Method for predicting therapeutic response to biological drugs by measuring anti-drug antibody concentrations in blood

Abstract

The present invention relates to a method for predicting therapeutic response to a biological drug by measuring the concentration of an anti-drug antibody in blood, and the therapeutic response prediction method according to one embodiment can predict immunotherapy response to a biological drug by defining a clinically relevant cutoff value of the anti-drug antibody concentration at an early time point while minimizing false positives, which is useful for providing data for guiding a patient's treatment decision and establishing an appropriate treatment plan.

Description

[Technical field]

[0001] The present invention relates to a method for predicting therapeutic response to a biological drug by measuring the concentration of anti-drug antibodies in the blood. [Background technology]

[0002] Atezolizumab is a humanized IgG1 antibody against programmed death ligand-1 (PD-L1) that has shown clinical benefit in various types of cancer. In particular, the combination of atezolizumab and bevacizumab (Atezo / Bev) has completely transformed the treatment of advanced hepatocellular carcinoma (HCC). The combination improved overall survival (OS) compared with sorafenib in the pivotal phase 3 IMbrave 150 trial.

[0003] Despite this remarkable progress, clinical responses to atezolizumab are highly variable, ranging from durable complete responses to rapidly progressive disease in patients with advanced cancer. To optimize atezolizumab-based immunotherapy, several tumor or circulating factors, such as PD-L1 expression, gene expression signatures, and tumor mutational burden (TMB), have been investigated to better predict atezolizumab efficacy, but the predictive accuracy is limited. According to several recent studies, serum levels of atezolizumab are correlated with clinical outcomes and immune responses.

[0004] Administration of humanized antibodies can be immunogenic and induce undesirable anti-drug antibody (ADA) responses. ADA can interfere with the action of therapeutic antibodies, affecting or neutralizing drug clearance and serum concentration. Furthermore, a significant decrease in therapeutic antibody serum concentration due to ADA may affect anticancer efficacy. Some studies have shown that administration of immune checkpoint inhibitors (ICIs) can generate ADA responses in cancer patients. In some cases, some patients may have pre-existing drug-reactive antibodies without ICI exposure.

[0005] Among the various ICIs, atezolizumab showed the highest ADA incidence at 29.8%, whereas other ICIs accounted for approximately 5%–10%. More importantly, the incidence of neutralizing ADA, which directly blocks and interferes with the binding of atezolizumab to PD-L1, was 14.5%. The results of previously reported studies were inconsistent, so the debate on the clinical implications of atezolizumab ADA continues. One reason for the mixed results is that ADA levels can change over time and can be influenced by various impairing factors, such as sampling timing, test format, and patient immune status. Furthermore, there is a lack of agreement on the cutoffs to define ADA positivity. It is unclear whether the presence of ADA is clinically significant even at very low concentrations, and it is also unclear whether the presence of ADA is significant only when concentrations are high enough to affect systemic atezolizumab exposure.

[0006] In the IMbrave 150 study, 29.6% of patients with HCC developed atezolizumab-induced ADAs at one or more time points after Atezo / Bev treatment, but there are no data on ADA development patterns outside of the clinical trial setting or to guide treatment decisions in patients treated with Atezo / Bev. Summary of the Invention [Problem to be solved by the invention]

[0007] To solve the above problem, one aspect is to provide a method for predicting therapeutic responsiveness of a biological drug, comprising a step of measuring anti-drug antibody concentrations against the biological drug from a biological sample isolated from a patient to whom the biological drug has been administered.

[0008] Another aspect is to provide a diagnostic kit for predicting therapeutic responsiveness of a biological drug comprising an agent capable of measuring anti-drug antibodies against the biological drug. [Means for solving the problem]

[0009] One aspect provides a method for predicting therapeutic responsiveness of a biological drug, comprising measuring anti-drug antibody concentrations against the biological drug from a biological sample isolated from a patient to whom the biological drug has been administered.

[0010] As used herein, the terms "drug", "biological drug" and their plurals are generic and refer to a drug consisting of or including biological molecules or substances, i.e. proteins, polypeptides, peptides, polynucleotides, oligonucleotides, polysaccharides, oligosaccharides and fragments thereof, as well as cells, tissues, biological fluids or extracts thereof, which can induce antibodies in a subject. Said biological drug may include or target any component involved in molecular and / or cellular processes such as cell cycle, cell survival, apoptosis, immunity, etc., and may be any checkpoint protein or any modulator or inhibitor thereof, or any combination thereof.

[0011] In one embodiment, the biologic drug may be a biologic drug for cancer treatment, which may be a targeted anti-cancer drug, an immune anti-cancer drug, or a personalized anti-cancer drug.

[0012] In this specification, the term "targeted anticancer drug" can refer to a drug that targets a protein in a specific part of cancer cells that is different from that in normal cells and selectively attacks only cancer cells. Depending on the type of drug, it can be classified into monoclonal antibody drugs that act on the cell surface and small molecule compound drugs that act inside the cell. In addition, depending on the target of action of the drug, it can be classified into EGFR inhibition, BCR-ABL inhibition, RAS / MAPK inhibition, PI3K / AKT / mTOR inhibition, angiogenesis inhibition, JAK2 inhibition, BTK inhibition, ALK / ROS1 inhibition, CDK4 / 6 inhibition, proteasome inhibition, CD20 inhibition, etc.

[0013] In the present specification, the term "immune anticancer agent" refers to a therapeutic agent that induces immune cells to selectively attack only cancer cells by injecting artificial immune proteins into the body to stimulate the immune system, unlike existing anticancer agents that attack cancer itself. It can be said to be a drug that restores or strengthens the immune system's tumor recognition or destruction ability to overcome the immune suppression or immune evasion mechanism acquired by cancer cells. The immune anticancer agent can be distinguished from those used in passive immunotherapy and those used in active immunotherapy. Passive immunotherapy includes immune checkpoint inhibitors, immune cell therapy, and therapeutic antibodies, while active immunotherapy includes cancer treatment vaccines and immune-modulating agents, but is not limited thereto.

