Patent Application
20200150117
According to the World Health Organization (WHO) cancer is one of the leading causes of morbidity and mortality worldwide, with approximately 14 million new cases and 8.2 million cancer deaths in 2012 worldwide (Ferlay et al., 2015). An estimated 21.6 million new cancer cases are predicted for 2030 (an increase of 53 percent from 2012.
The economic impact of cancer is significant is increasing. In the US the total annual economic cost of cancer in 2010 were estimated at approximately US$ 1.16 trillion.
According to GLOBOCON the four most commonly new diagnosed cancer types in 2012 were lung (1.82 million), breast (1.67 million), colorectal (1.36 million), and prostate cancer (1.1 million) (Ferlay et al., 2015).
There are many types of cancer treatment, which depend on the cancer type. These include classical treatments such as surgery with chemotherapy and/or radiation therapy or hormone therapy. New therapies aim to directly target the tumor or to inhibit the growth of the tumor with tyrosine kinase inhibitors, monoclonal antibodies, and proteasome inhibitors.
Despite improvements in current therapies, the low survival rates of cancer are due to inadequate early diagnosis, resistance to current therapies, and ineffective treatment. Thus, alternative treatment approaches are desperately needed for cancer.
In contrast to targeting cancer-specific oncogenes, which promote survival and metastasis of cancer, the primary goal of cancer immunotherapy is to stimulate the human immune system to identify and destroy developing tumors.
The concept of cancer immunotherapy is based on the finding that many tumor cells express abnormal proteins and molecules, which in theory should be recognized by the immune system. Proteins, which are present in the tumor and elicit an immune response, are called tumor-associated antigens (TAA). The group of TAA comprises mutated proteins, overexpressed or aberrantly expressed proteins, proteins produced by oncogenic viruses, germline-expressed proteins, glycoproteins or proteins, which are produced in small quantities or are not exposed to the immune system. The immune response to TAA includes cellular processes as well as the production of antibodies against TAA.
However, forcing immune cells to recognize the tumor as foreign is proving to be much more difficult than anticipated. This is because the tumor effectively suppresses immune responses by activating negative regulatory pathways. These negative regulatory pathways are called immune-checkpoints, which under normal physiologic conditions; maintain a careful balance between activating and inhibitory signals thereby protecting the normal tissue from damage.
Collectively, these findings have led to different immunotherapeutic approaches including active, passive and immunomodulatory approaches.
Active immunotherapies directly stimulate the immune system to target tumors using inflammatory factors such as cytokines or therapeutic cancer vaccines.
For example, PROSTVAC cancer vaccination is intended to trigger a specific and targeted immune response against prostate cancer. PROSTVAC is a virus-based vaccine that carries the tumor-associated antigen PSA/KLK3 (prostate-specific antigen) along with three natural human immune-enhancing costimulatory molecules collectively designated as TRICOM (LFA3, ICAM1, and B7.1/CD80). The PSA-TRICOM vaccines infects antigen-presenting cells (APCs) and generate proteins that are expressed on the surface of the APCs by major histocompatibility complex (MHC) proteins. This leads to T-cell activation.
PROSTVAC is currently tested in phase 3 clinical trials for treating minimally symptomatic metastatic prostate cancer (mCRPC). Prior phase 2 clinical studies showed that patients who received PROSTVAC had a median overall survival that was 8.5 months longer than the control group (25.1 versus 16.6 months) and a 44% reduction in the risk of death (stratified log-rank P=0.0061). PROSTVAC was generally well tolerated, with the most common side effects including injection site reactions, fever, fatigue, and nausea (Kantoff et al., 2017).
Passive immunotherapies usually utilize monoclonal antibodies targeting immune checkpoint molecules. The cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed death 1 (PD-1) immune checkpoints are negative regulators of T-cell immune function, when bound to their respective ligands CD80/86 and programmed cell-death ligand 1 and 2 (PDL1/PDL2).
In addition to anti-CTLA4 and anti-PD1/PDL1 antibodies, drugs targeting other checkpoints such as lymphocyte activation gene 3 protein (LAG3), T cell immunoglobulin mucin 3 (TIM-3), and IDO (Indoleamine 2,3-dioxygenase) are in development.
Inhibition of checkpoint inhibitors, resulting in increased activation of the immune system, has led to new immunotherapies for melanoma, non-small cell lung cancer, and other cancers (Buchbinder and Desai, 2016).
Ipilimumab, an inhibitor of CTLA-4, is approved for the treatment of advanced or unresectable melanoma.
Nivolumab and pembrolizumab, both PD-1 inhibitors, are approved to treat patients with advanced or metastatic melanoma and patients with metastatic, refractory non-small cell lung cancer.
Anti-PDL1 inhibitor avelumab has received orphan drug designation by the European Medicines Agency for the treatment of gastric cancer in January 2017. The US Food and Drug Administration (FDA) approved it in March 2017 for Merkel-cell carcinoma, an aggressive type of skin cancer.
Despite the fact that checkpoint inhibitors, demonstrated clinical efficacy across multiple cancer types, checkpoint inhibitor drugs are not effective against all cancer types, nor in every patient within a cancer type (Brahmer et al., 2012).
In addition, compared to cancer vaccination strategies, checkpoint inhibitors can induce severe immune-related adverse events (irAE). The main side effects include diarrhea, colitis, hepatitis, skin toxicities, arthritis, diabetes, endocrinopathies such as hypophysitis and thyroid dysfunction (Spain et al., 2016).
