A combined marker for evaluating the efficacy of chemotherapy for breast cancer and application thereof

Abstract

The present application belongs to the technical field of cancer detection, and particularly relates to a combination marker for evaluating the curative effect of breast cancer chemotherapy and application thereof. The present application provides application of a combination marker in preparation of a detection kit for evaluating the curative effect of breast cancer chemotherapy, wherein the combination marker is composed of lactic acid, pyruvic acid, 1-methyl histidine and formic acid. The present application provides a combination marker for evaluating the curative effect of breast cancer chemotherapy and application thereof, which provides a potential and long-term dynamic monitoring index for evaluating the curative effect of breast cancer chemotherapy. The present application determines a non-invasive and dynamic effective evaluation index for evaluating the curative effect of chemotherapy, which has high specificity and high sensitivity, and has high clinical application value in predicting the curative effect of breast cancer chemotherapy.

Description

Technical Field

[0001] This invention belongs to the field of cancer detection technology, specifically relating to a combination biomarker for evaluating the efficacy of chemotherapy for breast cancer and its application. Background Technology

[0002] Breast cancer is the leading cause of cancer-related morbidity and mortality among women. A 2020 report by the International Agency for Research on Cancer (IARC) stated that breast cancer has surpassed lung cancer to become the most common cancer type globally, accounting for 11.7% of all new cancer cases. Adjuvant chemotherapy is a common treatment after breast cancer surgery, effectively reducing the risk of micrometastasis and improving patient survival. A retrospective cohort study on the prognostic effects of chemotherapy on elderly breast cancer patients showed that patients receiving chemotherapy had better overall survival and prognosis. Subjects with HER-2 positivity or multiple lymph node metastases responded better to chemotherapy. The combination of hormone therapy and chemotherapy significantly reduced the risk of hormone receptor expression in patients with recurrent breast cancer. Furthermore, the combination of trastuzumab and chemotherapy significantly improved overall survival in patients with HER-2 overexpressing metastatic breast cancer. Chemotherapy is the primary treatment for triple-negative breast cancer (TNBC). Therefore, exploring patient responses to chemotherapy and assessing drug efficacy are crucial in determining the appropriate chemotherapy regimen.

[0003] Currently, the efficacy of chemotherapy is generally evaluated based on 2-year disease-free survival or 5-year survival. Some clinical biochemical indicators, such as CEA (carcinoembryonic antigen) and CA15-3 (cancer antigen 15-3), are often used as auxiliary evaluation indicators, but their low sensitivity limits their clinical application. Therefore, there is a lack of effective methods in clinical practice for long-term, dynamic, and non-invasive monitoring of chemotherapy efficacy. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a combination biomarker for assessing the efficacy of chemotherapy in breast cancer patients and its application, offering a potential, long-term, dynamic monitoring indicator for evaluating the efficacy of chemotherapy in breast cancer patients. This invention identifies a non-invasive, dynamic, and effective evaluation indicator for chemotherapy efficacy, exhibiting high specificity and sensitivity, and has significant clinical application value in predicting the efficacy of chemotherapy in breast cancer patients.

[0005] The technical solution of this invention is:

[0006] The application of a combination marker in the preparation of a test kit for evaluating the efficacy of chemotherapy in breast cancer, wherein the combination marker is composed of lactic acid, pyruvate, 1-methylhistidine and formic acid.

[0007] This invention detects the levels of four metabolites in the patient's isolated serum, and diagnoses whether the condition has improved or worsened based on the levels of these four metabolites and the patient's clinical symptoms, thereby predicting the efficacy of adjuvant chemotherapy for breast cancer.

[0008] Furthermore, the lactic acid, pyruvate, 1-methylhistidine, and formic acid are derived from serum.

[0009] Furthermore, the contents of lactic acid, pyruvate, 1-methylhistidine, and formic acid were determined by nuclear magnetic resonance. 1 H-spectrum detection.

[0010] This invention collects serum metabolic characteristics of breast cancer patients before and after chemotherapy, and screens out four metabolic biomarkers that can predict the efficacy of chemotherapy—lactic acid, pyruvate, 1-methylhistidine, and formic acid. The predictive value of these four metabolic biomarkers is verified using receiver operating characteristic (ROC) curves. Experiments have shown that the predictive value of these four metabolites for chemotherapy efficacy is as follows: lactate (AUC = 0.595, sensitivity = 95.08%, specificity = 21.74%), pyruvate (AUC = 0.731, sensitivity = 57.38%, specificity = 89.13%), 1-methylhistidine (AUC = 0.843, sensitivity = 83.61%, specificity = 71.74%), and formic acid (AUC = 0.851, sensitivity = 98.36%, specificity = 76.09%). The area under the ROC curve for the combined prediction of the efficacy of adjuvant chemotherapy for breast cancer using these four indicators in this invention was 0.958, with a sensitivity of 98.36% and a specificity of 91.30%. This indicates that the combination of these four metabolites has a good predictive effect on chemotherapy and provides a potential, long-term, non-invasive dynamic monitoring indicator for evaluating the efficacy of chemotherapy in breast cancer subjects.

