, which is a measure of the probability that the perceived measurement will allow correct identification of a condition (see, e.g., Hanley et al., Radiology 143: 29-36 (1982)).
[0119] Alternatively, or in addition, thresholds may be established by obtaining an earlier protein biomarker result from the same patient, to which later results may be compared. In these embodiments, the individual in effect acts as their own "control group." For protein biomarkers that decrease inversely with prognostic risk, a decrease over time in the same patient can indicate a worsening of the condition or a failure of a treatment regimen or poor outcome, while an increase over time can indicate remission of the condition or success of a treatment regimen or good outcome.
[0120] In some embodiments, a positive likelihood ratio, negative likelihood ratio, odds ratio, and/or AUC or receiver operating characteristic (ROC) values are used as a measure of a method's ability to prognose patient outcome. As used herein, the term "likelihood ratio" is the probability that a given test result would be observed in a subject with a particular prognostic outcome divided by the probability that that same result would be observed in a patient without the prognostic outcome. Thus, a positive likelihood ratio is the probability of a positive result observed in subjects with the specified prognostic outcome, divided by the probability of a positive results in subjects without the specified prognostic outcome. A negative likelihood ratio is the probability of a negative result in subjects without the specified prognostic outcome divided by the probability of a negative result in subjects with specified prognostic outcome. The term "odds ratio," as used herein, refers to the ratio of the odds of an event occurring in one group (e.g., one of the prognostic outcomes discloses herein) to the odds of it occurring in another group (e.g., another of the disclosed prognostic outcomes), or to a data-based estimate of that ratio. The term "area under the curve" or "AUC" refers to the area under the curve of a receiver operating characteristic (ROC) curve, both of which are well known in the art. AUC measures are useful for comparing the accuracy of a classifier across the complete data range. Classifiers with a greater AUC have a greater capacity to classify unknowns correctly between two groups of interest (e.g., a first disclosed prognostic outcome such as a poor prognosis (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM), and a second disclosed prognostic outcome such as good prognosis (e.g., a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM). ROC curves are useful for plotting the performance of a particular feature e.g., any of the salivary EV protein biomarkers disclosed herein and/or any item clinical parameter or symptom information) in distinguishing or discriminating between two populations (e.g., a first disclosed prognostic outcome and a second disclosed prognostic outcome). Typically, the feature data across the entire population (e.g., subjects with a first disclosed prognostic outcome and subjects with a second disclosed prognostic outcome) are sorted in ascending order based on the value of a single feature. Then, for each value for that feature, the true positive and false positive rates for the data are calculated. The sensitivity is determined by counting the number of cases above the value for that feature and then dividing by the total number of cases. The specificity is determined by counting the number of controls below the value for that feature and then dividing by the total number of controls. Alternatively, specificity may be calculated by ROC curve and threshold value. Although this definition refers to scenarios in which a feature is elevated in one patient group compared to another patient group, this definition also applies to scenarios in which a feature is lower in one patient group compared to the other patient group (in such a scenario, samples below the value for that feature would be counted). ROC curves can be generated for a single feature as well as for other single outputs, for example, a combination of two or more features (e.g., a combination of two or more biomarker values) can be mathematically combined (e.g., added, subtracted, multiplied, etc.) to produce a single value, and this single value can be plotted in a ROC curve. Additionally, any combination of multiple features (e.g., a combination of multiple biomarker values), in which the combination derives a single output value, can be plotted in a ROC curve. These combinations of features may comprise a test. The ROC curve is the plot of the sensitivity of a test against the specificity of the test, where sensitivity is traditionally presented on the vertical axis and specificity is traditionally presented on the horizontal axis. Thus, "AUC ROC values" are equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one. An AUC ROC value may be thought of as equivalent to the Mann-Whitney U test, which tests for the median difference between scores obtained in the two groups considered if the groups are of continuous data, or to the Wilcoxon test of ranks.
[0121] In some embodiments, a protein biomarker or a panel of protein biomarkers is selected to discriminate between subjects with a first disclosed prognostic outcome and subjects with a second a first disclosed prognostic outcome and a second disclosed prognostic outcome, with at least about 50%, 55% 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% accuracy or having a C- statistic of at least about 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95.
[0122] In the case of a positive likelihood ratio, a value of 1 indicates that a positive result is equally likely among subjects in both the "first condition" and "second condition" groups; a value greater than 1 indicates that a positive result is more likely in the first condition group; and a value less than 1 indicates that a positive result is more likely in the second condition group. In this context, "first condition" group is meant to refer to a group having one characteristic (e.g., a first disclosed prognostic outcome) and "second condition" group (e.g., a second disclosed prognostic outcome) lacking the same characteristic. In the case of a negative likelihood ratio, a value of 1 indicates that a negative result is equally likely among subjects in both the "first condition" and "second condition" groups; a value greater than 1 indicates that a negative result is more likely in the "first condition" group; and a value less than 1 indicates that a negative result is more likely in the "second condition" group. In the case of an odds ratio, a value of 1 indicates that a positive result is equally likely among subjects in both the "first condition" and "second condition" groups; a value greater than 1 indicates that a positive result is more likely in the "first condition" group; and a value less than 1 indicates that a positive result is more likely in the "second condition" group. In the case of an AUC ROC value, this is computed by numerical integration of the ROC curve. The range of this value can be 0.5 to 1.0. A value of 0.5 indicates that a classifier (e.g., a protein biomarker signature) is no better than a 50% chance to classify unknowns correctly between two groups of interest (e.g., a first disclosed prognostic outcome and a second disclosed prognostic outcome disclosed herein), while 1.0 indicates the relatively best diagnostic accuracy. In certain embodiments, individual protein biomarkers and/or protein biomarker panels are selected to exhibit a positive or negative likelihood ratio of at least about 1.5 or more or about 0.67 or less, at least about 2 or more or about 0.5 or less, at least about 5 or more or about 0.2 or less, at least about 10 or more or about 0.1 or less, or at least about 20 or more or about 0.05 or less.
[0123] In certain embodiments, individual protein biomarkers and/or protein biomarker panels are selected to exhibit an odds ratio of at least about 2 or more or about 0.5 or less, at least about 3 or more or about 0.33 or less, at least about 4 or more or about 0.25 or less, at least about 5 or more or about 0.2 or less, or at least about 10 or more or about 0.1 or less.
[0124] In certain embodiments, individual protein biomarkers and/or protein biomarker panels are selected to exhibit an AUC ROC value of greater than 0.5, preferably at least 0.6, more preferably at least 0.7, still more preferably at least 0.8, even more preferably at least 0.9, and most preferably at least 0.95.
[0125] In some cases, multiple thresholds may be determined in so-called "tertile," "quartile," or "quintile" analyses. In these methods, the "diseased" and "control groups" (or "high risk" and "low risk") groups are considered together as a single population, and are divided into 3, 4, or 5 (or more) "bins" having equal numbers of individuals. The boundary between two of these "bins" may be considered "thresholds." A risk (of a particular diagnosis or prognosis for example) can be assigned based on which "bin" a test subject falls into.
[0126] In other embodiments, particular thresholds for the protein biomarker(s) measured are not relied upon to determine if the biomarker level(s) obtained from a subject are correlated to a particular prognosis. For example, a temporal change in the protein biomarker(s) can be used to rule in or out one or more particular prognoses. Alternatively, protein biomarker(s) may be correlated to a prognosis by the presence or absence of one or more protein biomarkers in a particular assay format. In the case of protein biomarker panels, the detection methods disclosed herein may utilize an evaluation of the entire population or subset of protein biomarkers disclosed herein to provide a single result value (e.g., a "panel response" value expressed either as a numeric score or as a percentage risk).
[0127] In certain embodiments, a panel of protein biomarkers is selected to assist in distinguishing a pair of groups (/.e., assist in assessing whether a subject has an increased likelihood of being in one group or the other group of the pair) selected from a "favorable outcome group" (having outcomes selected from no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM) and an "unfavorable outcome group" (having outcomes selected from disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM), or "low risk" and "high risk" with at least about 70%, 80%, 85%, 90% or 95% sensitivity, suitably in combination with at least about 70% 80%, 85%, 90% or 95% specificity. In some embodiments, both the sensitivity and specificity are at least about 75%, 80%, 85%, 90% or 95%.
[0128] The phrases "assessing the likelihood" and "determining the likelihood," as used herein, refer to methods by which the skilled artisan can predict a favorable outcome and an unfavorable outcome in a patient. The skilled artisan will understand that this phrase includes within its scope an increased probability that the patient has one of the disclosed prognoses; that is, that the patient is more likely to have the prognosis. For example, the probability that an individual predicted to have a specified prognosis (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, or a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM) actually has the prognosis may be expressed as a "positive predictive value" or "PPV." Positive predictive value can be calculated as the number of true positives divided by the sum of the true positives and false positives. PPV is determined by the characteristics of the predictive methods disclosed herein as well as the prevalence of the condition in the population analyzed. The statistical algorithms can be selected such that the positive predictive value in a population having a condition prevalence is in the range of 70% to 99% and can be, for example, at least 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 85%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
[0129] In other examples, the probability that an individual predicted as not having a specified prognosis actually does not have that prognosis may be expressed as a "negative predictive value" or "NPV." Negative predictive value can be calculated as the number of true negatives divided by the sum of the true negatives and false negatives. Negative predictive value is determined by the characteristics of the prognostic method as well as the prevalence of the disease in the population analyzed. The statistical methods and models can be selected such that the negative predictive value in a population having a condition prevalence is in the range of about 70% to about 99% and can be, for example, at least about 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
[0130] In some embodiments, a subject is determined as having a significant likelihood of having or not having a specified prognosis (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, or a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM). By "significant likelihood" is meant that the subject has a reasonable probability (0.6, 0.7, 0.8, 0.9 or more) of having, or not having, a specified prognosis.
