WO2018229733A1 - Immuno-biomarkers distinguish responsiveness versus non-responsiveness during immunotherapeutic treatments - Google Patents

Abstract

A combination of biomarkers comprising a plurality of immuno-biomarkers associated with immune function that differentiates a responding patient from a non-responding one against a vaccine is provided. The combination of biomarkers comprises a plurality of immuno-biomarkers associated with immune function are three or more biomarkers selected from the group consisting of MIP-1α, SDF-1α, IL-27, IL-1β, IL-2, IL-4, IL-5, IP-10, IL-6, IL-7, IL-8, IL-10, IL-11, Eotaxin, IL-12p70, IL-13, IL-17A, IL-31, IL-35, IL-1RA, RANTES, IFN-g, GM-CSF, TNF-a, MIP-1beta, IFN-a, MCP-1, IL-9, TNF-β, GRO-alpha, IL-1α, IL-23, IL-15, IL-18, IL-21, IL-22, MIG, Granulocyte-colony stimulating factor (G-CSF or GCSF), C-X-C motif chemokine receptor 1 (CXCR1), CXCR2, CXCR3, CXCR4, CXCR5, CXCR6, CXCR7, CXCR8, CXCR9, CXCR10, CXCR11, CXCR12, CCR1, CCR2, CCR3, CCR4, CCR5, CCR6, CCR7, CCR8, CCR9, CCR10, CCL1, CCL2, CCL3, CCL4, CCL5, CCL6, CCL7, CCL8, CCL9, CCL10, CXCL1, CXCL2, CXCL3, CXCL4, CXCL5, CXCL6, CXCL7, CXCL8, CXCL9, CXCL10, CXCL11, CXCL12, CXCL13 and VEGF.

Description

IMMUNO-BIOMARKERS DISTINGUISH RESPONSIVENESS VERSUS NON- RESPONSIVENESS DURING IMMUNOTHERAPEUTIC TREATMENTS

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Application No. 62/520,875 filed on June 16, 2017 with the title of "IMMUNO-BIOMARKERS DISTINGUISH HBEAG SEROCONVERTED FROM NON-CONVERTED INDIVIDUALS IN CHRONIC HEPATITIS B PATIENTS TREATED WITH ANTIGEN- ANTIBODY COMPLEX THERAPEUTIC VACCINE," the entire content of which is incorporated herein by reference in this application.

FIELD OF THE INVENTION

[0002] The present invention relates to the field of biomedicine, in particular to Immuno-biomarkers, their uses and methods for distinguishing a patient who is responsive to a vaccine from a non-responsive one during immunotherapeutic treatments of chronic hepatitis B patients.

BACKGROUND OF THE INVENTION

[0003] Chronic hepatitis B virus (HBV) infection remains a major public health threat worldwide, with nearly 240 million people affected [1]. Infected individuals are at risk for the development of cirrhosis and hepatocellular carcinoma (HCC). In China, it was estimated that 120 million people carried HBsAg, and annually around 300,000 of them died of liver cirrhosis and HCC [2, 3]. Therefore, the HBV imposes a huge social burden in China.

[0004] HBV specific T cells are deleted, dysfunctional or become exhausted in chronic hepatitis B (CHB) patients. Several immunotherapeutic approaches have been developed to restore the T cell functions [4]. Wen et al. have developed an antigen-antibody (HBsAg-HBIG) immunogenic complex with alum adjuvant as a therapeutic vaccine (YIC), aiming to enhance the uptake of HBsAg-Ab complex by antigen presenting cells (APC) through their Fc receptors [5, 6]. In phase lla and lib clinical trials, efficacy has been demonstrated among CHB patients [7, 8]. At present, the YIC has completed a phase Ilia clinical trial (trial number: 2002L0038), and a phase 1Mb trial is underway [9].

[0005] In the phase Ilia trial, although there was no significant difference in the viral load at baseline, 47 patients had HBeAg seroconversion in the YIC group. Since the efficacy of immunotherapy is highly dependent on the immune elements (innate immunity and acquired immunity) of individual patients, at intervals the expression of cytokines and chemokines in the plasma was assayed. According to the published relevance to the pathogenesis and treatment of HBV and other hepatitis infections, we measured levels of 3 pro-inflammatory cytokines (I L- 1 β, IFN-α, TNF-α), 6 inflammatory cytokines (IFN-γ, IL-2, IL-7, IL-15, IL-21 , IL-33) and 1 anti-inflammatory cytokine IL-10, as well as 4 HBV related chemokines (MIG, IP-10, MIP-1a, and MIP- 1 β) [10-14]. In view of the complex immune status in CHB patients, a Boolean logic method was used to analyze the results of these cytokines/chemokine assays and compare HBeAg seroconverted with nonconverted patients.

[0006] Thus, there is an urgent need to develop methods for distinguishing responsiveness versus non-responsiveness during immunotherapeutic treatments of chronic hepatitis B patients. Further, there is an urgent need of immuno-biomarkers and their uses to distinguish HBeAg Seroconverted from non-converted Individuals in chronic hepatitis B patients, especially for those treated with antigen-antibody complex therapeutic vaccine.

[0007] To look for a correlation between initial immunological status and HBeAg seroconversion, Applicants examined the expression of cytokines and chemokines in the plasma. In view of the complex immune status among CHB patients, Applicants used a Boolean logic method to analyze the detection of multiple cytokines/chemokines, such as a combination of IL-10, IL-33 and MIP-1a, a combination of IL-10, IL-13, IL-23, IFN-a, IL-18 and MIG6 and a combination of IL- 13, IFN-a, and IL-18. The method differentiated seroconverted from non-converted YlC-immunized subjects.

SUMMARY OF THE INVENTION

[0008] In view of shortcomings of the current technology, the invention provides immuno-biomarkers and their uses, discloses a method for distinguishing HBeAg Seroconverted from non-converted Individuals in chronic hepatitis B patients, especially for those treated with a vaccine such as an antigen-antibody complex therapeutic vaccine. The specific technical solutions of the invention are listed as follows:

[0009] In one aspect, the invention provides a combination of biomarkers comprising a plurality of immuno-biomarkers associated with immune function that differentiates a responding patient from a non-responding one against a vaccine. The immuno-biomarkers associated with immune function are three or more biomarkers selected from the group consisting of MIP-1 a, SDF-1 a, IL-27, IL- β, IL-2, IL-4, IL-5, IP-10, IL-6, IL-7, IL-8, IL-10, IL-11 , Eotaxin, IL-12p70, IL-13, IL-17A, IL-31 , IL-35, IL-1 RA, RANTES, IFN-g, GM-CSF, TNF-a, MIP-1 beta, IFN-a, MCP-1 , IL-9, TNF-β, GRO-alpha, IL-1a, IL-23, IL-15, IL-18, IL-21 , IL-22, MIG, Granulocyte-colony stimulating factor (G-CSF or GCSF), C-X-C motif chemokine receptor 1 (CXCR1), CXCR2, CXCR3, CXCR4, CXCR5, CXCR6, CXCR7, CXCR8, CXCR9, CXCR10, CXCR11 , CXCR12, CCR1 , CCR2, CCR3, CCR4, CCR5, CCR6, CCR7, CCR8, CCR9, CCR10, CCL1 , CCL2, CCL3, CCL4, CCL5, CCL6, CCL7, CCL8, CCL9, CCL10, CXCL1 , CXCL2, CXCL3, CXCL4, CXCL5, CXCL6, CXCL7, CXCL8, CXCL9, CXCL10, CXCL1 1 , CXCL12, CXCL13 and VEGF.

[0010] In one embodiment, the vaccine is a hepatitis B vaccine.

[0011] In one embodiment, the vaccine is a chronic hepatitis B vaccine.

[0012] In one embodiment, the vaccine is an HBsAg-HBIG therapeutic vaccine (YIC).

[0013] In one embodiment, the vaccine is a hepatitis B vaccine in combination of GM-CSF.

[0014] In one embodiment, when the vaccine is an HBsAg-HBIG therapeutic vaccine (YIC), the immuno-biomarkers comprise IL-10, IL-33 and MIP-1 a. More preferably, the immuno-biomarkers consist of IL-10, IL-33 and MIP-1a.

[0015] In one embodiment, when the vaccine is a hepatitis B vaccine in combination of GM-CSF, the immuno-biomarkers either comprise IL-10, IL-13, IL-23, IFN-a, IL-18 and MIG6 or comprise IL-13, IFN-a, and IL-18. More preferably, the immuno-biomarkers either consist of IL-10, IL-13, IL-23, IFN-a, IL-18 and MIG6 or consist of IL-13, IFN-a, and IL-18.

[0016] In one embodiment, the combination of biomarkers can differentiate a responding patient from a non-responding one against the vaccine.

[0017] In one embodiment, the combination of biomarkers can differentiate a responding patient from a non-responding one against the vaccine before the vaccination.

[0018] In one embodiment, the combination of biomarkers can differentiate a responding patient from a non-responding one against the vaccine during the vaccination.

[0019] In another aspect, the present invention reveals a method for evaluating efficacy of a vaccine on a patient. The method comprises the steps of a) obtaining a biological sample of each of the patients including the patient from a patient pool including both seroconverted patients and non-seroconverted ones; b) quantifying concentrations of a combination of immuno-biomarkers of claim 1 in the biological sample for each of the patients; c) generating a profile of the immuno- biomarkers from the concentrations of the immuno-biomarkers of each of the patients by using a model; d) determining in the profile whether each of the immuno- biomarkers of each of the patients is expressed or not expressed, wherein one immuno-biomarker of each of the patients is expressed if its concentration is above a cut-off concentration and otherwise it is unexpressed; e) setting every possible combination of Boolean states as a criterion to distinguish converted patients from non-converted; and f) classifying the patient as a converted one (or a responding one) to the vaccine if the patient's Boolean state consists with the criterion state and as a (non-converted one (or a non-responding one) otherwise.

[0020] In one embodiment of the method, the vaccine is a hepatitis B vaccine.

[0021] In one embodiment of the method, the vaccine is a chronic hepatitis B vaccine.

[0022] In one embodiment, the vaccine is an HBsAg-HBIG therapeutic vaccine (YIC).

[0023] In another embodiment, the vaccine is a hepatitis B vaccine in combination of GM-CSF.

[0024] In one embodiment of the method, the model is a Boolean modeling.

[0025] In one embodiment of the method, the cut-off concentration is 0.1 pg/ml.

[0026] In one embodiment of the method, when the vaccine is an HBsAg- HBIG therapeutic vaccine (YIC), the immuno-biomarkers comprise IL-10, IL-33 and MIP-1 a. More preferably, the immuno-biomarkers consist of IL-10, IL-33 and M IP- la. [0027] In one embodiment of the method, when the vaccine is a hepatitis B vaccine in combination of GM-CSF, the immuno-biomarkers either comprise IL-10, IL-13, IL-23, IFN-a, IL-18 and MIG6 or comprise IL-13, IFN-a, and IL-18. More preferably, the immuno-biomarkers either consist of IL-10, IL-13, IL-23, IFN-a, IL-18 and MIG6 or consist of IL-13, IFN-a, and IL-18.

[0028] In one embodiment, the biological sample is obtained from the patient before vaccination by the vaccine.

[0029] In one embodiment, the biological sample is obtained from the patient during vaccination by the vaccine.

[0030] In one embodiment, the biological sample comprises cerebrospinal fluid, whole blood, blood serum, plasma, urine, saliva, other bodily fluid, breath, condensed breath, or an extract, purification, or dilution of any of these.

