Molecular marker for diagnosis and prognosis of brain metastatic tumor of lung cancer

Abstract

The invention provides application of a metabolite S-cysteine succinic acid (S-Cysteinosuccinic acid) as a molecular marker for diagnosis and prognosis of lung cancer brain metastasis tumors, and application of a reagent for detecting the metabolite in preparation of a reagent for diagnosis and prognosis of the lung cancer brain metastasis tumors. The invention further provides a method for evaluating the occurrence risk of the lung cancer brain metastasis tumor, and the method comprises the steps that subjects are divided into a high-risk group of the lung cancer brain metastasis tumor and a low-risk group of the lung cancer brain metastasis tumor according to the expression level of metabolites in samples of the subjects, and the metabolites comprise S-Cysteinosuccinic acid. The invention also relates to a kit or a chip containing the reagent for detecting the metabolite.

Description

Technical Field

[0001] The present invention relates to the field of clinical diagnosis and treatment of tumors, and specifically to metabolite molecular markers for evaluating the prognosis of lung cancer, and the diagnosis and prognosis of lung cancer brain metastases, and a method for evaluating the occurrence risk and prognosis of lung cancer brain metastases based on the molecular markers. Background Art

[0002] Lung cancer is the most common type of cancer worldwide and is mainly divided into non-small cell lung cancer (NSCLC, accounting for about 85%) and small cell lung cancer. According to the 2015 classification standard of the World Health Organization, NSCLC accounts for the majority of lung cancer cases and is further divided into three major subtypes: adenocarcinoma, squamous cell carcinoma (SSC) and large cell carcinoma (LCC). Adenocarcinoma and squamous cell carcinoma are the most common histological subtypes of NSCLC.

[0003] Common metastatic organs of lung cancer include the lungs, central nervous system, cervical lymph nodes, bones, adrenal glands, liver, etc. (Oktay et al., 2018).

[0004] The incidence of brain metastasis (BM) in lung cancer patients is very high. BM is common in advanced cases of lung cancer and is the main cause of morbidity and mortality in patients. About 10%-20% of NSCLC patients have BM at diagnosis, and about 40% of patients will develop BM during the course of the disease (Schoenmaekers et.al, 2018, Hubbs et.al, 2018). BM usually manifests as multiple lesions, but 1 / 3 of patients present with solitary lesions.

[0005] In a study of 933 patients with stage I non-small cell lung cancer who underwent surgical resection, 17.8% of patients developed distant metastasis after surgical resection, of which 54.7% were patients with lung adenocarcinoma. Among these patients, 29.2% had brain metastasis, which was the case when surgery was performed at an early stage (stage I) (Hung et al., 2010). There is a higher risk of recurrent brain metastasis in patients with lung adenocarcinoma, and the occurrence of BM can also lead to a shortened survival of patients.

[0006] BM is usually detected based on neurological symptoms, in which patients present with headaches, neurological deficits, seizures, stroke-like symptoms, and / or mild cognitive impairment.

[0007] Routine brain magnetic resonance imaging (MRI) can increase the detection rate of asymptomatic brain metastases, but routine MRI screening is not currently recommended for patients without neurological symptoms due to the burden on patients and the medical system. In addition, some screen-negative patients may develop brain metastases within 1 year. Therefore, the existing incidence of lung cancer BM may be underestimated.

[0008] In summary, it is hoped that there will be a way to identify the metastatic progression of the tumor before the patient has no neurological symptoms, effectively assess the risk of BM, and not increase the burden of MRI for patients. An effective method for detecting BM is crucial to improving treatment strategies and patient survival. Summary of the invention

[0009] Tumor cells and the microenvironment interact and adapt to each other through metabolites. The inventors conducted metabolomics analysis on tissues of patients with lung adenocarcinoma without brain metastasis and patients with lung adenocarcinoma with brain metastasis, aiming to find markers related to lung cancer prognosis and brain metastasis tumor risk. The inventors collected a large number of long-term follow-up clinical cases and studied patients with lung adenocarcinoma who developed BM after surgery.

[0010] The inventors compared the differences in metabolite distribution between lung cancer patients with and without BM after surgery, and screened out cancer tissue-paracancer differential metabolites from tumor tissue and paracancer tissue. After intersecting the cancer tissue-paracancer differential metabolites with the cerebrospinal fluid metabolites detected in the cerebrospinal fluid of patients with BM after surgery, a key metabolite that can detect brain metastases of lung adenocarcinoma with high accuracy was discovered: S-Cysteinosuccinic acid (S-cysteine ​​succinic acid).

[0011] S-Cysteinosuccinic acid is a cysteine ​​derivative, and there are few reports on it. The IUPAC name is 2-(2-amino-2-carboxyethyl)sulfanylbutanedioic acid, Chinese 2-(2-amino-2-carboxyethyl)sulfanylbutanedioic acid, chemical formula C7H 11 NO6S, molecular weight is 237.23 g / mol.

[0012] The inventors found through experiments that the expression of S-Cysteinosuccinic acid in the tissues and cerebrospinal fluid of lung cancer patients who developed BM after surgery was significantly lower than that in patients who did not develop BM after surgery. When lung adenocarcinoma patients were grouped independently according to the expression of the metabolite S-Cysteinosuccinic acid, patients in the group with high S-Cysteinosuccinicacid expression levels had a longer disease-free survival period, and the difference was significant.

[0013] Experiments have shown that the metabolite can be used as a molecular marker for the diagnosis of lung cancer brain metastasis and lung cancer prognosis. Reagents for detecting the molecular marker can be used to prepare diagnostic chips or diagnostic kits for diagnosing lung cancer brain metastasis and lung cancer prognosis.

[0014] A first aspect of the present disclosure provides a metabolite molecular marker for the diagnosis and prognosis of brain metastases from lung cancer, wherein the molecular marker is S-Cysteinosuccinic acid.

[0015] The second aspect of the present disclosure provides use of the molecular marker of the first aspect in preparing a reagent for diagnosis and prognosis of brain metastases from lung cancer.

