Method for predicting prognosis of subject having cancer, method for predicting therapeutic effect of immune checkpoint inhibitor in subject having cancer, kit for predicting prognosis of subject having cancer, and kit for predicting therapeutic effect of immune checkpoint inhibitor in subject having cancer

Abstract

This method for predicting the prognosis of a subject having cancer comprises a step (a1) of measuring the concentration of lysophosphatidylcholine in a blood sample of the subject having cancer, and a step (b1) of predicting the prognosis of the subject on the basis of the concentration of lysophosphatidylcholine measured in the step (a1).

Description

A method for predicting prognosis in a subject having cancer, a method for predicting the therapeutic effect of an immune checkpoint inhibitor in a subject having cancer, a kit for predicting prognosis in a subject having cancer, and a kit for predicting the therapeutic effect of an immune checkpoint inhibitor in a subject having cancer

[0001] The present disclosure relates to a method for predicting prognosis in a subject having cancer, a method for predicting the therapeutic effect of an immune checkpoint inhibitor in a subject having cancer, a kit for predicting prognosis in a subject having cancer, and a kit for predicting the therapeutic effect of an immune checkpoint inhibitor in a subject having cancer. The present application claims priority based on Japanese Patent Application No. 2025-053633 filed in Japan on March 27, 2025, the content of which is incorporated herein by reference.

[0002] In recent years, with the development of genome analysis technology based on next-generation sequencing (NGS), personalized medicine tailored to patients through molecular diagnosis of cancer has been widely implemented at the level of daily clinical practice. Particularly in adenocarcinoma, various gene mutations and molecular targeted drugs targeting these mutations have been reported and play an important role in cancer medical care. On the other hand, in squamous cell carcinoma, the usefulness of molecular diagnosis based on gene mutations is low, and the progress of personalized medicine has been delayed. In particular, advanced squamous cell carcinoma (e.g., esophageal cancer and head and neck cancer) has an extremely poor prognosis, and many patients are in a cachectic state accompanied by weight loss, loss of appetite, systemic inflammation, and the like at the time of diagnosis.

[0003] The therapeutic effect of immune checkpoint inhibitors for esophageal cancer and head and neck cancer is limited to approximately 30 to 40 percent of patients. Currently, tumor mutational burden (TMB) and PD-L1 expression are used as factors capable of stratifying the therapeutic effect of immune checkpoint inhibitors, which results in bifurcated stratification. However, there are cases where therapeutic effects are exhibited even in patients in whom these indicators are not observed.

[0004] Patent Document 1 reports a method for stratifying a subject regarding susceptibility to treatment with an immune checkpoint inhibitor, the method comprising the step of measuring the concentration of soluble PD-L1 (bsPD-L1) having binding ability to PD-1 receptor in a biological sample.

[0005] Japanese Unexamined Patent Publication No. 2020-148631

[0006] Predicting cancer prognosis is crucial for formulating treatment strategies. In squamous cell carcinoma, the lack of readily available biomarkers that can serve as prognostic factors poses a barrier to the search for novel therapeutic targets.

[0007] Immune checkpoint inhibitors are often antibody drugs and tend to be expensive. They also raise concerns about side effects. Therefore, there is a need for new factors that can stratify the therapeutic effects of immune checkpoint inhibitors.

[0008] This disclosure is made in view of the above circumstances and aims to provide a method for predicting the prognosis of a subject with cancer, which can be performed using a blood sample, and a prognosis prediction kit for a subject with cancer, which can be used in the said method. It also aims to provide a method for predicting the therapeutic effect of an immune checkpoint inhibitor in a subject with cancer, which can be performed using a blood sample, and a prediction kit for the therapeutic effect of an immune checkpoint inhibitor in a subject with cancer, which can be used in the said method.

[0009] This disclosure includes the following embodiments: [1] A method for predicting the prognosis of a subject having cancer, comprising the steps of: (a1) measuring the concentration of lysophosphatidylcholine in a blood sample of the subject; and (b1) predicting the prognosis of the subject based on the concentration of lysophosphatidylcholine measured in step (a1). [2] The method for predicting the prognosis according to [1], wherein the cancer is esophageal cancer or head and neck cancer. [3] The method for predicting the prognosis according to [1] or [2], wherein the lysophosphatidylcholine comprises one or more selected from the group consisting of LPC16:0, LPC17:0, LPC18:0, LPC18:1, and LPC26:1. [4] The method for predicting the prognosis according to any one of [1] to [3], wherein the lysophosphatidylcholine comprises LPC16:0, LPC17:0, LPC18:0, LPC18:1, and LPC26:1. [5] A method for predicting prognosis according to any one of [1] to [4], wherein the lysophosphatidylcholine comprises LPC14:0, LPC16:0, LPC16:1, LPC17:0, LPC18:0, LPC18:1, LPC18:2, LPC20:3, LPC20:4, LPC26:1, and LPC28:1. [6] A method for predicting the therapeutic effect of an immune checkpoint inhibitor in a subject having cancer, comprising the steps of: (a2) measuring the concentrations of two or more lysophosphatidylcholines in a blood sample of the subject; and (b2) predicting the therapeutic effect of the immune checkpoint inhibitor in the subject based on the sum of the concentrations of the two or more lysophosphatidylcholines measured in step (a2). [7] The method for predicting the therapeutic effect according to [6], wherein the cancer is esophageal cancer or head and neck cancer. [8] A method for predicting therapeutic effect according to [6] or [7], wherein the two or more lysophosphatidylcholines are LPC14:0, LPC16:0, LPC16:1, LPC17:0, LPC18:0, LPC18:1, LPC18:2, LPC20:3, LPC20:4, LPC26:1, and LPC28:1. [9] A prognosis prediction kit for a subject with cancer, comprising a reagent for measuring the concentration of lysophosphatidylcholine.

[10] The prognosis prediction kit according to [9], wherein the cancer is esophageal cancer or head and neck cancer.

[11] A prognosis prediction kit according to [9] or

[10] , wherein the lysophosphatidylcholine comprises one or more selected from the group consisting of LPC16:0, LPC17:0, LPC18:0, LPC18:1, and LPC26:1.

[12] A prognosis prediction kit according to any one of [9] to

[11] , wherein the lysophosphatidylcholine comprises LPC14:0, LPC16:0, LPC16:1, LPC17:0, LPC18:0, LPC18:1, LPC18:2, LPC20:3, LPC20:4, LPC26:1, and LPC28:1.

[13] A prediction kit for the therapeutic effect of an immune checkpoint inhibitor in a subject with cancer, comprising reagents for measuring the concentrations of two or more lysophosphatidylcholines.

[14] A prediction kit for the therapeutic effect according to

[13] , wherein the cancer is esophageal cancer or head and neck cancer.

[15] A therapeutic effect prediction kit according to

[13] or

[14] , comprising two or more types of lysophosphatidylcholine, LPC14:0, LPC16:0, LPC16:1, LPC17:0, LPC18:0, LPC18:1, LPC18:2, LPC20:3, LPC20:4, LPC26:1, and LPC28:1.

[0010] This disclosure provides a method for predicting the prognosis of a subject with cancer, which can be performed using a blood sample, and a prognosis prediction kit for a subject with cancer, which can be used in the said method. Furthermore, it provides a method for predicting the therapeutic effect of an immune checkpoint inhibitor in a subject with cancer, which can be performed using a blood sample, and a prediction kit for the therapeutic effect of an immune checkpoint inhibitor in a subject with cancer, which can be used in the said method.