[0014] As used herein, the term "immune barrier" can refer to a molecule that is also called an "immune checkpoint" and that regulates the activity of immune cells. Immune checkpoints exist for self-tolerance, preventing the immune system from indiscriminately attacking cells. The immune checkpoints can be classified into stimulatory checkpoints, which enhance immune activity, and inhibitory checkpoints, which reduce immune activity. Some cancers can target inhibitory immune checkpoints to protect themselves from immune responses.

[0015] As used herein, the term "immune checkpoint inhibitor" may refer to a drug that blocks the activity of a target immune checkpoint protein involved in the suppression of immune cells, activating immune cells to attack cancer cells. Immune checkpoints that can be targeted by immune checkpoint inhibitors include PD-1, PD-L1, CD80, CD86, CTLA4, B7-H3, -H4, -H5, BTLA, 4-1BB, Tim-3, TIGIT, CD94 / NKG2A, KIR2DL-1, -2, -3, etc., and the targets of immune checkpoint inhibitors currently in clinical use are PD-1, PD-L1, and CTLA4. More specifically, immune checkpoint inhibitors currently in clinical use include, but are not limited to, CTLA-4 monoclonal antibodies such as ipilimumab and tremelimumab, PD-1 monoclonal antibodies such as nivolumab, pembrolizumab, and cemiplimab, and PD-L1 monoclonal antibodies such as atezolizumab, durvalumab, and avelumab.

[0016] In one embodiment, the biological drug may be an antibody, a protein, a peptide, or a combination thereof, including proteins such as monoclonal antibodies, cytokines, soluble receptors, growth factors, hormones, enzymes, adhesion molecules, and fusion proteins and peptides specific for particular targets known to modulate disease mechanisms.

[0017] The term "antibody" as used herein includes immunoglobulin molecules having immunological reactivity with a specific antigen, and may include both monoclonal and polyclonal antibodies. Furthermore, the antibody may include both chimeric antibodies (e.g., humanized mouse antibodies) and forms produced by genetic engineering, such as heterologous conjugate antibodies (e.g., bispecific antibodies).

[0018] In one embodiment, the cancer is urothelial carcinoma (UC), non-small cell lung cancer, small cell lung cancer, triple-negative breast cancer (TNBC), hepatocellular carcinoma, colorectal cancer, lung cancer, glioblastoma multiforme, renal cell carcinoma. The cancer may be carcinoma, melanoma, bladder cancer, classical Hodgkin lymphoma (CHL), Esophageal squamous cell carcinoma, esophageal cancer, gastric cancer or Head and Neck Squamous Cell Carcinoma.

[0019] In one embodiment, the antibody may be an immune checkpoint inhibitor, a targeted anti-cancer drug, or a combination thereof.

[0020] In one embodiment, the immune checkpoint inhibitor may be a CTLA4 inhibitor, a PD-L1 inhibitor, a PD-1 inhibitor, or a combination thereof. The immune checkpoint inhibitor may be Ipilimumab, Tremelimumab, Pembrolizumab, Cemiplimab, Nivolumab, Durvalumab, Atezolizumab, Avelunab, or a combination thereof. The targeted anti-cancer drug can be Cetuximab, Trastuzumab, Ibritumomab, Rituximab, Brentuximab, Alemtuzumab, Bevacizumab, Panitumumab, or a combination thereof.

[0021] In one embodiment, the patient may be a cancer patient, and may be a patient receiving a combination of atezolizumab and bevacizumab.

[0022] In one embodiment, the biological sample may be one or more selected from the group consisting of blood cells, blood, serum, plasma, bone marrow, lymph, urine, sputum, saliva, faeces, semen, spinal cord, or CSF; external secretions of the skin, respiratory, intestinal and genitourinary tracts; tears, milk, any human organ or tissue, any sample obtained by lavage, optionally the breast ductal system, pleural effusion, in vitro or ex vivo cell cultures and samples of cell culture components.

[0023] In one embodiment, the biological sample is collected from the patient at 14-28 days, 14-27 days, 14-26 days, 14-25 days, 14-24 days, 14-23 days, 14-22 days, 14-21 days, 14-20 days, 14-19 days, 14-18 days, 15-28 days, 15-27 days, 15-26 days, 15-25 days, 15-24 days, 15-23 days, 15-24 days, 15-25 ... 22nd, 15th-21st, 15th-20th, 15th-19th, 15th-18th, 16th-28th, 16th-27th, 16th-26th, 16th-25th, 16th-24th, 16th-23rd, 16th-22nd, 16th-21st, 16th-20th, 16th-19th, 16th-18th, 17th-28th, 17th-27th, 17th-26th, 17th-25th, 17th-24th, 17th-23rd Sun, 17-22, 17-21, 17-20, 17-19, 17-18, 18-28, 18-27, 18-26, 18-25, 18-24, 18-23, 18-22, 18-21, 18-20, 18-19, 19-28, 19-27, 19-26, 19-25, 19-24, 19-23, The isolated product may be one that is isolated after 19 to 22 days, 19 to 21 days, 19 to 20 days, 20 to 28 days, 20 to 27 days, 20 to 26 days, 20 to 25 days, 20 to 24 days, 20 to 23 days, 20 to 22 days, 20 to 21 days, 21 to 28 days, 21 to 27 days, 21 to 26 days, 21 to 25 days, 21 to 24 days, 21 to 23 days, 21 to 22 days, or 21 days.