[0016] The third aspect of the present disclosure provides a metabolite-based method for diagnosing and predicting the prognosis of lung cancer brain metastases, comprising: dividing the subjects suffering from lung cancer into a first high-risk group and a first low-risk group according to the metabolite expression levels of the subjects;

[0017] The metabolites include S-Cysteinosuccinic acid, and subjects with high expression levels of the metabolites are classified into a low-risk group and have a lower risk of developing brain metastases from lung cancer.

[0018] The fourth aspect of the present disclosure also relates to a detection reagent for the molecular marker of the first aspect, and a kit or chip comprising the detection reagent.

[0019] A fifth aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for executing the following method:

[0020] (1) Obtaining the content of S-Cysteinosuccinic acid in samples from lung cancer patients / subjects;

[0021] (2) comparing the content with a preset threshold value, and judging the risk of occurrence of brain metastasis from lung cancer and / or the length of disease-free survival of the subject according to the comparison result; when the content is lower than the threshold value, judging that the risk of occurrence of brain metastasis from lung cancer is high and the length of disease-free survival of the subject is short.

[0022] In a sixth aspect, the present disclosure provides a system for evaluating the risk of occurrence of brain metastasis from lung cancer in a subject, which includes a detection device and a comparison device. When the S-Cysteinosuccinic acid content in a sample of a lung cancer patient / subject is lower than a threshold value, it is judged that the risk of occurrence of brain metastasis from lung cancer is high and the disease-free survival period of the subject is short.

[0023] Advantages of the present disclosure

[0024] 1. The metabolite S-Cysteinosuccinic acid has extremely good specificity for brain metastases from lung cancer, and has extremely good discrimination between people with and without the risk of brain metastases.

[0025] 2. Early detection. The molecular marker S-Cysteinosuccinic acid is used in clinical diagnosis. The risk of BM in the patient can be determined from the surgical tissue obtained after surgery, without waiting until the patient develops brain symptoms. This provides new strategies and treatment basis for early warning and intervention of brain metastasis of lung cancer.

[0026] 3. Sampling advantages. Tumor tissue samples taken during surgery can be used without increasing the number of postoperative visits to the hospital or using MRI screening, thus reducing the burden on patients and the medical system. Liquid biopsy of cerebrospinal fluid can also be performed, which is minimally invasive, low-cost, and can be repeated continuously.

[0027] 4. Currently, there is little research on S-Cysteinosuccinic acid. The present disclosure provides a clinical use of the metabolite, which is practical.

[0028] 5. The methods and systems disclosed herein do not require detection of gene and protein levels in samples. There are many methods for detecting metabolite levels in tissues and body fluids, which facilitates the selection of more flexible and less expensive methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 PLS-DA dimensionality reduction diagram of metabolite principal component analysis in patients with brain metastasis and those without brain metastasis and those with other metastasis. Among them, blue (right circle) shows patients without metastasis (up to the end of follow-up, the same below); magenta (middle circle) shows patients with brain metastasis; yellow (lower left circle) shows patients with metastasis to other parts of the body except brain.

[0030] Figure 2 Volcano plot of differentially expressed metabolites in tumor tissues of patients with brain metastasis and patients without metastasis. Magenta (upper right quadrant) shows metabolites that are upregulated in tumor tissues with brain metastasis; blue (upper left quadrant) shows metabolites that are downregulated in tumor tissues with brain metastasis. Screening conditions: change fold greater than or equal to 1, p value less than or equal to 0.1.

[0031] Figure 3Volcano plot of differential expression of metabolites in tumor tissues of patients with brain metastasis and patients with metastasis to other parts. Among them, magenta (upper right quadrant) shows metabolites that are upregulated in tumor tissues with brain metastasis; yellow (upper left quadrant) shows metabolites that are downregulated in tumor tissues with brain metastasis. Screening conditions: change fold greater than or equal to 1, p value less than or equal to 0.06.

[0032] Figure 4 KEGG signaling pathway analysis of metabolite differences between patients with brain metastases and those without brain metastases. The X-axis is the proportion of metabolites that overlap with pathway annotations, and the Y-axis is the pathway name.

[0033] Figure 5 Venn diagram of characteristic metabolites in cerebrospinal fluid samples and tissue samples. The brown (upper circle) shows the metabolites measured in cerebrospinal fluid tumor cells; the reddish brown (lower circle) shows the differential metabolites obtained by differential analysis of metabolites measured in lung cancer tissues and paired adjacent cancer tissues of lung cancer patients, and the light brown (middle spindle) is the intersection of the two.

[0034] Figure 6 The expression level of S-Cysteinosuccinic acid in patients with different prognoses. The leftmost group: the group without metastasis; the middle group: the group with metastasis to other parts (excluding brain); the rightmost group: the group with brain metastasis.

[0035] Figure 7 The expression level of S-Cysteinosuccinic acid in patients with different prognoses. Left group: group without brain metastasis; right group: group with brain metastasis.

[0036] Figure 8 Metabolite risk factor forest plot for DFS in COX analysis. Taking S-Cysteinosuccinic acid as a risk factor, the hazard ratio for disease-free survival was calculated and plotted. The P value and 95% Cl are shown in the figure, the vertical line at the position of X = 1 is the ineffective line, and HR: hazard ratio.

[0037] Fig. 9 Survival curves of lung adenocarcinoma patients grouped by S-Cysteinosuccinic acid expression level, with survival time expressed in months. According to the level of S-Cysteinosuccinic acid expression, patients were divided into a high expression group (upper curve) and a low expression group (lower curve), and their survival curves were compared. DETAILED DESCRIPTION OF THE INVENTION

[0039] 1. Definitions and Abbreviations

[0040] Prognosis: The development of a disease predicted based on experience. The following parameters are often used in describing prognosis: Progression-free survival, which describes the length of time that a disease does not worsen after treatment or medication for a disease (usually used for cancer). Survival rate, which describes the proportion of people who are still alive after a period of time after diagnosis in a group undergoing medical research or treatment. Survival time, if not otherwise specified, is generally calculated from the time the disease is diagnosed. The above information can be in days, months or years.

[0041] Lung cancer: A malignant tumor originating from the bronchial mucosa or glands of the lungs.