[0011] This table shows the results of evaluating the correlation between survival time and various clinical information in patients with esophageal squamous cell carcinoma and head and neck squamous cell carcinoma in Example 1. HR: Hazard Ratio. This heatmap shows the Pearson correlation coefficient between each module, which is a cluster of metabolites possessed by patients with esophageal squamous cell carcinoma and head and neck squamous cell carcinoma, and clinical information in Example 2. Each row corresponds to a module, and each column corresponds to clinical information. The value in each cell shows the Pearson correlation coefficient. This box plot shows the correlation between various metabolites and Glasgow prognostic score (GPS) in patients with esophageal squamous cell carcinoma, head and neck squamous cell carcinoma, or healthy individuals in Example 3. The vertical axis represents the blood concentration of each metabolite. Ref: Healthy individual data. This table shows the results of evaluating the correlation between survival time and various metabolites in patients with esophageal squamous cell carcinoma and head and neck squamous cell carcinoma in Example 3. HR: Hazard ratio. This graph shows the Kaplan-Meier survival curves for patients with esophageal squamous cell carcinoma and head and neck squamous cell carcinoma in Example 4, specifically for the group with high blood LPC concentration (high) or the group with low blood LPC concentration (low). HR: Hazard ratio, OS: Overall survival. This graph shows the Kaplan-Meier survival curves for patients with esophageal squamous cell carcinoma and head and neck squamous cell carcinoma in Example 4, specifically for the group with high blood LPC concentration (high) or the group with low blood LPC concentration (low). HR: Hazard ratio, OS: Overall survival. This graph shows the Kaplan-Meier survival curves for patients with esophageal squamous cell carcinoma in Example 4, specifically for the group with high blood LPC concentration (high (≧105 μM)) or the group with low blood LPC concentration (low (<105 μM)). ESCC: Esophageal squamous cell carcinoma, HR: Hazard ratio, OS: Overall survival. This graph shows the Kaplan-Meier survival curves for patients with head and neck squamous cell carcinoma in Example 4, specifically for the group with high (high) or low (low) blood LPC concentration. HNSCC: Head and neck squamous cell carcinoma, HR: Hazard ratio, OS: Overall survival. This graph shows the Kaplan-Meier survival curves for patients with esophageal squamous cell carcinoma and head and neck squamous cell carcinoma in Example 5, specifically for the group with high (high) or low (low) blood LPC concentration.HR: Hazard ratio, OS: Overall survival. This graph shows the Kaplan-Meier survival curves for esophageal squamous cell carcinoma patients and head and neck squamous cell carcinoma patients who received immune checkpoint inhibitor (ICI) therapy as first-line treatment in Example 5, for the group with high blood LPC concentration (high) or the group with low blood LPC concentration (low). HR: Hazard ratio, OS: Overall survival. This graph shows the Kaplan-Meier survival curves for lung adenocarcinoma patients who received immune checkpoint inhibitor therapy as first-line treatment in Example 6, for the group with high blood LPC concentration (≧median) or the group with low blood LPC concentration (<median). HR: Hazard ratio, OS: Overall survival.

[0012] The following describes in detail one embodiment of the present invention, but the scope of the present invention is not limited to the embodiment described herein, and various modifications can be made without departing from the spirit of the invention. Furthermore, if multiple upper and lower limits are given for a particular parameter, any combination of these upper and lower limits can be used to create a suitable numerical range.

[0013] A numerical range indicated using "~" signifies a range that includes the numbers before and after the "~" as the lower and upper limits, respectively. If multiple upper and lower limits are listed for a particular parameter, any combination of these upper and lower limits can be used to create a suitable numerical range.

[0014] Unless otherwise specified, "a," "an," and "the" are understood to mean "one or more," encompassing both singular and plural forms.

[0015] The term "comprise" means that it may include components other than the component being discussed. The term "consist of" means that it does not include components other than the component being discussed. The term "consistently of" means that it does not include components other than the component being discussed in a manner that performs a special function (such as a manner that completely negates the effect of the invention). In this specification, when "comprise" is used, it includes the "consist of" and "consistently of" manners.

[0016] [Method for predicting prognosis in subjects with cancer] A first aspect of this disclosure is a method for predicting prognosis in subjects with cancer, comprising steps (a1) and (b1). Step (a1) of this aspect is a step of measuring the concentration of lysophosphatidylcholine in a blood sample of the subject. Step (b1) of this aspect is a step of predicting the prognosis of the subject based on the concentration of lysophosphatidylcholine measured in step (a1).

[0017] The cancer is preferably a solid tumor. Examples of solid tumors include, but are not limited to, squamous cell carcinoma, breast cancer, malignant breast tumor, gastric cancer, melanoma, non-small cell lung cancer, gastric cancer, pancreatic cancer, ovarian cancer, uterine cancer, cervical cancer, hepatocellular carcinoma, prostate cancer, urothelial carcinoma, renal cell carcinoma, and bile duct cancer. Among these, squamous cell carcinoma is preferred.

[0018] Preferred squamous cell carcinomas are esophageal squamous cell carcinoma or head and neck squamous cell carcinoma. Examples of head and neck squamous cell carcinomas include nasopharyngeal cancer, oropharyngeal cancer, hypopharyngeal cancer, laryngeal cancer, oral cancer, nasal cavity cancer, paranasal sinus cancer, and external auditory canal cancer. Hereinafter, unless otherwise specified, "esophageal cancer" refers to esophageal squamous cell carcinoma. Unless otherwise specified, "head and neck cancer" refers to head and neck squamous cell carcinoma.

[0019] Examples of subjects with cancer include mammals. Humans are preferred among mammals.

[0020] <Step (a1)> In step (a1), the concentration of lysophosphatidylcholine in the target blood sample is measured.

[0021] The target blood sample is not particularly limited as long as the prediction method according to this embodiment is effective, and examples include whole blood, plasma, serum, etc., with plasma or serum being preferred.

[0022] Lysophosphatidylcholine (hereinafter also referred to as "LPC") is a general term for phospholipids having a structure in which choline is ester-bonded to a glycerophospholipid with one fatty acid chain. Lysophosphatidylcholine is widely distributed in living organisms and is produced by hydrolysis of phosphatidylcholine (hereinafter also referred to as "PC"), a glycerophospholipid with two fatty acid chains, by phospholipase A.

[0023] In this disclosure, specific lysophosphatidylcholine is referred to as "LPCX:Y". "X" represents the number of carbon atoms in the fatty acid chain in lysophosphatidylcholine (hereinafter also referred to as "fatty acid chain length"). "Y" represents the number of double bonds in the fatty acid chain in lysophosphatidylcholine (hereinafter also referred to as "degree of unsaturation").

[0024] Examples of lysophosphatidylcholine include LPC14:0, LPC15:0, LPC16:0, LPC16:1, LPC17:0, LPC18:0, LPC18:1, LPC18:2, LPC19:0, LPC20:0, LPC20:3, LPC20:4, LPC20:5, LPC22:6, LPC26:1, LPC28:1, etc.

[0025] Hereinafter, the lysophosphatidylcholine whose concentration in the blood sample is measured in step (a1) will be collectively referred to as "group a1 LPC." The concentration of group a1 LPC in the blood sample will also be referred to as "LPC concentration (a1)." Group a1 LPC may consist of one type of LPC or multiple types of LPC. If group a1 LPC consists of multiple types, the LPC concentration (a1) is calculated as the sum of the concentrations of those multiple types of LPC.

[0026] The method for measuring the concentration of lysophosphatidylcholine in a blood sample (hereinafter also referred to as "blood LPC concentration") is not particularly limited. For example, blood LPC concentration can be measured using methods known to those skilled in the art. Among such methods, examples of methods for measuring the total amount of blood LPC concentration include high-performance liquid chromatography (HPLC) and enzyme-linked immunosorbent assay (ELISA). Examples of methods for measuring the concentration of specific components of lysophosphatidylcholine in a blood sample include liquid chromatography-tandem mass spectrometry (LC-MS / MS) and gas chromatography (GC). From the viewpoint of ease of measurement, the method for measuring LPC concentration (a1) is preferably HPLC or ELISA. From the viewpoint of high sensitivity and high selectivity of measurement, the method for measuring LPC concentration (a1) is preferably LC-MS / MS or GC. LPC concentration (a1) can be measured in vitro.

[0027] The LPC in group a1 preferably contains one or more selected from the group consisting of LPC16:0, LPC17:0, LPC18:0, LPC18:1, and LPC26:1, and more preferably contains one or more selected from the group consisting of LPC16:0, LPC18:0, and LPC18:1, which are the main components of lysophosphatidylcholine in blood.

[0028] It is even more preferable that the LPC of group a1 includes LPC16:0, LPC17:0, LPC18:0, LPC18:1, and LPC26:1.

[0029] From the viewpoint of ease of measurement, the LPC in group a1 may be all lysophosphatidylcholine detectable in a blood sample (hereinafter also referred to as "total LPC"). Examples of lysophosphatidylcholine contained in total LPC include LPC14:0, LPC16:0, LPC16:1, LPC17:0, LPC18:0, LPC18:1, LPC18:2, LPC20:3, LPC20:4, LPC26:1, LPC28:1, etc. The LPC in group a1 may also include LPC14:0, LPC16:0, LPC16:1, LPC17:0, LPC18:0, LPC18:1, LPC18:2, LPC20:3, LPC20:4, LPC26:1, and LPC28:1.

[0030] In step (a1), the concentrations of lipids, proteins, nucleotides, etc. other than lysophosphatidylcholine in the target blood sample may be measured simultaneously.

[0031] <Step (b1)> In step (b1), the prognosis of the subjects is predicted based on the concentration of lysophosphatidylcholine measured in step (a1).