[0131] The protein biomarker analysis disclosed herein permits the generation of high- density data sets that can be evaluated using informatics approaches. High data density informatics analytical methods are known and software is available to those in the art, e.g., cluster analysis (Pirouette, Informetrix), class prediction (SIMCA-P, Umetrics), principal components analysis of a computationally modeled dataset (SIMCA-P, Umetrics), 2D cluster analysis (GeneLinker Platinum, Improved Outcomes Software), and metabolic pathway analysis (biotech.icmb.utexas.edu). The choice of software packages offers specific tools for questions of interest (Kennedy et al., Solving Data Mining Problems Through Pattern Recognition. Indianapolis: Prentice Hall PTR, 1997; Golub et al., (2999) Science 286:531-7; Eriksson et al., Multi and Megavariate Analysis Principles and Applications: Umetrics, Umea, 2001). In general, any suitable mathematic analyses can be used to evaluate at least one (e.g., 1, 2, 3, 4, etc.) protein biomarker in a population disclosed herein with respect to a disclosed prognosis (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, or a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM). For example, methods such as multivariate analysis of variance, multivariate regression, and/or multiple regression can be used to determine relationships between dependent variables (e.g., clinical measures) and independent variables (e.g., levels of protein biomarkers). Clustering, including both hierarchical and non-hierarchical methods, as well as non-metric Dimensional Scaling can be used to determine associations or relationships among variables and among changes in those variables.
[0132] In addition, principal component analysis is a common way of reducing the dimension of studies, and can be used to interpret the variance-covariance structure of a data set. Principal components may be used in such applications as multiple regression and cluster analysis. Factor analysis is used to describe the covariance by constructing "hidden" variables from the observed variables. Factor analysis may be considered an extension of principal component analysis, where principal component analysis is used as parameter estimation along with the maximum likelihood method. Furthermore, simple hypothesis such as equality of two vectors of means can be tested using Hotelling's T squared statistic.
[0133] In some embodiments, the data sets corresponding to protein biomarker panels disclosed herein are used to create a predictive rule or model based on the application of a statistical and machine learning algorithm. Such an algorithm uses relationships between a protein biomarker panel and a disclosed prognosis (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, or a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM), observed in control subjects or typically cohorts of control subjects (sometimes referred to as training data), which provides combined control or reference protein biomarker panels for comparison with protein biomarker panels of a subject. The data are used to infer relationships that are then used to predict the status of a subject, including the presence or absence of one of the conditions referred to herein.
[0134] Practitioners skilled in the art of data analysis recognize that many different forms of inferring relationships in the training data may be used without materially changing the detection methods disclosed herein. The data presented in the Tables, Examples and Figures herein have been used to generate illustrative minimal combinations of protein biomarkers (models) that differentiate between the disclosed prognoses (/.e., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, and a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM) using feature selection based on AUC maximization in combination with analytical model classification, including for example classification using one or more of: an additive model; a linear model; a support vector machine; a neural network model; a random forest model; a regression model; a genetic algorithm; an annealing algorithm; a weighted sum; a nearest neighbor model; and a probabilistic model. The protein biomarkers disclosed herein provide illustrative lists of protein biomarkers ranked according to their p value. Illustrative models comprising 1, 2, 3 or 4 protein biomarkers were able to develop a classifier or generative algorithm for discriminating between two control groups as defined above with significantly improved positive predictive values compared to conventional methodologies. This algorithm can be advantageously applied to determine presence or probability of one of the conditions or prognoses disclosed herein in a patient, and thus diagnose the patient as having or as likely to have the condition, or prognose the patient as having decreased or poor survival prognosis, or as having increased or good survival prognosis.
[0135] In some embodiments, evaluation of protein biomarkers includes determining the levels of individual protein biomarkers, which correlate with a prognosis, as defined above. In certain embodiments, the techniques used for detection of protein biomarkers may include internal or external standards to permit quantitative or semi-quantitative determination of those biomarkers, to thereby enable a valid comparison of the level of the protein biomarkers in a salivary EV sample with the corresponding protein biomarkers in a reference sample or samples. Such standards can be determined by the skilled practitioner using standard protocols. In specific examples, absolute values for the level or functional activity of individual expression products are determined.
[0136] In semi-quantitative methods, a threshold or cut-off value is suitably determined, and is optionally a predetermined value. In particular embodiments, the threshold value is predetermined in the sense that it is fixed, for example, based on previous experience with the assay and/or a population of affected and/or unaffected subjects. Alternatively, the predetermined value can also indicate that the method of arriving at the threshold is predetermined or fixed even if the particular value varies among assays or may even be determined for every assay run.
[0137] In some embodiments, the level of a protein biomarker is normalized. There is no intended limitation on the methodology used to normalize the values of the measured biomarkers provided that the same methodology is used for testing a human subject sample as was used to generate a risk categorization table or threshold value. Many methods for data normalization exist and are familiar to those skilled in the art. These include methods such as background subtraction, scaling, MoM analysis, linear transformation, least squares fitting, etc. The goal of normalization is to equate the varying measurement scales for the separate biomarkers such that the resulting values may be combined according to a weighting scale as determined and designed by the user or by the machine learning system and are not influenced by the absolute or relative values of the protein biomarker found within nature.
[0138] Composite scores may be calculated using standard statistical analysis well known to one of skill in the art wherein the measurements of each protein biomarker in the panel are combined, optionally with clinical parameters, to provide a probability value. For example, generalized or multivariate logistic regression analysis may be used to derive a mathematical function with a set of variables corresponding to each protein biomarker and optional clinical parameter, which provides a weighting factor for each variable. The weighting factors are derived to optimize the agency of the function to predict the dependent variable, which is the dichotomy of a first prognostic outcome (e.g., unfavorable outcome ) as compared to a second prognostic outcome (e.g., favorable outcome) disclosed herein. The weighting factors are specific to the particular variable combination (e.g., biomarker panel analyzed). The function can then be applied to the original samples to predict a probability of a disclosed condition. In this way, a retrospective data set may be used to provide weighting factors for a particular panel of salivary protein biomarkers, optionally in combination with clinical parameters, which is then used to calculate the probability of a disclosed condition in a patient where the outcome of the condition is unknown or indeterminate prior to screening using the present methods.
[0139] Composite scores may be calculated for example using the statistical methodology disclosed in US Publ. No. 2008/013314 for handling and interpreting data from a multiplex assay. In this methodology, the amount of any one biomarker is compared to a predetermined cut-off distinguishing positive from negative for that biomarker as determined from a control population study of patients with a prognostic outcome (e.g., unfavorable outcome) and suitably matched controls (e.g., patients with an unfavorable outcome) to yield a score for each biomarker based on that comparison; and then combining the scores for each biomarker to obtain a composite score for the biomarker(s) in the sample.
[0140] A predetermined cut-off can be based on ROC curves and the score for each biomarker can be calculated based on the specificity of the biomarker. Then, the total score can be compared to a predetermined total score to transform that total score to a qualitative determination of the likelihood or risk of having a condition as disclosed herein.
[0141] In certain embodiments, the protein biomarkers disclosed herein are measured and those resulting values normalized and then summed to obtain a composite score. In certain aspects, normalizing the measured biomarker values comprises determining the multiple of median (MoM) score. In other aspects, the present method further comprises weighting the normalized values before summing to obtain a composite score. In illustrative examples of this type, the median value of each biomarker is used to normalize all measurements of that specific biomarker, for example, as provided in Kutteh et a/. (Obstet. Gynecol. 1994;84:811-815) and Palomaki et a/. Clin. Chem. Lab. Med 2001;39: 1137-1145). Thus, any measured biomarker level is divided by the median value of a disclosed prognosis group (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, and a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM), resulting in a MoM value. The MoM values can be combined (namely, summed or added) for each biomarker in the panel resulting in a panel MoM value or aggregate MoM score for each sample.
[0142] If desired, a machine learning system may be utilized to determine weighting of the normalized values as well as how to aggregate the values (e.g., determine which protein biomarkers are most predictive, and assign a greater weight to these biomarkers).
[0143] In specific embodiments, a composite score for determining an indicator used in assessing a likelihood of having a disclosed prognostic outcome is determined by a statistical model based on analyzing protein significance by applying a linear mixed-effects model using MSstats, as described previously (Zhang et al., Theranostics. 2017;7(18):4350-8). This analysis consists of quantitative measurements for a targeted protein based on peptides, charge states, transitions, samples, and conditions. The method identifies protein alterations in abundance between conditions more systematically than random chance (Zhang et al., 2017; supra). The protein abundance levels between patients with unfavorable and favorable outcomes were compared using the Mann-Whitney test (GraphPad Prism). A p value < 0.05 was defined as statistically significant.
[0144] In some embodiments, composite scores include one or more clinical parameters or signs of the patient. Representative clinical parameters or signs include age, ethnicity, gender, tumor burden, pain, edema of the brain, frequency or severity of seizures, frequency or severity of vomiting, frequency or severity of headache, memory deficit, neurological deficit, and occurrence of tumor spread or metastasis.
[0145] In certain embodiments, the detection methods utilize a risk categorization table to generate a risk score for a patient based on a composite score by comparing the composite score with a reference set derived from a cohort of patients with one of the prognostic outcomes disclosed herein. The detection methods may further comprise quantifying the increased risk for the presence of a disclosed prognostic outcome in the patient as a risk score, wherein the composite score (combined obtained biomarker value and optionally obtained clinical parameter values) is matched to a risk category of a grouping of stratified patient populations wherein each risk category comprises a multiplier (or percentage) indicating an increased likelihood of having the prognostic outcome correlated to a range of composite scores. This quantification is based on the pre-determined grouping of a stratified cohort of subjects. In some embodiments, the grouping of a stratified population of subjects, or stratification of a prognosis cohort, is in the form of a risk categorization table. The selection of the prognosis cohort, the cohort of subjects that share disclosed prognostic outcome risk factors, are well understood by those skilled in the art of cancer research. However, the skilled person would also recognize that the resulting stratification, may be more multidimensional and take into account further environmental, occupational, genetic, or biological factors (e.g., epidemiological factors).
[0146] After quantifying the increased risk for presence of a disclosed prognostic outcome (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, or a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM) in the form of a risk score, this score may be provided in a form amenable to understanding by a physician. In certain embodiments, the risk score is provided in a report. In certain aspects, the report may comprise one or more of the following: patient information, a risk categorization table, a risk score relative to a cohort population, one or more biomarker test scores, a biomarker composite score, a master composite score, identification of the risk category for the patient, an explanation of the risk categorization table, and the resulting test score, a list of biomarkers tested, a description of the disease cohort, environmental and/or occupational factors, cohort size, biomarker velocity, genetic mutations, family history, margin of error, and so on.