[0031] In one embodiment, the quantifying step may use a technique comprising llluminex, flow cytometry, Chemiluminescence, Electroluminescence, Photoluminescence, Bioluminescence, ELISA, CyTOF, or combinations thereof.

[0032] In one embodiment, the quantifying step uses a technique comprising SELDI (-TOF) spectrometry, MALDI (-TOF) spectrometry, a 1-D gel-based analysis, a 2-D gel-based analysis, mass spectrometry (MS), liquid chromatography (LC), reverse phase liquid chromatography (RP-LC), size permeation chromatography, gel filtration chromatography, ion exchange chromatography, affinity chromatography, FPLC, HPLC, UPLC, other LC-based techniques, other LC-MS-based technique, or combinations thereof.

[0033] In one embodiment, the quantifying step uses a method comprising an immunological method, a biosensor method, a microanalytical method, a microengineered method, a microseparation method, an immunochromatography method, or combinations thereof.

[0034] In another aspect, the present invention reveals a kit for differentiating a responding patient from a non-responding one against a vaccine. The kit comprises a biosensor capable of detecting and/or quantifying the combination of biomarkers as defined in this application.

[0035] It should be understood that, within the scope of the present invention, each technical feature of the present invention described above and various technical features described in the following (as examples) may be combined with each other to form a new or preferred technical solution. Any equivalents or modifications without departing from the principle disclosed in the invention falls into the protection scope of the invention.

[0036] The invention will be further described with the figures to fully illustrate the objectives, technical features and technical effects of the invention. The foregoing and other aspects and advantages of the invention will appear from the following description. In the description, reference is made to the accompanying drawings which form a part hereof, and in which there is shown by way of illustration a preferred embodiment of the invention. Such embodiment does not necessarily represent the full scope of the invention, however, and reference is made therefore to the claims and herein for interpreting the scope of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0037] FIG. 1 (a) and FIG. 1 (b) are a set of graphs showing a baseline expression of 14 cytokines/chemokines in the HBeAg seroconverted (FIG. 1 (a)) and non-seroconverted (FIG. 1 (b)) patients. Heat map images are shown for I L- β, IFN- a, TNF-a, IFN-γ, IL-2, IL-7, IL-15, IL-21 , IL-33, IL-10, MIG, IP-10, MIP-1a, ΜΙΡ-1 β in pretreatment plasmas from 44 HBeAg seroconverted patients and 49 HBeAg non- seroconverted.

[0038] FIG. 2(a), FIG. 2(b), FIG. 2(c), FIG. 2(d), FIG. 2(e), FIG. 2(f) and FIG. 2(g) are a systematic diagram showing a scheme of Boolean modeling to distinguish HBeAg seroconverted patients from the non-converted ones based on the baseline immune status. FIG. 2(a): Each patient has a cytokine/chemokine expressing profile measured by the Luminex system. FIG. 2(b): Expression of these immune biomarkers are transformed into 1 (Expressed) or 0 (Not expressed) by cut-off 0.1 pg/ml. FIG. 2(c): The immune status depicted by means of a series of 0/1 s are assembled for all 93 CHB patients and a Boolean state pool is generated. FIG. 2(d): Patients who had HBeAg-seroconverted after the 76-week YIC treatments are represented with "+", and non-seroconverted ones are represented with A Boolean state is considered as a unique immune characteristic state. FIG. 2(e): Each possible combination of these Boolean states chosen randomly from the assigned pool can be set as a criterion to distinguish the converted and non- converted patients. Within the range of these combined Boolen states those associated with conversion are identified. FIG. 2(f): The numbers of HBeAg seroconverted and non-converted patients predicted by these combinations are counted. FIG. 2(g): The optimal combinational pattern used to differentiate converted patients from non-converted ones is based on the sensitivity and specificity according to the formulas described.

[0039] FIG. 3(a), FIG. 3(b), FIG. 3(c) and FIG. 3(d) are a set of graphs showing virological changes in the actual and predicted HBeAg converted and in the actual and predicted non-converted groups during the 76-week YIC phase III clinical trial. The predicted groups were distinguished by the cytokine combination analysis. The changes in plasma levels of HBeAg (FIG. 3(a)), HBV DNA (FIG. 3(b)), HBsAg (FIG. 3(c)) and HBe Ab (FIG. 3(d)) are shown.

[0040] FIG. 4(a), FIG. 4(b), FIG. 4(c) and FIG. 4(d) are a set of graphs showing that Boolean Model gives the smallest 2-factor ROC for the predicted endpoint. FIG. 4(a): Boolean flow diagram, IL-10 and MIG at week 0 predicted sAg seroconversion; FIG. 4(b): IL-13 and GRO-a at week 0 predicted sAg decreasing; FIG. 4(c): IL-23 and MIP-1 b at weeks 12 predicted sAg seroconversion; and FIG. 4(d): IL-18 and IFN-a at weeks 12 predicted sAg decreasing.

[0041] FIG. 5(a) and FIG. 5(b) are a set of graphs showing dynamic variation of Jmax for multiple factorial model. FIG. 5(a): Jmax of 4-8 factors random combinations. FIG. 5(b): Jmax of 3 factor random combination.

[0042] FIG. 6(a), FIG. 6(b), FIG. 6(c) and FIG. 6(d) are a set of graphs showing the comparison result of virological data of converted patients with the multiple-factor predicted data by the combination of IL-10, IL-13, IL-23, IFN-a, IL-18, and MIG. Actual sAg concentration dynamic changes of converted patients compared with predicted data (FIG. 6(a)) with week 0 as starting point and sAg seroconversion as end point; FIG. 6(b): with week 0 as starting point and sAg decreasing as end point; FIG. 6(c): with week 12 as starting point and sAg seroconversion as end point; FIG. 6(d): with week 12 as starting point and sAg decreasing as end point.

[0043] FIG. 7(a), FIG. 7(b), FIG. 7(c) and FIG. 7(d) are a set of graphs showing the comparison result of virological data of non-converted patients with IL- 13, IFN-a, and IL-18 multiple-factor predicted data. Actual sAg concentration dynamic changes of non-converted patients compared with predicted data (FIG. 7(a)) with week 0 as starting point and sAg seroconversion as end point; FIG. 7(b): with week 0 as starting point and sAg decreasing as end point; FIG. 7(c): with week 12 as starting point and sAg seroconversion as end point; FIG. 7(d) with week 12 as starting point and sAg decreasing as end point.

[0044] FIG. 8 is a set of graphs showing the receiver operating characteristic curves of TNF-a, MIP-1a, ΜΙΡ-1 β, MIG, and IP-10. Receiver operating characteristic (ROC) curves were performed for TNF-a (AUC, 0.604), ΜΙΡ-1 β (AUC, 0.559), IP-10 (AUC, 0.478), MIG (AUC, 0.454), and MIP-1a (AUC, 0.565). AUC, areas under the curves.

[0045] FIG. 9(a), FIG. 9(b) and FIG. 9(c) are a set of diagrams showing the selection of final prediction biomarkers from 14 cytokines/chemokines. FIG. 9(a): Among 14 cytokines/chemokines, TNF-a, ΜΙΡ-1 β, MIG and IP-10 were highly expressed in almost all CHB patients, did not contribute to discrimination between converted and non-converted patients, and were excluded from subsequent analysis. FIG. 9(b): The remaining 10 factors were set as Boolean nodes, representing the immune status before YIC treatments. However, seven of these factors made only minor contribution towards discrimination in the combined analysis. FIG. 9(c): In contrast, IL-10, IL-33 and MIP-1a, singly and in combination, were strongly associated with HBeAg seroconversion after YIC treatments.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

IN GENERAL

[0046] Before any embodiments of the invention are explained in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the following drawings. The invention is capable of other embodiments and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of "including," "comprising," or "having" and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. It is further understood that this invention is not limited to the particular methodology, protocols, materials, and reagents described, as these may vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present invention which will be limited only by any later-filed nonprovisional applications.

[0047] As used herein and in the appended claims, the singular forms "a", "an", and "the" include plural reference unless the context clearly dictates otherwise. As well, the terms "a" (or "an"), "one or more" and "at least one" can be used interchangeably herein. The terms "comprising", "including", and "having" can be used interchangeably.

[0048] Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, the preferred methods and materials are now described. All publications and patents specifically mentioned herein are incorporated by reference for all purposes including describing and disclosing the chemicals, instruments, statistical analysis and methodologies which are reported in the publications which might be used in connection with the invention. All references cited in this specification are to be taken as indicative of the level of skill in the art. Nothing herein is to be construed as an admission that the invention is not entitled to antedate such disclosure by virtue of prior invention.

I. DEFINITIONS

[0049] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. As used herein the following terms have the following meanings.

[0050] The term "comprising" or "comprises," as used herein, is intended to mean that the compositions and methods include the recited elements, but not excluding others. "Consisting essentially of when used to define compositions and methods, shall mean excluding other elements of any essential significance to the combination for the stated purpose. Thus, a composition consisting essentially of the elements as defined herein would not exclude other materials or steps that do not materially affect the basic and novel characteristic(s) of the claimed invention. "Consisting of" shall mean excluding more than trace elements of other ingredients and substantial method steps. Embodiments defined by each of these transition terms are within the scope of this invention.

[0051] The term "about," when used before a numerical designation, e.g., temperature, time, amount, and concentration, including range, indicates approximations which may vary by (+) or (-) 10%, 5% or 1 %.

[0052] The term "biomarker," as used herein in various embodiments, refers to a specific substance or biochemical in the body that has a particular molecular feature to make it useful for diagnosing and measuring the progress of disease or the effects of treatment.

[0053] The term "immune biomarker" or "immuno-biomarker," as used herein, refers to a specific substance or biochemical in the body that has a particular molecular feature to make it useful for diagnosing and measuring the progress of immuno-response of a patient with a disease or during a treatment.

[0054] The term "sample," as used herein, refers to any animal tissue or fluid containing, e.g., polynucleotides, polypeptides, antibodies, metabolites, and the like, including cells and other tissue containing DNA and RNA. Examples include adipose, blood, cartilage, connective, epithelial, lymphoid, muscle, nervous, sputum, and the like. A sample may be solid or liquid and may be DNA, RNA, cDNA, bodily fluids such as blood or urine, cells, cell preparations or soluble fractions or media aliquots thereof, chromosomes, organelles, and the like.

[0055] The term "biological sample," as used herein, refers to any sample from a patient for diagnosis and analysis, and the non-limiting examples comprise cerebrospinal fluid, whole blood, blood serum, plasma, urine, saliva, other bodily fluid, breath, condensed breath, or an extract, purification, or dilution of any of these.

[0056] The term "animal," as used herein, refers to a human or other animal, including avian, bovine, canine, equine, feline, hircine, murine, ovine, and porcine animals. When the term is used in the context of comparing test subjects, the animals that are compared are animals of the same species and possibly of the same race or breed. A "companion animal" is any domesticated animal, and includes, without limitation, cats, dogs, rabbits, guinea pigs, ferrets, hamsters, mice, gerbils, horses, cows, goats, sheep, donkeys, pigs, and the like. Preferably, the animal is a human or a companion animal such as a canine or feline. [0057] The term "3x," as used herein, refers to a frequency of administration as three times. For example, 3x GM-CSF means GM-CSF is administrated three times.

[0058] The term "pharmaceutical composition," as used herein, refers to any chemical or biological compound or substance, or a mixture or combination of two or more such compounds or substances, intended for use in the medical diagnosis, cure, treatment, or prevention of disease or pathology.