[0032] In the subjects, a low concentration of lysophosphatidylcholine in the blood sample indicates a high probability of a poor prognosis. Conversely, a high concentration of lysophosphatidylcholine in the blood sample indicates a low probability of a poor prognosis. Therefore, the likelihood of a subject having a poor prognosis can be predicted based on the LPC concentration (a1) measured in step (a1). A prediction of a "poor prognosis" may, for example, mean that the subject's five-year survival rate is less than 10%.

[0033] In step (b1), if the LPC concentration (a1) is lower than the threshold Th (b1), it may be predicted that there is a high probability of a poor prognosis in the subject, and if the LPC concentration (a1) is higher than the threshold Th (b1), it may be predicted that there is a low probability of a poor prognosis in the subject.

[0034] The threshold Th(b1) mentioned above is a threshold set according to the LPC concentration (a1).

[0035] The threshold Th(b1) may be a value calculated by statistically processing the LPC concentration (a1) measured in blood samples previously collected from multiple cancer patients whose prognosis has been determined (e.g., squamous cell carcinoma patients). The threshold Th(b1) may also be a cutoff value calculated by statistically processing the LPC concentration (a1) measured in blood samples previously collected from, for example, a group of cancer patients with a poor prognosis (e.g., a group of cancer patients who died within 5 years from the time of blood sample collection) and a group of cancer patients with a good prognosis (e.g., a group of cancer patients who survived for more than 5 years from the time of blood sample collection). It is preferable that the cancer the subject has and the cancers of the cancer patient group used to calculate the threshold Th(b1) are the same. For example, if the cancer the subject has is esophageal cancer, it is preferable to calculate the threshold Th(b1) from blood samples of the cancer patient group that has esophageal cancer. For example, if the cancer the subject has is head and neck cancer, it is preferable to calculate the threshold Th(b1) from blood samples of cancer patients with head and neck cancer. Alternatively, if the cancer the subject has is squamous cell carcinoma, the cancers in the cancer patient group used to calculate the threshold Th(b1) are not limited to cancers of a specific organ, but may include a mixture of squamous cell carcinomas of multiple organs.

[0036] The threshold Th(b1) may be set, for example, to maximize the positive likelihood ratio for the LPC concentration (a1). The threshold Th(b1) that maximizes the positive likelihood ratio can be set, for example, by performing ROC analysis. For example, a group of cancer patients with a poor prognosis (e.g., cancer patients who died within 5 years from the time of blood sample collection) may be defined as "positive," and a group of cancer patients who did not have a poor prognosis (e.g., cancer patients who survived for more than 5 years from the time of blood sample collection) may be defined as "negative," and ROC analysis may be performed.

[0037] The threshold Th(b1) may be a value obtained, for example, by acquiring LPC concentrations (a1) from blood samples of multiple cancer patients and statistically processing the LPC concentrations (a1) for each patient.

[0038] The threshold Th(b1) may be determined, for example, by dividing a group of cancer patients whose prognosis after blood sampling has been determined into two groups: a group with a high LPC concentration (a1) and a group with a low LPC concentration (l1), based on a predetermined LPC concentration (a1) (reference LPC concentration (a1)), and creating Kaplan-Meier curves for each group. If a statistically significant difference is found between the Kaplan-Minor curve of the high-concentration patient group (h1) and the Kaplan-Meier curve of the low-concentration patient group (l1), the reference LPC concentration (a1) may be used as the threshold Th(b1). For example, a log-rank test can be used to test for the significance of the Kaplan-Meier curves between the two groups.

[0039] In step (b1), the prognosis of the subject may be predicted by combining other prognostic indicators. Examples of other prognostic indicators include the Glasgow prognostic score (hereinafter also referred to as "GPS"), PaP score, PPI, and PiPS model. GPS is calculated based on the CRP concentration in the blood sample (hereinafter also referred to as "blood CRP concentration") and the ALB concentration in the blood sample (hereinafter also referred to as "blood ALB concentration"). A blood CRP concentration of less than 1.0 mg / dL and a blood ALB concentration of 3.5 g / dL or higher is classified as GPS 0, a blood CRP concentration of 1.0 mg / dL or higher or a blood ALB concentration of less than 3.5 g / dL is classified as GPS 1, and a blood CRP concentration of 1.0 mg / dL or higher and a blood ALB concentration of less than 3.5 g / dL is classified as GPS 2. GPS 0 is thought to reflect normal, GPS 1 reflects malnutrition or precachexia, and GPS 2 reflects cachexia. The higher the GPS score, the higher the likelihood of a poor prognosis. More specifically, a GPS 0 is likely to indicate a good prognosis, while GPS 1 or GPS 2 is likely to indicate a poor prognosis. A GPS 2 is more likely to indicate a poor prognosis than a GPS 1. By combining other prognostic indicators such as GPS, the accuracy of prognosis prediction in process (b1) is expected to improve. For example, if the LPC concentration (a1) of the target blood sample is lower than the threshold Th (b1) and the GPS is 1 or 2, it can be said that the subject is likely to have a poor prognosis.

[0040] In step (b1), for subjects predicted to have a high probability of having a poor prognosis, the treatment method may be changed, for example, because the treatment currently being performed may not be effective. In one embodiment, the present disclosure provides a method for treating cancer, which includes (b1-i) identifying subjects predicted to have a high probability of having a poor prognosis by steps (a1) and (b1) above, and (b1-ii) treating the subjects predicted to have a high probability of having a poor prognosis by changing to a treatment method different from the treatment currently being performed.

[0041] In step (b1-i), steps (a1) and (b1) described above are performed to identify subjects who are predicted to have a high probability of having a poor prognosis.

[0042] In step (b1-ii), for subjects predicted to have a poor prognosis in step (b1-i), cancer treatment can be changed to a different treatment method from the one currently being used. Examples of the changed treatment method include immunotherapy, radiotherapy, drug therapy, and palliative care. Examples of immunotherapy include the administration of immune checkpoint inhibitors. Examples of immune checkpoint inhibitors are the same as those listed in the [Method for Predicting the Therapeutic Efficacy of Immune Checkpoint Inhibitors in Subjects with Cancer] described later. Examples of radiotherapy include external beam and internal beam radiation. Examples of drugs used in drug therapy include chemotherapy drugs, molecular targeted drugs, antimetabolites, and endocrine therapy drugs. Examples of anticancer drugs used in chemotherapy include platinum-based drugs, fluorouracil (trade name 5-FU), and taxane-based drugs. Examples of platinum-based drugs include oxaliplatin (trade name Elplat), carboplatin (trade name Paraplatin), cisplatin (trade names Randa, Briplatin), and nedaplatin (trade name Akpra). Taxane-based preparations include paclitaxel (brand name Taxol), albumin-bound baclitaxel (brand name Abraxane), and docetaxel (brand names Taxotere, OneTaxotere). Molecularly targeted drugs include anti-EGFR antibodies. Examples of anti-EGFR antibodies include cetuximab (brand name Erbitux) and panitumumab (brand name Vectibix). Antimetabolites include pemetrexed (brand name Alimta). Drug therapy may be carried out systematically according to the treatment line. If a patient is receiving drug therapy and is predicted to have a high probability of a poor prognosis, the type of anticancer drug may be changed. Palliative care is not particularly limited and includes supportive care and palliative anticancer treatment. Supportive care includes, for example, pain management, relief of respiratory distress, relief of loss of appetite and gastrointestinal symptoms, and psychological care. Palliative anticancer treatment includes low-dose chemotherapy, palliative radiotherapy, molecularly targeted therapy, and immunotherapy. The revised treatment method can be selected considering the characteristics of the patient, the duration of use, side effects, etc.

[0043] [Method for Predicting Therapeutic Effect of Immune Checkpoint Inhibitor in Subject Having Cancer] A second aspect of the present disclosure is a method for predicting the therapeutic effect of an immune checkpoint inhibitor in a subject having cancer, comprising step (a2) and step (b2). Step (a2) of the present aspect is a step of measuring the concentrations of two or more lysophosphatidylcholines in a blood sample of the subject. Step (b2) of the present aspect is a step of predicting the therapeutic effect of the immune checkpoint inhibitor in the subject based on the total value of the concentrations of two or more lysophosphatidylcholines measured in step (a2).

[0044] The cancer is preferably a solid cancer. Examples of the solid cancer include those similar to those exemplified in the first aspect described above, and among these, squamous cell carcinoma or non-small cell lung cancer is preferred. As the squamous cell carcinoma, esophageal cancer or head and neck cancer is preferred. Examples of the head and neck cancer include those similar to those exemplified in the first aspect described above.