[0042] Lung adenocarcinoma: A type of lung cancer, it is a non-small cell carcinoma that originates from the bronchial mucosal epithelium, and a few originate from the mucous glands of the large bronchi. The classification of lung adenocarcinoma includes, for example, minimally invasive adenocarcinoma (MIA), invasive nonmucinous adenocarcinoma, invasive mucinous adenocarcinoma, colloid adenocarcinoma, fetal adenocarcinoma, enteric type adenocarcinoma, and adenocarcinoma not otherwise specified (NOS).

[0043] DFS (disease-free survival): refers to the time from randomization to the first tumor recurrence or metastasis, or the death of the subject due to any reason, usually used as an indicator of the main efficacy after surgery. For patients who were lost to follow-up, it is the last follow-up time, and for patients who are still alive at the end of the study, it is the end of follow-up.

[0044] Brain metastasis: A type of intracranial tumor, which refers to the metastasis of any primary tumor in an organ other than the brain to the brain and the formation of new lesions in the brain. In the present disclosure, brain metastasis includes meningeal metastasis. In terms of imaging, enhanced brain MRI is the gold standard for neuroradiological assessment of patients suspected of brain metastasis.

[0045] No brain metastasis: For the sake of brevity, in this disclosure, the absence of recurrence of brain metastasis or metastasis to other parts (excluding the brain) after surgery is collectively referred to as "no brain metastasis".

[0046] Distal metastasis: The process by which tumor cells migrate from the site of their original occurrence to other parts of the body by invading the circulatory system and continuing to grow.

[0047] Metabolites: refers to substances produced or consumed through metabolic processes, excluding biomacromolecules. Metabolites include precursors and degradation products of biomacromolecules, as well as small molecules generated or transformed by enzymes during metabolism.

[0048] Primary site tumor: In the present disclosure refers to a tumor at the primary site that is the source of the formation of metastatic cancer or metastatic tumor.

[0049] Neurological symptoms: In the present disclosure, "neurological symptoms", "brain discomfort symptoms", "brain involvement symptoms" and "neurological involvement symptoms" are used with the same meaning, referring to, for example, headache, neurological dysfunction, epileptic seizure, severe neurological and cognitive difficulties, stroke-like symptoms and / or mild cognitive dysfunction, etc.

[0050] The S-Cysteinosuccinic acid (S-cysteine ​​succinic acid) described in the present disclosure has an IUPAC name of 2-(2-amino-2-carboxyethyl)sulfanylbutanedioic acid, and its Chinese name is 2-(2-amino-2-carboxyethyl)sulfanylbutanedioic acid, PubChem CID: 169591.

[0051] The chemical formula of S-Cysteinosuccinic acid is C7H 11 NO6S, with a molecular weight of 237.23 g / mol, is a tricarboxylic acid and a cysteine ​​derivative. It is also called S-(1,2-Dicarboxyethyl)cysteine. In some reports, S-Cysteinosuccinic acid can be used as a flavor enhancing compound for food.

[0052] 2. Statistical methods

[0053] PCA: An unsupervised analysis method that selects a smaller number of important variables through linear transformation of multiple variables. It is a multivariate statistical analysis method, also known as principal component analysis. It can provide a preliminary understanding of the overall metabolite differences between groups of samples and the degree of variation between samples within a group.

[0054] PLS-DA: A supervised discriminant analysis method, which is a multivariate statistical analysis method. PLS-DA can maximize the difference between groups according to the pre-defined classification (Y variable) and obtain better separation effect than PCA.

[0055] Selection of cutoff value: The cutoff value or classification threshold can be defined according to the commonly used methods in the art. In one embodiment, the cutoff value can be the lower quartile of the expression of S-Cysteinosuccinic acid in a cohort of lung cancer patients in a public database, a clinical lung cancer patient cohort, etc., or others.

[0056] KEGG (Kyoto Encyclopedia of Genes and Genomes) is a comprehensive resource that includes bioinformatics databases and related tools, including a metabolic pathway database.

[0057] Risk factors: factors that increase the probability of a disease, injury or adverse outcome. Specifically, it can be understood that if a factor F is a risk factor for a disease D, then the presence of F does not necessarily mean that disease D must exist, but only that the possibility of disease D is higher.

[0058] Hazard Ratio (HR) is calculated by the Cox proportional hazard model and is used to evaluate the impact of a factor on the time of outcome events. It is mainly used for survival analysis (such as overall survival rate, OS, etc.). HR = 1 means that the exposure factor is not related to the outcome event, HR> 1 means that the survival time of the exposed group is shorter (risk factor), and HR< 1 means that the survival time of the exposed group is longer (protective factor). The size of the HR value does not reflect the degree of difference.

[0059] 3. Implementation Method

[0060] The present invention discloses a main embodiment, use of a molecular marker in preparing a reagent for diagnosing brain metastasis of lung cancer, wherein the molecular marker is S-Cysteinosuccinic acid.

[0061] In some embodiments, the lung cancer is non-small cell lung cancer.

[0062] In some embodiments, the non-small cell lung cancer is lung adenocarcinoma. The lung adenocarcinoma may be in stage I to IV. The lung adenocarcinoma may be multiple lesions or a single lesion. The lung adenocarcinoma includes, for example, minimally invasive adenocarcinoma (MIA), invasive nonmucinous adenocarcinoma, invasive mucinous adenocarcinoma, colloid adenocarcinoma, fetal adenocarcinoma, enteric type adenocarcinoma, adenocarcinoma not specified (NOS), etc.

[0063] In some embodiments, diagnosing brain metastases from lung cancer includes: distinguishing 1) subjects who develop brain metastases after surgery from 2) subjects who do not develop brain metastases after surgery or develop other metastases, wherein the other metastases do not include brain metastases.

[0064] In some embodiments, the other metastases are selected from the group consisting of lung, lymph nodes (eg, cervical lymph nodes, mediastinal-subcarinal lymph nodes), bone, adrenal gland, liver, pancreas, hip, ovary, pleura, etc.

[0065] In some other embodiments, the tissue sampling time and the body fluid sampling time can be any period during or after the operation of the subject. Preferably, the tissue sampling time is during the operation. Specifically, the body fluid sampling time can be when the subject shows or does not show symptoms of brain discomfort, or when the subject is suspected of having a brain metastasis of lung cancer. In some other embodiments, preferably, the body fluid is cerebrospinal fluid.