Therefore, biomarkers are needed to predict both clinical efficacy and toxicity. Such biomarkers may guide patient selection for both monotherapy and combination therapy (Topalian et al., 2016).
There are apparent differences between the CTLA4 and PD1 pathways of the immune response. CTLA4 acts more globally on the immune response by stopping potentially autoreactive T cells at the initial stage of naive T-cell activation, typically in lymph nodes. The PD-1 pathway regulates previously activated T cells at the later stages of an immune response, primarily in peripheral tissues (Buchbinder and Desai, 2016).
Substantial efforts have been undertaking to identify biomarkers for predicting which patient will respond best to immune checkpoint inhibition.
Given the mechanism of action of inhibiting the PD1 pathway, several studies have evaluated the expression of the PDL1 ligand in the tumor as a biomarker of clinical response. However, differences regarding the predictive value of PDL1 expression have been found. This limits the current use of PDL1 as a biomarker for predicting clinical response. The differences in the utility of PDL1 as biomarker may be caused by differences in the assay type used in different studies and by variable expression of PDL1 during therapy (Manson et al., 2016).
Since checkpoint inhibition is typically viewed as enhancing the activity of effector T cells in the tumor and tumor environment, other biomarker approaches have focused on identifying TAA recognized by T cells. However, this approach is limited to exploratory analyses and is not practical in a routine laboratory setting because it requires patient-specific MHC reagents (Gulley et al., 2014).
A largely overlooked immune cell type in the context of immunotherapies are B cells, which can exert both anti-tumor and tumor-promoting effects by providing co-stimulatory signals and inhibitory signals for T cell activation, cytokines, and antibodies (Chiaruttini et al., 2017). B-cells produce anti-tumor antibodies, which can potentially mediate antibody-dependent cellular cytotoxicity (ADCC) of tumor cells. It is well established that many cancer types induce an antibody response, which can be used for diagnostic purposes. Although some cancer patients show an antibody response to neo-antigens restricted to the tumor, the majority of antibodies in cancer patients are directed to self-antigens and are therefore autoantibodies (Bei et al., 2009). Breakthrough of tolerance and elevated levels of autoantibodies to self-antigens are also prominent features of many autoimmune diseases.
Thus, autoantibodies hold the potential to serve as biomarkers of a sustained humoral anti-tumor response and irAE in cancer patients treated with immunotherapeutic approaches.
Compared to biomarker strategies involving the identification of TAA-specific T-cells, the identification of autoantibodies can be performed using modern multiplex high-throughput screening approaches using minimal amounts of serum (Budde et al., 2016).
In one aspect is provided a method of identifying a tumor-associated antigen (TAA) for prostate cancer. A group of patients with prostate cancer is selected. Also, a group of patients who are healthy are selected. A sample from at least one patient in the group with prostate cancer is assayed for the level of an autoantibody to an antigen. The level of the autoantibody to an antigen in the group of patients with prostate cancer is compared to the level of the autoantibody in the group of healthy patients. The antigen is determined to be a TAA for prostate cancer if the level of the autoantibody to the antigen is statistically different between the group of patients with prostate cancer versus the group of healthy patients.
In another aspect is provided a method of identifying a TAA as a marker for prostate cancer vaccination response. A group of patients with prostate cancer who have been vaccinated with a vaccine effective to induce an immune response against a prostate cancer antigen is selected. Also, a group of patients with prostate cancer who have not been vaccinated with the vaccine is selected. A sample from at least one patient in the group with prostate cancer is assayed for the level of an autoantibody to an antigen. The level of the autoantibody to an antigen in the group of patients with prostate cancer who have been vaccinated is compared to the level of the autoantibody in the group of patients with prostate cancer who have not been vaccinated. The antigen is determined to be a TAA for prostate cancer if the level of the autoantibody to the antigen is statistically different between the group of patients who have been vaccinated and the group of patients who have not been vaccinated.
In another aspect is provided a method of identifying and treating a prostate cancer patient with PROSTVAC therapy or for vaccination with a prostate antigen. The level of one or more antigens encoded by a gene listed in Table 4 having a positive value for r_in_PROSTVAC Progression-free survival is determined in a sample from the prostate cancer patient who has undergone PROSTVAC therapy. The level of the same one or more antigens in a sample from a prostate cancer patient, or a group of prostate cancer patients, who have not undergone PROSTVAC therapy. The levels of the one or more antigens in the patient who has undergone PROSTVAC therapy are compared with the corresponding levels of the patient or group of patients who have not undergone PROSTVAC therapy. If the level of the one or more antigens in the patient (encoded by a gene listed in Table 4 having a positive value for r_in_PROSTVAC Progression-free survival) is greater than the average level of the one or more antigens in the group of patients with prostate cancer, then PROSTVAC therapy, Ipilimumab, and/or the vaccination with a prostate antigen is administered to the patient.
Additional aspects and embodiments are described below in the Detailed Description.
In one aspect is provided a method of identifying a tumor-associated antigen (TAA) for prostate cancer. A group of patients with prostate cancer is selected. Also, a group of patients who are healthy are selected. A sample from at least one patient in the group with prostate cancer is assayed for the level of an autoantibody to an antigen. The level of the autoantibody to an antigen in the group of patients with prostate cancer is compared to the level of the autoantibody in the group of healthy patients. The antigen is determined to be a TAA for prostate cancer if the level of the autoantibody to the antigen is statistically different between the group of patients with prostate cancer versus the group of healthy patients.