[0045] Non-small cell lung cancer is classified into lung adenocarcinoma, lung squamous cell carcinoma, and large cell lung cancer. When the cancer in the prediction method according to the present aspect is non-small cell lung cancer, it is preferably lung adenocarcinoma.

[0046] Examples of the subject having cancer include mammals. Humans are preferred as the mammals.

[0047] The term "immune checkpoint inhibitor" refers to an agent that inhibits the function of immune checkpoint molecules. The term "immune checkpoint molecule" refers to a molecule having a function of suppressing an immune response. Examples of immune checkpoint molecules include PD-1, PDL-1, CTLA-4, and the like. Examples of immune checkpoint inhibitors include anti-PD-1 antibodies, anti-PDL-1 antibodies, anti-CTLA-4 antibodies, and the like, and among these, anti-PD-1 antibodies or anti-PDL-1 antibodies are preferred. Examples of the anti-PD-1 antibody include pembrolizumab (trade name: Keytruda), nivolumab (trade name: Opdivo), cemiplimab (trade name: Libtayo), and the like. Examples of the anti-PDL-1 antibody include atezolizumab (trade name: Tecentriq), avelumab (trade name: Bavencio), durvalumab (trade name: Imfinzi), and the like. Examples of the anti-CTLA-4 antibody include ipilimumab (trade name: Yervoy), tremelimumab (trade name: Ijudo), and the like. The anti-CTLA-4 antibody may be used in combination with an anti-PD-1 antibody or an anti-PDL-1 antibody.

[0048] <Step (a2)> In step (a2), the concentrations of two or more lysophosphatidylcholines in a subject blood sample are measured.

[0049] The subject blood sample is not particularly limited as long as the effect of the method according to this embodiment is exhibited, and examples thereof include whole blood, plasma, serum, and the like, and among these, plasma or serum is preferred.

[0050] Hereinafter, the two or more lysophosphatidylcholines whose concentrations in the blood sample are measured in step (a2) are also collectively referred to as "group a2 LPC". The concentration of group a2 LPC in the blood sample is also referred to as "LPC concentration (a2)". The LPC concentration (a2) is calculated as the total value of the concentrations of the two or more LPCs.

[0051] The method for measuring the concentrations of two or more lysophosphatidylcholines in a blood sample (hereinafter also referred to as "blood LPCs concentration") is not particularly limited. For example, blood LPCs concentration can be measured using methods known to those skilled in the art. Among such methods, a method for measuring the total amount of blood LPCs concentration is the same as that listed in the first embodiment above for measuring the total amount of blood LPCs concentration. A method for measuring the concentration of a specific component of lysophosphatidylcholine in a blood sample is the same as that listed in the first embodiment above. From the viewpoint of ease of measurement, the method for measuring LPC concentration (a2) is preferably HPLC or ELISA. From the viewpoint of high sensitivity and high selectivity of measurement, the method for measuring LPC concentration (a2) is preferably LC-MS / MS or GC. LPC concentration (a2) can be measured in vitro.

[0052] The LPC in group a2 preferably contains two or more types selected from the group consisting of LPC16:0, LPC17:0, LPC18:0, LPC18:1, and LPC26:1, and more preferably contains one or more types selected from the group consisting of LPC16:0, LPC18:0, and LPC18:1, which are the main components of lysophosphatidylcholine in blood.

[0053] It is even more preferable that the LPC of group a2 includes LPC16:0, LPC17:0, LPC18:0, LPC18:1, and LPC26:1.

[0054] From the viewpoint of ease of measurement, the LPC in group a2 may be total LPC. Examples of lysophosphatidylcholine contained in total LPC include LPC14:0, LPC16:0, LPC16:1, LPC17:0, LPC18:0, LPC18:1, LPC18:2, LPC20:3, LPC20:4, LPC26:1, LPC28:1, etc. The LPC in group a2 may also include LPC14:0, LPC16:0, LPC16:1, LPC17:0, LPC18:0, LPC18:1, LPC18:2, LPC20:3, LPC20:4, LPC26:1, and LPC28:1.

[0055] In step (a2), the concentrations of lipids, proteins, nucleotides, etc. other than lysophosphatidylcholine in the target blood sample may be measured simultaneously.

[0056] <Step (b2)> In step (b2), the therapeutic effect of an immune checkpoint inhibitor (hereinafter also referred to as "ICI") in the subject is predicted based on the sum of the concentrations of two or more lysophosphatidylcholines measured in step (a2).

[0057] In a subject, a high concentration of lysophosphatidylcholine in the blood sample indicates a high probability that ICI treatment will be effective in that subject. On the other hand, a low concentration of lysophosphatidylcholine in the blood sample indicates a low probability that ICI treatment will not be effective in that subject. Therefore, the presence or absence of ICI treatment effectiveness in the subject can be predicted based on the LPC concentration (a2) measured in step (a2).

[0058] "An ICI is effective" means that the ICI treatment brings about beneficial results for the patient. Beneficial results include, for example, tumor reduction, inhibition of tumor progression, inhibition of tumor invasion, inhibition of tumor metastasis, alleviation of cancer symptoms, and extension of survival time. Regarding extension of survival time, for example, if a patient who has received ICI treatment survives beyond the life expectancy predicted based on the stage of cancer at the start of ICI treatment, it can be considered that the survival time has been extended due to the therapeutic effect of the ICI. "An ICI is ineffective" means that the ICI treatment does not bring about beneficial results for the patient.

[0059] In step (b2), if the LPC concentration (a2) is higher than the threshold Th (b2), it may be predicted that there is a therapeutic effect of ICI in the subject, and if the LPC concentration (a2) is lower than the threshold Th (b2), it may be predicted that there is no therapeutic effect of ICI in the subject.

[0060] The threshold Th(b2) mentioned above is a threshold set according to the LPC concentration (a2).

[0061] The threshold Th(b2) may be a value calculated by statistically processing the LPC concentration (a2) measured in blood samples collected in advance from cancer patients whose treatment effectiveness with ICI has been confirmed (e.g., squamous cell carcinoma patients). The threshold Th(b2) may also be a cutoff value calculated by statistically processing the LPC concentration (a2) measured in blood samples collected in advance from a group of cancer patients whose treatment effectiveness with ICI has been confirmed and a group of cancer patients whose treatment effectiveness with ICI has not been confirmed. It is preferable that the cancer of the subject and the cancer of the cancer patient group used to calculate the threshold Th(b2) are the same. For example, if the cancer of the subject is esophageal cancer, it is preferable to calculate the threshold Th(b2) from blood samples of the cancer patient group that has esophageal cancer. For example, if the cancer of the subject is head and neck cancer, it is preferable to calculate the threshold Th(b2) from blood samples of the cancer patient group that has head and neck cancer. Alternatively, if the cancer the subject has is squamous cell carcinoma, the cancers in the group of cancer patients used to calculate the threshold Th(b2) are not limited to cancers of a specific organ, but may include squamous cell carcinomas of multiple organs.

[0062] The threshold Th(b2) may be set, for example, to maximize the positive likelihood ratio for the LPC concentration (a2). The threshold Th(b2) that maximizes the positive likelihood ratio can be set, for example, by performing ROC analysis. For example, the group of cancer patients in whom the therapeutic effect of ICI was confirmed may be designated as "positive," and the group of cancer patients in whom the therapeutic effect of ICI was not confirmed may be designated as "negative," and then ROC analysis may be performed.

[0063] The threshold Th(b2) may be, for example, the median obtained by statistically processing the LPC concentration(a2) for each patient after obtaining LPC concentration(a2) from blood samples derived from multiple cancer patients who have received ICI treatment. ICI treatment may be performed as first-line treatment or as second-line or later treatment.

[0064] The threshold Th(b1) may be determined, for example, by dividing a group of cancer patients who have received ICI treatment and whose ICI treatment status is known into two groups: a group with a high LPC concentration (a2) and a group with a low LPC concentration (l2), by comparing them to a predetermined LPC concentration (a2) (reference LPC concentration (a2)), and creating Kaplan-Meier curves for each group. If a statistically significant difference is found between the Kaplan-Minor curve of the high-concentration patient group (h2) and the Kaplan-Meier curve of the low-concentration patient group (l2), the reference LPC concentration (a2) may be used as the threshold Th(b2). For example, a log-rank test can be used to test for the significance of the difference in Kaplan-Meier curves between the two groups.

[0065] In step (b2), ICI treatment can be performed on subjects who are predicted to be effective with ICI. In one embodiment, the present disclosure provides a method for treating cancer, which includes (b2-i) identifying subjects who are predicted to be effective with ICI by steps (a2) and (b2), and (b2-ii) performing ICI treatment on the subjects who are predicted to be effective with ICI.