[0024] Said biological drugs are known in some cases to induce the in vivo formation of "anti-drug antibodies (ADAs)" and their detection has generally become equivalent as a measure of immunogenicity. Most side effects due to ADA formation, such as pharmacological abrogation, effects on therapeutic exposure, or hypersensitivity reactions, are the result of the formation of immune complexes between ADAs and therapeutic proteins. Their levels, interaction kinetics, size, polyclonal diversity, distribution, and Fc-mediated physiological effects can potentially translate into clinically observable side effects. ADAs are generally polyclonal and can contain different isotypes [immunoglobulin (Ig) G, IgA, IgM or IgE], bind to different regions ("domains") of the drug molecule, and their affinity (binding strength) can be diverse and vary from patient to patient, so they can represent a very complex set of analytes.

[0025] The term "anti-drug antibodies" or "ADAs (Anti-Drug Antibodies)" as used herein can refer to antibodies that bind to therapeutic agents and can affect the serum concentration and function of therapeutic agents in an individual. The presence of ADAs can increase the clearance of therapeutic agents through the formation of immune complexes between the therapeutic agent and antibodies (neutralizing, non-neutralizing, or both), thus decreasing the half-life of the therapeutic agent. Furthermore, the activity and usefulness of the therapeutic agent can be decreased by the binding of antibodies to the therapeutic agent (especially in the case of neutralizing ADAs). ADAs can also be associated with other side effects, such as allergic or hypersensitivity reactions, and neutralization of host proteins.

[0026] In one embodiment, the anti-drug antibody may be an anti-ipirlimumab antibody, an anti-tremelimumab antibody, an anti-pembrolizumab antibody, an anti-cemiplimab antibody, an anti-nivolumab antibody, an anti-durvalumab antibody, an anti-atezolizumab antibody, an anti-avelumab antibody, an anti-cetuximab antibody, an anti-trastuzumab antibody, an anti-ibritumomab antibody, an anti-rituximab antibody, an anti-brentuximab antibody, an anti-alemtuzumab antibody, an anti-bevacizumab antibody, an anti-panitumumab antibody, or a combination thereof.

[0027] In one embodiment, the anti-drug antibody concentration may be measured by one or more methods selected from the group consisting of western blotting, radioimmunoassay (RIA), radioimmunodiffusion, enzyme-linked immunosorbent assay (ELISA), immunoprecipitation, flow cytometry, immunofluorescence, Ouchterlony, complement fixation assay, and protein chip, but is not limited thereto, and may be measured according to conventional methods known in the art.

[0028] In one embodiment, the method may further include determining that the therapeutic response of the biological drug is low when the measured concentration of the anti-drug antibody is equal to or higher than the cutoff value. In a patient having an anti-drug antibody concentration equal to or higher than the cutoff value, it is predicted that the patient will have a reduced systemic exposure to the drug during drug treatment and a reduced immunotherapeutic efficacy of the drug, compared to a patient having an anti-drug antibody concentration below the cutoff value.

[0029] As used herein, the term "cutoff value" refers to a numerical value used to mediate a classification between two or more states for a biological sample (e.g., hepatocellular carcinoma patients with high and low therapeutic response to immunological anticancer drugs). For example, if the parameter is equal to or less than the cutoff value, a first classification of the quantitative data (e.g., hepatocellular carcinoma patients with high therapeutic response to immunological anticancer drugs) is performed, or if the parameter exceeds the cutoff value, another classification of the quantitative data (e.g., hepatocellular carcinoma patients with low therapeutic response to immunological anticancer drugs) is performed.

[0030] As used herein, "PFS (Progression-free survival)" or "progression-free survival" refers to the period of time a patient survives from a state in which the disease does not progress until the patient's condition worsens or dies, and may be a method for observing whether a new treatment is effective. The "progression" may refer to a tumor size increase of 20% or more.

[0031] As used herein, "OS (Overall survival)" or "overall survival" refers to the time from randomization to death, and may refer to the value obtained by tracking the period from when a patient starts treatment to the moment of death.

[0032] In one embodiment, the cutoff value is 500 to 2000ng / ml, 500 to 1900ng / ml, 500 to 1800ng / ml, 500 to 1700ng / ml, 500 to 1600ng / ml, 500 to 1500ng / ml, 500 to 1400ng / ml, 500 to 1300ng / ml, 500 to 1200ng / ml, 500 to 1100ng / ml, 500 to 1000ng / ml, 500 to 900ng / ml, 500 to 800ng / ml, 500 to 700ng / ml, 500 to 600ng / ml, 600 to 2000ng / ml, 600 to 1900ng / ml, 600 to 1800ng / ml, g / ml, 600~1700ng / ml, 600~1600ng / ml, 600~1500ng / ml, 600~1400ng / ml, 600~1300ng / ml, 600~1200ng / ml, 600~1100ng / ml, 600~1000ng / ml, 600~9 00ng / ml, 600~800ng / ml, 600~700ng / ml, 700~2000ng / ml, 700~1900ng / ml, 700~1800ng / ml, 700~1700ng / ml, 700~1600ng / ml, 700~1500ng / ml, 700~ 1400ng / ml, 700~1300ng / ml, 700~1200ng / ml, 700~1100ng / ml, 700~1000ng / ml, 700~900ng / ml, 700~800ng / ml, 800~2000ng / ml, 800~1900ng / ml, 80 0~1800ng / ml, 800~1700ng / ml, 800~1600ng / ml, 800~1500ng / ml, 800~1400ng / ml, 800~1300ng / ml, 800~1200ng / ml, 800~1100ng / ml, 800~1000ng / m l, 800~900ng / ml, 900~2000ng / ml, 900~1900ng / ml, 900~1800ng / ml, 900~1700ng / ml, 900~1600ng / ml, 900~1500ng / ml, 900~1400ng / ml, 900~1300n g / ml, 900~1200ng / ml, 900~1100ng / ml, 900~1000ng / ml, 1000~2000ng / ml, 1000~1900ng / ml, 1000~1800ng / ml, 1000~1700ng / ml, 1000~1600ng / ml,1000~1500ng / ml, 1000~1400ng / ml, 1000~1300ng / ml, 1000~1200ng / ml, 1000~1100ng / ml, 1100~2000ng / ml, 1100~1900ng / ml, 1100~180 0ng / ml, 1100~1700ng / ml, 1100~1600ng / ml, 1100~1500ng / ml, 1100~1400ng / ml, 1100~1300ng / ml, 1100~1200ng / ml, 1200~2000ng / ml, 1 It may be 200-1900ng / ml, 1200-1800ng / ml, 1200-1700ng / ml, 1200-1600ng / ml, 1200-1500ng / ml, 1200-1400ng / ml, 1200-1300ng / ml, 1300-2000ng / ml, 1300-1900ng / ml, 1300-1800ng / ml, 1300-1700ng / ml, 1300-1600ng / ml, 1300-1500ng / ml, 1300-1400ng / ml or 1000ng / ml.