[0059] The term "hepatitis B virus" or "HBV," as used herein, is used to describe the virus (serum hepatitis virus) which produces viral hepatitis type B in humans. This is a viral disease with a long incubation period (about 50 to 160 days) in contrast to Hepatitis A virus (infectious hepatitis virus) which has a short incubation period. The virus is usually transmitted by injection of infected blood or blood derivatives or merely by use of contaminated needles, lancets or other instruments. Clinically and pathologically, the disease is similar to viral hepatitis type A; however, there is no cross-protective immunity. Viral antigen (HBAg) is found in the serum after infection.

[0060] The term "seroconversion," as used herein, refers to a time period during which a specific antibody develops and becomes detectable in the blood. After seroconversion has occurred, the disease can be detected in blood tests for the antibody. During an infection or immunization, antigens enter the blood, and the immune system begins to produce antibodies in response. Before seroconversion, the antigen itself may or may not be detectable, but the antibody is, by definition, absent. During seroconversion, the antibody is present but not yet detectable. Any time after seroconversion, the antibodies can be detected in the blood, indicating a prior or current infection.

[0061] The term "HBeAg seroconversion," is often used as an important milestone in the natural history of chronic hepatitis B virus, usually accompanied by clinical hepatitis disease remission and a good prognosis. Thus, the clinical doctors and researchers often turn to "whether patients HBeAg seroconversion after treatment" to determine whether treatment is effective as of the main indicators.

[0062] The term "treat," and "prevent" as well as words stemming therefrom, as used herein, do not necessarily imply 100% or complete treatment or prevention. Rather, there are varying degrees of treatment or prevention of which one of ordinary skill in the art recognizes as having a potential benefit or therapeutic effect.

[0063] In one embodiment, the inventive combination of immuno-biomarkers or the related methods can be used to predict efficacy of a vaccine such as a HBV vaccine by distinguishing a seroconverted patient from a non-converted one during a therapeutic vaccine treatment. After a prediction by using the combination of immuno-biomarkers or the related methods, one could better treat or prevent chronic hepatitis B in a patient by providing any amount of a vaccine or any level of treatment or prevention of chronic hepatitis B in a mammal. Furthermore, the treatment or prevention followed by the inventive combination of immuno-biomarkers or related methods can include treatment or prevention of one or more conditions or symptoms of the disease, e.g., cancer, being treated or prevented. Also, for purposes herein, "prevention" can encompass delaying the onset of the disease, or a symptom or condition thereof. With respect to the inventive methods, the cancer can be any cancer, including any of the cancers associated with any of the tumor antigens described herein.

[0064] The present invention is applicable to any mammal. As used herein, the term "mammal" refers to a warm-blooded vertebrate animal such as a human, dog or cat or the like. In one embodiment, mammal includes rodents such as rodent, rabbit, feline, canine, swine, and cattle.

[0065] The term "vaccine," as used herein, refers to a composition which can be administered to humans or to animals in order to induce an immune system response; this immune system response can result in a production of antibodies or simply in the activation of certain cells, in particular antigen-presenting cells, T lymphocytes and B lymphocytes. The vaccine composition can be a composition for prophylactic purposes or for therapeutic purposes, or both.

[0066] In one embodiment, the vaccine is a hepatitis B vaccine. In one preferred embodiment, the vaccine is a chronic hepatitis B vaccine.

[0067] In one embodiment, the present invention reveals a combination of immuno-biomarkers that can be used to differentiate a responding patient against a hepatitis B vaccine or a chronic hepatitis B vaccine from a non-responding patient against the same vaccine. In one embodiment, the combination of immuno- biomarkers of the present invention can be used wither before or during vaccination by the hepatitis B vaccine or chronic hepatitis B vaccine. [0068] The terms "administer" or "administration," as used herein, refers to their usual and ordinary meaning in the art of treating a patient with a substance such as a vaccine or a composition. The terms "co-administration" and "concomitant administration" as used herein are synonymous and refer to administering two substances or two compositions to a patient in such a manner and with such timing that both substances, or both compositions, reside in the patient's body at the same time. The co-administration may be simultaneous or sequential in time, and the coadministered substances or compositions may be administered to a patient at the same time, or separately but near in time, or on the same day, or otherwise in a way that results in substantial overlap of the residence periods for the respective substances or compositions in the body. The administration, e.g., parenteral administration, may include subcutaneous administration, intramuscular administration, transcutaneous administration, intradermal administration, intraperitoneal administration, intraocular administration, intranasal administration and intravenous administration.

[0069] The vaccine or the composition according to the invention may be administered to an individual according to methods known in the art. Such methods comprise application e.g. parenterally, such as through all routes of injection into or through the skin: e.g., intramuscular, intravenous, intraperitoneal, intradermal, mucosal, submucosal, or subcutaneous. Also, the vaccine may be applied by topical application as a drop, spray, gel or ointment to the mucosal epithelium of the eye, nose, mouth, anus, or vagina, or onto the epidermis of the outer skin at any part of the body. Other possible routes of application are by spray, aerosol, or powder application through inhalation via the respiratory tract. In this last case the particle size that is used will determine how deep the particles will penetrate into the respiratory tract. Alternatively, application may be via the alimentary route, by combining with the food, feed or drinking water e.g. as a powder, a liquid, or tablet, or by administration directly into the mouth as a: liquid, a gel, a tablet, or a capsule, or to the anus as a suppository.

II. DESCRIPTION OF PREFERRED EMBODIMENTS OF THE INVENTION

[0070] Before describing the invention, it is to be understood that this invention is not limited to particular methods and experimental conditions described, as such methods and conditions may vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, the scope of the invention being limited only by the appended claims.

[0071] After extensive studies and experimentations, Applicants discovered for the first time that a combination of biomarkers comprising a plurality of immuno- biomarkers associated with immune function that differentiates a responding patient from a non-responding one against a vaccine. Preferably, the combination of biomarkers comprising a plurality of immuno-biomarkers associated with immune function works best against a hepatitis B vaccine, more preferably against a chronic hepatitis B vaccine.

[0072] Depending on the vaccine, the combination of biomarkers may include a plurality of different immuno-biomarkers.

[0073] In one embodiment, the combination of biomarkers may include a plurality of different immuno-biomarkers selected from the group consisting of M IP- la, SDF-1a, IL-27, IL- β, IL-2, IL-4, IL-5, IP-10, IL-6, IL-7, IL-8, IL-10, IL-11 , Eotaxin, IL-12p70, IL-13, IL-17A, IL-31 , IL-35, IL-1 RA, RANTES, IFN-g, GM-CSF, TNF-a, MIP-1 beta, IFN-a, MCP-1 , IL-9, TNF-β, GRO-alpha, IL-1 a, IL-23, IL-15, IL-18, IL-21 , IL-22, MIG, Granulocyte-colony stimulating factor (G-CSF or GCSF), C-X-C motif chemokine receptor 1 (CXCR1), CXCR2, CXCR3, CXCR4, CXCR5, CXCR6, CXCR7, CXCR8, CXCR9, CXCR10, CXCR1 1 , CXCR12, CCR1 , CCR2, CCR3, CCR4, CCR5, CCR6, CCR7, CCR8, CCR9, CCR10, CCL1 , CCL2, CCL3, CCL4, CCL5, CCL6, CCL7, CCL8, CCL9, CCL10, CXCL1 , CXCL2, CXCL3, CXCL4, CXCL5, CXCL6, CXCL7, CXCL8, CXCL9, CXCL10, CXCL11 , CXCL12, CXCL13 and VEGF.

[0074] In one embodiment, the combination of biomarkers may include three or more different immuno-biomarkers selected from the group consisting of MIP-1 a, SDF-1 a, IL-27, I L- 1 , IL-2, IL-4, IL-5, IP-10, IL-6, IL-7, IL-8, IL-10, IL-1 1 , Eotaxin, IL- 12p70, IL-13, IL-17A, IL-31 , IL-35, IL-1 RA, RANTES, IFN-g, GM-CSF, TNF-a, MIP-1 beta, IFN-a, MCP-1 , IL-9, TNF-β, GRO-alpha, IL-1 a, IL-23, IL-15, IL-18, IL-21 , IL-22, MIG, Granulocyte-colony stimulating factor (G-CSF or GCSF), C-X-C motif chemokine receptor 1 (CXCR1), CXCR2, CXCR3, CXCR4, CXCR5, CXCR6, CXCR7, CXCR8, CXCR9, CXCR10, CXCR1 1 , CXCR12, CCR1 , CCR2, CCR3, CCR4, CCR5, CCR6, CCR7, CCR8, CCR9, CCR10, CCL1 , CCL2, CCL3, CCL4, CCL5, CCL6, CCL7, CCL8, CCL9, CCL10, CXCL1 , CXCL2, CXCL3, CXCL4, CXCL5, CXCL6, CXCL7, CXCL8, CXCL9, CXCL10, CXCL11 , CXCL12, CXCL13 and VEGF.

[0075] For example, in one embodiment, when the vaccine is an HBsAg-HBIG therapeutic vaccine (YIC), the combination of biomarkers may include IL-33, IL-10, and MIP-1 a. In one preferred embodiment, when the vaccine is an HBsAg-HBIG therapeutic vaccine (YIC), the combination of biomarkers consists to IL-33, IL-10, and MIP-1 a.

[0076] When the vaccine is a hepatitis B vaccine with 3x GM-CSF, the combination of biomarkers may include IL-10, IL-13, IL-23, IFN-a, IL-18 and MIG6 for predicting sAg seroconversion and sAg decreasing. In one preferred embodiment, when the vaccine is a hepatitis B with 3x GM-CSF, the combination of biomarkers consists of IL-10, IL-13, IL-23, IFN-a, IL-18 and MIG6 for predicting sAg seroconversion and sAg decreasing.

[0077] When the vaccine is a hepatitis B with 3x GM-CSF, the combination of biomarkers may include IL-13, IFN-a, and IL-18 to predict non sAg seroconversion and non-sAg decreasing. In one preferred embodiment, when the vaccine is a hepatitis B with 3x GM-CSF, the combination of biomarkers consists of IL-13, IFN-a, and IL-18 to predict non sAg seroconversion and non-sAg decreasing.

[0078] Thus, the present invention is applicable to any hepatitis B vaccine or any combination of vaccine with additional components (e.g., immune enhancers).

[0079] Applicants envision that the combination of biomarkers may include a plurality of different immuno-biomarkers when a different hepatitis B vaccine or a different chronic hepatitis B vaccine is used. Applicants further envision that the combination of biomarkers may include a plurality of different immuno-biomarkers selected from the group consisting of MIP-1a, SDF-1a, IL-27, I L- β, IL-2, IL-4, IL-5, IP-10, IL-6, IL-7, IL-8, IL-10, IL-11 , Eotaxin, IL-12p70, IL-13, IL-17A, IL-31 , IL-35, IL- 1 RA, RANTES, IFN-g, GM-CSF, TNF-a, MIP-1 beta, IFN-a, MCP-1 , IL-9, TNF-β, GRO-alpha, IL-1 a, IL-23, IL-15, IL-18, IL-21 , IL-22, MIG, Granulocyte-colony stimulating factor (G-CSF or GCSF), C-X-C motif chemokine receptor 1 (CXCR1), CXCR2, CXCR3, CXCR4, CXCR5, CXCR6, CXCR7, CXCR8, CXCR9, CXCR10, CXCR11 , CXCR12, CCR1 , CCR2, CCR3, CCR4, CCR5, CCR6, CCR7, CCR8, CCR9, CCR10, CCL1 , CCL2, CCL3, CCL4, CCL5, CCL6, CCL7, CCL8, CCL9, CCL10, CXCL1 , CXCL2, CXCL3, CXCL4, CXCL5, CXCL6, CXCL7, CXCL8, CXCL9, CXCL10, CXCL1 1 , CXCL12, CXCL13 and VEGF.