Within the scope of this invention, the term “patient” is understood to mean any test subject (human or mammal), with the provision that the test subject is tested for prostate cancer.
Autoantibodies can be formed by a patient before prostate cancer progresses or otherwise shows symptoms. Early detection, diagnosis and also prognosis and (preventative) treatment would therefore be possible years before the visible onset of progression. Devices and means (arrangement, array, protein array, diagnostic tool, test kit) and methods described herein can enable a very early intervention compared with known methods, which considerably improves the prognosis and survival rates. Since the prostate cancer-associated autoantibody profiles change during the establishment and treatment/therapy of prostate cancer, the invention also enables the detection and the monitoring of prostate cancer at any stage of development and treatment and also monitoring within the scope of aftercare in the case of prostate cancer. The means according to the invention also allow easy handling at home by the patient himself and cost-effective routine precautionary measures for early detection and also aftercare.
Different patients may have different prostate cancer-associated autoantibody profiles, for example different cohorts or population groups differ from one another. Here, each patient may form one or more different prostate cancer-associated autoantibodies during the course of the development of prostate cancer and the progression of the disease of prostate cancer, that is to say also different autoantibody profiles. In addition, the composition and/or the quantity of the formed prostate cancer-associated autoantibodies may change during the course of the prostate cancer development and progression of the disease, such that a quantitative evaluation is necessary. The therapy/treatment of prostate cancer also leads to changes in the composition and/or the quantity of prostate cancer-associated autoantibodies. The large selection of prostate cancer-associated marker sequences according to the invention allows the individual compilation of prostate cancer-specific marker sequences in an arrangement for individual patients, groups of patients, certain cohorts, population groups, and the like. In an individual case, the use of a prostate cancer-specific marker sequence may therefore be sufficient, whereas in other cases at least two or more prostate cancer-specific marker sequences have to be used together or in combination in order to produce a meaningful autoantibody profile.
Compared with other biomarkers, the detection of prostate cancer-associated autoantibodies for example in the serum/plasma has the advantage of high stability and storage capability and good detectability. The presence of autoantibodies also is not subject to a circadian rhythm, and therefore the sampling is independent of the time of day, food intake and the like.
In addition, the prostate cancer-associated autoantibodies can be detected with the aid of the corresponding antigens/autoantigens in known assays, such as ELISA or Western Blot, and the results can be checked for this.
In some embodiments, the antigen is an antigen encoded by a gene listed in Table 1. In some embodiments, the TAA is encoded by a gene listed in Table 2.
Various ways of performing the assay can be undertaken. A portion of serum from the patient with prostate cancer is contacted with a sample of an antigen. The antigen may be immobilized onto a solid support, in particular a filter, a membrane, a bead or small plate or bead, for example a magnetic or fluorophore-labelled bead, a silicon wafer, glass, metal, plastic, a chip, a mass spectrometry target or a matrix. A microsphere as a solid support may also be used. Multiple antigens may be coupled to multiple different solid supports and then arranged on an array.
The array may be in the form of a “protein array”, which in the sense of this invention is the systematic arrangement of prostate cancer-specific marker sequences on a solid support, wherein the prostate cancer-specific marker sequences are proteins or peptides or parts thereof, and wherein the support is preferably a solid support.
The sample comprising any of the TAAs, autoantigens, autoantibodies, are part of, found in, or otherwise present in, a bodily fluid. The bodily fluid may be blood, whole blood, blood plasma, blood serum, patient serum, urine, cerebrospinal fluid, synovial fluid or a tissue sample, for example from tumour tissue from the patient. These bodily fluids and tissue samples can be used for early detection, diagnosis, prognosis, therapy control and aftercare.
The level of a TAA, autoantibody or antigen is assayed by measuring the degree of binding between a sample and the antigen. Binding according to the invention, binding success, interactions, for example protein-protein interactions (for example protein to prostate cancer-specific marker sequence, such as antigen/antibody) or corresponding “means for detecting the binding success” can be visualised for example by means of fluorescence labelling, biotinylation, radio-isotope labelling or colloid gold or latex particle labelling in the conventional manner. Bound antibodies are detected with the aid of secondary antibodies, which are labelled using commercially available reporter molecules (for example Cy, Alexa, Dyomics, FITC or similar fluorescent dyes, colloidal gold or latex particles), or with reporter enzymes, such as alkaline phosphatase, horseradish peroxidase, etc. and the corresponding colorimetric, fluorescent or chemiluminescent substrates. A readout is performed, for example, by means of a microarray laser scanner, a CCD camera or visually.
Comparisons may be performed by any number of statistical analyses, such as those described in Example 5 herein.
In another aspect is provided a method of identifying a TAA as a marker for prostate cancer vaccination response. A group of patients with prostate cancer who have been vaccinated with a vaccine effective to induce an immune response against a prostate cancer antigen is selected. Also, a group of patients with prostate cancer who have not been vaccinated with the vaccine is selected. A sample from at least one patient in the group with prostate cancer is assayed for the level of an autoantibody to an antigen. The level of the autoantibody to an antigen in the group of patients with prostate cancer who have been vaccinated is compared to the level of the autoantibody in the group of patients with prostate cancer who have not been vaccinated. The antigen is determined to be a TAA for prostate cancer if the level of the autoantibody to the antigen is statistically different between the group of patients who have been vaccinated and the group of patients who have not been vaccinated.