[0066] In step (b2-i), steps (a2) and (b2) are performed as described above to identify subjects for whom treatment of ICI is expected to be effective.

[0067] In step (b2-ii), ICI treatment is performed on subjects who were predicted to be effective in ICI treatment in step (b2-i). ICI treatment can be performed by administering ICI to the subjects.

[0068] For patients in step (b2) who are predicted to be ineffective against ICI, treatment other than ICI may be administered. Other treatments are not limited to ICI and include radiotherapy and drug therapy. Radiotherapy includes external and internal beam radiation. Drug therapy includes the same treatments as those listed in step (b1) above. The content of the treatment can be selected considering the characteristics of the patient, the duration of use, side effects, etc. Chemotherapy as drug therapy may be administered systematically according to the treatment line. In cancer treatment, radiotherapy and drug therapy may be combined.

[0069] Alternatively, in step (b2), for subjects for whom the treatment of ICI is not expected to be effective, the blood LPC concentration can be increased by dietary therapy, drugs that regulate metabolic pathways, etc., thereby enhancing the treatment effect of ICI.

[0070] [Prognosis prediction kit for subjects with cancer] A third aspect of the present disclosure is a prognosis prediction kit for subjects with cancer, comprising a reagent for measuring the concentration of lysophosphatidylcholine.

[0071] The kit according to this embodiment is suitably used in the prediction method according to the first embodiment described above.

[0072] The cancer is preferably a solid tumor. Examples of solid tumors are the same as those listed in the first embodiment above, and among them, squamous cell carcinoma is preferred. As squamous cell carcinoma, esophageal cancer or head and neck cancer is preferred. Examples of head and neck cancer are the same as those listed in the first embodiment above.

[0073] Examples of subjects with cancer include mammals. Humans are preferred among mammals.

[0074] The target blood sample is not particularly limited as long as the kit according to this embodiment is effective, and examples include whole blood, plasma, serum, etc., with plasma or serum being preferred.

[0075] The reagent for measuring the concentration of lysophosphatidylcholine is a reagent used to measure the concentration of lysophosphatidylcholine (blood LPC concentration) in the target blood sample. The reagent may be a reagent that specifically binds to a structure common to lysophosphatidylcholine (for example, a structure including a glycerol skeleton, choline ester-bonded to the glycerol skeleton, and a binding site for a fatty acid chain attached to the glycerol skeleton, hereinafter also referred to as the "LPC main structure"), or it may be a reagent that specifically binds to any one of the lysophosphatidylcholines (LPCs of group a1) whose concentration in the blood sample is measured in step (a1) above. The reagent for measuring the concentration of lysophosphatidylcholine may be, for example, an antibody or aptamer that specifically binds to any one of the lysophosphatidylcholines (LPCs of group a1) that are either the LPC main structure or LPCs of group a1. In the following section, under the heading "[Prognostic Prediction Kit for Patients with Cancer]", lysophosphatidylcholine selected from the group consisting of LPCs in group a1 may be referred to as "specific LPC (a1)".

[0076] When measuring blood LPC concentration using analytical instruments such as HPLC, LC-MS / MS, and GC, one or more specific LPC (a1) standard samples can be used as reagents for measuring blood LPC concentration. Standard samples are used to create calibration curves in the measurement of lysophosphatidylcholine using the aforementioned analytical instruments. Blood LPC concentration can be calculated based on the calibration curve created using the standard samples.

[0077] The standard sample of lysophosphatidylcholine may be a purified product of one or more specific LPC(a1). The purity of the specific LPC(a1) in the standard sample of the specific LPC(a1) is, for example, 96% by mass or more, 97% by mass or more, 98% by mass or more, or 99% by mass or more.

[0078] The test kit according to this embodiment may include a reagent for measuring the total amount of lysophosphatidylcholine by specifically binding to the main structure of LPC. Alternatively, it may include a reagent for measuring one or more, two or more, three or more, four or more, or five types selected from the group consisting of LPC16:0, LPC17:0, LPC18:0, LPC18:1, and LPC26:1. Alternatively, it may include a reagent for measuring LPC14:0, LPC16:0, LPC16:1, LPC17:0, LPC18:0, LPC18:1, LPC18:2, LPC20:3, LPC20:4, LPC26:1, and LPC28:1.

[0079] The test kit according to this embodiment preferably includes a reagent capable of measuring lysophosphatidylcholine containing LPC16:0, LPC18:0, and LPC18:1. The reagent may be a reagent for measuring the main structure of LPC, or it may be a reagent for measuring LPC16:0, LPC18:0, and LPC18:1 individually.

[0080] When measuring blood LPC concentration using an analytical instrument, the kit according to this embodiment may include an internal standard substance. The internal standard substance is added to the target blood sample. The blood LPC concentration can be calculated from the amount of internal standard substance added to the target blood sample and the ratio of the measured value of lysophosphatidylcholine to the measured value of the internal standard substance in the blood sample.

[0081] An internal standard substance can be a substance not present in the target blood sample. For example, a protein known not to exist in the human body can be used as an internal standard substance. Preferably, the internal standard substance has an extraction rate similar to that of the lysophosphatidylcholine being measured, relative to the solvent used to extract lysophosphatidylcholine from the target blood sample. Examples of such internal standard substances include monoclonal antibodies and stable isotope-labeled compounds. Examples of stable isotope-labeled compounds include LPC16:0-d5, LPC17:0-d5, LPC18:0-d7, LPC18:1-d7, and LPC26:1-d9. Note that "d5," "d7," and "d9" indicate that 5, 7, and 9 hydrogen atoms are substituted with deuterium, respectively.

[0082] If the test kit according to this embodiment includes a monoclonal antibody as a reagent for measuring the concentration of lysophosphatidylcholine, it may further include a secondary antibody that binds to the monoclonal antibody. Examples of secondary antibodies include enzyme-labeled antibodies and fluorescently labeled antibodies.

[0083] By using the kit according to this embodiment, it becomes possible to easily measure the LPC concentration (a1) in the target blood sample. This makes it possible to easily obtain the prediction results of the prediction method according to the first embodiment.

[0084] [Prediction Kit for Therapeutic Efficacy of Immune Checkpoint Inhibitors in Patients with Cancer] A fourth aspect of this disclosure is a prediction kit for the therapeutic efficacy of immune checkpoint inhibitors in patients with cancer, comprising reagents for measuring the concentrations of two or more lysophosphatidylcholines.

[0085] The kit according to this embodiment is suitably used in the prediction method according to the second embodiment described above.

[0086] The cancer is preferably a solid tumor. Examples of solid tumors are the same as those listed in the first embodiment above, and among them, squamous cell carcinoma or non-small cell lung cancer is preferred. As squamous cell carcinoma, esophageal cancer or head and neck cancer is preferred. As head and neck cancer, examples are the same as those listed in the first embodiment above. As non-small cell lung cancer, lung adenocarcinoma is preferred.

[0087] Examples of subjects with cancer include mammals. Humans are preferred among mammals.

[0088] The target blood sample is not particularly limited as long as the prediction kit according to this embodiment is effective, and examples include whole blood, plasma, serum, etc., with plasma or serum being preferred.

[0089] The reagent for measuring the concentrations of two or more lysophosphatidylcholines is a reagent used to measure the concentrations of two or more lysophosphatidylcholines (blood LPCs concentration) in the target blood sample. The reagent may be a reagent that specifically binds to the main structure of LPC, or it may be a reagent that specifically binds to any one of the lysophosphatidylcholines (LPCs of group a2) whose concentration in the blood sample is measured in step (a2) above. The reagent for measuring the concentration of lysophosphatidylcholine may be, for example, an antibody or aptamer that specifically binds to any one of the lysophosphatidylcholines, either the main structure of LPC or one of the LPCs of group a2. Hereinafter, in the section on [Prediction Kit for the Therapeutic Efficacy of Immune Checkpoint Inhibitors in Subjects with Cancer], lysophosphatidylcholines selected from the group consisting of LPCs of group a2 may be referred to as "specific LPC (a2)".

[0090] When measuring blood LPCs concentration using analytical instruments such as HPLC, LC-MS / MS, and GC, reagents for measuring blood LPCs concentration include standard samples of two or more specific LPCs (a2). These standard samples are used to create a calibration curve in the measurement of lysophosphatidylcholine using the aforementioned analytical instruments. Blood LPCs concentration can then be calculated based on the calibration curve created using the standard samples.