3. Kits
[0147] All the essential reagents required for detecting and quantifying the protein biomarkers disclosed herein may be assembled together in a kit. In some embodiments, the kit comprises a reagent that permits quantification of at least one protein biomarker or each protein biomarker of a biomarker panel disclosed herein. In the context of the present disclosure, "kit" is understood to mean a product containing the different reagents necessary for carrying out the methods of the disclosure packed so as to allow their transport and storage. Additionally, the kits of the present disclosure can contain instructions for the simultaneous, sequential or separate use of the different components contained in the kit. The instructions can be in the form of printed material or in the form of an electronic support capable of storing instructions such that they can be read by a subject, such as electronic storage media (magnetic disks, tapes and the like), optical media (CD-ROM, DVD) and the like. Alternatively or in addition, the media can contain internet addresses that provide the instructions. The kits may contain software for interpreting assay data to determine the likelihood of a GBM patient having a poor prognosis or a good prognosis. In some embodiments, the kits may provide a means to access a machine learning system provided, for example, as a software as a service (SaaS) deployment.
[0148] Reagents that allow quantification of a protein biomarker include compounds or materials, or sets of compounds or materials, which allow quantification of the protein biomarker. In specific embodiments, the compounds, materials or sets of compounds or materials permit determining the level or abundance of a protein biomarker (e.g., a salivary EV protein biomarker disclosed herein) include without limitation the isolation or preparation of EVs from a saliva sample, the determination of the level of a corresponding protein biomarker, etc., antibodies for specifically binding to disclosed protein biomarkers, etc.
[0149] Kit reagents can be in liquid form or can be lyophilized. Suitable containers for the reagents include, for example, bottles, vials, syringes, and test tubes. Containers can be formed from a variety of materials, including glass or plastic. The kit can also comprise a package insert containing written instructions for methods of diagnosing a condition disclosed herein or prognosis patient survival.
[0150] The kits may also optionally include appropriate reagents for detection of labels, positive and negative controls, washing solutions, blotting membranes, microtiter plates, dilution buffers and the like. The kit can also feature various devices (e.g., one or more) and reagents (e.g., one or more) for performing one of the assays described herein; and/or printed instructions for using the kit to quantify at least one protein biomarker disclosed herein and/or carry out an indicator-determining method, as broadly described above and elsewhere herein.
[0151] The reagents described herein, which may be optionally associated with detectable labels, can be presented in the format of a microfluidics card, a reaction vessel, a microarray or a kit adapted for use with the assays described in the examples.
4. Treatment embodiments
[0152] The indicator-determining methods, apparatuses, composition and kits of the present disclosure are useful for managing treatment decision for GBM, including managing the development or progression GBM, in a human subject. In cases where a subject is positively identified as having a poor prognosis (e.g., an unfavorable outcome selected from disease recurrence, disease progression and death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM), for instance after being administered a cancer therapy (e.g., surgery), the patient may be administered an alternative cancer therapy including combination therapy, or with an increased dosage of a cancer therapy agent, or may be placed into palliative care. In certain embodiments, a patient identified as having a poor prognosis may be exposed to a more aggressive treatment regimen for treating GBM (also referred to herein as "GBM therapy"). Representative treatments include: surgery, radiotherapy, chemotherapy and other cancer therapies.
[0153] Radiotherapies include radiation and waves that induce DNA damage for example, -/-irradiation, X-rays, UV irradiation, microwaves, electronic emissions, radioisotopes, and the like. Therapy may be achieved by irradiating the localized tumor site with the above described forms of radiations. It is most likely that all of these factors effect a broad range of damage DNA, on the precursors of DNA, the replication and repair of DNA, and the assembly and maintenance of chromosomes. Dosage ranges for X-rays range from daily doses of 50 to 200 roentgens for prolonged periods of time (3 to 4 weeks), to single doses of 2000 to 6000 roentgens. Dosage ranges for radioisotopes vary widely, and depend on the half-life of the isotope, the strength and type of radiation emitted, and the uptake by the neoplastic cells. Non-limiting examples of radiotherapies include conformal external beam radiotherapy (50-100 Grey given as fractions over 4-8 weeks), either single shot or fractionated, high dose rate brachytherapy, permanent interstitial brachytherapy, systemic radio-isotopes (e.g., Strontium 89). In some embodiments the radiotherapy may be administered in combination with a radiosensitizing agent. Illustrative examples of radiosensitizing agents include but are not limited to efaproxiral, etanidazole, fluosol, misonidazole, nimorazole, temoporfin and tirapazamine.
[0154] Numerous cancer therapy agents exists including the following:
[0155] (1) Chemotherapeutic agents, which may be cytostatic or cytotoxic, non-limiting examples of which include:
[0156] (i) antiproliferative/antineoplastic drugs and combinations thereof, as used in medical oncology, such as alkylating agents (for example cis-platin, carboplatin, cyclophosphamide, nitrogen mustard, melphalan, chlorambucil, busulphan and nitrosoureas); antimetabolites (for example antifolates such as fluoropyridines like 5-fluorouracil and tegafur, raltitrexed, methotrexate, cytosine arabinoside and hydroxyurea; anti-tumor antibiotics (for example anthracyclines like adriamycin, bleomycin, doxorubicin, daunomycin, epirubicin, idarubicin, mitomycin-C, dactinomycin and mithramycin); antimitotic agents (for example vinca alkaloids like vincristine, vinblastine, vindesine and vinorelbine and taxoids like paclitaxel and docetaxel; and topoisomerase inhibitors (for example epipodophyllotoxins like etoposide and teniposide, amsacrine, topotecan and camptothecin);
[0157] (ii) cytostatic agents such as antiestrogens (for example tamoxifen, toremifene, raloxifene, droloxifene and idoxifene), oestrogen receptor down regulators (for example fulvestrant), antiandrogens (for example bicalutamide, flutamide, nilutamide and cyproterone acetate), UH antagonists or LHRH agonists (for example goserelin, leuprorelin and buserelin), progestagens (for example megestrol acetate), aromatase inhibitors (for example as anastrozole, letrozole, vorozole and exemestane) and inhibitors of 5a-reductase such as finasteride; and
[0158] (iii) agents which inhibit cancer cell invasion (for example metalloproteinase inhibitors like marimastat and inhibitors of urokinase plasminogen activator receptor function);
[0159] (2) inhibitors of growth factor function, for example growth factor antibodies, growth factor receptor antibodies (for example the anti-erbb2 antibody trastuzumab [Herceptin™] and the anti-erbbl antibody cetuximab [C225]), farnesyl transferase inhibitors, MEK inhibitors, tyrosine kinase inhibitors and serine/threonine kinase inhibitors, for example other inhibitors of the epidermal growth factor family (for example other EGFR family tyrosine kinase inhibitors such as N-(3-chloro-4-fluorophenyl)-7-methoxy-6-(3-morpholinopropoxy)quinazolin-4-amine (gefitinib, AZD1839), N-(3-ethynylphenyl)-5,7-bis(2-methoxyethoxy)quinazolin-4-amine (erlotinib, OSI-774) and 6-acrylamido-N-(3-chloro-4-fluorophenyl)-7-(3-morpholinopropoxy)quinazoli- n-4-amine (CI 1033)), for example inhibitors of the platelet-derived growth factor family and for example inhibitors of the hepatocyte growth factor family;
[0160] (3) anti-angiogenic agents such as those which inhibit the effects of vascular endothelial growth factor, (for example the anti-vascular endothelial cell growth factor antibody bevacizumab [Avastin™], compounds such as those disclosed in International Patent Applications WO 97/22595, WO 97/30035, WO 97/32855 and WO 98/13354) and compounds that work by other mechanisms (for example linomide, inhibitors of integrin av|33 function and angiostatin);
[0161] (4) vascular damaging agents such as Co mbreta statin A4 and compounds disclosed in International Patent Applications WO 99/02155, WO00/40529, WO 00/41569, WO01/92224, W002/04434 and W002/08213;
[0162] (5) antisense therapies, for example those which are directed to the targets listed above, such as ISIS 2503, an anti-ras antisense; and
[0163] (5) gene therapy approaches, including for example approaches to replace aberrant genes such as aberrant p53 or aberrant GDEPT (gene-directed enzyme pro-drug therapy) approaches such as those using cytosine deaminase, thymidine kinase or a bacterial nitroreductase enzyme and approaches to increase patient tolerance to chemotherapy or radiotherapy such as multi-drug resistance gene therapy.
[0164] (7) immunotherapy approaches, including for example immune checkpoint such as: those that target CTLA-4 and thus block or inhibit the interaction between CTLA-4 and CD80/CD86 (i.e. CTLA-4 inhibitors, such as ipilimumab or tremelimumab); those that target PD-1 and thus block or inhibit the interaction between PD-1 and PD-L1 (i.e. PD-1 inhibitors, representative examples of which include pembrolizumab, pidilizumab, nivolumab, REGN2810, CT- 001, AMP-224, BMS-936558, MK-3475, MEDI0680 and PDR001); and those that target PD-L1 and thus block or inhibit the interaction between PD-1 and PD-L1 (i.e. PD-L1 inhibitors such as atezolizumab, durvalumab, avelumab, BMS-935559 and MEDI4735). Alternatively, or in addition, ex vivo and in vivo approaches may be used to increase the immunogenicity of patient tumor cells, such as transfection with cytokines such as interleukin 2, interleukin 4 or granulocyte-macrophage colony stimulating factor, approaches to decrease T-cell anergy, approaches using transfected immune cells such as cytokine-transfected dendritic cells, approaches using cytokine-transfected tumor cell lines and approaches using anti-idiotypic antibodies. These approaches generally rely on the use of immune effector cells and molecules to target and destroy cancer cells. The immune effector may be, for example, an antibody specific for some marker on the surface of a malignant cell. The antibody alone may serve as an effector of therapy or it may recruit other cells to actually facilitate cell killing. The antibody also may be conjugated to a drug or toxin (chemotherapeutic, radionuclide, ricin A chain, cholera toxin, pertussis toxin, etc.) and serve merely as a targeting agent. Alternatively, the effector may be a lymphocyte carrying a surface molecule that interacts, either directly or indirectly, with a malignant cell target. Various effector cells include cytotoxic T cells and NK cells.