[0080] The objective of the invention is to predict efficacy of a vaccine (e.g., any known HBV vaccine) against chronic hepatitis B and to distinguish a seroconverted patient from a non-converted one before or during a therapeutic vaccine treatment. Following the successful prediction and differentiation of the present invention, one could better treat or prevent chronic hepatitis B on a patient. For example, once a patient is determined to be responding to a specific chronic hepatitis B vaccine, one could provide the patient the specific chronic hepatitis B vaccine or any known and similar chronic hepatitis B vaccine or related immunotherapeutic pharmaceutical compositions to treat chronic hepatitis B or prevent the patient from chronic hepatitis B. If a patient is determined to be non- responding to a specific chronic hepatitis B vaccine, one could further provide the patient a different chronic hepatitis B vaccine or related immunotherapeutic pharmaceutical compositions to treat chronic hepatitis B or prevent the patient from chronic hepatitis B.

[0081] Applicants envision that any known hepatitis B vaccine or chronic hepatitis B vaccine may be used to treat chronic hepatitis B or prevent the patient from chronic hepatitis B.

[0082] In one embodiment, Applicants use an HBsAg-HBIG therapeutic vaccine (YIC) as an exemplary hepatitis B vaccine (See Example 1). In another embodiment, Applicants use GM-CSF or a hepatitis B vaccine along with GM-CSF as exemplary hepatitis B vaccines (See Example 2).

[0083] As shown in Examples 1 and 2, Applicants use chronic hepatitis B virus as an example. Applicants envision that the methods of the present invention may be used to predict efficacy of other vaccine for the treatment of many diseases or viruses such as herpes virus, HPV, HIV, Merkel cell virus, influenza virus and RSV.

[0084] In one embodiment, the immuno-biomarkers of the combination of the biomarkers associated with immune function comprising three or more biomarkers selected from the group consisting of MIP-1a, SDF-1a, IL-27, I L- β, IL-2, IL-4, IL-5, IP-10, IL-6, IL-7, IL-8, IL-10, IL-11 , Eotaxin, IL-12p70, IL-13, IL-17A, IL-31 , IL-35, IL- 1 RA, RANTES, IFN-g, GM-CSF, TNF-a, MIP-1 beta, IFN-a, MCP-1 , IL-9, TNF-β, GRO-alpha, IL-1 a, IL-23, IL-15, IL-18, IL-21 , IL-22, MIG, Granulocyte-colony stimulating factor (G-CSF or GCSF), C-X-C motif chemokine receptor 1 (CXCR1), CXCR2, CXCR3, CXCR4, CXCR5, CXCR6, CXCR7, CXCR8, CXCR9, CXCR10, CXCR11 , CXCR12, CCR1 , CCR2, CCR3, CCR4, CCR5, CCR6, CCR7, CCR8, CCR9, CCR10, CCL1 , CCL2, CCL3, CCL4, CCL5, CCL6, CCL7, CCL8, CCL9, CCL10, CXCL1 , CXCL2, CXCL3, CXCL4, CXCL5, CXCL6, CXCL7, CXCL8, CXCL9, CXCL10, CXCL1 1 , CXCL12, CXCL13 and VEGF.

[0085] In one embodiment, the immuno-biomarkers of the combination of the biomarkers associated with immune function comprising three or more biomarkers selected from the group consisting of IL- β, IFN-α, TNF-α, IFN-γ, IL-2, IL-7, IL-15, IL- 21 , IL-33, IL-10, MIG, IP-10, MIP-1a and ΜΙΡ-1 β.

[0086] In one preferred embodiment, when YIC is used as a vaccine, the immuno-biomarkers of the combination of the biomarkers associated with immune function comprise IL-33, IL-10, and MIP-1 a.

[0087] More preferably, when YIC is used as a vaccine, the immuno- biomarkers of the combination of the biomarkers associated with immune function consists of IL-33, IL-10, and MIP-1a. Example 1 shows the methods of identifying the immuno-biomarkers of IL-33, IL-10, and MIP-1a as the decisive factors associated with responsiveness among CHB patients before and during YIC treatment.

[0088] In another embodiment, when a hepatitis B vaccine and GM-CSF are used, the immuno-biomarkers of the combination of the biomarkers associated with immune function comprises IL-10, IL-13, IL-23, IFN-a, IL-18 and MIG6.

[0089] In another embodiment, when a hepatitis B vaccine and GM-CSF are used, the immuno-biomarkers of the combination of the biomarkers associated with immune function consists of IL-10, IL-13, IL-23, IFN-a, IL-18 and MIG6.

[0090] In one specific embodiment, the combination of the biomarkers of IL- 10, IL-13, IL-23, IFN-a, IL-18 and MIG6 may be used to predict HBsAg seroconversion and decreasing. Example 2 shows the methods of identifying the immuno-biomarkers of IL-10, IL-13, IL-23, IFN-a, IL-18 and MIG6 as the decisive factors associated with responsiveness among CHB patients before and during the treatment of a hepatitis B vaccine and GM-CSF.

[0091] In one embodiment, when a hepatitis B vaccine and GM-CSF are used, the immuno-biomarkers of the combination of the biomarkers associated with immune function comprises IL-13, IFN-a, and IL-18 to predict non-HBsAg seroconversion and non-decreasing.

[0092] In another embodiment, when a hepatitis B vaccine and GM-CSF are used, the immuno-biomarkers of the combination of the biomarkers associated with immune function consists of IL-13, IFN-a, and IL-18.

[0093] In one specific embodiment, the combination of the biomarkers of IL- 13, IFN-a, and IL-18 may be used to predict non-HBsAg seroconversion and non- HBsAg decreasing. Example 2 shows the methods of identifying the immuno- biomarkers of IL-13, IFN-a, and IL-18 as the decisive factors associated with responsiveness among CHB patients before and during the treatment of a hepatitis B vaccine and GM-CSF.

[0094] In one embodiment, the combination of biomarkers comprising a plurality of immuno-biomarkers associated with immune function can differentiate a responding patient from a non-responding one against a hepatitis B vaccine. For example, the combination of biomarkers comprising a plurality of immuno- biomarkers associated with immune function can distinguish a seroconverted patient from a non-converted one before or during a therapeutic vaccine treatment.

[0095] In another embodiment, the combination of biomarkers comprising a plurality of immuno-biomarkers associated with immune function can distinguish a responding patient from a non-responding one against a chronic hepatitis B vaccine. For example, the combination of biomarkers comprising a plurality of immuno- biomarkers associated with immune function can distinguish a seroconverted patient from a non-converted one before or during a therapeutic vaccine treatment by using a chronic hepatitis B vaccine.

[0096] In one specific embodiment, the vaccine is an HBsAg-HBIG therapeutic vaccine (YIC). Example 1 shows the prediction and differentiation of a responding patient from a non-responding one by using the combination of biomarkers before or during vaccination of an HBsAg-HBIG therapeutic vaccine (YIC).

[0097] In one embodiment, the combination of biomarkers can differentiate a responding patient from a non-responding one against the vaccine before the vaccination. Thus, the combination of biomarkers can predict efficacy of a vaccine before the vaccine is administered to a patient.

[0098] In another embodiment, the combination of biomarkers can differentiate a responding patient from a non-responding one against a vaccine during the vaccination. As such, the combination of biomarkers can provide critical information of efficacy of different vaccines on a patient so that one could better design a scheme for treating the patient.

[0099] In another aspect, the present invention reveals a method for evaluating efficacy of a vaccine on a patient. Specifically, the present invention reveals a method for evaluating efficacy of a chronic hepatitis B vaccine on a patient.

[00100] In one embodiment, the method comprises the steps of a) obtaining a biological sample of each of the patients including the patient from a patient pool including both seroconverted patients and non-seroconverted ones; b) quantifying concentrations of a combination of immuno-biomarkers of claim 1 in the biological sample for each of the patients; c) generating a profile of the immuno-biomarkers from the concentrations of the immuno-biomarkers of each of the patients by using a model; d) determining in the profile whether each of the immuno-biomarkers of each of the patients is expressed or not expressed, wherein one immuno-biomarker of each of the patients is expressed if its concentration is above a cut-off concentration and otherwise it is unexpressed; e) setting every possible combination of Boolean states as a criterion to distinguish converted patients from non-converted; and f) classifying the patient as a converted one (or a responding one) to the vaccine if the patient's Boolean state consists with the criterion state and as a (non-converted one (or a non-responding one) otherwise.

[00101] During the first step, a biological sample from each of the patients including the patient from a patient pool including both seroconverted patients and non-seroconverted ones is obtained. Applicants envision that any sample from a patient for diagnosis and analysis may be obtained. The non-limiting examples may include cerebrospinal fluid, whole blood, blood serum, plasma, urine, saliva, other bodily fluid, breath, condensed breath, or an extract, purification, or dilution of any of these.

[00102] After the biological sample is obtained, concentrations of a combination of immuno-biomarkers as discussed above in the biological sample are quantified for each of the patients.

[00103] In one embodiment, the step of quantification may use any known technique such as llluminex, flow cytometry, Chemiluminescence, Electroluminescence, Photoluminescence, Bioluminescence, ELISA, CyTOF, or combinations thereof.

[00104] In one embodiment, the step of quantification may use any known technique and non-limiting examples of the techniques may comprise SELDI (-TOF) spectrometry, MALDI (-TOF) spectrometry, a 1-D gel-based analysis, a 2-D gel- based analysis, mass spectrometry (MS), liquid chromatography (LC), reverse phase liquid chromatography (RP-LC), size permeation chromatography, gel filtration chromatography, ion exchange chromatography, affinity chromatography, FPLC, HPLC, UPLC, other LC-based techniques, other LC-MS-based technique, or combinations thereof.

[00105] Further, the quantifying step may use any known method of diagnosis and/or analysis. The non-limiting examples may comprise an immunological method, a biosensor method, a microanalytical method, a microengineered method, a microseparation method, an immunochromatography method, or combinations thereof.

[00106] By using the above techniques or methods, the concentration of each of the immuno-biomarkers is quantified for each of the patients. In one embodiment, the concentrations of the following immuno-biomarkers may be quantified for each of the patients: MIP-1a, SDF-1a, IL-27, ΙΙ_-1 β, IL-2, IL-4, IL-5, IP-10, IL-6, IL-7, IL-8, IL- 10, IL-1 1 , Eotaxin, IL-12p70, IL-13, IL-17A, IL-31 , IL-35, IL-1 RA, RANTES, IFN-g, GM-CSF, TNF-a, MIP-1 beta, IFN-a, MCP-1 , IL-9, TNF-β, GRO-alpha, IL-1a, IL-23, IL-15, IL-18, IL-21 , IL-22, MIG, Granulocyte-colony stimulating factor (G-CSF or GCSF), C-X-C motif chemokine receptor 1 (CXCR1), CXCR2, CXCR3, CXCR4, CXCR5, CXCR6, CXCR7, CXCR8, CXCR9, CXCR10, CXCR11 , CXCR12, CCR1 , CCR2, CCR3, CCR4, CCR5, CCR6, CCR7, CCR8, CCR9, CCR10, CCL1 , CCL2, CCL3, CCL4, CCL5, CCL6, CCL7, CCL8, CCL9, CCL10, CXCL1 , CXCL2, CXCL3, CXCL4, CXCL5, CXCL6, CXCL7, CXCL8, CXCL9, CXCL10, CXCL11 , CXCL12, CXCL13 and VEGF. Applicants envision that other immuno-biomarkers may be added to the list.