Another aspect provides a method of identifying and treating a prostate cancer patient with PROSTVAC therapy or for vaccination with a prostate antigen. The level of one or more antigens encoded by a gene listed in Table 4 having a positive value for r_in_PROSTVAC Progression-free survival is determined in a prostate cancer patient. The level of the one or more antigens in the prostate cancer patient is compared with an average level of the one or more antigens for a group of patients with prostate cancer. PROSTVAC therapy, Ipilimumab, and/or vaccination with a prostate antigen is administered if the level of the one or more antigens in the patient is greater than the average level of the one or more antigens in the group of patients with prostate cancer.
Any number of antigens may be tested, such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20.
In some embodiments, the patient further has a reduced level of one or more antigens encoded by a gene listed in Table 4 having a negative value for r_in_PROSTVAC Progression-free survival as compared to the level in the group of patients with prostate cancer.
PROSTVAC is under development by Bavarian Nordic as a vaccine to be administered to prevent spread of metastatic prostate cancer. PROSTVAC may be helpful to treat men who have symptomatic or minimally symptomatic metastatic castration-resistant prostate cancer (mCRPC). PROSTVAC is a vaccine targeting PSA and is administered by a proprietary prime-boost method. PROSTVAC may be administered subcutaneously. Without wishing to be bound by theory, PROSTVAC may induce a direct immune response that attacks PSA-bearing metastatic prostate cancer cells.
Another aspect provides a method of identifying and treating a prostate cancer patient with PROSTVAC therapy or for vaccination with a prostate antigen. The level of one or more antigens encoded by a gene listed in Table 4 having a negative value for r_in_PROSTVAC Progression-free survival is determined in a prostate cancer patient. The level of the one or more antigens in the prostate cancer patient is compared with an average level of the one or more antigens for a group of patients with prostate cancer. PROSTVAC therapy, Ipilimumab, and/or the vaccination with a prostate antigen is administered if the level of the one or more antigens in the patient is less than the average level of the one or more antigens in the group of patients with prostate cancer.
Another aspect provides a method of monitoring the effectiveness of therapy in a prostate cancer patient previously treated with PROSTVAC vaccination or prostate antigen vaccination. The level of one or more antigens encoded by a gene listed in Table 4 having a negative value for r_in_PROSTVAC Progression-free survival is determined by assaying a sample from a prostate cancer patient. The level of the one or more antigens from the sample of the prostate cancer patient is compared with an average level of the one or more antigens for a group of patients with prostate cancer. A determination that PROSTVAC therapy is effective is made if the level of the one or more antigens in the patient is less than the average level of the one or more antigens in the group of patients with prostate cancer.
Another aspect provides a method of monitoring the effectiveness of therapy in a prostate cancer patient previously treated with PROSTVAC vaccination or prostate antigen vaccination. The level of one or more antigens encoded by a gene listed in Table 4 having a positive value for r_in_PROSTVAC Progression-free survival is determined by assaying the level of one or more antigens in a sample from a prostate cancer patient. The level of the one or more antigens from the sample is compared with an average level of the one or more antigens for a group of patients with prostate cancer. A determination is made that the therapy is effective if the level of the one or more antigens in the patient is greater than the average level of the one or more antigens in the group of patients with prostate cancer.
The therapy may include one or more of Ipilimumab administration, prostate antigen vaccination, and PROSTVAC therapy.
In another aspect is provided a method of identifying and treating a prostate cancer patient previously treated with PROSTVAC vaccination or prostate antigen vaccination. The level of one or more antigens encoded by a gene listed in Table 4 having a positive value for r_in_PROSTVAC Progression-free survival is determined by assaying the level of one or more antigens in a sample from a prostate cancer patient. The level of the one or more antigens is compared with an average level of the one or more antigens for a group of patients with prostate cancer. The therapy or the vaccination with a prostate antigen is administered if the level of the one or more antigens in the patient is greater than the average level of the one or more antigens in the group of patients with prostate cancer
In some embodiments, the administered therapy comprises one or more of Ipilimumab administration, prostate antigen vaccination, and PROSTVAC therapy.
In another aspect is provided a method of identifying and treating a prostate cancer patient previously treated with PROSTVAC vaccination or prostate antigen vaccination. The level of one or more antigens encoded by a gene listed in Table 4 having a negative value for r_in_PROSTVAC Progression-free survival is determined by assaying the level of one or more antigens in a sample from a prostate cancer patient. The level of the one or more antigens from the prostate cancer patient is compared with an average level of the one or more antigens for a group of patients with prostate cancer. Therapy is administered if the level of the one or more antigens in the patient is less than the average level of the one or more antigens in the group of patients with prostate cancer.
In some embodiments, the therapy comprises one or more of Ipilimumab administration, prostate antigen vaccination, and PROSTVAC therapy.
In some embodiments, the patient also has an increased level of one or more antigens encoded by a gene listed in Table 4 having a positive value for r_in_PROSTVAC Progression-free survival as compared to the level in the group of patients with prostate cancer.
In another aspect is provided a method of monitoring the effectiveness of PROSTVAC therapy in a prostate cancer patient previously treated with PROSTVAC therapy or vaccination with a prostate antigen. The level of one or more antigens encoded by a gene listed in Table 4 having a negative value for r_in_PROSTVAC Progression-free survival is determined by assaying the level of one or more antigens in a sample from a prostate cancer patient. The level of the one or more antigens from the prostate cancer patient is compared with an average level of the one or more antigens for a group of patients with prostate cancer. A determination is made that the PROSTVAC therapy is effective if the level of the one or more antigens in the patient is less than the average level of the one or more antigens in the group of patients with prostate cancer.