[0165] Typically, cancer therapy agents are administered in pharmaceutical (or veterinary) compositions together with a pharmaceutically acceptable carrier and in an effective amount to achieve their intended purpose. The dose of active compounds administered to a subject should be sufficient to achieve a beneficial response in the subject over time, such as a reduction in tumor burden and the like. The quantity of the pharmaceutically active compounds(s) to be administered may depend on the subject to be treated inclusive of the age, sex, weight and general health condition thereof. In this regard, precise amounts of the active compound(s) for administration will depend on the judgment of the practitioner. In determining the effective amount of the active compound(s) to be administered in the treatment of GBM, the medical practitioner may evaluate one or more clinical signs associated with the presence of GBM, including the severity of clinical signs. In any event, those of skill in the art may readily determine suitable dosages of the therapeutic agents and suitable treatment regimens without undue experimentation.
[0166] The GBM therapy may be administered in concert with an adjunctive cancer therapy, representative examples of which include agents to reduce pain, hair loss, vomiting, immune suppression, nausea, diarrhea, rash, sensory disturbance, anemia and fatigue.
[0167] In cases where a subject is positively identified as having a good prognosis (e.g., a favorable outcome selected from no disease recurrence, no disease progression and no death from disease within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM), for instance after being administered a cancer therapy (e.g., surgery), the patient may be continued to be administered the cancer therapy or cease to be administered the cancer therapy.
[0168] The present disclosure also contemplates use of the instant methods, apparatuses, composition and kits for monitoring the prognostic status of a GBM patient. In representative embodiments, a first sample is obtained from a GBM patient at an earlier time point to determine a first indicator and a second sample is obtained from the GBM patient at a later time point to determine a second indicator. The first and second indicators are then compared, wherein a difference between the first and second indicators is indicative of a change in prognostic status of the GBM patient, and wherein a similarity between the first and second indicators is indicative of no or negligible change in prognostic status of the GBM patient. For instance, the first indicator may be determined before administering a GBM therapy, and the second indicator may be determined after administration of the GBM therapy, to the patient. A change in indicator from a poor prognosis (/.e., first indicator) to a good prognosis (/.e., second indicator) indicates a likelihood that the GBM therapy was effective in treating the GBM and that the cancer is not progressing. However, if there is no change in indicator and the prognosis remains poor, this indicates a likelihood that the GBM therapy was ineffective in treating the GBM and that the cancer is progressing. In some examples, the time difference between the early time point and the later time point is at least 1 week, or at least 2 weeks, or at least 3 weeks, or at least 4 weeks, or at least 5 weeks, or at least 6 weeks, or at least 7 weeks, or at least 8 weeks, or at least 9 weeks, or at least 10 weeks, or at least 11 weeks, or at least 12 weeks, or at least 1 month, or at least 2 months, or at least 3 months, or at least 4 months, or at least 5 months, or at least 6 months, or at least 7 months, or at least 8 months, or at least 7 months, or at least 8 months, or at least 9 months, or at least 10 months, or at least 11 months, or at least 12 months, or at least 1 year, or at least 2 years, or at least 3 years, or at least 4 years, or at least 5 years. The time difference could also be determined by the number of treatment cycles. In some examples, the time difference between the early time point and the later time point is 1 treatment cycle, or 2 treatment cycles, or 3 treatment cycles, or 4 treatment cycles, or 5 treatment cycles, or 6 treatment cycles, or 7 treatment cycles, or 8 treatment cycles, or 9 treatment cycles, or 10 treatment cycles, or 11 treatment cycles, or 12 treatment cycles.
5. Device embodiments
[0169] Also contemplated herein are embodiments in which the indicator-determining method of the invention is implemented using one or more processing devices. In representative embodiments of this type, the method that is implemented by the processing device(s) determines an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, as disclosed herein (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM from diagnosis of GBM, or a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM), wherein the method comprises: (1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2 or 3 protein biomarkers) in a salivary extracellular vesicle (EV) sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample, and wherein the at least one protein biomarker is selected from six-month signature biomarkers, as defined herein; (2) determining the indicator using the biomarker value(s); (3) retrieving previously determined indicator references from a database, the indicator references being determined based on indicators determined from a reference population consisting of individuals diagnosed with the condition or individual having the survival prognosis; (4) comparing the indicator to the indicator references to thereby determine a probability indicative of the subject having or not having a disclosed prognosis; and (5) generating a representation of the probability, the representation being displayed to a user to allow the user to assess the likelihood of the subject having disclosed prognosis.
[0170] In other embodiments, the method that is implemented by the processing device(s) determines an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, as disclosed herein (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM from diagnosis of GBM, or a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after nine months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM), wherein the method comprises: (1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2, 3 or 4 protein biomarkers) in a salivary extracellular vesicle (EV) sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample, and wherein the at least one protein biomarker is selected from nine- month signature biomarkers, as defined herein; (2) determining the indicator using the biomarker value(s); (3) retrieving previously determined indicator references from a database, the indicator references being determined based on indicators determined from a reference population consisting of individuals diagnosed with the condition or individual having the survival prognosis; (4) comparing the indicator to the indicator references to thereby determine a probability indicative of the subject having or not having a disclosed prognosis; and (5) generating a representation of the probability, the representation being displayed to a user to allow the user to assess the likelihood of the subject having disclosed prognosis. [0171] In specific embodiments, an apparatus is provided for determining a likelihood of a human GBM patient having a poor prognosis or a good prognosis, as disclosed herein (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, or a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM). The apparatus typically includes at least one electronic processing device that:
• determines a biomarker value for at least one protein biomarker (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more biomarkers) disclosed herein in a saliva sample obtained from the subject; and
• determines the indicator using the biomarker value(s).
[0172] The apparatus may further include any one or more of:
• (A) a sampling device that obtains a salivary EV sample taken from a subject, the sample including at least one protein biomarker (e.g., 1, 2, 3, or more biomarkers) disclosed herein;
• (B) a measuring device that quantifies for each of the protein biomarkers a corresponding a biomarker value;
• (C) at least one processing device that: o (i) receives the biomarker value(s) from the measuring device; o (ii) determines an indicator that is indicative of a disclosed prognosis using the biomarker values optionally in combination with one or more clinical parameters or signs of the subject; o (iii) compares the indicator to at least one indicator reference; o (iv) determines a likelihood of the subject having or not having the disclosed prognosis using the results of the comparison; and o (v) generates a representation of the indicator and the likelihood for display to a user.
[0173] In some embodiments, the apparatus comprises a processor configured to execute computer readable media instructions (e.g., a computer program or software application, e.g., a machine learning system, to receive the biomarker values from the evaluation of EV protein biomarkers in a saliva sample and, in combination with other risk factors (e.g., medical history of the patient, publically available sources of information pertaining to a risk of GBM) may determine a master composite score and compare it to a grouping of stratified cohort population comprising multiple risk categories (e.g., a risk categorization table) and provide a risk score. Methods and techniques for determining a master composite score and a risk score are known in the art.
[0174] The apparatus can take any of a variety of forms, for example, a handheld device, a tablet, or any other type of computer or electronic device. The apparatus may also comprise a processor configured to execute instructions (e.g., a computer software product, an application for a handheld device, a handheld device configured to perform the method, a world- wide-web (WWW) page or other cloud or network accessible location, or any computing device. In other embodiments, the apparatus may include a handheld device, a tablet, or any other type of computer or electronic device for accessing a machine learning system provided as a software as a service (SaaS) deployment. Accordingly, the correlation may be displayed as a graphical representation, which, in some embodiments, is stored in a database or memory, such as a random access memory, read-only memory, disk, virtual memory, etc. Other suitable representations, or exemplifications known in the art may also be used.
[0175] The apparatus may further comprise a storage means for storing the correlation, an input means, and a display means for displaying the status of the subject in terms of the particular prognosis disclosed herein (e.g., an unfavorable outcome such as disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, or within nine months from diagnosis of GBM, or a favorable outcome such as no disease recurrence, no disease progression or survival within and/or after six months from diagnosis of GBM, or within and/or after nine months from diagnosis of GBM). The storage means can be, for example, random access memory, read-only memory, a cache, a buffer, a disk, virtual memory, or a database. The input means can be, for example, a keypad, a keyboard, stored data, a touch screen, a voice-activated system, a downloadable program, downloadable data, a digital interface, a hand-held device, or an infrared signal device. The display means can be, for example, a computer monitor, a cathode ray tube (CRT), a digital screen, a light-emitting diode (LED), a liquid crystal display (LCD), an X-ray, a compressed digitized image, a video image, or a hand-held device. The apparatus can further comprise or communicate with a database, wherein the database stores the correlation of factors and is accessible to the user.
[0176] In certain embodiments, the apparatus is a computing device, for example, in the form of a computer or hand-held device that includes a processing unit, memory, and storage. The computing device can include, or have access to a computing environment that comprises a variety of computer-readable media, such as volatile memory and non-volatile memory, removable storage and/or non-removable storage. Computer storage includes, for example, RAM, ROM, EPROM & EEPROM, flash memory or other memory technologies, CD ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other medium known in the art to be capable of storing computer-readable instructions. The computing device can also include or have access to a computing environment that comprises input, output, and/or a communication connection. The input can be one or several devices, such as a keyboard, mouse, touch screen, or stylus. The output can also be one or several devices, such as a video display, a printer, an audio output device, a touch stimulation output device, or a screen reading output device. If desired, the computing device can be configured to operate in a networked environment using a communication connection to connect to one or more remote computers. The communication connection can be, for example, a Local Area Network (LAN), a Wide Area Network (WAN) or other networks and can operate over the cloud, a wired network, wireless radio frequency network, and/or an infrared network.
[0177] In order that the disclosure may be readily understood and put into practical effect, particular preferred embodiments will now be described by way of the following non-limiting examples.