[00107] After quantification of each of the concentration of each of the related immuno-biomarkers for each of the patients, a profile of the immuno-biomarkers is generated from the concentrations of the immuno-biomarkers of all the patients by using a model. In one embodiment, the model is Boolean modeling. [00108] It is determined in the profile whether each of the immuno-biomarkers of each of the patients is expressed or not expressed, wherein one immuno- biomarker of each of the patients is expressed if its concentration is above a cut-off concentration and otherwise it is unexpressed.

[00109] Example 1 provides detail information of Boolean modeling. Specifically, a Boolean model may be adapted to construct the correlation between immune biomarkers and final seroconverstion. For example, expression data of immune biomarkers may be first transformed into 1 (Expressed) or 0 (Not expressed) by comparing their concentrations with a cut-off concentration. In one embodiment, the cut-off concentration is the detection limit of the system used. In one specification, the cut-off concentration is 0.1 pg/ml, which is the detection limit of the Luminex system.

[00110] Further, under the Boolean modeling, the Boolean state of the biomarkers was generated from each patient as a serial of 1/0s to form a Boolean state pool. Even further, a Boolean states pool was generated for all the patients including the HBeAg-seroconverted and non-converted ones.

[00111] As shown in FIG. 2, Patients were divided into the converted and the non-converted groups and their summarized immune status depicted according to this Boolean model, e.g., as "0000000000," "11 111 11 111 ," "0000000001 , etc."

[00112] After the formation of a Boolean state pool, each patient's position on the Boolean state pool is rearranged to achieve better separation between the seroconverted ones and non-converted ones. Every possible combination of Boolean states is set as a criterion to distinguish converted patients from non- converted.

[00113] In one embodiment, prediction panels for best separation between converter and non-converter may be evaluated by receiver operating characteristic curves (ROC) performance of random combinations from the Boolean states pool (random combination of the Boolean states generated 22! = 1.1*10²¹ possibilities).

[00114] Specifically, ROC performance may be analyzed with a commercial software such as Origin 8.0 graphing software. In one specific embodiment, the sensitivity and specificity calculation formulas were: Sensitivity = True positive/ (True positive + False negative); Specificity = True negtive/ (False positive + True negtive).

[00115] To determine a correction between the immune biomarkers and final seroconverstion, statistical analysis may be further performed by using unpaired t tests and two-way (ANOVA) analysis with Prism 6.0 software. Examples 1 and 2 show the detailed Statistical analyses. Several on-off characteristic states may be combined with an enumeration method to distinguish the converted and non- converted patients based on the sensitivity and specificity of every possible combination listed.

[00116] Further, the patient may be classifies as a converted one (or a responding one) to the vaccine if the patient's Boolean state consists with the criterion state and as a non-converted one (or a non-responding one) otherwise. Thus, the patients with Boolean states that were not consistent with the criterion states may be classified as non-converted patients.

[00117] Even further, the present method also includes the step of grouping the patients according to multiple expression statuses of immuno-biomarkers, wherein the expression statuses include expression of each of the immuno-biomarkers, expression of any two of the immuno-biomarkers, expression of any three of the immuno-biomarkers, . . . and expression of all the immuno-biomarkers; and the step of correlating different groups of expression status with whether the corresponding patients are converted or non-converted to determine sensitivity and specificity of the predication. Example 1 provides detailed information of the steps of grouping and correlating.

[00118] After determination of the correction between immune biomarkers and final seroconverstion, a specific combination of immuno-biomarkers may be chosen for each of the vaccine.

[00119] For example, as shown in Example 1 , the specific combination of immuno-biomarkers of IL-33, IL-10, and MIP-1a is chosen when YIC is used as a vaccine. Further, as shown in Example 2, the specific combination of immuno- biomarkers of IL-10, IL-13, IL-23, IFN-a, IL-18 and MIG6 may be used to predict HBsAg seroconversion and decreasing before and during the treatment of a hepatitis B vaccine and 3x GM-CSF. Even further, as shown in Example 2, the specific combination of immuno-biomarkers of IL-13, IFN-a, and IL-18 to predict non- sAg seroconversion and non-sAg decreasing before and during the treatment of a hepatitis B vaccine and 3x GM-CSF.

[00120] Thus, the present invention provides methods of identifying a specific combination of immuno-biomarkers against a vaccine such as a chronic hepatitis B vaccine to evaluate efficacy of the vaccine on a patient. Further, the present invention provides a specific combination of immuno-biomarkers against a vaccine such as a chronic hepatitis B vaccine and related methods to distinguish a patient's responsiveness versus non-responsiveness during immunotherapeutic treatments of chronic hepatitis B. Specifically, the specific combination of immuno-biomarkers identified in the present invention allows one to distinguish a seroconverted patient from a non-converted one before or during a therapeutic vaccine treatment.

[00121] In one embodiment, after the identification of a specific combination of immuno-biomarkers against a vaccine, one can determine in the profile whether each of the immuno-biomarkers of the patient is expressed or not expressed. Specifically, one immuno-biomarker of the patient is expressed if its concentration is equal or above a cut-off concentration and otherwise it is unexpressed. In one embodiment, the cut-off concentration is the detection limit of the system used. In one specification, the cut-off concentration is 0.1 pg/ml, which is the detection limit of the Luminex system.

[00122] In one embodiment, a patient is classified as a responding one to the related hepatitis B vaccine provided that the patient's Boolean state consists with any of the criterion states and as a non-converted one (or a non-responding one) otherwise. Applicants note that for the predicted responding patient, one single or some of the specific immuno-biomarkers of the patient in the profile may be expressed. Similarly, a patient is classified as a non-responding one to the related hepatitis B vaccine provided that the patient's Boolean state does not consists with any of the criterion states and one single biomarkers or some of the specific immuno-biomarkers of the patient in the profile may be expressed.

[00123] In another embodiment, a patient who is classified as a responding one to the related hepatitis B vaccine may have certain combinations of the specific immuno-biomarkers of the patient in the profile expressed. Similarly, a patient who is classified as a non-responding one to the related hepatitis B vaccine may have certain combinations of the specific immuno-biomarkers of the patient in the profile expressed.

[00124] In one embodiment, the present invention is applicable when the vaccine is a hepatitis B vaccine. In one preferred embodiment, the present invention is applicable when the vaccine is a chronic hepatitis B vaccine.

[00125] Example 1 shows one exemplary case when the vaccine is an HBsAg-

HBIG therapeutic vaccine (YIC).

[00126] Example 2 shows another exemplary case when the vaccine is a hepatitis B vaccine with 3x GM-CSF.

[00127] In one specific embodiment, when the vaccine is an HBsAg-HBIG therapeutic vaccine (YIC), the specific combination of immuno-biomarkers comprises IL-33, IL-10, and MIP-1 a. Preferably, when the vaccine is an HBsAg-HBIG therapeutic vaccine (YIC), the specific combination of immuno-biomarkers consists of IL-33, IL-10, and MIP-1a.

[00128] In another specific embodiment, the specific combination of immuno- biomarkers of IL-10, IL-13, IL-23, IFN-a, IL-18 and MIG6 may be used to predict HBsAg seroconversion and decreasing before and during the treatment of a hepatitis B vaccine and GM-CSF. A specific combination of immuno-biomarkers of IL-13, IFN-a, and IL-18 may be used to predict non sAg seroconversion and non-sAg decreasing before and during the treatment of a hepatitis B vaccine and 3x GM-CSF.

[00129] In one aspect, the present invention provides a method of treating or preventing hepatitis B, specifically chronic hepatitis B by combining the specific combination of immuno-biomarkers with a known vaccine or an existing vaccine treatment. The present invention provides a method of identifying a specific combination of immuno-biomarkers to distinguish a patient who responds to a vaccine from a non-responsive one during immunotherapeutic treatments of hepatitis B, preferably during immunotherapeutic treatments of chronic hepatitis B. By using the specific combination of immuno-biomarkers as identified against a specific vaccine, one could better design a treatment scheme for each individual patient depending upon his/her status related to the specific combination of immuno- biomarkers. Thus, the present invention provides a method of treating or preventing hepatitis B, specifically chronic hepatitis B by combining the specific combination of immuno-biomarkers as discussed herein with a known hepatitis B vaccine, specifically a chronic hepatitis B vaccine or treatment. Any known or existing vaccine composition against hepatitis B, specifically against chronic hepatitis B would be applicable to the present invention. Non-limiting examples of vaccine compositions are discussed as follow: [00130] Vaccine Composition

[00131] The vaccine compositions in the present invention may be prophylactic (prevent infection) and may also be therapeutic. The vaccine composition comprises immunogenic antigen(s) (such as protein antigen) and is combined with "pharmaceutically acceptable carrier(s)" which does not induce carrier-specific antibodies. Suitable carriers are typically large, slowly metabolized macromolecules such as aluminum hydroxide, dextrose, mannitol, trehalose, cyclodextrin, carrier proteins, polysaccharides, polylactic acid, polyglycolic acid, amino acid polymers, amino acid copolymers, lipid aggregates such as oil droplets or liposomes and/or any combinations of them. These vectors are well known to the general technical persons in this field. In addition, these vectors function as immunostimulants ("adjuvants"). Antigens can also be conjugated to bacterial toxins (toxoids such as diphtheria, tetanus, cholera, diphtheria CRM 197) or carrier proteins (L1 from HPV16 or HPV18, VP1 from Merkel's cell virus, Ovalbumin, keyhole limpet hemocyanin).

[00132] In addition to the above-mentioned immunostimulants in the present invention, other adjuvants that enhance the effect of the immunogenic composition may also be added to the vaccine composition may include, but are not limited to: (1) alum, such as aluminum hydroxide, aluminum phosphate, Aluminum sulfate and the like; (2) oil-in-water emulsion formulations such as (a) MF59 (see WO90 / 14837), (b) SAF, and (c) Ribi™ Adjuvant System (RAS); (3) Freund complete adjuvant (CFA) and Freund's incomplete adjuvant (IFA); (4) TLR agonists, eg. CpG, MPL, poly l:C, Pam3Cys, flagellin, resiquimod; (5)cytokines such as interleukins (such as IL-1 , IL- 2 and IL- 4, IL-5, IL-6, IL-7, IL-12 and the like), tumor necrosis factor (TNF) and the like; (5) bacterial ADP-ribosylating toxins detoxification variants such as cholera toxin CT, pertussis toxin PT or E. coli Heat-labile toxin LT, for example WO 93/13302 and WO 92/19265; and (6) other substances that enhance the effectiveness of the composition.

[00133] In one embodiment, Vaccine compositions include immunogenic compositions such as antigens, pharmaceutically acceptable carriers, and adjuvants, typically contain diluents such as water, saline, glycerol, buffer, and the like. In addition, auxiliary substances such as wetting, emulsifying agents, pH buffering substances and the like may also be included.

[00134] More specifically, vaccines, including immunogenic compositions, contain an immunologically effective amount of an immunogenic polypeptide, as well as the other desired components described above. An "immunologically effective amount" means that the amount administered to a subject as a single dose or as part of a continuous dosages is effective for treatment or prophylaxis. The amount can vary depending on the health and physiology of the individual, the type of individual (e.g., human), the ability of the individual's immune system to produce antibodies, the degree of protection desired, the formulation of the vaccine, the medical physician's assessment of medical condition, and other related factors. It is expected that this amount will be within a relatively wide range and can be determined by routine experimentation.