[0033] The term "therapeutic responsiveness" as used herein may refer to whether or not a particular drug has a therapeutic effect on a disease of an individual patient.

[0034] The term "prediction" herein may refer to predetermining a particular outcome, such as therapeutic response, by identification of a characteristic, such as an anti-drug antibody concentration to a biological drug, via a biological sample.

[0035] As used herein, the term "prediction of therapeutic response of a cancer patient" may mean predicting in advance whether administration of a drug will be useful in treating cancer before administration, and may mean predicting therapeutic response to a drug by measuring the expression levels of genes, proteins, antibodies, or metabolites.

[0036] Another embodiment provides a diagnostic kit for predicting therapeutic responsiveness of a biological drug comprising an agent capable of measuring anti-drug antibodies against the biological drug.

[0037] The biological drug and anti-drug antibody are as explained above.

[0038] In one embodiment, the subject for predicting treatment response is 14 to 28 days, 14 to 27 days, 14 to 26 days, 14 to 25 days, 14 to 24 days, 14 to 23 days, 14 to 22 days, 14 to 21 days, 14 to 20 days, 14 to 19 days, 14 to 18 days, 15 to 28 days, 15 to 27 days, 15 to 26 days, 15 to 25 days, 15 to 24 days, 15 to 23 days after the start of treatment of the patient. , 15-22, 15-21, 15-20, 15-19, 15-18, 16-28, 16-27, 16-26, 16-25, 16-24, 16-23, 16-22, 16-21, 16-20, 16-19, 16-18, 17-28, 17-27, 17-26, 17-25, 17-24, 1 7-23, 17-22, 17-21, 17-20, 17-19, 17-18, 18-28, 18-27, 18-26, 18-25, 18-24, 18-23, 18-22, 18-21, 18-20, 18-19, 19-28, 19-27, 19-26, 19-25, 19-24, 19- The patient may have been 23 days, 19-22 days, 19-21 days, 19-20 days, 20-28 days, 20-27 days, 20-26 days, 20-25 days, 20-24 days, 20-23 days, 20-22 days, 20-21 days, 21-28 days, 21-27 days, 21-26 days, 21-25 days, 21-24 days, 21-23 days, 21-22 days, or 21 days.

[0039] The patient may be a cancer patient, and may be a patient receiving a combination of atezolizumab and bevacizumab treatment.

[0040] In one embodiment, the agent capable of measuring the anti-drug antibody may be an antibody, the anti-drug antibody being as described above.

[0041] In one embodiment, the biological drug may be a biological drug for cancer treatment. The cancers include urothelial carcinoma (UC), non-small cell lung cancer, small cell lung cancer, triple-negative breast cancer (TNBC), hepatocellular carcinoma, colorectal cancer, lung cancer, glioblastoma multiforme, and renal cell carcinoma. The cancer may be carcinoma, melanoma, bladder cancer, classical Hodgkin lymphoma (CHL), Esophageal squamous cell carcinoma, esophageal cancer, gastric cancer or Head and Neck Squamous Cell Carcinoma.

[0042] In one embodiment, the biological agent may be an antibody, a protein, a peptide, or a combination thereof, with the antibody being as defined above.

[0043] In one embodiment, the antibody may be an immune checkpoint inhibitor, a targeted anti-cancer drug, or a combination thereof, wherein the immune checkpoint inhibitor and the targeted anti-cancer drug are as described above.

[0044] In one embodiment, when the concentration of the anti-drug antibody is equal to or higher than the cutoff value, it may be determined that the therapeutic response to the biological drug is low. The content of the determination is as described above.

[0045] The meaning of the cutoff value is as explained above. Effect of the Invention

[0046] The method for predicting therapeutic response according to one embodiment can predict immunotherapy response of biological drugs by defining a clinically relevant cut-off value of anti-drug antibody concentration at an early time point while minimizing false positives, which is useful for providing data for guiding patient treatment decisions and establishing appropriate treatment plans. [Brief description of the drawings]