[0091] The standard sample of lysophosphatidylcholine may be a purified product of two or more specific LPC(a2). The purity of the specific LPC(a2) in the standard sample of the specific LPC(a2) is, for example, 96% by mass or more, 97% by mass or more, 98% by mass or more, or 99% by mass or more.

[0092] The test kit according to this embodiment may include a reagent for measuring the total amount of lysophosphatidylcholine by specifically binding to the main structure of LPC. Alternatively, it may include a reagent for measuring two or more, three or more, four or more, or five types selected from the group consisting of LPC16:0, LPC17:0, LPC18:0, LPC18:1, and LPC26:1. Alternatively, it may include a reagent for measuring LPC14:0, LPC16:0, LPC16:1, LPC17:0, LPC18:0, LPC18:1, LPC18:2, LPC20:3, LPC20:4, LPC26:1, and LPC28:1.

[0093] The test kit according to this embodiment preferably includes a reagent capable of measuring lysophosphatidylcholine containing LPC16:0, LPC18:0, and LPC18:1. The reagent may be a reagent for measuring the main structure of LPC, or it may be a reagent for measuring LPC16:0, LPC18:0, and LPC18:1 individually.

[0094] When measuring blood LPCs concentration using an analytical instrument, the kit according to this embodiment may include an internal standard substance. The internal standard substance is added to the target blood sample. The blood LPCs concentration can be calculated from the amount of internal standard substance added to the target blood sample and the ratio of the measured value of lysophosphatidylcholine to the measured value of the internal standard substance in the blood sample.

[0095] The internal standard can be a substance not present in the target blood sample. For example, a protein known not to exist in the human body can be used as the internal standard. Preferably, the internal standard has an extraction rate similar to that of the lysophosphatidylcholine being measured, relative to the solvent used to extract lysophosphatidylcholine from the target blood sample. Examples of such internal standards are the same as those described in the third embodiment above.

[0096] If the test kit according to this embodiment includes a monoclonal antibody as a reagent for measuring the concentration of lysophosphatidylcholine, it may further include a secondary antibody that binds to the monoclonal antibody. Examples of secondary antibodies are the same as those described in the third embodiment above.

[0097] By using the kit according to this embodiment, it becomes possible to easily obtain the total value of LPC concentration (a2) in the target blood sample. This makes it possible to easily obtain the prediction results of the prediction method according to the second embodiment.

[0098] [Other Embodiments] In one embodiment, the present disclosure provides a biomarker comprising lysophosphatidylcholine for predicting prognosis in subjects with cancer.

[0099] In one embodiment, the disclosure provides a biomarker, including lysophosphatidylcholine, for predicting the therapeutic effect of immune checkpoint inhibitors in subjects with cancer.

[0100] In one embodiment, the present disclosure provides a method for collecting data for predicting the prognosis of a subject having cancer, comprising the steps of: (a1) measuring the concentration of lysophosphatidylcholine in a blood sample of the subject; and (b1) predicting the prognosis of the subject based on the concentration of lysophosphatidylcholine measured in the first step.

[0101] In one embodiment, the present disclosure provides a method for collecting data to predict the therapeutic effect of an immune checkpoint inhibitor in a subject having cancer, the method comprising: a1) measuring the concentrations of two or more lysophosphatidylcholines in a blood sample of the subject; and b2) predicting the therapeutic effect of the immune checkpoint inhibitor in the subject based on the sum of the concentrations of the two or more lysophosphatidylcholines measured in step (a2).

[0102] In one embodiment, the present disclosure provides a method for examining a blood sample of a subject having cancer, comprising the steps of: measuring the concentration of lysophosphatidylcholine in the blood sample of the subject; and identifying a blood sample of the subject that is likely to have a poor prognosis based on the concentration of lysophosphatidylcholine measured in the first step.

[0103] In one embodiment, the present disclosure provides a method for testing a blood sample of a subject having cancer, comprising the steps of: measuring the concentrations of two or more lysophosphatidylcholines in the subject's blood sample; and identifying a subject's blood sample that is likely to be effective in obtaining an immune checkpoint inhibitor based on the sum of the concentrations of the two or more lysophosphatidylcholines measured in the first step.

[0104] In one embodiment, the present disclosure provides a reagent for measuring the concentration of lysophosphatidylcholine, which is used to diagnose the prognosis in subjects with cancer.

[0105] In one embodiment, the present disclosure provides a reagent for measuring the concentrations of two or more lysophosphatidylcholines, which is used to diagnose the therapeutic effect of immune checkpoint inhibitors in subjects with cancer.

[0106] In one embodiment, the disclosure provides the use of a reagent for measuring the concentration of lysophosphatidylcholine in the manufacture of a diagnostic agent for diagnosing the prognosis in a subject with cancer.

[0107] In one embodiment, the present disclosure provides the use of a reagent for measuring the concentrations of two or more lysophosphatidylcholines in the manufacture of a diagnostic agent for diagnosing the therapeutic effect of immune checkpoint inhibitors in subjects with cancer.

[0108] In one embodiment, the disclosure provides the use of a reagent for measuring the concentration of lysophosphatidylcholine for diagnosing the prognosis in subjects with cancer.

[0109] In one embodiment, the present disclosure provides the use of a reagent for measuring the concentrations of two or more lysophosphatidylcholines to diagnose the therapeutic effect of immune checkpoint inhibitors in subjects with cancer.

[0110] In each of the above embodiments, the reagent for measuring the concentration of lysophosphatidylcholine is a substance that specifically binds to one of the lysophosphatidylcholine types, either the LPC main structure or the LPCs of group a1 (for example, an antibody, an aptamer, etc.).

[0111] In each of the above embodiments, a reagent for measuring the concentrations of two or more lysophosphatidylcholines is a substance (for example, an antibody, aptamer, etc.) that specifically binds to one of the lysophosphatidylcholines, either the LPC main structure or the LPCs of group a2.

[0112] The present invention will be described below with reference to examples, but the present invention is not limited to the following examples.

[0113] <Selection of Target Patients> The analysis included 149 patients with esophageal cancer or head and neck skin cancer (hereinafter also referred to as "squamous cell carcinoma patients") who received chemotherapy at the Department of Medical Oncology, Tohoku University Hospital from September 2018 to March 2023 and were confirmed to have stage 4 squamous cell carcinoma by histological examination. Chemotherapy was administered according to the guidelines of each institution. Esophageal cancer patients were mainly administered platinum-based drugs, fluorouracil, anti-PD-1 antibodies, and taxane-based drugs, while head and neck cancer patients were mainly administered platinum-based drugs, fluorouracil, anti-PD-1 antibodies, anti-EGFR antibodies, and taxane-based drugs. The median observation period from first-line treatment was 601 days. Patient information was obtained from the electronic medical record system. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the Graduate School of Medicine, Tohoku University (Approval Numbers: 2017-1-346, 2018-4-059). Prior to registration, we obtained written informed consent from all patients.

[0114] Table 1 shows the characteristics of the patients. 85% of the patients were elderly men. None of the patients had concomitant esophageal cancer and head and neck cancer.

[0115]

[0116] <Preparation of Plasma Samples> Plasma samples were collected from the above-mentioned patients and stored in the biobank at Tohoku University Hospital. A fixed amount of plasma sample was manually dispensed into designated tubes and stored at -80°C until analysis.

[0117] Plasma samples were collected from various treatment lines during chemotherapy. Table 2 shows the proportion of each treatment line at the time of blood collection for the above patients. All plasma samples were collected during the period when chemotherapy was discontinued.

[0118]

[0119] <Analysis Method> The univariate Cox proportional hazards analysis described below was performed using the statistical software "R version 1.73". In this example, the analysis was performed to evaluate the correlation between patient survival time and a predetermined univariate. Patient survival time corresponds to patient prognosis, and a short survival time was judged to indicate a poor prognosis.

[0120] The Kaplan-Meier analysis described below was performed using the statistical software "R version 1.73". In this example, the analysis was performed to estimate the distribution of overall survival (hereinafter also referred to as "OS") among the target patients. As a result of this analysis, a Kaplan-Meier survival curve was created. In the Kaplan-Meier survival curve, the horizontal axis represents OS, the vertical axis represents the survival rate, and "Number at risk" represents the number of surviving patients at the corresponding OS. Furthermore, the statistical difference between the two groups in the Kaplan-Meier analysis was examined using the log-rank test.