[00135] Generally, the vaccine composition or the immunogenic composition can be formulated as an injection, for example as a liquid solution or suspension; it can also be produced as a solid form suitable for preparation as a solution or suspension before injection. The formulation can also be emulsified or encapsulated in liposomes to enhance the adjuvant effect.

[00136] In addition, the vaccine composition of the present invention may be monovalent or multivalent.

[00137] Routes of Administration and Dosage

[00138] The composition can be administered directly to a subject. The subject may be a human or non-human mammal, preferably a human. When used as a vaccine, the composition of the invention can be administered directly to the individual using any known methods. These vaccines are usually administered using the same routes as used by conventional vaccines and/or mimicking the pathogen's infection.

[00139] Routes of administration of the pharmaceutical composition or vaccine composition of the invention may include, but are not limited to, intramuscular, subcutaneous, intradermal, intrapulmonary, intravenous, intranasal, vaginal, oral or other parenteral routes of administration. If desired, the route of administration may be combined or adjusted according to the condition of the disease. Vaccine compositions can be administered in a single dose or in multiple doses and can include the administration of booster doses to elicit and/or maintain immunity.

[00140] The vaccine should be given in a "therapeutically effective amount," that is, the amount of vaccine is sufficient to elicit an immune response in the chosen route of administration and can effectively promote the protection of the host against viral infections.

[00141] The amount of antigen selected for each dose is based on the amount that would trigger a protective response without significant side effects. Generally, after infection of the host cell, each dose contains the protein antigen from about O. ^g to about 1000C^g, preferably about ^g to about 10C^g, and more preferably about 1 C^g to about 5C^g. Antibody titers and other reactions in the subject can be used as standard methods to determine the optimal dosage for a particular vaccine. The level of immunity can be used to determine if a booster dose is needed. After assessing antibody titers in serum or T cell functions of PBMC, booster doses may be required. Administration of an adjuvant and/or an immunostimulant may increase the immune responses to antigens of the invention. The preferred method is to administer the immunogenic composition by injection from the subcutaneous or intramuscular route.

[00142] In another aspect, the present invention reveal a kit for differentiating a responding patient from a non-responding one against a vaccine. In one embodiment, the kit can differentiate a responding patient from a non-responding one against a hepatitis B vaccine, preferably against a chronic hepatitis B vaccine.

[00143] In one embodiment, the kit comprises a biosensor capable of detecting and/or quantifying the combination of biomarkers as defined herein.

[00144] The invention is further described below with reference to specific examples. It should be understood that these examples are only used for illustrating the invention and are not intended to limit the scope of the invention. The following examples did not indicate the specific conditions of the experimental methods, usually in accordance with conventional conditions, such as those described in Sambrook et al., Molecular Cloning: A Laboratory Manual (New York: Cold Spring Harbor Laboratory Press, 1989), or manufacturer's recommended conditions. Unless otherwise indicated, percentages and parts are referred by weight.

EXAMPLE 1

[00145] Biomarkers distinguish HBeAg seroconverted from non-converted individuals in chronic hepatitis B patients treated with a therapeutic vaccine [00146] ABSTRACT

[00147] Cytokine assays of host immune responses to vaccination can indicate vaccine efficacy. Here we tested the hypothesis that assays of the cytokine status of infected individuals prior to therapeutic vaccination might provide a guide to vaccine therapeutic efficacy. If so, cytokine analysis might be used to select appropriate patients for therapeutic vaccination. Data were obtained from a panel of 14 cytokine/chemokine assays that were done during a phase III clinical trial of HBsAg- HBIG therapeutic vaccine (YIC) treatment of chronic hepatitis B (CHB) patients. Summarized assay results were compared between patients who responded by HBeAg seroconversion and non-responders. Though no single cytokine or chemokine showed clear correlation with responsiveness, by bio-mathematical analysis with Boolean modelling, the combined results revealed that plasma IL-10, IL-33 and MIP-1a together correlated best with responsiveness. However, the difference between HBeAg seroconverted and non-converted YlC-treated CHB patients was maximized when results of all 14 cytokine/chemokine assays were included and showed a sensitivity around 0.59, and a specificity of 0.8. It suggested that the combined analysis of these elements may be useful to screen appropriate CHB patients for therapeutic vaccination with YIC.

[00148] Keywords: Hepatitis B virus; Antigen-Antibody complex therapeutic vaccine; biomarker; Boolean analysis model

[00149] Materials and methods

[00150] 1.1 Study Samples

[00151] All plasma samples had been collected at baseline and weeks 12, 24, 40, 52, and 76 after initial vaccine injection, tested for HBeAg, anti-HBe, HBV DNA, and HBsAg and stored at -80 °C. Patient's consent for retrospective serum analysis had been obtained in the phase Ilia clinical tria. Plasma samples from a total of 44 patients who had HBeAg-seroconverted in the trial and from 49 patients randomly picked from those who had not seroconverted were thawed, mixed by vortexing, and centrifuged to remove particulates prior to the cytokine/chemokine assays. Frozen plasma samples used for the main analysis had been taken on the day before treatment (baseline).

[00152] 1.2 Luminex cytokine/chomekine multiplex assays

[00153] Fourteen cytokines/chemokines were measured using a human MILLIPLEX MAP kit (Millipore) that assays ΙΙ_-1 β, IFN-α, TNF-α, IFN-γ, IL-2, IL-7, IL- 15, IL-21 , IL-33, IL-10, MIG, IP-10, ΜΙΡ-1α, ΜΙΡ-1 β. Plates were read with a Luminex instrument according the manufacturer's instruction (Luminex 200, Austin Luminex, USA). Concentrations of each target were analyzed using MILLIPLEX Analyst 5.1 software (Merck Millipore Darmstadt, Germany) according to the manufacturer's standard curve. The concentrations of various cytokines/chemokines were displayed as heat maps by MutiExperiment Viewer 4 software (TM4 Group; Dana Farber Cancer Institute, Boston, MA).

[00154] 1.3 Boolean modeling

[00155] To construct the correlation between immune biomarkers and final seroconverstion, a Boolean model was adapted [15]. In detail, expression data of immune biomarkers were first transformed into 1 (Expressed) or 0 (Not expressed) by cut-off 0.1 pg/ml, which is the detection limit of the Luminex system. Secondly, the boolean state of the biomarkers was generated from each patient as a serial of 1/0s. Thirdly, a Boolean states pool was generated for all 93 patients including the HBeAg- seroconverted and non-converted ones. The theoretical pool size is 2ⁿ possibilities (n= number of immune biomarkers, which is 10 here). However, in fact only 22 individual states existed in our data. Fourthly, prediction panels for best separation between converter and non-converter were evaluated by receiver operating characteristic curves (ROC) performance of random combinations from the boolean states pool (random combination of the boolean states generated 22! = 1.1*10²¹ possibilities). ROC performace was analysed with Origin 8.0 graphing software. The sensitivity and specificity calculation formulas were: Sensitivity = True positive/ (True positive + False negative); Specificity = True negtive/ (False positive + True negtive).

[00156] 1.4 Statistical analyses

[00157] The statistical analyses were done by performing unpaired t tests and two-way (ANOVA) analysis with Prism 6.0 software. Statistical significance was defined by P < 0.05. Several on-off characteristic states were combined with an enumeration method to distinguish the converted and non-converted patients based on the sensitivity and specificity of every possible combination listed.

[00158] 2. Results

[00159] 2.1 Characteristics of the study population

[00160] A total of 44 HBeAg seroconverted patients and 49 randomly picked non-seroconverted patients were selected for this study. The two groups showed similar baseline characteristics (Table 1). There were no significant differences between the levels of ALT (35 to 471 IU/L) or HBV DNA (average 4* 10⁶ copies/ml) observed in the seroconverted group when compared to the levels in the HBeAg non-seroconverted group (ALT ranging from 21 to 433.7 IU/L and average of 3.8x10⁶ copies/ml HBV DNA). This result indicated that although the groups had dramatically different outcomes under the same YIC treatments, any correlation with baseline viral loads and HBeAg seroconversion was weak and that other parameters might correlate better.

[00161] Table 1. HBeAg seroconversion and non-seroconversion characteristics.

Patient Characteristics

Characteristic HBeAg HBeAg Non-

Seroconversion seroconversion

Number 44 49

Age range 25.5 (18-38) 27.2 (17-53)

Sex ratio (male/female) 33/11 35/14

Weight (kg) 60.8 62.4

ALT (IU/L) 178.0 (35-471) 150.5 (21-433.7)

HBV DNA (copies/mL)

0 week 4.0x10⁶ 3.8x10⁶

76 week 2.6x10⁴ 2.5x10⁶

HBV genotype

B 27 27

C 14 17

Unknown 3 5

Notes: The characteristics of 44 HBeAg seroconverted and 49 non-seroconverted patients treated with YIC in the phase Ilia clinical trial are displayed above including age, sex ratio, weight, ALT, HBV DNA copies and HBV genotype.

ALT, alanine aminotransferase.

[00162] 2.2 Levels of multiple cytokines and chemokines in HBeAg converted and non-converted groups

[00163] Host factors, particularly anti-viral and associated immune responses, are the keys to be considered. The combined effects of pro-inflammatory, inflammatory, anti-inflammatory and chemo-attracting factors determine the outcome and efficacy of the host immune responses. We measured 14 potentially relevant cytokines and chemokines in the plasma samples of the HBeAg converted (FIG. 1 (a)) and non-converted groups (FIG. 1 (b)). Of the 14 analyzed, TNF-a, MIP-1 a, ΜΙΡ-1 β, MIG and IP-10 appeared as possible markers to distinguish between the groups, and among these five, the IP-10 and MIG were the most abundant factors according to the assay. The other 9 factors were below detectable limits in both groups except for a few patients. Among the five possible markers, the levels of expressed TNF-a and IP-10 were elevated in the HBeAg converted group compared to the non-converted group (TNF-a pO.0001 , IP-10 p=0.0235), and conversely the MIP-1 a and ΜΙΡ-1 β were decreased in the HBeAg seroconverted group (MIP-1 a, p<0.0001 and ΜΙΡ-1 β p=0.0056). High levels of MIG were found in both groups, but there was no significant difference between them. Although several cytokines/chemokines had statistically significant differences between the two groups, the TNF-a, MIP-1 a, ΜΙΡ-1 β, MIG and IP-10 data gave HBeAg serocenversion AUCs of only 0.604, 0.435, 0.559, 0.454 and 0.478 respectively (FIG. 8). Hence, the poor ROC curves meant that we did not find a satisfactory cutoff value that would permit use of any single analysis alone to distinguish between the groups. This indicated that we should consider a more complex or multiple parameter method of comparison.

[00164] 2.3 Development of a Boolean model to reflect the difference between seroconverted and non-converted patients before the YIC therapeutic vaccinations.