[0047] [Figure 1] This is a CONSORT (Consolidated Standards of Reporting Trials) diagram of a hepatocellular carcinoma (HCC) cohort study. [Diagram 2] This is a CONSORT (Consolidated Standards of Reporting Trials) diagram of a urothelial carcinoma (UC) cohort study. [Diagram 3] FIG. 1 is a graph comparing ADA (anti-drug antibody) levels between baseline and C2D1 (cycle 2 day 1) in the discovery cohort. [Figure 4] FIG. 1 is a graph comparing ADA levels in C2D1 with best response in the discovery cohort. [Diagram 5] Graph comparing ADA levels between baseline and C2D1 in the validation cohort. [Figure 6] Graph comparing ADA levels in C2D1 with best response in the validation cohort. [Figure 7] 1 is a pooled analysis graph of ADA levels at baseline and C2D1. [Figure 8] Graphs showing optimal response, progression-free survival (PFS) and overall survival (OS) according to ADA levels in the discovery cohort (HCC, n=50). [Figure 9]1 is a graph showing progression-free survival (PFS) according to ADA levels in the discovery cohort (HCC, n=50). [Figure 10] 1 is a graph showing overall survival (OS) according to ADA levels in the discovery cohort (HCC, n=50). [Figure 11] FIG. 1 is a graph showing optimal response according to ADA levels in an independent multicentric validation cohort (HCC, n=82). [Figure 12] 1 is a graph showing progression-free survival (PFS) according to ADA levels in an independent multicentric validation cohort (HCC, n=82). [Figure 13] FIG. 1 is a graph showing overall survival (OS) according to ADA levels in an independent multicentric validation cohort (HCC, n=82). [Figure 14] Graphs showing optimal response, progression-free survival (PFS) and overall survival (OS) according to ADA levels in the UC validation cohort (n=30). [Figure 15] Forest plot of the meta-analysis showing PFS according to age, sex, Eastern Cooperative Oncology Group (ECOG) performance status, Child-Pugh score, alpha-fetoprotein (AFP), macrovascular invasion (MVI), extrahepatic metastasis, neutrophil-lymphocyte ratio (NLR) and ADA status. [Figure 16] Forest plot of meta-analysis showing OS by age, sex, ECOG performance status, Child-Pugh score, α-fetoprotein, macrovascular invasion, extrahepatic metastases, neutrophil-lymphocyte ratio and ADA status. [Figure 17]Forest plot of meta-analysis showing PFS according to various cutoffs for ADA positivity. [Figure 18] Forest plot of meta-analysis showing OS according to different cutoffs for ADA positivity. [Figure 19] Receiver operating curve (AUC 0.78, 95% CI, 0.63-0.82) graph for 12-month OS. [Figure 20] 1 is a graph comparing serum atezolizumab concentrations by ADA level. [Figure 21] 1 is a graph showing the correlation between serum atezolizumab and ADA concentrations by Pearson correlation analysis. (Solid line: regression curve, dotted line: 95% confidence interval (CI) bands) [Figure 22] Representative flow cytometry plots and comparison graphs of Ki-67+ expanded CD8+ T cells between baseline and C2D1 by ADA status. [Figure 23] Representative flow cytometry plots and comparison graphs of intracellular interferon-γ and tumor necrosis factor-α from CD8+ T cells by ADA status. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0048] The present invention will be described in more detail with reference to the following examples. However, these examples are merely for illustrative purposes and are not intended to limit the scope of the present invention.

[0049] Reference Example 1 Patients and Methods We aimed to discover the clinical impact of ADA among prospectively enrolled patients diagnosed with advanced HCC and treated with Atezo / Bev at Bundang CHA Hospital, Korea (discovery cohort).We expanded patient enrollment at four independent tertiary cancer centers in Korea (Bundang Medical Center, University of Ulsan Hospital, Haeundae Paik Hospital, and St. Louis University Hospital) for two independent cohorts: patients with advanced HCC (validation cohort) and patients with advanced UC (UC validation cohort).

[0050] Eligibility criteria for the HCC discovery and validation cohort were age 20 years or older, locally advanced or unresectable HCC identified by histological or cytological analysis or clinical characteristics according to the American Association for the Study of Liver Diseases criteria for cirrhosis, no prior systemic therapy, Child-Pugh class A, and Eastern Cooperative Oncology Group (ECOG) score 0-1.

[0051] Eligibility criteria for the UC validation cohort were age 20 years or older, locally advanced or metastatic UC of the urinary tract identified by histological or cytological analysis, ongoing or post-platinum-based chemotherapy, and ECOG score 0–2.

[0052] The study was approved by the relevant institutional review boards (Bundang CHA Hospital, CHA-2017-11-052, CHA-2017-11-054; Ulsan University Hospital, 2020-12-006; Haeundae Paik Hospital, 2020-12-019-001, and St. Vincent's Hospital, VC16TISI0208). All patients signed written informed consent.

[0053] Reference Example 2 Treatment and Evaluation All patients in the HCC discovery and validation cohort were treated with a combination of atezolizumab (fixed dose 1200 mg) and bevacizumab (15 mg / kg) every 3 weeks. Patients in the UC validation cohort were treated with atezolizumab (fixed dose 1200 mg) every 3 weeks. Dose interruptions or reductions were performed according to the IMbrave 150 or IMvigor 210 study protocols. Treatment continued until intolerable toxicity, disease progression, or the patient requested withdrawal of consent. Response was assessed according to RECIST 1.1. Response assessments were performed every 6 or 9 weeks using computed tomography or magnetic resonance imaging.

[0054] Reference Example 3 Sample collection and ADA quantification Blood samples were collected in Vacutainer tubes (BD Biosciences) before the first atezolizumab administration (cycle 1 day 1, hereafter referred to as baseline) and before the second atezolizumab administration (cycle 2 day 2, hereafter referred to as C2D1). Samples were allowed to clot, and serum was centrifuged at 1000Хg for 5 min and stored at -80°C. Peripheral blood mononuclear cells were subjected to density gradient centrifugation on Ficoll-Paque PLUS (GE Healthcare) and stored in liquid nitrogen. C2D1 blood samples taken before 18 days or more than 24 days after the start of the first treatment were excluded from the analysis to ensure consistent timing.