In another aspect is provided a method of monitoring the effectiveness of PROSTVAC therapy in a prostate cancer patient previously treated with PROSTVAC therapy or vaccination with a prostate antigen. The level of one or more antigens encoded by a gene listed in Table 4 having a positive value for r_in_PROSTVAC Progression-free survival is determined by assaying the level of one or more antigens in a sample from a prostate cancer patient. The level of the one or more antigens in the prostate cancer patient is compared with an average level of the one or more antigens for a group of patients with prostate cancer. A determination is made that the PROSTVAC therapy is effective if the level of the one or more antigens in the patient is greater than the average level of the one or more antigens in the group of patients with prostate cancer.
In another aspect is provided a method of assessing overall survival of a patient who has been treated with PROSTVAC. The level of one or more antigens encoded by a gene listed in Table 5 having a positive value for r_in_Prostvac Overall Survival is determined by assaying the level of one or more antigens in a sample from a prostate cancer patient. The level of the one or more antigens is compared with an average level of the one or more antigens for a group of patients with prostate cancer.
In another aspect is provided a method of monitoring the effectiveness of combined PROSTVAC with Ipilimumab therapy in a prostate cancer patient previously treated with combined PROSTVAC with Ipilimumab therapy. The level of one or more antigens encoded by a gene listed in Table 6 having a positive value for r-value Study.Day is determined by assaying the level of one or more antigens in a sample from a prostate cancer patient. The level of the one or more antigens is compared with an average level of the one or more antigens for a group of patients with prostate cancer. The combined PROSTVAC with Ipilimumab therapy is determined to be effective if the level of the one or more antigens in the patient is greater than the average level of the one or more antigens in the group of patients with prostate cancer.
In yet another aspect is provided a method of monitoring the effectiveness of combined PROSTVAC with Ipilimumab therapy in a prostate cancer patient previously treated with combined PROSTVAC with Ipilimumab therapy. The level of one or more antigens encoded by a gene listed in Table 7 having a positive value for r_in_prostvac_ipi_Best.Response is determined by assaying the level of one or more antigens in a sample from a prostate cancer patient. The level of the one or more antigens is compared with an average level of the one or more antigens for a group of patients with prostate cancer. The combined PROSTVAC with Ipilimumab therapy is determined effective if the level of the one or more antigens in the patient is greater than the average level of the one or more antigens in the group of patients with prostate cancer.
In another aspect is provided a method of assessing overall survival of a patient who has been treated with PROSTVAC and Ipilimumab. The level of one or more antigens encoded by a gene listed in Table 8 having a positive value for r_in_prostvac_ipi_Overall.Survival is determined by assaying the level of one or more antigens in a sample from a prostate cancer patient. The level of the one or more antigens is compared with an average level of the one or more antigens for a group of patients with prostate cancer.
In another aspect is provided a method of monitoring for immune-related adverse events arising from combined PROSTVAC with Ipilimumab therapy in a prostate cancer patient previously treated with combined PROSTVAC with Ipilimumab therapy. The level of one or more antigens encoded by a gene listed in Table 9 having a positive value for Pearson'r is determined by assaying the level of one or more antigens in a sample from a prostate cancer patient. The level of the one or more antigens is compared with an average level of the one or more antigens for a group of patients with prostate cancer. A determination that there is risk for an immune-related adverse event arising from combined PROSTVAC with Ipilimumab therapy is made if the level of the one or more antigens in the patient is greater than the average level of the one or more antigens in the group of patients with prostate cancer.
The present invention is not to be limited in scope by the specific embodiments described herein. Indeed, various modifications of the invention in addition to those described herein will become apparent to those skilled in the art from the foregoing description and the accompanying figures. Such modifications are intended to fall within the scope of the appended claims. It is further to be understood that all values are approximate, and are provided for description.
Patents, patent applications, publications, product descriptions, and protocols are cited throughout this application, the disclosures of which are incorporated herein by reference in their entireties for all purposes.
Recombinant antigens were produced in Escherichia coli. Five cDNA libraries originating from different human tissues (fetal brain, colon, lung, liver, CD4 induced and non-induced T cells) were used for the recombinant production of human antigens. All of these cDNA libraries were oligo(dT)-primed, containing the coding region for an N-terminally located hexa-histidine-tag and were under transcriptional control of the lactose inducible promoter from E. coli]. Sequence integrity of the cDNA libraries was confirmed by 5′ DNA sequencing. Additionally, expression clones representing the full-length sequence derived from the human ORFeome collection were included. Individual antigens were designed in silico, synthesized chemically (Life Technologies, Carlsbad, USA) and cloned into the expression vector pQE30-NST fused to the coding region for the N-terminal-located His6-tag. Of the antigens. Recombinant gene expression was performed in E. coli SCS1 cells carrying plasmid pSE111 for improved expression of human genes. Cells were cultivated in 200 ml auto-induction medium (Overnight Express auto-induction medium, Merck, Darmstadt, Germany) overnight and harvested by centrifugation. Bacterial pellets were lysed by resuspension in 15 ml lysis buffer (6 M guanidinium-HCl, 0.1 M NaH2PO4, 0.01 M Tris-HCl, pH 8.0).
Soluble proteins were affinity-purified after binding to Protino® Ni-IDA 1000 Funnel Column (Macherey-Nagel, Duren, Germany). Columns were washed with 8 ml washing buffer (8 M urea, 0.1 M NaH2PO4, 0.01 M Tris-HCl, pH 6.3). Proteins were eluted in 3 ml elution buffer (6 M urea, 0.1 M NaH2PO4, 0.01 M Tris-HCl, 0.5% (w/v) trehalose pH 4.5). Each protein preparation was transferred into 2D-barcoded tubes, lyophilized and stored at −20° C.