6. Representative embodiments
1. A method for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, the method comprising, consisting or consisting essentially of:
(1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2 or 3 protein biomarkers) in a salivary extracellular vesicle (EV) sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample, and wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kallikrein-1 (KLK1); and
(2) determining the indicator using the biomarker value(s).
2. The method of embodiment 1, wherein biomarker values are determined for at least two protein biomarkers.
3. The method of embodiment 1, wherein biomarker values are determined for each of LKHA4, H4 and KLK1.
4. The method of any one of embodiments 1 to 3, wherein the indicator indicates a likelihood of a poor prognosis if:
• H4 is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome; and/or
• LKHA4 is present in the salivary EV sample obtained from the GBM patient at a lower level than in control salivary EV samples obtained from a reference population of GBM patients with a favorable outcome; and/or
• KLK1 is present in the salivary EV sample obtained from the GBM patient at a lower level than in control salivary EV samples obtained from a reference population of GBM patients with a favorable outcome.
5. The method of any one of embodiments 1 to 3, wherein the indicator indicates a likelihood of a good prognosis if:
• H4 is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome; and/or
• LKHA4 is present in the salivary EV sample obtained from the GBM patient at a higher level than in control salivary EV samples obtained from a reference population of GBM patients with an unfavorable outcome; and/or
• KLK1 is present in the salivary EV sample obtained from the GBM patient at a higher level than in control salivary EV samples obtained from a reference population of GBM patients with an unfavorable outcome.
6. The method of any one of embodiments 1 to 4, wherein the poor prognosis is selected from disease recurrence, disease progression and death from disease within six months from diagnosis of GBM.
7. The method of any one of embodiments 1 to 3 and 5, wherein the good prognosis is selected from no disease recurrence, no disease progression and no death from disease within and/or after six months from diagnosis of GBM.
8. A method for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, the method comprising, consisting or consisting essentially of: (1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2, 3 or 4 protein biomarkers) in a salivary extracellular vesicle (EV) sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample, and wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI); and
(2) determining the indicator using the biomarker value(s).
9. The method of embodiment 8, wherein biomarker values are determined for at least two protein biomarkers.
10. The method of embodiment 8, wherein biomarker values are determined for at least three protein biomarkers.
11. The method of embodiment 8, wherein biomarker values are determined for each of ALDOA, 1433E, TM11B and ECHI.
12. The method of any one of embodiments 8 to 11, wherein the indicator indicates a likelihood of a poor prognosis if:
• ALDOA is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome; and/or
• 1433E is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome; and/or
• TM11B is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome; and/or
• ECHI is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome.
13. The method of any one of embodiments 8 to 11, wherein the indicator indicates a likelihood of a good prognosis if:
• ALDOA is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome; and/or
• 1433E is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome; and/or
• TM11B is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome; and/or
• ECHI is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome.
14. The method of any one of embodiments 8 to 12, wherein the poor prognosis is selected from disease recurrence, disease progression and death from disease within nine months from diagnosis of GBM. 15. The method of any one of embodiments 8 to 11 and 13, wherein the good prognosis is selected from no disease recurrence, no disease progression and no death from disease within and/or after nine months from diagnosis of GBM.
16. The method of any one of embodiments 1 to 15, wherein the GBM patient has undergone a treatment regimen for treating GBM.
17. The method of any one of embodiments 1 to 15, wherein the GBM patient has not undergone a treatment regimen for treating GBM.
18. The method of any one of embodiments 1 to 17, wherein EVs of the sample are small EVs.
19. The method of any one of embodiments 1 to 17, wherein EVs of the sample have a diameter of less than about 200 nm.
20. The method of embodiment 19, wherein the EVs have a diameter ranging from about 30 nm to about 200 nm.
21. The method of any one of embodiments 1 to 20, further comprising applying a function to biomarker values to yield at least one functionalized biomarker value and determining the indicator using the at least one functionalized biomarker value.
22. The method of embodiment 21, wherein the function includes at least one of: (a) multiplying biomarker values; (b) dividing biomarker values; (c) adding biomarker values; (d) subtracting biomarker values; (e) a weighted sum of biomarker values; (f) a log sum of biomarker values; (g) a geometric mean of biomarker values; (h) a sigmoidal function of biomarker values; and (i) normalization of biomarker values.
23. The method of any one of embodiments 1 to 22, further comprising combining the biomarker values to provide a composite score and determining the indicator using the composite score.
24. The method of embodiment 23, wherein the biomarker values are combined by adding, multiplying, subtracting, and/or dividing biomarker values
25. The method of any one of embodiments 1 to 24, further comprising analyzing the biomarker value(s), functionalized biomarker value(s) or composite score with reference to one or more reference biomarker values, value ranges or cut-off values, or reference composite scores, composite score ranges or composite score cut-offs, to determine the indicator.
26. The method of embodiment 25, wherein a respective reference biomarker value, biomarker value range, functionalized biomarker value, functionalized biomarker value range, biomarker value cut-off or functionalized biomarker value cut-off, or reference composite score, composite score range or composite score cut-off may be a biomarker value, biomarker value range, functionalized biomarker value, functionalized biomarker value range, biomarker value cutoff or functionalized biomarker value cut-off, or reference composite score, composite score range or composite score cut-off corresponding to a control subject or control population of subjects.
27. The method of embodiment 26, wherein the control subject or control population of subjects is selected from a subject or population of subjects with an unfavorable outcome within a period (e.g., six months or nine months) from diagnosis of GBM, or a subject or population of subjects with a favorable outcome within and/or after a period (e.g., six months or nine months) from diagnosis of GBM.
28. The method of any one of embodiments 25 to 27, wherein the indicator indicates a likelihood of a poor prognosis, if the biomarker value(s), functionalized biomarker value(s) or composite score is(are) indicative of the level of the bioma rker(s) in the sample that correlates with an increased likelihood of a poor prognosis relative to a predetermined reference biomarker value, value range or cut-off value, or to a predetermined reference functionalized biomarker value, value range or cut-off value, or to a predetermined reference composite score, composite score range or composite score cut-off.
29. The method of any one of embodiments 25 to 27, wherein the indicator indicates a likelihood of a good prognosis, if the biomarker value(s), functionalized biomarker value(s) or composite score is(are) indicative of the level of the bioma rker(s) in the sample that correlates with an increased likelihood of a good prognosis relative to a predetermined reference biomarker value, value range or cut-off value, or to a predetermined reference functionalized biomarker value, value range or cut-off value, or to a predetermined reference composite score, composite score range or composite score cut-off.
30. A method for monitoring prognostic status or treatment of a GBM patient, the method comprising, consisting or consisting essentially of:
(1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2 or 3 protein biomarkers) in a first salivary EV sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the first sample, and wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kallikrein-1 (KLK1);
(2) determining a first indicator using the biomarker value(s);
(3) determining a biomarker value for the at least one protein biomarker in a second salivary EV sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the second sample; and
(4) determining a second indicator using the biomarker value(s); and
(5) comparing the first indicator with the second indicator, thereby monitoring the prognostic status or treatment of a GBM patient.
31. A method for monitoring prognostic status or treatment of a GBM patient, the method comprising, consisting or consisting essentially of: (1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2, 3 or 4 protein biomarkers) in a first salivary EV sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the first sample, and wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI);
(2) determining a first indicator using the biomarker value(s);
(3) determining a biomarker value for the at least one protein biomarker in a second salivary EV sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the second sample; and
(4) determining a second indicator using the biomarker value(s); and
(5) comparing the first indicator with the second indicator, thereby monitoring the prognostic status or treatment of a GBM patient.
32. The method of embodiment 30 or embodiment 31, wherein the first sample is obtained from the patient before undergoing a therapeutic regimen for treating GBM and the second sample is obtained from the patient after undergoing the therapeutic regimen.
33. An apparatus for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, the apparatus comprising at least one electronic processing device that:
• determines a biomarker value for at least one protein biomarker (e.g., 1, 2 or 3 protein biomarkers) in a salivary EV sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample, and wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kallikrein-1 (KLK1); and
• determines the indicator using the derived biomarker value(s).
34. The apparatus of embodiment 33, wherein the poor prognosis is disease recurrence, disease progression or death from disease within six months from diagnosis of GBM, and wherein the good prognosis is no disease recurrence, no disease progression or no death from disease within and/or after six months from diagnosis of GBM.
35. An apparatus for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, the apparatus comprising at least one electronic processing device that:
• determines a biomarker value for at least one protein biomarker (e.g., 1, 2, 3 or 4 protein biomarkers) in a salivary EV sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample, and wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI); and
• determines the indicator using the derived biomarker value(s). 36. The apparatus of embodiment 35, wherein the poor prognosis is disease recurrence, disease progression or death from disease within nine months from diagnosis of GBM, and wherein the good prognosis is no disease recurrence, no disease progression or no death from disease within and/or after nine months from diagnosis of GBM.
37. A composition comprising a mixture of a salivary EV sample obtained from a GBM patient, and for one or a plurality of protein biomarkers (e.g., 1, 2 or 3 protein biomarkers) in the sample an antibody or antigen-binding fragment that binds specifically to the protein biomarker, wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kallikrein-1 (KLK1).
38. A composition comprising a mixture of a salivary EV sample obtained from a GBM patient, and for one or a plurality of protein biomarkers (e.g., 1, 2, 3 or 4 protein biomarkers) in the sample an antibody or antigen-binding fragment that binds specifically to the protein biomarker, wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI).
39. The composition of embodiment 37 or embodiment 38, wherein individual antibodies or antigen-binding fragments are labeled.
40. The composition of embodiment 39, wherein the composition comprises a plurality of antibodies or antigen-binding fragments, each of which specifically binds to a different protein biomarker and comprises the same label or a different label, as compared to the protein biomarker specificity and label of other antibodies or antigen-binding fragments of the composition.
41. The composition of embodiment 40, wherein the labels of different antibodies or antigen-binding fragments are detectably distinct.