[0066] The brain discomfort symptoms or brain involvement symptoms can be, for example, headache, neurological dysfunction, epileptic seizure, severe neurological and cognitive difficulties, stroke-like symptoms and / or mild cognitive dysfunction, etc.

[0067] The present invention discloses an embodiment of a reagent for diagnosing brain metastasis of lung cancer, wherein

[0068] The reagent detects the expression level of S-Cysteinosuccinic acid in the tissue or body fluid of a subject in need thereof.

[0069] Another embodiment of the present disclosure is a kit or chip for diagnosing brain metastases from lung cancer, wherein the kit or chip comprises a reagent for detecting the expression level of S-Cysteinosuccinic acid in the tissue or body fluid of a subject in need thereof.

[0070] In some embodiments, the reagent detects non-quantitatively, detects quantitatively, specifically recognizes, hydrolyzes or binds to S-Cysteinosuccinic acid.

[0071] The reagent may be a lipid quantification kit (enzymatic reaction).

[0072] In some embodiments, the detection methods include high performance liquid chromatography, mass spectrometry, nuclear magnetic resonance, fluorescence, capillary electrophoresis and the like.

[0073] In some embodiments, the cerebrospinal fluid is analyzed using a single cell metabolic detection method.

[0074] In some embodiments, the steps of the single-cell metabolic detection method include but are not limited to: a sample preparation step, a single-cell quenching step, and a metabolite data collection step; preferably, it includes at least a sample preparation step and a metabolite data collection step.

[0075] The sample preparation method used in the sample preparation step includes: lumbar puncture collection, fluorescence activated cell sorting (FACS), microfluidic array, and direct visualization and penetration / extraction by atomic force microscopy (AFM) probe, etc.

[0076] Examples of the quenching method used in the single cell quenching step include an organic acid or primary solvent method, a liquid nitrogen quick freezing method, and an organic solvent method.

[0077] The metabolite data acquisition step employs methods such as single cell mass spectrometry, electrospray ionization mass spectrometry (ESI-MS), matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS), and secondary ion mass spectrometry (SIMS).

[0078] Another embodiment of the present disclosure is a method for diagnosing brain metastases from lung cancer, comprising:

[0079] Obtaining the expression level of S-Cysteinosuccinic acid in a tissue or body fluid of a subject in need thereof; and

[0080] Subjects whose S-Cysteinosuccinic acid expression level is lower than the cutoff value are classified into a first high-risk group, and subjects whose S-Cysteinosuccinic acid expression level is higher than the cutoff value are classified into a first low-risk group. The first high-risk group is predicted to have a high risk of recurrence of lung cancer brain metastasis, and the first low-risk group is predicted to have a low risk of recurrence of lung cancer brain metastasis.

[0081] Another embodiment of the present disclosure is a method for predicting the prognosis of lung cancer, comprising obtaining the expression level of S-Cysteinosuccinic acid in a tissue or body fluid of a subject in need thereof; and

[0082] Subjects whose S-Cysteinosuccinic acid expression level is lower than the cutoff value are classified into a first high-risk group, and subjects whose S-Cysteinosuccinic acid expression level is higher than the cutoff value are classified into a first low-risk group, and it is determined that the first high-risk group has a short survival period, and the first low-risk group has a long survival period.

[0083] In another embodiment of the present disclosure, a method for classifying a subject suffering from lung cancer comprises:

[0084] Obtaining the expression level of S-Cysteinosuccinic acid in a tissue or body fluid of a subject in need thereof; and

[0085] Subjects whose S-Cysteinosuccinic acid expression level is lower than the cutoff value are classified into a first high-risk group, and subjects whose S-Cysteinosuccinic acid expression level is higher than the cutoff value are classified into a first low-risk group, and it is determined that the first high-risk group has a high risk of recurrence of lung cancer brain metastasis, and the first low-risk group has a low risk of recurrence of lung cancer brain metastasis.

[0086] In some embodiments, the cutoff value is the lower quartile, median, etc. of S-Cysteinosuccinic acid expression in a cohort of subjects with lung cancer, preferably the lower quartile. In some embodiments, the cutoff value can be 4.5366.

[0087] In some embodiments, the subject may be a mammal, preferably a primate, more preferably a human. In some embodiments, the subject may be of any gender. The subject may have or not have a history of smoking. The subject may be of any age, and may be 26-75 years old.

[0088] Applicable population of the diagnostic method: The method or system of the present disclosure is applicable to subjects who have been diagnosed with lung cancer, especially lung adenocarcinoma. The subject may be a subject who is about to undergo or has undergone lung cancer surgery, and the subject may or may not have shown symptoms of brain (or nervous system) involvement.

[0089] In some embodiments, the subject is pathologically diagnosed with lung cancer, non-small cell lung cancer. In some embodiments, the subject is pathologically diagnosed with adenocarcinoma. The differentiation of the subject can be low, medium, or high. The stage of the subject is not limited, and can be IA1-IA3, IB, IIA-IIB, IIIA-IIIB, IVA. The TNM stage of the subject is not limited. In some embodiments, the subject optionally has an EGFR mutation type selected from the following: 19Del, L858R.

[0090] TNM staging criteria

[0091] 1. T stage:

[0092] T0: Indicates no evidence of a primary tumor.

[0093] T1: The tumor diameter is less than or equal to 3 cm and has not invaded surrounding tissues. It is specifically divided into T1a≤1 cm, T1b 1-2 cm, and T1c 2-3 cm.

[0094] T2: The tumor diameter is greater than 3 cm but less than 5 cm, or it invades the main bronchus but does not involve the carina, or it is accompanied by atelectasis or obstructive pneumonia.

[0095] T3: The tumor is larger than 5 cm but less than 7 cm in diameter and invades the chest wall, pericardium, diaphragm, mediastinal pleura, or is less than 2 cm from the carina.

[0096] T4: The tumor invades the heart, great blood vessels, esophagus, vertebral body, carina, or produces malignant pleural effusion.

[0097] 2. N stage:

[0098] N0: No lymph node metastasis.

[0099] N1: metastasis to peribronchial or ipsilateral hilar lymph nodes.

[0100] N2: Metastasis to the ipsilateral mediastinal and subcarinal lymph nodes.