[0121] The metabolome analysis described below was performed using the MxP® Quant 500 XL kit (Biocrates Life Science AG), which is equipped with a triple quadrupole mass spectrometer (Xevo TQ-XS, Waters Corporation) and an ultra-high-performance liquid chromatography (UPLC) system (ACQUITY UPLC® I-Class, Waters Corporation). This analysis was performed with the aim of comprehensively analyzing metabolites, and in this example, the blood concentrations of 635 metabolites were quantified. Calibration standards and quality control methods were set according to the kit manual, and plasma samples from patients or healthy individuals were measured. WebIDQ software (Biocrates Life Science AG) was used to quantify the blood metabolite concentrations. This analysis was conducted by the Tohoku Medical Megabank Organization (hereinafter also referred to as "ToMMo").

[0122] The weighted gene correlation network analysis (WGCNA), described below, was performed using the WGCNA package of the statistical software "R version 1.73". In this example, the analysis was performed to evaluate the correlation between metabolites and clinical information. The metabolites analyzed were a total of 503 metabolites detected in more than 80% of the target plasma samples. The value of "soft-threshold power" was set to 7 (R ２Clustering of the metabolites was performed with a p-value of ≥0.90. The following parameters were applied to determine the modules by clustering: TOMType = "unsigned", minModuleSize = 10, deepSplit = 2, reassignThreshold = 0. As a result of clustering, 18 modules were obtained. Subsequently, the first principal component was shown, and the Pearson correlation coefficient was calculated between module eigengene, which summarizes the metabolic profile of each module, and clinical information, with a p-value of less than 0.1 being considered statistically significant.

[0123] Unless otherwise specified, a p-value of less than 0.05 for a two-tailed test was considered statistically significant.

[0124] [Example 1] Univariate Cox proportional hazards analysis was performed to evaluate the correlation between survival time in squamous cell carcinoma patients and various clinical information. Kaplan-Meier analysis showed that the median survival time in esophageal cancer patients was 22.5 months, and the median survival time in head and neck cancer patients was 21.7 months, and the Kaplan-Meier survival curves for both groups were similar (figure omitted). From this, it was concluded that the survival time in squamous cell carcinoma patients is not strongly influenced by either esophageal cancer or head and neck cancer alone. The clinical information was from before the start of primary treatment, and included body mass index, age, sex, tumor size, etc.

[0125] Figure 1 is a table showing the evaluation results. In Figure 1, CI stands for "Confidence Interval". "Severe body weight loss" includes patients whose weight loss over 6 months exceeds 5%, or whose weight loss over 6 months exceeds 2% and whose BMI is less than 20.

[0126] In this analysis, among the clinical information examined, only the factor of "Glasgow prognostic score (GPS) of 1 or higher" showed a significant correlation with the prognosis of squamous cell carcinoma patients (HR: 1.52, 95% CI: 1.02-2.25, p-value: 0.04). This result indicates that a GPS score of 1 or higher is associated with a poor prognosis in squamous cell carcinoma patients.

[0127] [Example 2] Metabolome analysis was used to quantify the blood metabolite concentrations of squamous cell carcinoma patients. Subsequently, WCGNA was performed to evaluate the correlation between 18 modules clustered with metabolites and clinical information.

[0128] Figure 2 is a heatmap showing the Pearson correlation coefficients between the module Eigengene and clinical information for each module. Each row corresponds to a module, and each column corresponds to clinical information. The value in each cell shows the Pearson correlation coefficient. Module 6 ("MEgreenyellow") showed the strongest negative correlation with GPS among the 18 modules (Pearson correlation coefficient: -0.42, p-value: 3.0 × 10⁻¹⁰). －８ ).

[0129] [Example 3] In [Example 2], module 6, which showed a negative correlation with GPS, was examined for its individual metabolites. Metabolome analysis identified 11 LPCs, 68 phosphatidylcholines (hereinafter also referred to as "PCs"), and 11 cholesteryl esters (hereinafter also referred to as "CEs"). Module 6 contained 10 LPCs (LPC14:0, 16:0, 16:1, 17:0, 18:0, 18:1, 18:2, 20:3, 20:4, 26:1, and 28:1), 8 PCs (PC28:1, 30:0, 32:1, 32:2, 34:1, 34:3, 34:4, and 36:1), and 3 CEs (CE16:1, 18:1, and 20:3) (hereinafter also referred to as "metabolites of module 6").

[0130] Figure 3 is a box plot showing the correlation between the metabolites of module 6 and GPS for each metabolite. "Ref" represents data from healthy individuals and was prepared as a control group. The vertical axis represents the blood concentration of each metabolite (hereinafter also referred to as "metabolic level"). The asterisk indicates a group whose p-value was less than 0.05 compared to the GPS 0 group. For each metabolic level of LPC, PC, and CE, the GPS 0 group was equivalent to the average value of healthy individual data. For all 10 LPCs, the metabolic levels of the GPS 1 and GPS 2 groups were significantly lower than those of the GPS 0 group, and a clear negative correlation was observed between GPS and metabolic level. Significant correlations were also observed for PC and CE, but not as pronounced as with LPC.

[0131] Univariate Cox proportional hazards analysis was performed to evaluate the correlation between individual metabolites in module 6 and patient prognosis.

[0132] Figure 4 is a table showing the evaluation results. In Figure 4, CI stands for "Confidence Interval". Five LPCs (LPC16:0, 17:0, 18:0, 18:1, and 26.2) showed a p-value of less than 0.05 when compared to patient survival time.

[0133] Figure 3 shows that LPC can be a predictive biomarker for the prognosis of squamous cell carcinoma patients. Furthermore, Figure 4 suggests that among LPCs, LPC16:0, 17:0, 18:0, 18:1, and 26.2 are particularly useful as such biomarkers.

[0134] [Example 4] Kaplan-Meier analysis was performed to evaluate the relationship between blood LPC concentration (hereinafter also referred to as "LPC level") and the distribution of patient survival time. LPC levels were calculated by the sum of the blood concentrations of 11 LPCs (14:0, 16:0, 16:1, 17:0, 18:0, 18:1, 18:2, 20:3, 20:4, 26:1, and 28:1) or the sum of the blood concentrations of 5 LPCs (16:0, 17:0, 18:0, 18:1, and 26:2). The patients were divided into a group with high LPC levels (hereinafter also referred to as the "high LPC group") and a group with low LPC levels (hereinafter also referred to as the "low LPC group") based on predetermined criteria.

[0135] Figure 5 shows the Kaplan-Meier survival curves for high-LPC and low-LPC groups in squamous cell carcinoma patients. LPC levels were calculated by summing the blood concentrations of the 11 LPCs listed above. Based on the median LPC level, patients with LPC levels higher than the median were classified as the "high-LPC group," and those with LPC levels lower than the median were classified as the "low-LPC group." The median OS in the low-LPC group (18.6 months) was significantly lower than the median OS in the high-LPC group (28.4 months) (HR: 1.56, 95% CI: 1.05-2.30, p: 0.03).

[0136] Figure 6 also shows Kaplan-Meier survival curves for high-LPC and low-LPC groups in squamous cell carcinoma patients. However, LPC levels were calculated by summing the blood concentrations of the five LPCs mentioned above. Based on the median LPC level, patients with LPC levels higher than the median were classified as the "high-LPC group," and those with LPC levels lower than the median were classified as the "low-LPC group." The median OS in the low-LPC group (18.6 months) was significantly lower than the median OS in the high-LPC group (29.4 months) (HR: 1.61, 95% CI: 1.09-2.38, p: 0.02).

[0137] Figure 7 shows the Kaplan-Meier survival curves for high-LPC and low-LPC groups in esophageal cancer patients. LPC levels were calculated by summing the blood concentrations of the 11 LPCs listed above. Based on an LPC level of 105 μM, patients with an LPC level of 105 μM or higher were classified as the "high-LPC group," and those with an LPC level below 105 μM were classified as the "low-LPC group." The median OS in the low-LPC group (16.7 months) was significantly lower than the median OS in the high-LPC group (26.2 months) (HR: 2.10, 95% CI: 1.07–4.12, p: 0.03).

[0138] Figure 8 shows the Kaplan-Meier survival curves for high-LPC and low-LPC groups in head and neck cancer patients. LPC levels were calculated by summing the blood concentrations of the 11 LPCs listed above. Based on the median LPC level, patients with LPC levels higher than the median were classified as the "high-LPC group," and those with LPC levels lower than the median were classified as the "low-LPC group." The median OS in the low-LPC group (15.9 months) was significantly lower than the median OS in the high-LPC group (34.7 months) (HR: 1.88, 95% CI: 1.08-3.27, p: 0.03).