[0055] Reference Example 4 Measurement of anti-atezolizumab antibody and atezolizumab The concentration of anti-atezolizumab antibodies was measured using a commercially available enzyme-linked immunosorbent assay (ELISA) (KBI2027 ver 4.0, KRISHGEN BioSystems) according to the manufacturer's guidelines. Briefly, samples were incubated in microwells pre-coated with atezolizumab for 120 min at 37°C. After several washes, horseradish peroxidase (HRP)-conjugated atezolizumab was added and the plate was incubated for 60 min at 37°C. After another wash, TMB substrate solution was added and the plate was incubated for 30 min at 37°C in the dark. The absorbance of each well was measured at 450 nm using a microplate reader (Bio Tek). The concentration of atezolizumab in serum was measured using a commercially available ELISA assay (KBI1027, KRISHGEN BioSystems) according to the manufacturer's guidelines.

[0056] Reference Example 5 Cytokine secretion analysis and flow cytometry To assess cytokine secretion, peripheral blood mononuclear cells were stimulated with plate-bound anti-CD3 antibody (1ug / ml). After 4 hours, brefeldin A (eBioscience) and monensin (eBioscience) were added to the culture medium. After 20 hours, activated cells were harvested. To examine the proliferation capacity of CD8+ T cells, cryopreserved peripheral blood mononuclear cells were used after thawing. Before antibody staining, dead cells were removed with Fixable Viability Dye eFluor™ 780 (eBioscience) for 30 minutes on ice, and then treated with human Fc receptor binding inhibitor (eBioscience) for 15 minutes at room temperature. Surface proteins were stained with fluorochrome-conjugated antibodies, anti-human CD3 antibody (clone SK7, eBioscience), anti-human CD4 antibody (clone RPA-T4, BioLegend) and anti-human CD8 antibody (clone RPA-T8, eBioscience) for 30 minutes on ice.

[0057] For intracellular staining, cells were fixed and permeabilized with FoxP3 staining buffer kit (Thermo Fisher Scientific), and then stained with fluorochrome-conjugated antibodies, anti-human Ki-67 antibody (clone Ki-67, BioLegend), anti-human interferon-γ antibody (clone B27, BD Bioscience), and anti-human tumor necrosis factor-α antibody (clone Mab11, BD Bioscience).

[0058] Flow cytometry was performed using a CytoFLEX flow cytometer (Beckman Coulter), and result statistics were analyzed using FlowJo software (Tree Star Inc., Ashland, OR, USA).

[0059] Reference example 6 Statistical analysis Unpaired or paired t-tests, ANOVA and chi-square tests were used to compare variables. OS was defined as the time from initiation of treatment to death from any cause. Progression-free survival (PFS) was defined as the time between initiation of treatment and disease progression or death from any cause. Survival outcomes were analyzed using the Kaplan-Meier method, and subgroups were compared using the log-rank test. Univariate and multivariate analyses of OS and PFS were performed using Cox proportional hazards models. ROC curves were plotted using R 4.1.2. Spearman correlation was used to correlate serum ADA with atezolizumab. Statistical significance was defined as two-sided P<0.05. Statistical analysis was performed using SPSS (IBM, NY, USA) version 18.0.

[0060] Example 1 Baseline Demographic Analysis of Hepatocellular Carcinoma (HCC) and Urothelial Carcinoma (UC) Patients A total of 132 patients with advanced HCC treated with first-line Atezo / Bev were prospectively enrolled into a discovery cohort (50 patients from a single center) and a validation cohort (82 patients from four centers) from June 2020 to July 2021. For the UC validation cohort, 30 patients with advanced UC treated with atezolizumab were enrolled between November 2019 and July 2021. The CONSORT diagram for the study is shown in Figure 1 .

[0061] The baseline characteristics of patients with advanced hepatocellular carcinoma are shown in Table 1 below.

[0062] [Table 1] JPEG2025514738000003.jpg78165The mean age was 61 years, and 84.1% were male. Most patients had Child-Pugh grade A6 and BCLC stage C. Hepatitis B virus infection was the most common etiology of HCC. The discovery and validation cohorts had similar clinical and laboratory characteristics. The objective response rate (ORR) in the discovery cohort was 30.0%, the 1-year OS rate was 66.6%, and the mean follow-up was 19.4 months. The ORR in the validation cohort was 25.6%, the 1-year OS rate was 65.2%, and the median follow-up was 13.4 months.

[0063] The baseline characteristics of patients with advanced urothelial carcinoma are shown in Table 2 below.

[0064] [Table 2] The mean age was 68 years, and 83.3% were male. The most common tumor site was the bladder (50.0%), and most patients (90.0%) had visceral metastases. All patients in the UC validation cohort were treated with atezolizumab after failure of prior platinum-based therapy, with an ORR of 13.3%, a mean OS of 9.0 months, and a median follow-up of 5.9 months.

[0065] Example 2 Comparison of serum atezolizumab ADA levels at baseline and C2D1 (Cycle 2 Day 1)

[0066] To compare serum atezolizumab ADA levels at baseline and C2D1, the methods of Reference Examples 1 to 4 and Reference Example 6 were performed.

[0067] According to Figure 3, atezolizumab ADA levels were elevated in C2D1 in the advanced HCC discovery cohort compared to baseline. The elevation of ADA in C2D1 was prominent in the non-responders, but not in the responders. Furthermore, according to Figure 3, ADA levels in C2D1 were significantly higher in patients with progressive disease (PD) compared to those with complete response (CR), partial response (PR) or stable disease (SD). These results were consistently observed in the validation cohort. According to Figure 5, ADA levels were significantly elevated in C2D1, and the non-responders had a notable increase in ADA levels in C2D1. Furthermore, according to Figure 6, ADA levels in C2D1 were higher in PD patients compared to those who experienced CR / PR in the validation cohort.