[0101] N3: Metastasis to the contralateral hilar, mediastinal, or supraclavicular lymph nodes.

[0102] 3. M stage:

[0103] M0: No distant metastasis.

[0104] M1: There is distant metastasis, such as liver, bone, brain, etc.

[0105] TNM staging not only helps determine the severity of lung cancer, but also directly guides the formulation of treatment plans.

[0106] Some embodiments are a computer-readable storage medium, characterized in that it stores computer-executable instructions for performing the following method:

[0107] (1) obtaining the content of a molecular marker in a sample of a subject, wherein the molecular marker is S-Cysteinosuccinic acid;

[0108] (2) comparing the content with a preset threshold value, and determining the risk of occurrence of brain metastasis of lung cancer and / or the length of disease-free survival of the subject based on the comparison result.

[0109] Some embodiments provide a system for assessing the risk of developing brain metastases from lung cancer in a subject, comprising:

[0110] A detection device for determining the content of a molecular marker in a sample of a subject, wherein the molecular marker is S-Cysteinosuccinic acid;

[0111] The comparison device is used to compare the content with a preset threshold value, and determine the risk of occurrence of brain metastasis of lung cancer and / or the length of the subject's disease-free survival period based on the comparison result. DETAILED DESCRIPTION

[0112] The present application is further described in detail below through the accompanying drawings and embodiments. Through these descriptions, the characteristics and advantages of the present application will become clearer and more specific.

[0113] The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise noted.

[0114] In addition, the technical features involved in different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0115] Example 1 Different distribution characteristics of metabolites in tissues with brain metastases

[0116] The main cause of death in lung cancer patients is due to the metastasis of the tumor to distant organs, including the lungs themselves, the central nervous system, cervical lymph nodes, bones, adrenal glands, and liver. Studies on early-stage (stage I) NSCLC patients showed that despite surgical resection, 17.8% of patients still experienced distant metastasis, of which the proportion of patients with lung adenocarcinoma was as high as 54.7%, and nearly one-third of these patients had brain metastasis.

[0117] Another retrospective analysis of patients with lung adenocarcinoma further confirmed the high incidence of brain metastasis, revealing that brain metastasis not only occurred frequently (accounting for 44.5%), but also significantly shortened the survival of patients. Among patients with lung cancer, a large proportion of them developed brain metastasis after surgical resection.

[0118] At present, some studies have described the mechanism and treatment of brain metastasis of lung cancer. For example, some studies have shown that the age, KPS (Karnofsky Performance Status), presence of extracranial metastasis, number of brain metastases, and EGFR and ALK positivity of patients with lung adenocarcinoma are all related to prognosis. Other studies have reported that there is no significant difference in the frequency of brain metastases between patients with EGFR mutations and wild-type patients at the time of initial diagnosis in lung adenocarcinoma; however, after treatment, patients with EGFR mutations are more likely to develop brain metastases.

[0119] In a study comparing in situ lung adenocarcinoma tissue with brain metastasis tissue, it was found that, compared with K-ras mutation, the status of EGFR mutation in lung adenocarcinoma was relatively consistent between primary and metastatic sites, but a few lung adenocarcinoma cases still showed inconsistent EGFR mutation status. If tissue from metastatic or recurrent sites is available for evaluation of targeted therapy, it is recommended to repeat the analysis of EGFR mutations.

[0120] In this example, the metabolomics of a clinical cohort of 53 patients with lung adenocarcinoma was studied, and it was found that metabolites had different distribution characteristics between tissues with postoperative recurrent brain metastases and tissues with postoperative non-recurrent brain metastases (= no metastasis or other metastasis, the same below).

[0121] 1. Materials and methods

[0122] Unless otherwise specified, the same materials and methods were used in the subsequent examples.

[0123] 1.1.1 Metabolomics data acquisition

[0124] The clinical cohort included 53 lung cancer patients with pathological diagnosis of adenocarcinoma. The specific sample information of the patients, such as age, gender, OS (overall survival rate), DFS survival rate, smoking history, recurrence and metastasis, and TNM stage, is shown in Table 1.

[0125] As shown in Table 1 , in the cohort, there were no patients with any postoperative recurrence or metastasis (n=27), no patients with postoperative brain metastasis (n=9), and no patients with postoperative other metastasis (n=17).

[0126] Trial Materials: Tumor tissue (surgical resection) from each subject in the clinical cohort.

[0127] Table 1 Sample information of lung adenocarcinoma clinical cohort patients

[0128]

[0129]

[0130]

[0131] Single cell metabolic assay

[0132] Step 1: Sample preparation

[0133] 1. Collect cerebrospinal fluid samples from patients through lumbar puncture, or prepare single-cell suspensions from lung tumor tissue samples obtained during surgery;

[0134] 2. Centrifuge the cell suspension at 1000 rpm at 4°C for 4 minutes and discard the supernatant;

[0135] 3. Add 500ul of cell buffer and gently pipette and wash the cells;

[0136] 4. Repeat steps 2 and 3 once, and resuspend with 500ul cell buffer.

[0137] Step 2: Quality Check

[0138] 1. Use a pipette to absorb 10ul of the above cell suspension and drop it on a clean glass slide;

[0139] 2. Use a 10x microscope to examine the cells in the above droplets. If the single cell morphology can be clearly seen, the field of view is clean and there is no obvious cell debris, the sample is considered qualified.

[0140] Step 3: Single cell metabolite extraction and mass spectrometry detection

[0141] 1. Prepare cell smears using the above cell suspension samples;

[0142] 2. Start the single-cell mass spectrometer (SinCell-100, Ningbo Huayi Ningchuang Intelligent Technology Co., Ltd.) and use the fully automatic single-cell pre-processing function to pre-process the single-cell samples.

[0143] The instrument can realize automated single-cell localization (precise addressing of target single cells), single-cell metabolite extraction (extraction of metabolites in single cells based on extraction methods), and single-cell sample ionization (ionization of single-cell metabolites using picoliter electrospray ionization technology);

[0144] Single-cell metabolite data were acquired using a Thermo Q Exactive HF mass spectrometer.

[0145] The metabolomic data of tumor tissues of patients with lung adenocarcinoma were divided into three groups based on prognostic information: Group I without BM after surgery, Group II with BM after surgery, and Group III with other metastases (but no brain metastasis) after surgery.