[00165] The Boolean model uses logical values ('on' (1) or Off (0)) to determine if the factor is active/present or not. The on/off events can be linked to multiple factors by a biological association or functional role fitted into a regulatory network, which can consequently be encoded by Boolean operations on the network nodes. We sought to use this Boolean model to analyze multiple cytokines relevant to the immune status of CHB patients of the two groups. The scheme of Boolean modeling is detailed in FIG. 2(a)-FIG. 2(g). Among the 14 cytokines/chemokines, TNF-a, ΜΙΡ-1 β, MIG and IP-10 were highly expressed by most of the patients, made no contribution to discriminate between converted and non-converted patients and were therefore excluded (See FIG. 9). Ten cytokines, other than TNF-a, ΜΙΡ-1 β, MIG and IP-10 which were highly expressed among most of the patients, were assigned as a set of nodes whose status was binary in an immune regulatory network that described the immune status of each CHB patient before the YIC treatment. Patients were divided into the converted and the non-converted groups and their summarized immune status depicted according to this Boolean model, e.g. as "0000000000", "111 111 11 11", "0000000001 , etc", as shown in Table 2. In fact, there were a total of 22 immune states that were summarized as a serial of "0" or "1 " in this study. Any possible combination of these Boolean states were picked up from the 22 states pool and defined as a criterion to distinguish converted and non- converted patients. Each criterion set randomly generated the predictive numbers of converted and non-converted patients. According to the predictive and actual numbers, each possible combinational pattern was evaluated by its calculated sensitivity and specificity. Based on the enumeration method in this combined factors analysis, the optimal pattern was used to distinguish the group of patients who subsequently converted after the YIC treatments from those who did not.

Table 2. The Boolean model of each CHB patient's immune status

Note: The cytokine/chemokine expression profile of each patient in the HBeAg seroconversion and non-seroconversion groups before YIC treament was depicted in the Boolean model using logical values ('on' (1) or Off (0)). The number of patients with the same on-off characteristics is summarized.

[00167] Based on the enumeration method in this combined factors analysis, the baseline status could distinguish the group of patients who subsequently converted after the YIC treatments from those who did not. Six comparisons contributed to distinguishing the converted from the non-converted patients. These were: (1-3) single expression of either IL-10 alone, IL-33 alone, or MIP-1a (0.1-20 pg/ml) alone, (4) dual expressions of IL-10 and MIP1 a, (5) triple expressions of IL- 10, IL-33 and MIP1a, and (6) all 10 cytokines expressed (FIG. 9 and Table 3). For instance, four patients had only IL-33 detectable prior to the YIC treatments and all four had HBeAg seroconverted at the endpoint; none of the patients in the HBeAg non-seroconverted group had expressed IL-33. This result suggested that the IL-33 expression may reflect a patient more likely becoming an HBeAg seroconverted individual if he finished the YIC treatments. However, the number of patients who expressed IL-33 alone was limited and more study would be needed to confirm this point.

[00168] By combining the six cytokine expression patterns listed above to predict patients who would reach the HBeAg seroconversion, we found that 26 of the 44 HBeAg seroconverted patients and 39 of the 49 non-converted patients would have been correctly predicted. Patients with Boolean patterns of assay results that were not within the range of these combinations were considered to be non- converted ones. The sensitivity of the cytokine combination analysis to distinguish converted and non-converted patients at baseline was about 0.59 and its specificity was about 0.80 (Table 4).

[00169] Table 3. Combined analysis of Cytokines in patients screening

Combined Converted Non- analysis of group converted

cytokines in CHB (n) group

patients (n)

screening

MIP-1a (<20 5 5

pg/mL)

IL-10 4 2

Notes: The IL-33 4 0 optimal

IL-10, ΜΙΡ1α 10 3 combinations of

IL-10, IL-33, ΜΙΡ1α Ϊ 0

10 All 2 0

Conformance (n) 26 10

Non-conformance 18 39

(n)

cytokine/chemokines at baseline level used to discriminate the HBeAg seroconverted patients from the non-converted ones are listed and the numbers of patients within these combinations are shown.

[00170] Table 4. The sensitivity and specificity of Single analyte MIP1 a, IL-10, IL-33 and combined analysis

Biomarker Sensitivity Specificity

MIP-1 a (<20 pg/mL) 0.70 0.32

IL-10 0.22 0.61

Notes: The IL-33 0.14 0.80 sensitivity and specificity of IL-10, IL-33, MIP-1a 0.59 0.80 single biomarker (<20 pg/mL) including MIP-1 a (<20 pg/mL), IL-10 10 cytokines all or IL-33 and the optimal expressed combinations were calculated according to the sensitivity and specificity calculation formulas described.

[00171] 2.4 Virological data to validate the reliability of this combined analysis

[00172] To validate the accuracy of this Boolean analysis model, we examined the levels of HBeAg, HBV DNA, HBsAg and HBeAb that were found during course of the entire 76-week clinical trial in the groups with known conversion (n=44) and non- conversion (n=49) outcomes and the groups with predicted conversion (n=26) and predicted non-conversion (n=39) outcomes. We observed that there was no significant difference between the clinically tested groups with the corresponding predicted groups (FIG. 3(a), FIG. 3(b), FIG. 3(c) and FIG. 3(d)). This result demonstrated that the prediction parameters that were obtained from the multifactorial analysis in the Boolean model were consistent with the clinical outcomes. Although there was a difference between clinically tested and predicted groups in HBeAb, this study worked as a proof of principle and more biomarkers should be enrolled to improve the accuracy of prediction.

[00173] 3. Discussion

[00174] Host factors, particularly anti-viral and associated immune responses, are key factors to be considered as determinants of immunotherapeutic efficacy. Analysis of the combined contribution of pro-inflammatory, inflammatory, antiinflammatory and chemo-attracting factors is required.

[00175] Due to the complex interactions in these networks, a single change in cytokine/chemokine may be insufficient to reveal the complete immune status of patients [16]. To circumvent this, we have adopted a Boolean modeling approach using multi-cytokines-chemokines as biomarkers to characterize signatures of patients who subsequently responded or did not respond to the YIC immunotherapy. Of the 14 cytokines/chemokines tested, combinations of IL-10, IL-33 and MIP-1a were able to discriminate the HBeAg seroconverted patients from the non-converted ones. However, when all 14 cytokines/chemokines were included the sensitivity of this method increased to 0.59, while the specificity was 0.8.

[00176] In clinical trials, accurate assessment of immune status is essential prior to selecting the appropriate patients for a specific immunotherapeutic treatment. The YIC was designed to enhance the up-taking HBsAg by macrophages and dendritic cells via their Fc receptors and in turn to generate robust CD4⁺ and CD8⁺ T- cell activations. As the activation of functional T cells can be monitored by their expressions of appropriate cytokines or chemokines, the sum of changes in these factors in a complex immune network needs to be examined. In this study, we showed that assaying a single cytokine/chemokine was not useful for distinguishing HBeAg seroconverters for non-converters. In contrast, multi-factor analysis by Boolean modeling did provide such a distinction.

[00177] We found that IL-33, IL-10 and MIP-1 a were three critical factors highly corresponding to the HBeAg seroconversion after the YIC treatments. IL-33, a member of the IL-1 cytokine family, binds to the ST2 receptor and regulates the NF- kB signaling pathway. In the liver, epithelial cells, hepatocytes and hepatic stellate cells are considered to be the main source of IL-33 [13]. IL-33 can enhance both innate and adaptive immune responses. Endogenous IL-33 has been found to act as an alarmin and be necessary for induction of potent CD8⁺ T cell (CTL) responses to virus infections in mice [17]. Although IL-10 is generally considered a central immunoregulatory cytokine, especially in maintaining immune tolerance in chronic virus infections [18, 19], a beneficial role of IL-10 expression in the earlier HBeAg seroconversion is also observed, suggesting that the IL-10 may be bifunctional during the course of HBV infection [20]. Chemokine MIP-1 a is involved in the recruitment and activation of NK cells, T cells and macrophages through binding to its receptors CCR5, CCR3 and CCR1 [21]. CCR5 is a key regulator of immune responses to HBV infection, hepatic fibrosis and HCC progression [22-24], and has been observed to be decreased on the peripheral blood cytotoxic T lymphocytes of CHB patients [25]. However, whether the lower initial expression of MIP-1 a in plasma is more favorable for the migration of T cells to infected liver is a hypothesis that remains to be confirmed.

[00178] Boolean modeling has previously been successfully applied in analysis of biological complex networks at the level of genes, proteins, cells and populations [26-28]. Boolean analysis can be applied to simplify the multi-factor profiles and display them in a variety of ways [29, 30]. In this study, the same principle was used after simplifying the state of these cytokine/chemokine nodes as 1 (ON) if it was detectable and as 0 (OFF) if undetectable based on the limitations of the test kit employed. Any node that signaled in this network and seemed disconnected could indicate either a positive or a negative mediator since the mechanisms in such networks are still vague. Accordingly, this Boolean model method and results offered limited insights into the mechanism of HBeAg seroconversion by YIC treatment in CHB patients. More data, to be collected in future clinical trials, may verify its use for selecting the most appropriate CHB patients for YIC treatment.

[00179] We used the Boolean method to retrospectively analyze CHB patients' sera that had been obtained prior to YIC treatment. We measured relevant cytokine/chemokine profiles in pre-treatment samples from CHB patients who had or had not seroconverted following YIG treatment. Indeed, this Boolean model method and results could offer unique insights into mechanism of CHB patients' HBeAg seroconversion by YIC treatment in addition to possibly providing a valuable tool to prescreen for responders within the CHB patients for future YIC clinical practices. Ultimately, CHB patients selected in this way may have not only higher responding rates, but could reach higher cure rates after immune therapy.

[00180] In conclusion, we identified a combination panel of IL-33, IL-10, and MIP-1 a that may play a role in triggering antiviral immune responses and controlling virus replication upon YIC treatment. These observations may expand the scope of precision medicine if HBV patients who are selected on the basis of plasma cytokine/cytokine content have a better favorable response rate to immunotherapy.

[00181] We herein introduce the use of Boolean modeling to analyze the significance of multiple cytokines/chemokines for associated patient responsiveness to YIC therapeutic vaccine treatment. With this model, we defined a method to obtain optimal sensitivity and specificity for prediction of patient responsiveness before therapeutic vaccination. The advantage of using this model of cytokines/chemokines analysis may be further explored in other clinical studies.

EXAMPLE 2

[00182] Establishment and Verification of Multi- Factor Predictive Model for HBV therapeutic vaccine

[00183] Study Samples

[00184] 76 HBV patients were included in this study.

[00185] Included criteria: 1) Age 18-65 years, not limited on gender; 2) HBeAg- positive chronic hepatitis B patients and HBeAg seroconversion after nucleoside analogue antiviral treatment, HBV DNA lower than limit of detection, normal ALT, and the treatment consolidated for one year.

[00186] All patients were randomly divided into four groups: the first group of 23 people continued oral adefovir dipivoxil (ADV) once a day; the second group of 17 people was injected IFN-a intramuscularly every two days on the basis of the first group; the third group of 17 people was injected GM-CSF intramuscularly once a day for 3 days on the basis of the second group. The fourth group of 19 people received hepatitis B vaccine after 3 needles GM-CSF. All patients stopped treatments after 48 weeks except follow-up oral ADV to 72 weeks.

[00187] Method

[00188] 1. 35 factors were detected at weeks 0 and 12 in HBV patients plasma by Luminex 200, including MIP-1 alpha, SDF-1 alpha, IL-27, IL-1 beta, IL-2, IL-4, IL-5, IP-10, IL-6, IL-7, IL-8, IL-10, Eotaxin, IL-12p70, IL-13, IL-17A, IL-31 , IL- 1 RA, RANTES, IFN-g, GM-CSF, TNF-a, MIP-1 beta, IFN-a, MCP-1 , IL -9, TNF-beta, GRO-alpha, IL-1a, IL-23, IL-15, IL-18, IL-21 , IL-22, MIG. In detail, the blank hole density was set up as the cutoff value, the detected samples were higher than the cutoff value was defined as positive result (1), and lower than or equal to the cutoff value was defined as negative result (0). The 34 factor (REF: EPX340-12167-901) and MIG (REF: EPX01 A- 10285-901) kits were purchased from Invitrogen.