42. A method managing treatment of a GBM patient, the method comprising:
• not exposing the patient to a treatment regimen or exposing the subject to a standard care treatment regimen at least in part on the basis that the patient is determined by the indicator-determining method of any one of embodiments 1 to 3, 7 to 11, 13, 15 to 27 and 29 as having a likelihood of a good prognosis; or
• exposing the patient to a more aggressive treatment regimen than standard care at least in part on the basis that the patient is determined by the indicator-determining method any one of embodiments 1 to 4, 6, 8 to 12, 14, and 16 to 28 as having a likelihood of a poor prognosis.
43. The method of embodiment 42, wherein the GBM patient has been administered a treatment regimen prior to undertaking the indicator-determining method.
44. The method of embodiment 42, wherein the GBM patient has not undergone a treatment regimen prior to undertaking the indicator-determining method. 45. The method of any one of embodiments 42 to 44, further comprising: taking a sample from the patient and determining an indicator indicative of a likelihood of a disclosed prognosis using the indicator-determining method.
46. The method of embodiment 45, further comprising: sending a sample obtained from the patient to a laboratory at which the indicator is determined according to the indicatordetermining method.
47. The method of embodiment 46, further comprising: receiving the indicator from the laboratory.
48. A kit for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, the kit comprising : for one or a plurality of protein biomarkers (e.g., 1, 2 or 3 protein biomarkers) an antibody or antigen-binding fragment that binds specifically to the protein biomarker, wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kail ikrei n-1 (KLK1).
49. A kit for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, the kit comprising: for one or a plurality of protein biomarkers (e.g., 1, 2, 3 or 4 protein biomarkers) an antibody or antigen-binding fragment that binds specifically to the protein biomarker, wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI).
50. The kit of embodiment 48 or embodiment 49, further comprising at least one reagent for preparing EVs from a saliva sample.
51. The kit of any one of embodiments 48 to 50, further comprising one or more of deoxynucleotides, buffer(s), positive and negative controls, and reaction vessel(s).
52. The kit of any one of embodiments 48 to 51, further comprising instructions for performing the indicator-determining method of any one of embodiments 1 to 19.
EXAMPLES
EXAMPLE 1
PROTEOME PROFILING OF SALIVARY SMALL EXTRACELLULAR VESICLES IN GLIOBLASTOMA PATIENTS
Demographic and clinical information from patients
[0178] Demographic and clinical information from GBM patients were collected (see, TABLE 1). TABLE 1. Participants' demographics and clinical information.
Figure imgf000047_0001
[0179] Within the cohort of GBM patients, 9 were confirmed with disease recurrence or were deceased within six months from diagnosis (unfavorable outcome), and 9 had no recurrence or confirmed recurrence after six months from diagnosis (favorable outcome).
Isolation and characterization of salivary small extracellular vesicles in glioblastoma patients
[0180] Following isolation of salivary small EVs, size distribution and concentrations were assessed by NTA (Figure 1A). The average size of salivary EVs from pre and postoperative samples were 163.5 nm and 142.8 nm, respectively. The average concentration (particles/mL of saliva) of EVs was 7.67 x 109 in preoperative samples and 4.26 x 109 in postoperative samples. Interestingly, although not statistically significant, there is a decrease in EV concentration in GBM patients after surgery. Additionally, TEM images corroborated the presence of small EVs, showing their typical cup-shaped structure (Figure IB). Immunoblotting of small EV markers was performed in a subset of samples (n=7) (Figure 1C), showing positive bands for CD9 and CD63 (Figure 1C). The negative marker, GM130, was absent in all isolated salivary EV samples, suggesting no contamination of larger EVs or cells (16) (Figure 1C). Taken together, these results confirm the successful isolation of small EVs from saliva of GBM patients.
Proteomic profiling of small extracellular vesicles in pre and postoperative saliva samples from glioblastoma patients
[0181] Following the characterization step confirming the enrichment of small EVs in our samples, a total of 12.5 pg of protein was used for mass spectrometry analysis. The workflow is shown in Figure 2A. A total of 507 proteins were identified in the small EVs fraction isolated from saliva samples of GBM patients. Out of these, 238 (47.0%) were found only in pre-operative samples, 215 (42.4%) were detected in both conditions, and only 54 (10.6%) were found exclusively in postoperatively (Figure 2B). [0182] The identified proteins were compared to the ones reported in known EVs databases. The present inventors cross-referenced their findings with ExoCarta (v5) (41-43) and Vesiclepedia (v4.1) (44, 45) (Figure2C).
[0183] In addition, quantitative analysis was performed on the identified proteins using SWATH mass spectrometry. A total of 89 significant differentially abundant proteins (DAPs) were identified between pre and postoperative saliva samples from GBM patients. Among them, 69 were more abundant in patients before surgery, while 20 were less abundant (Figure 2D).
Prognostic potential of salivary small extracellular vesicles in glioblastoma
[0184] Using the proteomics analysis from small EVs of patients before surgery, performance of partial least squares discriminant analysis (PLS-DA) was performed to cluster patients. Figure 3A shows that the majority of patients with good (blue dots) or poor (red dots) outcomes clustered together.
[0185] The analysis of the protein content of exosomes from patients with good or poor outcomes is shown in Figure 3B. The volcano plot identifies proteins by their Iog2 fold changes against their corresponding p-value in patients before surgery (Figure 3B). The present inventors observed 1 abundant protein (H4_ HUMAN) and 6 less abundant proteins (KV116_HUMAN, GCFC2JHUMAN, KLK1_ HUMAN, KV230_HUMAN, ACTZ_HUMAN, LKHA4JHUMAN) in EVs of patients with poor outcomes compared to good outcomes (TABLE 2).
TABLE 2. Name, log2FC and p-value for differentially abundant between patients with good and poor outcomes.
Figure imgf000048_0001
[0186] The detected immunoglobulins were excluded from further analyses. A receiver operating characteristics analysis was performed for each protein individually and combined panels to evaluate the prognostic performance of a 3 proteins panel. The present inventors chose H4_HUMAN, LKHA4_HUMAN and KLK1_HUMAN and a panel combining these 3 protein markers (Figure 4 and 5). A logistic regression predictive model was applied to the biomarkers, calculating a predictive score for samples individually and plotted into the groups analyzed (Figure 5). Comparing both groups (good and poor outcomes) the present inventors were able to identify a 3- protein panel that can distinguish patients who had a good outcome versus patients with a poor outcome, with an area under the curve of 0.903. EXAMPLE 2
THE PROGNOSTIC VALUE OF SALIVARY EXOSOMES IN GLIOBLASTOMA PATIENTS
NINE MONTHS FROM DIAGNOSIS
[0187] Since GBM patients experience recurrence within 6 to 9 months from diagnosis (Twelves et al., BrJ Cancer. 2021; 124(8): 1379-87; Weller et al., Neuro Oncol. 2013;15(l):4-27; Shergalis et al., Pharmacol Rev. 2018;70(3):412-45), it was decided to analyze a more conservative cut-off for the prognostic analysis of salivary EVs in GBM patients. Accordingly, in addition to the 6-month cut-off for predicting good or poor outcomes described in Example 1, the present inventors also analyzed patients using a 9-month cut-off. This means that GBM patients with recurrence or death within nine months from diagnosis were considered to have 'poor outcomes', while patients with no recurrence or recurrence after nine months from diagnosis were considered to have 'good outcomes'.
[0188] The demographics and clinical information from GBM patients included in this study are shown in TABLE 3. The average age of GBM patients was 60 years (ranging from 37 to 82 years) and for the control group was 63.5 years (ranging from 58 to 71 years). The healthy control group consisted of three women and two men, while the GBM cohort included nine women and nine men. Nearly all patients were classified as IDH-wild type GBM (n = 17) and one with IDH1R132H mutation. At the time of diagnosis, which was prior to the new 2021 WHO classification of central nervous system tumors, patients presenting IDH1R132H mutation had their tumors still classified as GBM. Within the cohort of GBM patients, 11 were confirmed with disease recurrence or were deceased within nine months from diagnosis (unfavorable outcome), while four had no recurrence or confirmed recurrence after nine months from diagnosis (favorable outcome). For three patients, follow up information was obtained less than nine months from diagnosis, so although no progression was evident, these GBM patients were excluded from further analysis on the prognostic significance of small EVs (Table 3). Due to the known prognostic differences in patients with IDH mutation and the lack of information on the prognosis according to our 9 months cut-off, the only patient with IDH mutation was excluded from the favorable/unfavorable analysis.
TABLE 3. Participants' demographics and clinical information.
Figure imgf000049_0001
Figure imgf000050_0001
[0189] Following the prognostic analysis using the 9 months cut-off, 66 differentially abundant proteins (DAPs) were observed in the pre-operative groups and 15 DAPs in the postoperative setting between groups with unfavorable vs favorable outcomes (TABLE 4). TABLE 4. Differentially abundant proteins in salivary EVs between patients with unfavorable and favorable prognoses in pre and post-operative samples.
Figure imgf000050_0002
Figure imgf000051_0001
Figure imgf000052_0001
• SBP = Salivary EVs before surgery with poor outcomes.
• SBG = Salivary EVs before surgery with good outcomes.
• SAP = Salivary EVs after surgery with poor outcomes.
• SAG = Salivary EVs after surgery with good outcomes.
[0190] To investigate the relationship between the protein content of small EVs and patients' clinical outcomes, the GBM cohort was separated into patients with favorable outcomes (progression-free survival (PFS) > 9 months) and unfavorable outcomes (PFS < 9 months). This conservative cut-off was established considering that the average time for a GBM patient to present disease recurrence is usually within six to nine months from diagnosis (Twelves et al., 2021; supra; Weller et al., 2013; supra; Shergalis et a/., 2018; supra).
[0191] The vesicles size was not altered according to the patients' prognosis (Figure 7A). However, there is a significant difference (p = 0.0242) when comparing EV concentration between favorable and unfavorable outcomes preoperatively (Figure 7B). The inventors further performed a partial least squares-discriminant analysis (PLS-DA) of proteome signatures in pre (Figure 7C) and postoperative (Figure 7D) samples of patients with favorable (blue) or unfavorable (red) outcomes. The resulting score plots of the analysis showed a clear separation between patients with favorable and unfavorable prognoses in the preoperative condition. However, postoperatively, a partial overlap was observed between patients. Next, DAPs were identified in each group pre (Figure 7E) and postoperatively (Figure 7F). Before surgery, a total of 65 DAPs were detected, among them, 54 were more abundant in patients with unfavorable outcomes, while two were less abundant. After surgery, 15 DAPs were identified, five more abundant and ten less abundant in patients with unfavorable outcomes. A list of all DAPs is presented in Table 4.