[0146] 1.1.2 Principal component dimensionality reduction

[0147] The principal component analysis of the metabolome data of the three groups was performed by PCA and PLS-DA analysis using R (4.4.0) software, and the PLS-DA loading diagram was obtained ( Figure 1 ). The X-axis is the principal component 1 obtained by dimensionality reduction, and the Y-axis is the principal component 2 obtained by dimensionality reduction, wherein blue (right) shows patients who had no metastasis by the end of follow-up; magenta (lower middle) shows patients who had brain metastasis by the end of follow-up; and yellow (left) shows patients who had metastasis to other parts of the body except the brain by the end of follow-up.

[0148] 1.2 Results

[0149] like Figure 1 As shown, the circle is the confidence ellipse (confidence interval 95%) of the principal component distribution of the corresponding samples, indicating that the three groups of metabolites have different distribution characteristics. Among them, the metabolite distribution difference between the postoperative no BM group and the postoperative BM group and the postoperative other metastasis group is relatively large; compared with the postoperative BM group and the postoperative other metastasis group, the metabolite distribution between them partially overlaps.

[0150] The results showed that the distribution of metabolites in the intraoperative tumor tissue of patients who developed brain metastases from lung cancer was different from that of patients who did not develop brain metastases after surgery. This difference may help to distinguish those who are at higher risk of developing brain metastases from lung cancer.

[0151] Example 2 BM differentially expressed metabolites

[0152] 1. Materials and methods

[0153] 2.1.1 Metabolome data acquisition

[0154] The metabolomic data of the tumor tissues of the above-mentioned lung adenocarcinoma patients were divided into three groups according to the presence or absence of BM information: Group 1 was the postoperative brain metastasis group (Brain_Trans), Group 2 was the postoperative non-recurrence metastasis group (No_Trans), and Group 3 was the other metastasis group without brain metastasis (Other_Trans). In addition, Group 2 and Group 3 were combined into a postoperative non-brain metastasis group (Not Brain Trans) for analysis.

[0155] 2.1.2 Differentially expressed metabolites in BM

[0156] The differentially expressed metabolite profiles between group 1 brain metastasis group and group 2 non-metastasis group were obtained by R (4.4.0) software using the limma package and screening conditions: change fold greater than or equal to 1, p value less than or equal to 0.1.

[0157] The differentially expressed metabolites between group 1 and group 2 were plotted into a volcano plot, as shown in Figure 2 Among them, magenta, the upper right quadrant shows metabolites that are up-regulated in tumor tissues with brain metastasis; blue, the upper left quadrant shows metabolites that are down-regulated in tumor tissues with brain metastasis.

[0158] from Figure 2 It can be seen that 26 metabolites are upregulated in tumor tissues with brain metastasis, and 81 metabolites are downregulated in tumor tissues with brain metastasis.

[0159] The differentially expressed metabolites between group 1 brain metastasis group and group 3 other site metastasis groups were plotted into a volcano plot, as shown in Figure 3 Among them, magenta, the upper right quadrant shows metabolites that are up-regulated in tumor tissues with brain metastasis; yellow, the upper left quadrant shows metabolites that are down-regulated in tumor tissues with brain metastasis.

[0160] from Figure 3 It can be seen that compared with patients with metastasis to other parts of the body, 21 metabolites were upregulated in tumor tissues with brain metastasis, and 45 metabolites were downregulated in tumor tissues with brain metastasis.

[0161] Then, statistical analysis was performed on these differentially expressed metabolites.

[0162] 2.1.3 KEGG signaling pathway enrichment analysis

[0163] Enrichment Analysis (EA) is a technique used to analyze high-throughput experimental data. The purpose of enrichment analysis is to determine whether the genes or metabolites observed in the experiment are concentrated in a specific function or pathway.

[0164] The gene name data of differentially expressed metabolites were searched for pathway enrichment through the KEGG Mapper website (https: / / www.kegg.jp / kegg / mapper / ), and species information was input. The enrichment results with biological significance were selected for visualization and shown in Figure 4 The screening criteria for screening significantly enriched pathways were: the corrected p-adjusted value was less than 0.05.

[0165] 2.2 Results

[0166] Figure 4The results showed that in tumor tissues with brain metastasis, the pathways that were significantly upregulated included the sulfur relay system, C-type lectin receptor signaling pathway, oxytocin signaling pathway, cAMP signaling pathway, pantothenic acid and coenzyme A biosynthesis, thiamine metabolism, and β-alanine metabolism, and the pathways that were significantly downregulated included glycerophospholipid metabolism, histidine metabolism, and sphingolipid metabolism. The results suggest that molecular markers with significant expression differences can be screened from metabolites.

[0167] discuss

[0168] Existing studies have found that in the process of brain metastasis tumor formation, the main steps of cancer metastasis to the brain include cell separation from the primary tumor, invasion of the surrounding matrix and basement membrane, cancer cell intravasation, extravasation and destruction of the blood-brain barrier, followed by invasion and colonization of the central nervous system. The process of tumor cells crossing the blood-brain barrier is a key step in the spread of tumors to the brain.

[0169] The experiments disclosed in this paper show that metabolites upregulated by the C-type lectin receptor signaling pathway and cAMP signaling pathway are enriched in the autophagy and complement pathways, suggesting that during the process of lung cancer brain metastasis, the increase of these metabolites may be involved in the anti-apoptosis of circulating tumor cells and the destruction of the blood-brain barrier.

[0170] Example 3 Screening of differential metabolites

[0171] Single-cell metabolomics technology: Traditional metabolomics studies usually require a large number of cells for each sample. Multi-cellular samples can fully detect chemical components, but may mask metabolic differences between different cell types. Single-cell metabolomics technology can determine the metabolic profile of individual cells and is very suitable for decoding heterogeneity at the cellular metabolic level.

[0172] 1. Materials and methods

[0173] 3.1.1 Metabolome data acquisition

[0174] Metabolome data were obtained from lung cancer tissue and paired adjacent paracancerous tissue of each lung cancer patient in the clinical cohort in Example 1.