[0068] According to Figure 7, Tables 1 and 2, 17.4% (n=23) of advanced HCC patients treated with Atezo / Bev showed high levels of ADA at C2D1. Consistently, 13.3% (n=4) of advanced UC patients treated with Atezolizumab also showed high levels of ADA (≥1000ng / ml) at C2D1.

[0069] This confirmed that ADA levels were higher in C2D1 compared to baseline.

[0070] Example 3 Setting of cutoff value Most patients did not develop atezolizumab ADA or had very low ADA levels at C2D1, but some patients had very rapid and strong ADA responses at C2D1. Baseline ADA values ​​were <500ng / ml in all patients, and 1000ng / ml was set as the cutoff value to group patients as ADA-high (≥1000ng / ml) or ADA-low (<1000ng / ml) to minimize false positives as much as possible.

[0071] 17 and 18 are forest plots of a meta-analysis showing PFS and OS according to various cutoffs for ADA positivity in the clinical outcome analysis of Example 4 below.

[0072] According to Figures 17 and 18, the adverse clinical impact of ADA on PFS was significant when the cutoff value was high enough (≥1000ng / ml) and gradually decreased when the cutoff was lowered to the upper quartile (257.9ng / ml) or median (45.95ng / ml). High ADA levels in terms of OS are associated with poor prognosis of atezolizumab-based treatment, regardless of various cutoffs. ADA levels showed good predictive value for 12-month OS in the receiver operating curve analysis (AUC 0.78, 95% CI, 0.63-0.82) in Figure 19. The sensitivity and specificity of a cutoff ≥1000ng / ml were 77.8% and 71%, respectively.

[0073] 20 and 21 are graphs showing the correlation between serum atezolizumab and ADA concentrations in Example 5 below.

[0074] According to Figures 20 and 21, the inverse correlation between ADA concentrations and serum atezolizumab concentrations was significant in patients with ADA levels ≧1000 ng / ml, but was not significant in patients with ADA levels <1000 ng / ml.

[0075] Example 4 Confirmation of clinical outcomes for atezolizumab-based therapy in patients with high ADA levels in C2D1 Example 4-1 Analysis of hepatocellular carcinoma (HCC) discovery cohort, hepatocellular carcinoma (HCC) independent multicentric validation cohort, and urothelial carcinoma (UC) validation cohort

[0076] To confirm the clinical significance of high atezolizumab ADA levels in C2D1 on Atezo / Bev treatment efficacy in patients with advanced hepatocellular carcinoma, the discovery cohort and an independent multicentric validation cohort were analyzed using the methods of Reference Examples 1 to 4 and Reference Example 6. Clinical outcomes according to ADA status in patients with advanced UC treated with atezolizumab were also analyzed in the same manner as above.

[0077] The results of the hepatocellular carcinoma (HCC) discovery cohort analysis are shown in Figures 8 to 10, the results of the hepatocellular carcinoma (HCC) independent multicentric validation cohort analysis are shown in Figures 11 to 13, and the urothelial carcinoma (UC) validation cohort analysis is shown in Figure 14.

[0078] 8-10, in the discovery cohort, patients with high ADA levels in C2D1 had a worse ORR than patients with low ADA levels. Patients with high ADA levels also had a substantially worse PFS and OS than patients with low ADA levels.

[0079] According to Figures 11-13, the results in the validation cohorts were similar. In C2D1, patients with high ADA levels had reduced ORR compared with those with low levels. PFS and OS were significantly worse in the ADA-high group compared with the ADA-low group.

[0080] In the UC validation cohort, patients with high ADA levels in C2D1 also had a lower ORR compared with patients with low ADA levels, according to Figure 14. Furthermore, UC patients with high ADA levels experienced worse PFS and OS.

[0081] These results indicate that the occurrence of high levels of ADA in C2D1 was significantly associated with poor clinical outcomes of atezolizumab-based immunotherapy.

[0082] Example 4-2 Multivariate survival analysis of ADA status after covariate adjustment Because various factors may confound the inferred clinical meaning of ADA, the results were further verified by multivariate analysis. Age, sex, ECOG performance status, Child-Pugh score, alpha-fetoprotein, macroscopic vascular invasion, extrahepatic spread, and neutrophil-lymphocyte ratio were used as variables and analyzed by the method described in Reference Example 6.

[0083] 15 and 16 show the results of the multivariate analysis.

[0084] After adjustment for age, sex, ECOG performance status, Child-Pugh score, alpha-fetoprotein, macroscopic vascular invasion, extrahepatic spread, and neutrophil:lymphocyte ratio in multivariate analysis, high ADA levels in C2D1 were independently associated with shorter PFS and OS.

[0085] These results confirm that patients with high ADA levels in C2D1 have poor clinical outcomes on atezolizumab-based therapy.

[0086] Example 5. Confirmation of correlation between decreased serum atezolizumab concentrations and impaired T cell responses with ADA levels

[0087] To further explain the impact of high ADA levels on the efficacy of atezolizumab immunotherapy, we evaluated serum atezolizumab concentrations according to ADA status in patients with advanced HCC. Furthermore, to investigate the effect of high ADA levels on T cell immunity, we compared the T cell proliferation responses and cytokine production between the ADA-high and ADA-low groups.

[0088] According to Figures 20 and 21, atezolizumab concentrations in C2D1 were decreased by 23.5% in patients with ADA values ​​of 0-1000 ng / ml compared to ADA-negative (0 ng / ml) patients. Furthermore, patients with ADA values ​​of 1000 ng / ml or more showed a 29.8% decrease in atezolizumab concentrations in C2D1 compared to the ADA-negative group. Atezolizumab concentrations in C2D1 were inversely correlated with ADA levels in all patients.