Candidate antigens were selected for this cancer screen to cover immune-related processes and autoimmune disease antigens, cancer signaling processes, and antigens preferentially expressed in different cancer types. In total, 842 potential antigens were selected.
For the production of bead-based arrays (BBA), the proteins were coupled to magnetic carboxylated color-coded beads (MagPlex™ microspheres, Luminex Corporation, Austin, Tex., USA). The manufacturer's protocol for coupling proteins to MagPlexm microspheres was adapted to use liquid handling systems. A semi-automated coupling procedure of one BBA encompassed 384 single, separate coupling reactions, which were carried out in four 96-well plates. For each single coupling reaction, up to 12.5 μg antigen and 8.8×105 MagPlex™ beads of one color region (ID) were used. All liquid handling steps were carried out by either an eight-channel pipetting system (Starlet, Hamilton Robotics, Bonaduz, Switzerland) or a 96-channel pipetting system (Evo Freedom 150, Tecan, Mannderdorf, Switzerland). For semi-automated coupling, antigens were dissolved in H2O, and aliquots of 60 microliters were transferred from 2D barcode tubes to 96-well plates. MagPlex™ microspheres were homogeneously resuspended and each bead ID was transferred in one well of a 96-well plate. The 96-well plates containing the microspheres were placed on a magnetic separator (LifeSep™, Dexter Magnetic Technologies Inc., Elk Grove Village, USA) to sediment the beads for washing steps and on a microtiter plate shaker (MTS2/4, IKA) to facilitate permanent mixing for incubation steps.
For coupling, the microspheres were washed three times with activation buffer (100 mM NaH2PO4, pH 6.2) and resuspended in 120 μl activation buffer. To obtain reactive sulfo-NHS-ester intermediates, 15 μl 1-ethly-3-(3-dimethlyaminopropyl) carbodiimide (50 mg/ml) and 15 μl N-hydroxy-succinimide (50 mg/ml) were applied to microspheres. After 20 minutes incubation (900 rpm, room temperature (RT)) the microspheres were washed three times with coupling buffer (50 mM MES, pH 5.0) and resuspended in 65 μl coupling buffer. Immediately, 60 μl antigen solution was added to reactive microspheres and coupling took place over 120 minutes under permanent mixing (900 rpm, RT). After three wash cycles using washing buffer (PBS, 0.1% Tween20) coupled beads were resuspended in blocking buffer (PBS, 1% BSA, 0.05% ProClin300), incubated for 20 minutes (900 rpm, RT) and then transferred to be maintained at 4-8° C. for 12-72 h.
Finally, a multiplex BBA was produced by pooling 384 antigen-coupled beads.
Serum samples were transferred to 2D barcode tubes and a 1:100 serum dilution was prepared with assay buffer (PBS, 0.5% BSA, 10% E. coli lysate, 50% Low-Cross buffer (Candor Technologies, Nurnberg, Germany)) in 96-well plates. The serum dilutions were first incubated for 20 minutes to neutralize any human IgG eventually directed against E. coli proteins. The BBA was sonicated for 5 minutes and the bead mix was distributed in 96-well plates. After three wash cycles with washing buffer (PBS, 0.05% Tween20) serum dilutions (50 μl) were added to the bead mix and incubated for 20 h (900 rpm, 4-8° C.). Supernatants were removed from the beads by three wash cycles, and secondary R-phycoerythrin-labeled antibody (5 μg/ml, goat anti-human, Dianova, Hamburg, Germany) was added for a final incubation of 45 minutes (900 rpm, RT). The beads were washed three times with washing buffer (PBS, 0.1% Tween20) and resuspended in 100 μl sheath fluid (Luminex Corporation). Subsequently, beads were analyzed in a FlexMap3D device for fluorescent signal readout (DD gate 7.500-15.000; sample size: 80 μl; 1000 events per bead ID; timeout 60 sec). The binding events were displayed as median fluorescence intensity (MFI). Measurements were disregarded when low numbers of bead events (<30 beads) were counted per bead ID.
Data processing and analysis were performed using the programming language R (http://www.r-project.org/ version 3.3.0), KNIME 3.2 (https://www.knime.org/), DataWarrior (www.openmolecules.org/datawarrior), and tMeV 4.9 (http://www.tm4.org).
To identify autoantibodies that have higher reactivity to the test antigen in a group of patients compared to a control group, the permutation based statistical technique Significance of microarrays in the R-programming language (SAMR) was used (Tusher et al., 2001). The strength of differences between the two test groups is computed as SAMR score_d. A positive fold-change value is indicative of higher autoantibody reactivity in the cancer group compared to healthy control samples. Furthermore, receiver-operating characteristics were calculated to provide area under the curve (AUC) values for each antigen. The ROC curves were generated using the package pROC (Robin et al., 2011).
To identify biomarkers correlating with clinical response, overall survival, study day, or irAE the Pearson's correlation coefficient “r” was calculated.
To explore the data and to identify biomarkers that enable classification and prediction, partial least squares regression (PLS) was applied to the autoantibody (antigens) data set (Palermo et al., 2009). The orthogonal scores algorithm was used to perform the PLS regression using the programming language “R”. Results of PLS modeling were visualized as “biplots” of autoantibodies and demographic, study data and clinical data reflecting the study design. For each antigen coordinate, the distance to the origin indicates the variance in the reduced two-dimensional space. Antigens without variance would lie in the middle of the bi-plot. The identified autoantibody biomarkers were used as landmarks in the graphical representation of the multivariate model.