[00189] 2. Boolean Model [00190] 2.1 Determining factors that contribute to prediction

[00191] Screen factors that are useful for predicting the end point, obtain the optimal solution (Jmax = Sensitivity + 1 - Specificity, more Jmax was better prediction effect; When 100% prediction, Jmax = 1) through 35 factors expression as beginning and sAg negative (<=0.07 IU/ML) or decreasing sAg (decreasing one log) as end point. The optimal solution is defined as the highest true positive rate

(sensitivity) and the lowest false positive rate (1 -specificity).

[00192] 2.2 Determining a single factor that affects the optimal prediction

[00193] Find out the factors that are indispensable for predicting the end point, retain that factors maintaining the Jmax on the optimal solution through gradually eliminating a single factor as a starting point, sAg negative or sAg falling (down by one log) as ending point.

[00194] 2.3 Finding the main factor

[00195] Find the factor that contributes most to the optimal solution, select 1-2 factor with the Jmax maximum through a single factor as starting point, sAg negative or sAg decreased (decrease by one log) as the end point.

[00196] 2.4 ROC Curve

[00197] Find the single factor matching with the main factor to obtain the optimal solution. That is, the main factor is matched with any single factor that definitely affects Jmax, and finds the best match for Jmax.

[00198] 3. The overall prediction

[00199] The smallest 2 factors were matched by pairs, which were week 0 biomarker data as starting with sAg seroconversion as end point, week 12 biomarker data as starting with sAg seroconversion as end point, week 0 biomarker data as starting with sAg decreasing as end point and week 12 biomarker data as starting with sAg decreasing as end point. Jmax close to 1was selected, that was the greatest impact on Jmax.

[00200] 4. Virological data to validate the reliability of this combined analysis

[00201] The predictive model effect was validated by comparing the actual end- point virological data with the predicted data.

[00202] Result

[00203] Research object

[00204] A total of 76 HBV patients were included in this trial. 10 patients were sAg seroconversion, and sAg of 22 patients was decreased by one log (Table 5).

[00205] Table 5. Patients state at the end of treatment

End of patient Number of patients [ C^e⁾

^■ l itf

[00206] The minimum 2 factors to distinguish patients from reaching the end point by Boolean Model

[00207] The combination of low IL-10 and high MIG represent 50% of sAg seroconversion patients (FIG. 4(a)), as well as low IL-13 and high GRO-alpha combination can distinguish patients with a decrease in 54.5% sAg (FIG. 4(b)), with the patient's baseline 35 factors expression level as starting points and sAg seroconversion or sAg decreased as endpoints. Similarly, low IL-13 and high MIP- 1 b can distinguish 70% of sAg seroconversion patients (FIG. 4(c)), and high IL-18 and high IFN-a can distinguish 86.4% sAg decreased patients (FIG. 4(d)) through 35 factors at weeks 12 expression level as starting points and sAg seroconversion or sAg decreased as endpoints.

[00208] Establish multi-factor prediction model

[00209] To select the optimal multi-factor combination, we combine all the smallest 2 factors and then gradually reduce the factor by comparing the Jmax (the least number of factors and the largest value of Jmax). Because of the small number of sAg converted or decreasing patients, so we gradually reduced the number of factors in combination and found that the Jmax of the IL-10, IL-13, IL-23, IFN-a, IL- 18, and MIG combinations changed most drastically (the number of factors was the least and Jmax was maximum). Similarly, due to the large number of non-converted and non-decreasing patients, we gradually increased the factor based on the minimum 2 factor and the mean value of Jmax of the combination of IL-13, IFN-a, and IL-23 was the largest. [00210] Review of virological data

[00211] We found similar decline between the actual virological curve of converted patients and multiple-factor predicted curve using no matter sAg seroconversion or sAg decreasing as end point and week 0 or week 12 data of IL- 10, IL-13, IL-23, IFN-a, IL-18 and MIG as starting point. Actual sAg-decreasing patients compared with predicted sAg-decreasing patients were no statistic difference. Therefore, IL-10, IL-13, IL-23, IFN-a, IL-18 and MIG6 factors could be used to predict sAg seroconversion and sAg decreasing.

[00212] We found similar decline between the actual virological curve of non- converted patients and IL-13, IFN-a, and IL-18 predicted curve using no matter week 0 or week 12 data as starting point and sAg seroconversion or sAg decreasing as end point. Actual non-converted patients compared with predicted non-converted patients were no statistic difference using week 0 data as starting point and sAg seroconversion as end point. Similarly, actual non-converted patients compared with predicted non-converted patients were no statistic difference using week 12 data as starting point and sAg seroconversion as end point. Therefore, the combination of IL- 13, IFN-a, and IL-18 could be used to predict non sAg seroconversion and non sAg decreasing.

[00213] It is understood that the foregoing detailed description and accompanying examples are merely illustrative and are not to be taken as limitations upon the scope of the invention, which is defined solely by the appended claims and their equivalents. Various changes and modifications to the described embodiments will be apparent to those skilled in the art. Such changes and modifications, including without limitation those relating to the biomarkers, vaccine formulations, or methods of vaccination, or any combination of such changes and modifications of use of the invention, may be made without departing from the spirit and scope thereof. REFERENCES

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Claims

1. A combination of biomarkers comprising a plurality of immuno-biomarkers associated with immune function that differentiates a responding patient from a non- responding one against a vaccine, wherein immuno-biomarkers associated with immune function are three or more biomarkers selected from the group consisting of MIP-1 a, SDF-1 a, IL-27, ΙΙ_-1 β, IL-2, IL-4, IL-5, IP-10, IL-6, IL-7, IL-8, IL-10, IL-1 1 , Eotaxin, IL-12p70, IL-13, IL-17A, IL-31 , IL-35, IL-1 RA, RANTES, IFN-g, GM-CSF, TNF-a, MIP-1 beta, IFN-a, MCP-1 , IL-9, TNF-β, GRO-alpha, IL-1 a, IL-23, IL-15, IL- 18, IL-21 , IL-22, MIG, Granulocyte-colony stimulating factor (G-CSF or GCSF), C-X- C motif chemokine receptor 1 (CXCR1), CXCR2, CXCR3, CXCR4, CXCR5, CXCR6, CXCR7, CXCR8, CXCR9, CXCR10, CXCR1 1 , CXCR12, CCR1 , CCR2, CCR3, CCR4, CCR5, CCR6, CCR7, CCR8, CCR9, CCR10, CCL1 , CCL2, CCL3, CCL4, CCL5, CCL6, CCL7, CCL8, CCL9, CCL10, CXCL1 , CXCL2, CXCL3, CXCL4, CXCL5, CXCL6, CXCL7, CXCL8, CXCL9, CXCL10, CXCL1 1 , CXCL12, CXCL13 and VEGF.

2. The combination of claim 1 , wherein the vaccine is a hepatitis B vaccine.

3. The combination of claim 1 , wherein the vaccine is a chronic hepatitis B vaccine.

4. The combination of claim 1 , wherein the vaccine is an HBsAg-HBIG therapeutic vaccine (YIC).

5. The combination of claim 1 , wherein the vaccine is a hepatitis B vaccine in combination of GM-CSF.

6. The combination of claim 4, wherein the immuno-biomarkers comprise IL-10, IL-33 and MIP-1 a.

7. The combination of claim 4, wherein the immuno-biomarkers consist of IL-10, IL-33 and MIP-1 a.

8. The combination of claim 5, wherein the immuno-biomarkers either comprise IL-10, IL-13, IL-23, IFN-a, IL-18 and MIG6 or comprise IL-13, IFN-a, and IL-18.

9. The combination of claim 5, wherein the immuno-biomarkers either consist of IL-10, IL-13, IL-23, IFN-a, IL-18 and MIG6 or consist of IL-13, IFN-a, and IL-18.

10. The combination of claim 1 , wherein the combination of biomarkers can differentiate a responding patient from a non-responding one against the vaccine.

11. The combination of claim 1 , wherein the combination of biomarkers can differentiate a responding patient from a non-responding one against the vaccine before the vaccination.

12. The combination of claim 1 , wherein the combination of biomarkers can differentiate a responding patient from a non-responding one against the vaccine during the vaccination.

13. A method for evaluating efficacy of a vaccine on a patient, the method comprising:

a) obtaining a biological sample of each of the patients including the patient from a patient pool including both seroconverted patients and non- seroconverted ones;

b) quantifying concentrations of a combination of immuno-biomarkers of claim 1 in the biological sample for each of the patients;

c) generating a profile of the immuno-biomarkers from the concentrations of the immuno-biomarkers of each of the patients by using a model;

d) determining in the profile whether each of the immuno-biomarkers of each of the patients is expressed or not expressed, wherein one immuno-biomarker of the patient is expressed if its concentration is above a cut-off concentration and otherwise it is unexpressed;

e) setting every possible combination of Boolean states as a criterion to distinguish converted patients from non-converted; and

f) classifying the patient as a converted one (or a responding one) to the vaccine if the patient's Boolean state consists with the criterion state and as a non-converted one (or a non-responding one) otherwise.

14. The method of claim 13, wherein the vaccine is a hepatitis B vaccine.

15. The method of claim 13, wherein the vaccine is a chronic hepatitis B vaccine.

16. The method of claim 13, wherein the vaccine is an HBsAg-HBIG therapeutic vaccine (YIC).

17. The method of claim 13, wherein the vaccine is a hepatitis B vaccine in combination of GM-CSF.

18. The method of claim 13, wherein the model is a Boolean modeling.

19. The method of claim 13, wherein the cut-off concentration is 0.1 pg/ml.

20. The method of claim 16, wherein the immuno-biomarkers comprises IL-10, IL-33 and MIP-1 a.

21. The method of claim 16, wherein the immuno-biomarkers consists of IL-10, IL-33 and MIP-1 a.

22. The method of claim 17, wherein the immuno-biomarkers either comprise IL- 10, IL-13, IL-23, IFN-a, IL-18 and MIG6 or comprise IL-13, IFN-a, and IL-18.

23. The method of claim 17, wherein the immuno-biomarkers either consist of IL- 10, IL-13, IL-23, IFN-a, IL-18 and MIG6 or consist of IL-13, IFN-a, and IL-18.

24. The method of claim 13, wherein the biological sample is obtained from the patient before vaccination by the vaccine.

25. The method of claim 13, wherein the biological sample is obtained from the patient during vaccination by the vaccine.

26. The method of claim 13, wherein the biological sample comprises cerebrospinal fluid, whole blood, blood serum, plasma, urine, saliva, other bodily fluid, breath, condensed breath, or an extract, purification, or dilution of any of these.

27. The method of claim 13, wherein the quantifying step uses a technique comprising SELDI (-TOF) spectrometry, MALDI (-TOF) spectrometry, a 1-D gel- based analysis, a 2-D gel-based analysis, mass spectrometry (MS), liquid chromatography (LC), reverse phase liquid chromatography (RP-LC), size permeation chromatography, gel filtration chromatography, ion exchange

chromatography, affinity chromatography, FPLC, HPLC, UPLC, other LC-based techniques, other LC-MS-based technique, or combinations thereof.

28. The method of claim 13, wherein the quantifying step uses a method comprising an immunological method, a biosensor method, a microanalytical method, a microengineered method, a microseparation method, an

immunochromatography method, or combinations thereof.

29. A kit for differentiating a responding patient from a non-responding one against a vaccine, the kit comprising a biosensor capable of detecting and/or quantifying the combination of biomarkers as defined in any of claims 1 to 12.