[0011] Furthermore, the breast cancer is stage I breast cancer, stage II breast cancer, stage III breast cancer, and / or stage IV breast cancer.

[0012] Furthermore, the breast cancer is Luminal A molecular subtype breast cancer, Luminal B molecular subtype breast cancer, HER2+ molecular subtype breast cancer, and / or TNBC molecular subtype breast cancer.

[0013] Furthermore, the present invention also provides a combined marker as described above, the combined marker being composed of lactic acid, pyruvate, 1-methylhistidine, and formic acid.

[0014] Metabolic reprogramming occurs during the development and progression of breast cancer. Metabolomics, a field involving cancer phenotypic research, is a new omics technology following genomics, transcriptomics, and proteomics. This field uses high-throughput, non-invasive analytical techniques to identify tumor markers in bodily fluids to explore the impact of metabolic changes on cancer development and can be used to evaluate the efficacy of chemotherapy in cancer patients. Nuclear magnetic resonance spectroscopy (NMR) is a commonly used technique in metabolomics research. This invention combines NMR-based metabolomics with multivariate analysis to collect serum metabolic characteristics of breast cancer patients before and after chemotherapy. Four metabolic markers—lactic acid, pyruvate, 1-methylhistidine, and formic acid—were screened to predict chemotherapy efficacy, and the predictive value of these four metabolic markers was verified using receiver operating characteristic (ROC) curves. This invention provides a potential, long-term, non-invasive, dynamic monitoring indicator for evaluating the efficacy of chemotherapy in breast cancer patients.

[0015] Compared with existing technologies, the present invention provides a combination biomarker for evaluating the efficacy of chemotherapy in breast cancer and its application, which has the following advantages:

[0016] (1) This invention provides the application of a combination of biomarkers in the preparation of a diagnostic kit for evaluating the efficacy of chemotherapy in breast cancer. The combination of biomarkers (i.e., metabolites or metabolic markers) consists of lactate, pyruvate, 1-methylhistidine, and formic acid. The levels of these four metabolites are not affected by nodule metastasis, clinical stage, or tumor size, and are correlated with chemotherapy efficacy—the area under the ROC curve for the combined prediction of the efficacy of adjuvant chemotherapy in breast cancer is 0.958, with a sensitivity of 98.36% and a specificity of 91.30%, indicating that the combination of these four metabolites has good predictive value for chemotherapy. This combination of biomarkers identifies a non-invasive, dynamic, and effective evaluation indicator for assessing the efficacy of chemotherapy, and has high clinical application value in predicting the efficacy of chemotherapy in breast cancer patients.

[0017] (2) The four metabolic biomarkers used in this invention to predict the efficacy of adjuvant chemotherapy for breast cancer are differentially expressed metabolites screened from the serum metabolomes of breast cancer patients at different stages and with different molecular subtypes using nuclear magnetic resonance spectroscopy. The levels of lactate, pyruvate, and formic acid significantly decreased in the serum of breast cancer patients after chemotherapy, while the level of 1-methylhistidine increased. These biomarkers exhibit high specificity and sensitivity and can be used to predict the efficacy of adjuvant chemotherapy for breast cancer. This invention represents a novel discovery. Decreased levels of lactate, pyruvate, and formic acid in serum, coupled with increased levels of 1-methylhistidine, can serve as metabolic biomarkers for predicting the efficacy of adjuvant chemotherapy for breast cancer. The combined use of these four biomarkers demonstrates the best sensitivity and specificity, and their predictive effect is superior to that of using them individually. Attached Figure Description

[0018] Figure 1Representative serum 1 ¹H NMR spectra. (A) Control group, (B) Breast cancer patients before chemotherapy, (C) Breast cancer patients after chemotherapy. δ 6.0-9.0 range magnified 100 times. 1. Lipids (mainly LDL / LDL), 2. Leucine / Isoleucine, 3. Valine, 4. Lactic acid, 5. Alanine, 6. Acetic acid, 7. Glycoprotein, 8. Glutamic acid, 9. Acetoacetic acid; 10. Pyruvate, 11. Glutamine, 12. Citrate, 13. Creatine, 14. GPC / PC, 15. TMAO, 16. Taurine, 17. α,β-glucose, 18. Glycine, 19. Isoniazid, 20. Tyrosine, 21. 1-Methylhistidine, 22. Phenylalanine, 23. Formate.