Serum samples from 24 prostate cancer patients treated with PROSTVAC cancer vaccine were tested for the presence of autoantibodies against 842 preselected antigens (Gulley et al., 2014). Samples were collected prior to treatment (T0 samples) and two timepoints during treatment. The T1 corresponds to 90 days (3 month) and the T2 samples corresponds to 180 days (6 month) The PROSTVAC regimen consists of an initial PSA-TRICOM vaccinia-based priming dose, followed by six subsequent PSA-TRICOM boosting doses. These seven injections are given within a 5-month treatment period. To enhance the immune response to weakly immunogenic autoantigens such as PSA, GM-CSF/CSF2 is given at the start of the therapy.
Table 1 includes all identified autoantibody reactivities and antigens.
Markers correlating with different clinical endpoints are extracted and shown in separate tables (T).
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The GeneID is found on NCBI website available at www.ncbi.nlm.nih.gov. More information about the gene can be found by accessing the NCBI website and entering the GeneID or Gene Symbol, for instance. The sequence listing provided with the application contains the sequences of the above-identified antigen sequences encoded by the gene identified by the corresponding “Gene ID”.
A tumor-associated antigen (TAA) is defined as an antigenic substance produced in the tumor, vascular or tumor surrounding tissue, which triggers an immune response in the host. A higher autoantibody level against a TAA is useful to determine the immuno-competence of cancer patients before treating a patient with an immuno-oncology (IO) therapy. Furthermore, TAA expressed in tumor cells or surrounding tissue are potential targets for use in cancer therapy. A further use of TAA is to diagnose cancer patients.
Group 1 comprises the best 49 tumor-associated antigens identified in prostate cancer. Group 1 antigens were identified by comparing the autoantibody levels in prostate cancer patients and with those in healthy control patients. Markers were identified by using the statistical technique Significance of microarrays in the R-programming language (SAMR). The strength of differences between the two test groups is computed as SAMR score_d. A positive fold-change value is indicative of higher autoantibody reactivity in the cancer group compared to healthy control samples. Shown below in Table 2 are the data on 49 TAA which elicit an immune response in prostate cancer.
The GeneID is found on NCBI website available at www.ncbi.nlm.nih.gov. More information about the gene can be found by accessing the NCBI website and entering the GeneID or Gene Symbol, for instance.
Long-term positive effects on the overall survival of prostate cancer patients treated with the PROSTVAC vaccine may involve the stimulation of the humoral immune response in cancer patients. This may involve the induction of B cells and antibodies, which target additional antigens that are not directly included in the vaccine. This generation of a broader immune response is called antigen-spreading and could be important to achieve a sustainable anti-tumor response in patients.
Thus any new antibody and antigen, which is not part of the PROSTVAC vaccine, is a potential biomarker to measure the vaccination response in prostate cancer patients. In order to investigate if the vaccination with PROSTVAC can induce a post-treatment antibody response, the change in antibody levels between T0 (pre-treatment samples), T1 (3 month) and T2 (6 month) samples was analyzed. In total, antibody responses towards 842 antigens were analyzed. The post-treatment increase in the antibody levels from baseline was analyzed by correlation analysis using Pearson's correlaton (Study Day 0,1,2).
Table 3 includes the Pearson's r-value of 39 antigens, which induce a post-treatment antibody response in prostate cancer patients treated with PROSTVAC.
The GeneID is found on NCBI website available at www.ncbi.nlm.nih.gov. More information about the gene can be found by accessing the NCBI website and entering the GeneID or Gene Symbol, for instance.
One of the reasons to terminate a patient's cancer therapy or to change the therapy is disease progression. The time from the beginning of the intervention until a patient shows signs of disease progression is called Progression-free survival (PFS). In PROSTVAC clinical studies a patient's PSA levels were determined pre-treatment and post-treatment. A biochemical progression was defined as a decrease in PSA levels of greater than or equal to 30% from baseline (T0) (https://clinicaltrials.gov/ct2/show/NCT00060528).
Biomarkers correlating with progression-free survival were calculated using Pearson's correlation.
Table 4 shows 50 markers correlating positively or negatively with progression-free survival in PROSTVAC treated patients.
Biomarkers correlating with progression-free survival were calculated using Pearson's correlation. Biomarkers with a positive r-value show positive correlation with progression-free survival and show higher intensity values in patients with longer PFS. Markers showing a positive correlation can be used to identify patients who are more likely to respond to PROSTVAC therapy.
In contrast, biomarkers with a negative r-value show a negative correlation with PFS and higher levels were found in patients with lower PFS. Patients who have higher levels of these markers are less likely to respond to therapy.
The GeneID is found on NCBI website available at www.ncbi.nlm.nih.gov. More information about the gene can be found by accessing the NCBI website and entering the GeneID or Gene Symbol, for instance.
An important clinical outcome measure in clinical trials is the Overall Survival (OS). The overall survival is defined as the date of on-study to the date of death from any cause or last follow-up.
Biomarkers correlating with OS were calculated using Pearson's correlation. Biomarkers with a positive r-value show positive correlation with OS and show higher intensity values in patients with longer OS. These markers can be used to identify patients who have a better overall survival time and may be more likely to benefit from PROSTVAC therapy.
In contrast, biomarkers with a negative r-value show a negative correlation with OS and higher levels were found in patients with lower OS.