[0175] The test samples further included cerebrospinal fluid of other patients with clinical postoperative recurrent brain metastasis (18 cases), and the sampling time was: some samples were collected within 1 month after the diagnosis of meningeal metastasis, and some samples were collected one year after intrathecal chemotherapy. Metabolome data were obtained from the above cerebrospinal fluid samples using the method in Example 1 using single-cell metabolomics technology.

[0176] 3.1.2 Differential metabolites between cancer tissue and adjacent tissue

[0177] The metabolome data of lung cancer tissues of lung cancer patients and paired adjacent paracancerous tissues were differentially analyzed to obtain differential metabolites between cancer tissues and adjacent paracancerous tissues.

[0178] 3.1.3 Cerebrospinal fluid metabolites

[0179] The metabolite expression was detected in cerebrospinal fluid samples using single-cell metabolomics technology to obtain cerebrospinal fluid metabolites.

[0180] The key metabolites measured in single tumor cells in cerebrospinal fluid are shown in Table 2.

[0181] Table 2 Key metabolites detected in single tumor cells in cerebrospinal fluid

[0182]

[0183]

[0184] 3.1.3 Key differential metabolites

[0185] The metabolite intersection is shown in Figure 2. Figure 5 .

[0186] Figure 5 In the figure, brown (upper circle) shows 305 cerebrospinal fluid metabolites in cerebrospinal fluid tumor cells; reddish brown (lower circle) shows 443 cancer tissue-paracancer differential metabolites obtained by differential analysis of metabolites in lung cancer tissue and paired adjacent cancer tissues, and light brown (middle spindle) shows 6 intersections of cerebrospinal fluid metabolites and cancer tissue-paracancer differential metabolites.

[0187] After the intersection of cerebrospinal fluid metabolites and cancer tissue-paracancer differential metabolites, six key differential metabolites were obtained, including: Glutamylalanine, Pantothenic acid, gamma-Glutamylisoleucine, L-Cystine, Anserine and S-Cysteinosuccinic acid.

[0188] 3.2 Results

[0189] The results suggest that the above six metabolites show characteristic expression in the cerebrospinal fluid of patients with BM, and they also have significant differential expression in lung cancer and adjacent normal tissues, which has the potential to be used as markers for diagnosing brain metastases of lung adenocarcinoma. These six metabolites can be used to characterize lung adenocarcinoma and indicate the occurrence of brain metastases of lung adenocarcinoma in cerebrospinal fluid samples.

[0190] from Figure 2 and Figure 3It was found that S-Cysteinosuccinic acid was a metabolite that was downregulated in people with BM. We then conducted further studies on it.

[0191] discuss

[0192] Liquid biopsy of cerebrospinal fluid is a minimally invasive alternative to tissue biopsy, especially for difficult-to-access tissues such as the brain. In addition, liquid biopsy is minimally invasive and low-cost, so it can be repeated serially. Markers that can be used in both tissue and cerebrospinal fluid have better practicality than markers that are only applicable to tissue samples.

[0193] Example 4 Correlation between S-Cysteinosuccinic acid and lung adenocarcinoma BM

[0194] 4.1.1 Expression of S-Cysteinosuccinic acid in the three groups

[0195] The expression of S-Cysteinosuccinic acid in the three groups described in Example 1 was calculated, and the expression of S-Cysteinosuccinic acid in patients with lung adenocarcinoma with different prognoses is shown in Figure 6 The figure shows the postoperative no BM group (no metastasis), postoperative other metastasis group (but not brain metastasis) and postoperative BM group (brain metastasis). The Y axis is the expression value measured by metabolic profile.

[0196] like Figure 6 As shown in the figure, in the group with BM after surgery, the content of S-Cysteinosuccinic acid in the tumor tissue of patients was lower than that in the other two groups. In particular, between the group without BM after surgery and the group with other metastases after surgery, p was 0.6, and the difference in the content of S-Cysteinosuccinic acid was very insignificant.

[0197] The above results suggest that:

[0198] 1) In tumor tissues, the difference in the content of S-Cysteinosuccinic acid specifically points to brain metastasis, but not to other distant metastases, indicating that not all types of metastatic tumors will show differences in the tissue content of S-Cysteinosuccinic acid, suggesting that this metabolite has extremely good BM specificity.

[0199] 2) There was no difference in the level of S-Cysteinosuccinic acid in tumor tissue between patients with other distant metastatic tumors and those without any recurrence of metastasis after surgery, with neither upregulation nor downregulation, suggesting that this metabolite has extremely good discrimination between those with and without BM.

[0200] 4.1.2 Expression of S-Cysteinosuccinic acid in the presence and absence of BM

[0201] For verification, the inventors divided the metabolomics data into two groups according to the grouping method of Example 2. The expression of S-Cysteinosuccinic acid in lung adenocarcinoma patients with different prognoses is shown in Figure 7 .

[0202] Figure 7 The results showed that even after being divided into two groups, the S-Cysteinosuccinic acid content in the group with BM after surgery was still significantly lower than that in the group without brain metastasis.

[0203] 4.2 Results

[0204] Statistical results verified that this metabolite had extremely good discrimination between those with and without BM.

[0205] The results showed that the content of the metabolite S-Cysteinosuccinic acid was correlated with the recurrence of brain metastasis after lung adenocarcinoma surgery, which was reflected in the fact that in patients with recurrence of brain metastasis after lung adenocarcinoma surgery, the content of the metabolite S-Cysteinosuccinic acid was lower than that in patients without brain metastasis.

[0206] A decrease in the level of S-Cysteinosuccinic acid indicates a high risk of brain metastasis in lung cancer patients.

[0207] Example 5 COX Analysis of S-Cysteinosuccinic Acid and Disease-Free Survival

[0208] 1. Materials and methods

[0209] 5.1.1 Metabolome data acquisition

[0210] In this example, the disease-free survival data of the clinical cohort obtained by follow-up were further introduced to analyze the correlation between S-Cysteinosuccinic acid and the prognosis of lung cancer.

[0211] the term

[0212] Risk factors: factors that increase the probability of a disease, injury or adverse outcome. Specifically, it can be understood that if a factor F is a risk factor for a disease D, then the presence of F does not necessarily mean that disease D must exist, but only that the possibility of disease D is higher.