[0192] Considering the more representative separation from the PLS-DA of preoperative samples (Figure 7C), four proteins were selected for further investigation, namely aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI) (Figure 8A). Our criteria for selection included 1) a fold change of at least 1.5, 2) p-value < 0.05 and 3) biological relevance. All protein candidates presented increased abundance in patients with unfavorable outcomes compared to patients with favorable outcomes. ALDOA, 1433E and ECHI abundance was also verified by western blotting considering their biological relevance. For ALDOA (Figure 8B), it was confirmed that patients with unfavorable outcomes presented with visually stronger bands compared to patients with a good outcome. Additionally, a receiver operator characteristic (ROC) curve analysis of ALDOA was performed, which showed a sensitivity of 90.91% and specificity of 100.0% (Figure 8C).
[0193] A receiver operating characteristics analysis was performed for each of ALDOA, 1433E, TM11B and ECHI individually and a combined panel to evaluate prognostic performance Figure 9). A logistic regression predictive model was applied to the biomarkers, calculating a predictive score for samples individually and plotted into the groups analyzed (Figure 9A-D). Comparing both groups (good and poor outcomes) the present inventors were able to identify a 4- protein panel that can distinguish patients who had a good outcome versus patients with a poor outcome, with an area under the curve of 0.803 (Figure 9E).
MATERIALS AND METHODS
CLINICAL SAMPLES COLLECTION AND PROCESSING
[0194] The present experiments were approved by the human research ethics committee (HREC) of Royal Brisbane and Women's Hospital (Brisbane Australia), approval number: HREC/2019/QRBW/48780, and the Queensland University of Technology (QUT) (approval number: 1900000292). Pre and postoperative (within two weeks of brain surgery) whole mouth saliva samples, hereafter referred to as 'saliva', from GBM patients (n = 18) were obtained as previously described (Tang K et al., Mol Diagn Ther. 2021, Zhang et al., 2017; supra). Briefly, prior to saliva sample collection, GBM patients were asked to refrain from eating and drinking and were asked to rinse out their mouths with water. During saliva collection, volunteers were seated comfortably in an upright position with their heads slightly tilted forward so that saliva pools to the front of their mouth for about 2-5 minutes and expectorate into a specimen collection falcon. All samples were placed on ice, aliquoted and stored in the -80°C freezer until further analysis. Saliva samples were collected between June 2019 and November 2020. The favorable or unfavorable outcome was calculated using the time elapsed between the preoperative saliva collection date and the date of progression, death, or the last follow-up visit with an MRI scan image.
ISOLATION OF EXTRACELLULAR VESICLES
[0195] Small EVs (<200 nm) from saliva were pelleted by differential centrifugations and ultracentrifugation as previously described (Langevin et al., 2017; supra Zhang et al., 2017: supra; Raposo et al., J Exp Med. 1996; 183: 1161-72). Briefly, saliva (200 pL) was centrifuged at 2,000 x g for 10 min to pellet cells and apoptotic bodies; the supernatant was then collected and centrifuged at 16,000 x g for 20 min to remove larger EVs (e.g. microvesicles), followed by ultracentrifugation at 120,000 x g for 3 h to pellet the small EVs. All steps were carried out at 4°C. Small EVs pellets were then resuspended (50 pL) in filtered (0.22 pm) PBS and stored at -80°C for further experiments.
SMALL EXTRACELLULAR VESICAL CHARACTERIZATION
Nanoparticle tracking analysis
[0196] EVs were diluted (1:200) with filtered (0.22 pm) PBS and analyzed using the NanoSight NS300 with a 405-nm laser (NanoSight Ltd., Malvern, UK). This instrument explores the Brownian motion resulting from the light of the equipped laser being scattered by the particles, measuring their size and concentration. Three videos of 30 seconds were recorded for each sample, and a report was generated on the size distribution and concentration of particles.
Transmission electron microscopy
[0197] EV morphology was assessed using Transmission Electron Microscopy (TEM). Samples were mixed by vortexing vigorously. Five-microliter drops of resuspended samples were placed onto a parafilm, and the mounting grid was placed over the droplet. The mount was then incubated with 2% uranyl acetate (negative staining). EVs were imaged on a JEOL JEM-1400 TEM at lOOkV mounted with a 2K TVIPS CCD camera at the Central Analytical Research Facility (CARF) - QUT.
WESTERN BLOT
[0198] Total protein concentration was quantified using Pierce™ BCA Protein Assay Kit (Thermo Fisher Scientific). For western blotting, equal amounts of protein (5 pg) were loaded onto 10% SDS-PAGE gels and ran at 100 V for 90 min. An equal amount of protein from a GBM cell line (U251MG) was also loaded onto each gel as a positive control. U251MG cell line was gifted by Prof. Bryan W. Day (QIMR, Brisbane, Australia). Proteins were then transferred onto a polyvinylidene difluoride (PVDF) membrane at 100 V for 90 min at 4°C. The membranes were blocked for 1 h at room temperature (RT) with 5% bovine serum albumin (BSA) in tris-buffered saline, 0.1% Tween 20 (TBS-T). After blocking, membranes were washed (3 times) with TBS-T and incubated overnight at 4°C with the following primary antibodies: CD53 (Santa Cruz - #15363), CD9 (Cell signaling - #13174), GM-130 (Cell signaling - #12480), Aldolase A (C-10) (Santa Cruz - #390733), ECHI (B- 3) (Santa Cruz #515270), 14-3-3 E (8C3) (Santa Cruz #23957). After washing (3 times) with TBS- T, membranes were incubated with anti-rabbit IgG-HRP secondary antibody (Cell signaling - #7074) for 1 h at RT. All primary antibodies were diluted 1 : 1000 and secondary 1:2000. The membranes were incubated with Pierce ECL Western Blotting substrate (Thermo Fisher Scientific) and imaged using ChemiDoc XRS+ System (Bio-Rad Laboratories).
SAMPLE PREPARATION FOR MASS SPECTROMETRY
[0199] Sample processing was carried out as previously described (Zhang et al., 2017: supra). Briefly, for each sample, a total of 12.5 pg of protein was aliquoted into Protein LoBinding tubes (Catalog No. 0030108442, Eppendorf, Hamburg, Germany). For reduction of proteins, samples were mixed with 15 pL of SDS-Tris buffer (4% sodium dodecyl sulfate (SDS), 100 mM Tris-HCI pH 8.5, 100 mM Dithiothreitol (DTT)) and 200 pL of DTT-Urea buffer (25 mM DTT, 8 M urea in 100 mM Tris-HCI pH 8.5) within a 30 kDa Microcon YM-30 centrifugal filter device (Merck Millipore, MA, USA) and incubated at RT for 60 minutes under agitation. Following agitation, samples were centrifuged at 14,000 x g for 15 min, washed with Urea-Tris buffer, and centrifuged again at 14,000 x g for 15 min. Proteins were alkylated using 50 mM iodoacetamide (IAM) in 8 M urea in 100 mM Tris-HC for 20 min at room temperature in the dark. For digestion, samples were mixed with trypsin (catalogue No.V5280, Promega, WI, USA, enzyme:protein ratio 1: 50) at 37°C overnight. The peptides were then collected, samples were dried using a vacuum concentrator, and reconstituted with 0.1% trifluoroacetic acid in 2% acetonitrile. The desalting, cleaning and concentration of peptides was performed using strong cation excha nge(SCX)-StageTips according to Rappsilber et a/. (Nat Protoc. 2007;2(8): 1896-906).
MASS SPECTROMETRY SYSTEM
[0200] The mass spectrometry system utilized was a nanoflow liquid chromatographytandem mass spectrometry (LC-MS/MS) with a Prominence nanoLC system (Shimadzu) coupled with a TripleTOF 5600+ mass spectrometer system with a Nanospray III interface (AB SCIEX) as previously described (34). Briefly, approximately 2 pg of peptides were injected and separated using an analytical column packed with ChromXP C18 (150 mm x 75 pm, Eksigent Technologies, Dublin, CA). Trapping was performed at a flow rate of 5 pL/min for 5 min using mobile phase C (2% acetonitrile and 0.1% formic acid), followed by elution for 40 min using mobile phases A (1% acetonitrile and 0.1% formic acid) and B (80% acetonitrile and 0.1% formic acid) at a conserved flowrate of 300 niymin. Gas and voltage settings were adjusted as required.
DATA-DEPENDENT ACQUISITION (DDA)
[0201] A TripleTOF® 5600+ (SCIEX) was used to analyze peptide ions in data- dependent acquisition (DDA) mode, obtaining high resolution (30,000) TOF-MS scans over a range of 350 - 1350 m/z, followed by up to 40 high sensitivity MS/MS scans of the most abundant peptide ions per cycle over the range of 100- 2000 m/z. Peptide ions meeting the criteria of intensity greater than 150 cps and charge state of 2-5 were included. Each survey (TOF-MS) scan lasted 250 ms and the product ion (MS/MS) scan was acquired for 50 ms resulting in a total cycle time of 2.3 s. The ions were fragmented in the collision cell and the collected peptide ion fragmentation spectra were stored in .wiff format (SCIEX).
DATA-INDEPENDENT ACQUISITION (DIA)
[0202] Peptides were subjected to data-independent acquisition (DIA/SWATH-MS™ acquisition) using cycling isolation windows of equal mass ranges across a 65 min gradient method. LC conditions were the same as described above. For peptide detection, a survey scan data (MS) was performed for 80 ms, followed by MS/MS on all precursors in a cyclic manner using an accumulation time of 80 ms per individual SWATH-MS window. A total of 36 overlapping windows, each 26 m/z units wide, were used to cover the peptide ions in a range of 350 - 1500 m/z which resulted in a cycle time of 3 s. Fragment ions were recorded in a high sensitivity mode and a range of 100 - 1800 m/z. The collected data were saved in .wiff format.