Table 5 shows 70 markers correlating positively or negatively with OS in PROSTVAC treated patients.
The GeneID is found on NCBI website available at www.ncbi.nlm.nih.gov. More information about the gene can be found by accessing the NCBI website and entering the GeneID or Gene Symbol, for instance.
Although PROSTVAC vaccination has been shown to improve the overall survival of prostate cancer patients, some patients experienced progression or relapse of the disease. There is evidence that cytotoxic T cells upregulate the T-lymphocyte-associated protein 4 (CTLA4), a negative regulatory molecule. Ipilimumab (Bristol-Myers Squibb, New York, N.Y., USA) is an antagonistic anti-CTLA4 monoclonal antibody that blocks the activity of CTLA4. Ipilimumab has been assessed in the treatment of prostate cancer, in which a minority (about 20%) of patients had significant PSA declines. Clinical data suggest, that combining immune checkpoint inhibition with therapeutic cancer vaccines, has the potential to improve the proportion of patients seeing long-term durable responses with these therapies.
In a phase I clinical trial 30 study participants with metastatic castration-resistant prostate cancer (mCRPC) were treated with PROSTVAC and escalating doses of ipilimumab (Madan et al., 2012). Serum samples from 24 patients treated with PROSTVAC plus ipilimumab were tested for the presence of autoantibodies against 842 preselected antigens Samples were collected prior to treatment (T0 samples) and two timepoints during treatment. The T1 corresponds to 90 days (3 month) and the T2 samples corresponds to 180 days (6 month).
Long-term positive effects on the overall survival of prostate cancer patients treated with the PROSTVAC plus Ipilimumab may involve the stimulation of the humoral immune response in cancer patients. This may involve the induction of B cells and antibodies, which target additional antigens that are not directly included in the vaccine. This generation of a broader immune response is called antigen-spreading and could be important to achieve a sustainable anti-tumor response in patients.
Thus, any new antibody and antigen, which is not part of the PROSTVAC plus Ipilimumab treatment regime, is a potential biomarker to measure the vaccination response in prostate cancer patients. In order to investigate if PROSTVAC plus Ipilimumab can induce a post-treatment antibody response, the change in antibody levels between T0 (pre-treatment samples) and T1 (3 month) and T2 (6 month) samples was analyzed. In total, antibody responses towards 842 antigens were analyzed. The post-treatment increase in the antibody levels from baseline was analyzed by correlation analysis using Pearson's correlaton (Study Day 0,1,2).
Furthermore, the post-treatment samples T1 and T2 were compared to T0 samples using SAMR.
Table 6 includes the Pearson's r-value of 25 antigens, which induce a post-treatment antibody response in prostate cancer patients treated with PROSTVAC plus Ipilimumab.
The GeneID is found on NCBI website available at www.ncbi.nlm.nih.gov. More information about the gene can be found by accessing the NCBI website and entering the GeneID or Gene Symbol, for instance.
One of the reasons to terminate a patient's cancer therapy or to change the therapy is disease progression. The predicted median overall survival (OS) by the Halabi nomogram is prognostic model for patients with metastatic castration-resistant prostate cancer (mCRPC) that can be used to compute individual predicted survival probability at different time points (Halabi et al., 2014).
Biomarkers correlating with OS-Halabi were calculated using Pearson's correlation.
Table 7 shows 64 markers correlating positively or negatively with OS-Halabi in PROSTVAC plus Ipilimumab treated patients.
The GeneID is found on NCBI website available a www.ncbi.nlm.nih.gov. More information about the gene can be found by accessing the NCBI website and entering the GeneID or Gene Symbol, for instance.
Biomarkers correlating with OS were calculated using Pearson's correlation. Biomarkers with a positive r-value show positive correlation with OS and show higher intensity values in patients with longer OS. These markers can be used to identify patients who have a better overall survival time and may be more likely to benefit from PROSTVAC plus Ipilimumab therapy.
In contrast, biomarkers with a negative r-value show a negative correlation with OS and higher levels were found in patients with lower OS.
Table 8 shows 70 markers correlating positively or negatively with OS in PROSTVAC treated patients.
The GeneID is found on NCBI website available at www.ncbi.nlm.nih.gov. More information about the gene can be found by accessing the NCBI website and entering the GeneID or Gene Symbol, for instance.
Despite important clinical benefits, checkpoint inhibitors area associated with immune-related adverse events (irAEs) The mechanisms by which checkpoint inhibitors induce irAEs are not completely understood. It is believed that by blocking negative checkpoints a general immunologic enhancement occurs. It is also possible that by unleashing the immune-checkpoints that control tolerance, autoreactive lymphocytes are activated, which could be either T cells or B cells. It is well known that in autoimmune diseases autoreactive B cells produce autoantibodies that can induce tissue damage via ADCC. Thus, epitope spreading towards self-antigens may be an indicator for irAEs.
Autoantibodies correlating with irAEs were identified by Pearson's correlation analysis and SAMR.
Table 9 includes 87 biomarkers that are associated with irAE in PROSTVAC plus ipilimumab treated prostate cancer patients.
These biomarkers may be used to predict irAE in baseline samples of patients and prior to therapy or are induced following treatment.
The GeneID is found on NCBI website available at www.ncbi.nlm.nih.gov. More information about the gene can be found by accessing the NCBI website and entering the GeneID or Gene Symbol, for instance.
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| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2018/066840 | 6/23/2018 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 62524220 | Jun 2017 | US |