[0089] According to Figure 22, the fraction of Ki-67+CD8+ proliferating T cells was significantly increased in C2D1 of the ADA-low group, while CD8+ T cell proliferation did not change significantly in the ADA-high group. Furthermore, in Figure 23, CD8+ T cells in the ADA-high group showed decreased secretion of interferon-γ and tumor necrosis factor-α compared with the ADA-low group.

[0090] The inverse correlation between ADA and serum atezolizumab concentrations was prominent in patients with ADA levels ≥1000 ng / ml, but was not significant in those with ADA levels <1000 ng / ml. Overall, high ADA levels were associated with decreased atezolizumab concentrations, decreased CD8+ T-cell proliferation, and decreased secretion of effector cytokines from CD8+ T cells.

Claims

1. 23. A method for predicting therapeutic responsiveness of a biological drug, comprising the step of measuring an anti-drug antibody concentration against the biological drug from a biological sample isolated from a patient to whom the biological drug has been administered.

2. 2. The method of claim 1, wherein the biological sample is at least one selected from the group consisting of blood, plasma, serum, bone marrow, tissue, cells, saliva, sputum, and urine.

3. The method of claim 1, wherein the biological sample is isolated 18-24 days after initiation of patient treatment.

4. The method of claim 1 , wherein the biologic drug is a biologic drug for the treatment of cancer.

5. The cancers include urothelial carcinoma, non-small cell lung cancer, and small cell lung cancer. triple-negative breast cancer (TNBC), hepatocellular carcinoma, colorectal cancer, lung cancer, glioblastoma multiforme, renal cell carcinoma, melanoma, bladder cancer, classical Hodgkin lymphoma lymphoma, CHL), esophageal squamous cell carcinoma, esophageal cancer, gastric cancer, or head and neck squamous cell carcinoma 5. The method according to claim 4, wherein the method is Carcinoma.

6. The method of claim 1 , wherein the biological drug is an antibody, a protein, a peptide, or a combination thereof.

7. 7. The method of claim 6, wherein the antibody is an immune checkpoint inhibitor, a targeted anti-cancer drug, or a combination thereof.

8. 8. The method of claim 7, wherein the immune checkpoint inhibitor is a CTLA4 inhibitor, a PD-L1 inhibitor, a PD-1 inhibitor, or a combination thereof.

9. 8. The method of claim 7, wherein the immune checkpoint inhibitor is Ipilimumab, Tremelimumab, Pembrolizumab, Cemiplimab, Nivolumab, Durvalumab, Atezolizumab, Avelumab, or a combination thereof.

10. 8. The method of claim 7, wherein the targeted anti-cancer drug is Cetuximab, Trastuzumab, Ibritumomab, Rituximab, Brentuximab, Alemtuzumab, Bevacizumab, Panitumumab, or a combination thereof.

11. The method of claim 1, wherein the anti-drug antibody concentration is measured by enzyme-linked immunosorbent assay (ELISA).

12. The method according to claim 1, further comprising determining that the therapeutic response of the biological drug is low if the measured anti-drug antibody concentration is equal to or greater than a cutoff value.

13. The method of claim 11, wherein the cutoff value is 500 to 2000 ng / ml.

14. A diagnostic kit for predicting therapeutic responsiveness to a biological drug, comprising an agent capable of measuring anti-drug antibodies against the biological drug.

15. The diagnostic kit according to claim 14, wherein the subject for predicting therapeutic response is a patient for whom 18 to 24 days have passed since starting biological drug treatment.

16. The diagnostic kit according to claim 14, wherein the agent capable of measuring the anti-drug antibody is an antibody.

17. The diagnostic kit of claim 14, wherein the biological drug is a biological drug for the treatment of cancer.

18. The cancers include urothelial carcinoma, non-small cell lung cancer, and small cell lung cancer. triple-negative breast cancer (TNBC), hepatocellular carcinoma, colorectal cancer, lung cancer, glioblastoma multiforme, renal cell carcinoma, melanoma, bladder cancer, classical Hodgkin lymphoma lymphoma, CHL), esophageal squamous cell carcinoma, esophageal cancer, gastric cancer, or head and neck squamous cell carcinoma 18. The diagnostic kit according to claim 17, which is a diagnostic kit of Carcinoma.

19. The diagnostic kit of claim 14 , wherein the biological drug is an antibody, a protein, a peptide, or a combination thereof.

20. 20. The diagnostic kit of claim 19, wherein the antibody is an immune checkpoint inhibitor, a targeted anti-cancer drug, or a combination thereof.

21. The diagnostic kit of claim 20, wherein the immune checkpoint inhibitor is a CTLA4 inhibitor, a PD-L1 inhibitor, a PD-1 inhibitor, or a combination thereof.

22. 21. The diagnostic kit of claim 20, wherein the immune checkpoint inhibitor is Ipilimumab, Tremelimumab, Pembrolizumab, Cemiplimab, Nivolumab, Durvalumab, Atezolizumab, Avelumab, or a combination thereof.

23. 21. The diagnostic kit of claim 20, wherein the targeted anti-cancer drug is Cetuximab, Trastuzumab, Ibritumomab, Rituximab, Brentuximab, Alemtuzumab, Bevacizumab, Panitumumab, or a combination thereof.

24. The diagnostic kit according to claim 14, wherein, when the concentration of the anti-drug antibody is equal to or higher than a cutoff value, the therapeutic responsiveness of the biological drug is judged to be low.

25. The diagnostic kit according to claim 20, wherein the cutoff value is 500 to 2000 ng / ml.

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