[0213] Protective factors: Factors that reduce the likelihood of a disease, injury, or adverse outcome.

[0214] 5.1.2 COX analysis

[0215] The expression of S-Cysteinosuccinic acid was used as a risk factor by COX analysis to calculate the risk ratio for disease-free survival. Figure 8 .

[0216] The diamond represents the 95% credible interval of the combined effect value, the midpoint of the diamond represents the point estimate of the combined effect value, and the length of the diamond represents the credible interval of the combined effect value. The larger the sample size, the narrower the credible interval.

[0217] Figure 8 The hazard ratio (HR) of S-Cysteinosuccinic acid for disease-free survival was 0.940.

[0218] result

[0219] Hazard Ratio (HR) is calculated by the Cox proportional hazard model and is used to evaluate the effect of a factor on the time of occurrence of an outcome event. It is mainly used for survival analysis (such as overall survival rate, OS, etc.). HR = 1 means that the exposure factor is not related to the outcome event, HR>1 means that the survival time of the exposed group is shorter (risk factor), and HR<1 means that the survival time of the exposed group is longer (protective factor). The size of the HR value does not reflect the degree of difference.

[0220] The results suggested that the expression level of S-Cysteinosuccinic acid was a protective factor for disease-free survival in patients with lung adenocarcinoma after surgery.

[0221] 5.1.3 System for Assessing Prognosis of Subjects with Lung Adenocarcinoma

[0222] The expression level of S-Cysteinosuccinic acid was used as a grouping factor alone, and the lower quartile of the expression level of S-Cysteinosuccinic acid in tumor tissues in the clinical cohort was used as a cutoff value, specifically, the cutoff value was 4.5366. The metabolome data in tumor tissues of the clinical cohort were divided into an S-Cysteinosuccinic acid high expression group (Experssion High) and an S-Cysteinosuccinic acid low expression group (Experssion Low).

[0223] The survival curves of lung adenocarcinoma patients grouped according to the expression of S-Cysteinosuccinic acid are shown in Fig. 9 . S-Cysteinosuccinic acid high expression group (upper curve), S-Cysteinosuccinic acid low expression group (lower curve).

[0224] 5.2 Results

[0225] Fig. 9 The results showed that compared with the low expression group, the lung cancer patients classified as the S-Cysteinosuccinic acid high expression group had a longer disease-free survival, and the difference was significant.

[0226] The experimental results suggest that the expression of S-Cysteinosuccinic acid in tumor tissue is a protective factor for postoperative BM recurrence in patients with lung adenocarcinoma and can be used as a molecular marker to predict brain metastasis after lung cancer surgery.

[0227] The results also suggest that S-Cysteinosuccinic acid has a predictive value for lung cancer prognosis. In lung cancer patients, when the tumor tissue has a higher S-Cysteinosuccinic acid content, the patient has a better prognosis and is predicted to have a longer disease-free survival period.

[0228] There have been many explorations on the mechanism of brain metastasis of lung cancer, but its core causes and specific mechanisms still need to be revealed. At the same time, the prediction research on brain metastasis after lung cancer surgery is relatively scarce. This paper provides specific molecular markers for diagnosing brain metastasis of lung cancer through metabolomics research on brain metastasis of lung cancer, and discovers metabolite pathways related to the occurrence of brain metastasis of lung cancer.

[0229] The metabolite S-Cysteinosuccinic acid can be used as a marker to predict and differentiate lung cancer patients with a high risk of developing BM after surgery. Therefore, it can be used to detect patients with asymptomatic brain metastases, which is conducive to timely clinical decision-making and the early adoption of treatment strategies such as focused observation, increased follow-up, and MRI screening.

[0230] The present application has been described above in conjunction with preferred embodiments, but these embodiments are only exemplary and serve only as an illustration. On this basis, various replacements and improvements may be made to the present application, all of which fall within the scope of protection of the present application.

[0231] References

[0232] Schoenmaekers, JJAO; Dingemans, A.-MC; Hendriks, LELBrain Imaging in Early Stage Non-Small Cell Lung Cancer: Still a Controversial Topic? J.Thorac.Dis.2018,10,S2168-S2171.

[0233] Hubbs, JL; Boyd, JA; Hollis, D.; Chino, JP; Saynak, M.; Kelsey, CR

Claims

1. Use of molecular markers in the preparation of reagents for diagnosing brain metastases from lung cancer, The molecular marker is S-Cysteinosuccinic acid.

2. The use according to claim 1, wherein the primary tumor corresponding to the brain metastasis of lung cancer is lung cancer, and the lung cancer is non-small cell lung cancer.

3. The use according to claim 1, wherein the non-small cell lung cancer is lung adenocarcinoma.

4. The use according to claim 1, wherein the diagnosis of brain metastases from lung cancer comprises: Distinguish between 1) subjects who developed brain metastases after surgery and 2) subjects who did not develop brain metastases or developed other metastases after surgery.

5. The use according to any one of claims 1 to 4, wherein the sample used for the diagnosis is tissue and / or body fluid.

6. The use according to claim 5, wherein the body fluid is cerebrospinal fluid.

7. The use according to claim 1, wherein The reagent detects the level of S-Cysteinosuccinic acid by at least one method selected from the following methods: mass spectrometry, electrochemical method, chromatography, preferably mass spectrometry.

8. The use according to claim 1, wherein The reagent can semi-quantitatively detect, quantitatively detect, and specifically recognize or bind to S-Cysteinosuccinic acid.

9. A computer-readable storage medium, characterized in that: The computer executable instructions are used to perform the following method: (1) obtaining the content of a molecular marker in a sample of a subject, wherein the molecular marker is S-Cysteinosuccinic acid; (2) comparing the content with a preset threshold value, and determining the risk of occurrence of brain metastasis of lung cancer and / or the length of disease-free survival of the subject based on the comparison result.

10. A system for assessing the risk of developing brain metastases from lung cancer in a subject, characterized in that: include: A detection device for determining the content of a molecular marker in a sample of a subject, wherein the molecular marker is S-Cysteinosuccinic acid; The comparison device is used to compare the content with a preset threshold value, and determine the risk of occurrence of brain metastasis of lung cancer and / or the length of the disease-free survival period of the subject according to the comparison result.

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