SPECTRAL LIBRARY PREPARATION AND PEPTIDE QUANTIFICATION
[0203] Protein Pilot software version 5.0.2 (AB SCIEX) was used for peptide identification. Selected DDA files included seven EV samples obtained from saliva of GBM patients representing the unfavorable and favorable group (.wiff format). To generate a spectral library, DDA files were searched using ProteinPilot with the following parameters: iodoacetamide for cysteine alkylation, digestion with trypsin, and no special factors using the human SwissProt database (March 2021 release). Peptide identification was performed as previously described (34). Briefly, Protein Pilot 5.0.2 software was used to perform a false discovery rate (FDR) analysis for all searches, and further analyses were performed in peptides identified with a greater than 99% confidence and an FDR of less than 1%. The abundance of peptides was determined using PeakView Software (version 2.2) with standard settings as previously described (34, 38). Peptide abundance was determined by the sum of the integrated are of six fragment ion, and up to six peptides per protein were used to determine protein abundance.
BIOINFO MATICS ANALYSIS
[0204] Gene Ontology (GO) enrichment analysis of differentially abundant proteins was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Resources 6.8 (https://david.ncifcrf.gov/). GO annotation was classified into two categories biological process and molecular function. An adjusted p-value < 0.05 was considered significant. The GO Protein class analysis was generated using Panther classification (Mi et al., Nucleic Acids Res. 2021;49(Dl): D394-D403), http://www.pantherdb.org/. Pathway analyses were performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp/kegg/) and Reactome (https://reactome.or) to identify enriched pathways and generate a report, respectively. A Protein-Protein Interaction (PPI) network was built using Cytoscape (http://www.cytoscape.org) based on findings from the STRING database (https://stringdb.org)
STATISTICAL ANALYSIS
[0205] Statistical analyses were performed using GraphPad Prism Software and package MSStats version 2.0 (40) in R (Zhang et al., 2017: supra). Protein significance analysis was performed by applying a linear mixed-effects model using MSstats, as described previously (34). This analysis consisted of quantitative measurements for a targeted protein based on peptides, charge states, transitions, samples, and conditions. The method identifies protein alterations in abundance between conditions more systematically than random chance (Zhang et al., 2017: supra). The protein abundance levels between patients with unfavorable and favorable outcomes were compared using the Mann-Whitney test (GraphPad Prism). A p value < 0.05 was defined as statistically significant.
[0206] The disclosure of every patent, patent application, and publication cited herein is hereby incorporated herein by reference in its entirety.
[0207] The citation of any reference herein should not be construed as an admission that such reference is available as "Prior Art" to the instant application.
[0208] Throughout the specification the aim has been to describe the preferred embodiments of the disclosure without limiting the disclosure to any one embodiment or specific collection of features. Those of skill in the art will therefore appreciate that, in light of the instant disclosure, various modifications and changes can be made in the particular embodiments exemplified without departing from the scope of the present disclosure. All such modifications and changes are intended to be included within the scope of the appended claims.

Claims

WHAT IS CLAIMED IS:
1. A method for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, the method comprising, consisting or consisting essentially of:
(1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2 or 3 protein biomarkers) in a salivary extracellular vesicle (EV) sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample, and wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kallikrein-1 (KLK1); and
(2) determining the indicator using the biomarker value(s).
2. The method of claim 1, wherein biomarker values are determined for each of LKHA4, H4 and KLK1.
3. The method of claim 1 or claim 2, wherein the poor prognosis is selected from disease recurrence, disease progression and death from disease within six months from diagnosis of GBM, and wherein the good prognosis is selected from no disease recurrence, no disease progression and no death from disease within and/or after six months from diagnosis of GBM.
4. The method of any one of claims 1 to 3, wherein the indicator indicates a likelihood of a poor prognosis if:
• H4 is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome; and/or
• LKHA4 is present in the salivary EV sample obtained from the GBM patient at a lower level than in control salivary EV samples obtained from a reference population of GBM patients with a favorable outcome; and/or
• KLK1 is present in the salivary EV sample obtained from the GBM patient at a lower level than in control salivary EV samples obtained from a reference population of GBM patients with a favorable outcome, and wherein the indicator indicates a likelihood of a good prognosis if:
• H4 is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome; and/or
• LKHA4 is present in the salivary EV sample obtained from the GBM patient at a higher level than in control salivary EV samples obtained from a reference population of GBM patients with an unfavorable outcome; and/or
• KLK1 is present in the salivary EV sample obtained from the GBM patient at a higher level than in control salivary EV samples obtained from a reference population of GBM patients with an unfavorable outcome.
5. A method for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, the method comprising, consisting or consisting essentially of:
(1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2, 3 or 4 protein biomarkers) in a salivary extracellular vesicle (EV) sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample, and wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI); and
(2) determining the indicator using the biomarker value(s).
6. The method of claim 5, wherein biomarker values are determined for each of ALDOA, 1433E, TM11B and ECHI.
7. The method of claim 5 or claim 6, wherein the poor prognosis is selected from disease recurrence, disease progression and death from disease within nine months from diagnosis of GBM, and wherein the good prognosis is selected from no disease recurrence, no disease progression and no death from disease within and/or after nine months from diagnosis of GBM.
8. The method of any one of claims 5 to 7, wherein the indicator indicates a likelihood of a poor prognosis if:
• ALDOA is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome; and/or
• 1433E is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome; and/or
• TM11B is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome; and/or
• ECHI is present in the salivary EV sample obtained from the GBM patient at a higher level than in a reference population of GBM patients with a favorable outcome, and wherein the indicator indicates a likelihood of a good prognosis if:
• ALDOA is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome; and/or
• 1433E is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome; and/or • TM11B is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome; and/or
• ECHI is present in the salivary EV sample obtained from the GBM patient at a lower level than in a reference population of GBM patients with an unfavorable outcome.
9. The method of any one of claims 5 to 8, wherein the GBM patient has undergone a treatment regimen for treating GBM.
10. The method of any one of claims 5 to 8, wherein the GBM patient has not undergone a treatment regimen for treating GBM.
11. A method for monitoring prognostic status or treatment of a GBM patient, the method comprising, consisting or consisting essentially of:
(1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2 or 3 protein biomarkers) in a first salivary EV sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the first sample, and wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kallikrein-1 (KLK1);
(2) determining a first indicator using the biomarker value(s);
(3) determining a biomarker value for the at least one protein biomarker in a second salivary EV sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the second sample; and
(4) determining a second indicator using the biomarker value(s); and
(5) comparing the first indicator with the second indicator, thereby monitoring the prognostic status or treatment of a GBM patient.
12. A method for monitoring prognostic status or treatment of a GBM patient, the method comprising, consisting or consisting essentially of:
(1) determining a biomarker value for at least one protein biomarker (e.g., 1, 2, 3 or 4 protein biomarkers) in a first salivary EV sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the first sample, and wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI);
(2) determining a first indicator using the biomarker value(s);
(3) determining a biomarker value for the at least one protein biomarker in a second salivary EV sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the second sample; and
(4) determining a second indicator using the biomarker value(s); and
(5) comparing the first indicator with the second indicator, thereby monitoring the prognostic status or treatment of a GBM patient.
13. The method of claim 11 or claim 12, wherein the first sample is obtained from the patient before undergoing a therapeutic regimen for treating GBM and the second sample is obtained from the patient after undergoing the therapeutic regimen.
14. An apparatus for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, the apparatus comprising at least one electronic processing device that:
• determines a biomarker value for at least one protein biomarker (e.g., 1, 2 or 3 protein biomarkers) in a salivary EV sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample, and wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kallikrein-1 (KLK1); and
• determines the indicator using the derived biomarker value(s).
15. An apparatus for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, the apparatus comprising at least one electronic processing device that:
• determines a biomarker value for at least one protein biomarker (e.g., 1, 2, 3 or 4 protein biomarkers) in a salivary EV sample obtained from the patient, wherein a respective biomarker value is indicative of a level of a corresponding protein biomarker in the sample, and wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI); and
• determines the indicator using the derived biomarker value(s).
16. A composition comprising a mixture of a salivary EV sample obtained from a GBM patient, and for one or a plurality of protein biomarkers (e.g., 1, 2 or 3 protein biomarkers) in the sample an antibody or antigen-binding fragment that binds specifically to the protein biomarker, wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kallikrein-1 (KLK1).
17. A composition comprising a mixture of a salivary EV sample obtained from a GBM patient, and for one or a plurality of protein biomarkers (e.g., 1, 2, 3 or 4 protein biomarkers) in the sample an antibody or antigen-binding fragment that binds specifically to the protein biomarker, wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI).
18. A method managing treatment of a GBM patient, the method comprising:
• not exposing the patient to a treatment regimen or exposing the subject to a standard care treatment regimen at least in part on the basis that the patient is determined by the indicator-determining method of any one of claims 1, 2, 4 and 6 as having a likelihood of a good prognosis; or • exposing the patient to a more aggressive treatment regimen than standard care at least in part on the basis that the patient is determined by the indicator-determining method any one of claims 1 to 3 and 5 as having a likelihood of a poor prognosis.
19. A kit for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, the kit comprising: for one or a plurality of protein biomarkers (e.g., 1, 2 or 3 protein biomarkers) an antibody or antigenbinding fragment that binds specifically to the protein biomarker, wherein the at least one protein biomarker is selected from leukotriene A-4 hydrolase (LKHA4), histone H4 (H4) and kallikrein-1 (KLK1).
20. A kit for determining an indicator used in assessing a likelihood of a human GBM patient having a poor prognosis or a good prognosis, the kit comprising: for one or a plurality of protein biomarkers (e.g., 1, 2, 3 or 4 protein biomarkers) an antibody or antigenbinding fragment that binds specifically to the protein biomarker, wherein the at least one protein biomarker is selected from aldolase A (ALDOA), 14-3-3 protein epsilon (1433E), transmembrane protease serine 11B (TM11B) and enoyl CoA hydratase 1 (ECHI).
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