[0139] Figures 5-8 show that squamous cell carcinoma patients with high blood LPC concentrations (esophageal cancer patients and head and neck cancer patients), esophageal cancer patients, or head and neck cancer patients had significantly longer overall survival (OS) compared to squamous cell carcinoma patients with low blood LPC concentrations. These results suggest that blood LPC concentration can be a prognostic factor for cancer patients, particularly squamous cell carcinoma patients. Furthermore, it was shown that both the sum of the blood concentrations of the 11 LPCs mentioned above, and the sum of the blood concentrations of the 5 LPCs mentioned above, can serve as predictive indicators for the prognosis of squamous cell carcinoma patients.

[0140] [Example 5] Kaplan-Meier analysis was performed to evaluate the relationship between LPC levels and the distribution of survival time in patients treated with immune checkpoint inhibitors (hereinafter also referred to as "ICIs"). LPC levels were calculated by summing the blood concentrations of the 11 LPCs mentioned above. Based on the median LPC level, the patients were divided into a "high LPC group" whose LPC levels were higher than the median and a "low LPC group" whose LPC levels were lower than the median.

[0141] Figure 9 shows the Kaplan-Meier survival curves for high-LPC and low-LPC groups in squamous cell carcinoma patients treated with ICI. The median OS in the low-LPC group (18.2 months) was significantly shorter than the median OS in the high-LPC group (28.3 months) (HR: 1.63, 95% CI: 1.05–2.52, p: 0.03).

[0142] Figure 10 shows the Kaplan-Meier survival curves for high-LPC and low-LPC groups in squamous cell carcinoma patients treated with ICI as first-line therapy. The median OS in the low-LPC group (8.6 months) was significantly shorter than the median OS in the high-LPC group (25.8 months) (HR: 2.39, 95% CI: 1.17–4.90, p: 0.02).

[0143] Figure 9 shows that among squamous cell carcinoma patients treated with ICI, those with high blood LPC concentrations had significantly longer overall survival (OS) compared to those with low blood LPC concentrations. A longer OS in ICI-treated patients suggests that the ICI treatment was effective for those patients. Therefore, the sum of the blood concentrations of the 11 LPCs mentioned above could serve as a predictive biomarker for the effectiveness of ICI treatment in squamous cell carcinoma patients. Furthermore, Figure 10 suggests that the sum of the blood concentrations of the 11 LPCs is a useful indicator, particularly in predicting the effectiveness of ICI treatment as a first-line therapy.

[0144] [Example 6] Kaplan-Meier analysis was performed on 17 patients (hereinafter referred to as "lung adenocarcinoma patients") who received chemotherapy at the Department of Medical Oncology, Tohoku University Hospital from April 2019 to September 2024 and were confirmed to have stage IV lung adenocarcinoma by histological examination. The relationship between LPC level and the distribution of survival time among lung adenocarcinoma patients who received ICI treatment was evaluated. Chemotherapy was performed according to the guidelines of each institution, and mainly anti-PD-1 antibodies (pembrolizumab, nivolumab), anti-CTLA-4 antibodies (ipilimumab), platinum-based drugs (carboplatin, cisplatin), antimetabolites (pemetrexed), and taxane-based drugs (albumin-bound paclitaxel) were administered. The median observation period from first-line treatment was 908 days. Patient information was obtained from the electronic medical record system. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the Graduate School of Medicine, Tohoku University (Approval Number: 2023-1-116). Prior to registration, written informed consent was obtained from all patients. Plasma samples were collected from the lung adenocarcinoma patients in question and stored in the biobank at Tohoku University Hospital. A fixed amount of plasma sample was manually dispensed into designated tubes and stored at -80°C until analysis.

[0145] LPC levels were calculated based on the sum of the blood concentrations of the 11 LPCs listed above. Based on the median LPC level, the patients were divided into a "high LPC group" (those with LPC levels higher than the median) and a "low LPC group" (those with LPC levels lower than the median).

[0146] Figure 11 shows Kaplan-Meier survival curves for high-LPC (≥median) and low-LPC (<median) lung adenocarcinoma patients who received ICI treatment as first-line therapy. The median OS in the low-LPC group was significantly smaller than the median OS in the high-LPC group (HR: 3.97, 95% CI: 1.007–15.7, p: 0.049).

[0147] Figure 11 shows that, not only in squamous cell carcinoma patients, but also in lung adenocarcinoma patients treated with ICI, patients with high blood LPC concentrations had significantly longer overall survival (OS) compared to patients with low blood LPC concentrations. This suggests that the sum of the blood concentrations of the 11 LPCs mentioned above can serve as a predictive biomarker for the effectiveness of ICI treatment in lung adenocarcinoma patients. Furthermore, it is inferred that the sum of the blood concentrations of the 11 LPCs is a particularly useful indicator for predicting the effectiveness of ICI treatment as a first-line therapy.

[0148] While preferred embodiments of the present invention have been described above, the present invention is not limited to these embodiments. Additions, omissions, substitutions, and other modifications are possible without departing from the spirit of the invention. The present invention is not limited by the foregoing description, but only by the scope of the appended claims.

[0149] The present disclosure aims to provide a method for predicting the prognosis of a subject with cancer, which can be performed using a blood sample, and a prognosis prediction kit for a subject with cancer, which can be used in the said method. Furthermore, the present disclosure also provides a method for predicting the therapeutic effect of an immune checkpoint inhibitor in a subject with cancer, which can be performed using a blood sample, and a prediction kit for the therapeutic effect of an immune checkpoint inhibitor in a subject with cancer, which can be used in the said method.

Claims

1. A method for predicting the prognosis of a subject with cancer, comprising: a step (a1) measuring the concentration of lysophosphatidylcholine in a blood sample of the subject; and a step (b1) predicting the prognosis of the subject based on the concentration of lysophosphatidylcholine measured in step (a1).

2. The method for predicting prognosis according to claim 1, wherein the cancer is esophageal cancer or head and neck cancer.

3. The method for predicting prognosis according to claim 1, wherein the lysophosphatidylcholine comprises one or more selected from the group consisting of LPC16:0, LPC17:0, LPC18:0, LPC18:1, and LPC26:

1.

4. The method for predicting prognosis according to claim 1, wherein the lysophosphatidylcholine comprises LPC16:0, LPC17:0, LPC18:0, LPC18:1, and LPC26:

1.

5. The method for predicting prognosis according to claim 1, wherein the lysophosphatidylcholine comprises LPC14:0, LPC16:0, LPC16:1, LPC17:0, LPC18:0, LPC18:1, LPC18:2, LPC20:3, LPC20:4, LPC26:1, and LPC28:

1.

6. A method for predicting the therapeutic effect of an immune checkpoint inhibitor in a subject with cancer, comprising: a step (a2) measuring the concentrations of two or more lysophosphatidylcholines in a blood sample of the subject; and a step (b2) predicting the therapeutic effect of the immune checkpoint inhibitor in the subject based on the total value of the concentrations of the two or more lysophosphatidylcholines measured in step (a2).

7. The method for predicting the therapeutic effect according to claim 6, wherein the cancer is esophageal cancer, head and neck cancer, or lung adenocarcinoma.

8. The method for predicting therapeutic effects according to claim 6, wherein the two or more lysophosphatidylcholines include LPC14:0, LPC16:0, LPC16:1, LPC17:0, LPC18:0, LPC18:1, LPC18:2, LPC20:3, LPC20:4, LPC26:1, and LPC28:

1.

9. A prognosis prediction kit for cancer patients, comprising reagents for measuring lysophosphatidylcholine concentrations.

10. The prognosis prediction kit according to claim 9, wherein the cancer is esophageal cancer or head and neck cancer.

11. The prognosis prediction kit according to claim 9, wherein the lysophosphatidylcholine comprises one or more selected from the group consisting of LPC16:0, LPC17:0, LPC18:0, LPC18:1, and LPC26:

1.

12. The prognosis prediction kit according to claim 9, wherein the lysophosphatidylcholine comprises LPC14:0, LPC16:0, LPC16:1, LPC17:0, LPC18:0, LPC18:1, LPC18:2, LPC20:3, LPC20:4, LPC26:1, and LPC28:

1.

13. A kit for predicting the therapeutic effect of immune checkpoint inhibitors in cancer patients, comprising reagents for measuring the concentrations of two or more lysophosphatidylcholines.

14. The therapeutic effect prediction kit according to claim 13, wherein the cancer is esophageal cancer, head and neck cancer, or lung adenocarcinoma.

15. The therapeutic effect prediction kit according to claim 13, wherein the two or more lysophosphatidylcholines include LPC14:0, LPC16:0, LPC16:1, LPC17:0, LPC18:0, LPC18:1, LPC18:2, LPC20:3, LPC20:4, LPC26:1, and LPC28:1.

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