[0019] Figure 2 For healthy control groups and breast cancer patients before and after chemotherapy 1 PCA score plot of H NMR data. R2X = 0.905, Q2 = 0.751, control group is marked with ▲, breast cancer before chemotherapy is marked with ●, and breast cancer after chemotherapy is marked with ■.

[0020] Figure 3 For healthy control groups and breast cancer patients before and after chemotherapy 1 OPLS-DA score plots of H NMR data. (A) Breast cancer in the control group and before chemotherapy, R2Y = 0.942, Q2 = 0.589, (B) Breast cancer before chemotherapy and after chemotherapy, R2Y = 0.927, Q2 = 0.376. The control group is marked with ▲, breast cancer before chemotherapy is marked with ●, and breast cancer after chemotherapy is marked with ■, (C) VIP values ​​of metabolites in breast cancer patients before chemotherapy compared with the control group. (D) VIP values ​​of metabolites in breast cancer patients after chemotherapy compared with before chemotherapy.

[0021] Figure 4 ROC analysis of chemotherapy-related metabolic markers. (A) Lactate, (B) Pyruvate, (C) 1-methylhistidine, (D) Formic acid, (E) Combination of the four metabolites. Detailed Implementation

[0022] The present invention will be further described below through specific embodiments, but this is not a limitation of the present invention. Those skilled in the art can make various modifications or improvements based on the basic idea of ​​the present invention, but as long as they do not depart from the basic idea of ​​the present invention, they are all within the protection scope of the present invention.

[0023] Example 1

[0024] 1. Criteria for Participants

[0025] A total of 51 healthy controls and 61 breast cancer patients who underwent surgery participated in our study. Among the 61 postoperative patients, 46 received adjuvant chemotherapy and demonstrated good clinical treatment outcomes based on 2-year disease-free survival assessment. The inclusion criteria were as follows: (1) pathological diagnosis of breast cancer; (2) routine chemotherapy administered according to the doctor's prescribed regimen after surgery; (3) complete clinical data and corresponding immunohistochemical examinations; and (4) no recurrence within two years after chemotherapy. Serum samples were collected from 61 patients before chemotherapy and from 46 patients at the end of the third chemotherapy cycle after surgery. This study was approved by the Medical Ethics Committee of the First Affiliated Hospital of Guangdong Pharmaceutical University.

[0026] 2. Sample collection and storage

[0027] All patients fasted for 12 hours before sample collection to avoid dietary interference. Fasting peripheral blood was collected into blood collection tubes without anticoagulants, allowed to stand for 30 minutes, centrifuged at 1500 rpm for 10 minutes at 4°C, and the supernatant was obtained and then stored at -80°C until batch MRI analysis was performed.

[0028] 3. Serum preparation and 1 H NMR experiment

[0029] Thaw serum samples stored at -80°C and centrifuge at 12,000 rpm for 10 min at 4°C. Add 300 μL of supernatant to a 5 mm NMR tube, then dilute with 150 μL PBS (0.2 mol / L Na₂HPO₄ / NaH₂PO₄, pH 7.4) and 100 μL L₂O. Store samples at 4°C before testing.

[0030] Nuclear magnetic resonance (NMR) metabolic analysis was performed on a Bruker AVANCE III 500M superconducting NMR spectrometer (Bruker). Spectra were collected using a Carr-Purcell-Meiboom-Gill [CPMG, recycle delay -90 - (τ - 180 - τ)n - acquisition] pulse sequence. The experimental temperature was set to 298 K, and the total spin echo time was 100 ms (2nτ). The linewidth was 10 kHz, and 128 data points were obtained for each sample. Topspin 4.0 (Bruker BioSpin, Germany) software was used for manual phase and baseline correction of all NMR samples. The lactate peak was scaled with a chemical shift of δ 1.33. For ease of subsequent analysis, the spectra in the δ 0.5–9.0 range (excluding δ 4.7–5.2 to eliminate the influence of residual water peaks) were divided into integral intervals of 0.004 ppm using AMIX software (4.0.2, Bruker BioSpin, Germany).

[0031] 4. Multivariate analysis

[0032] Unsupervised principal component analysis (PCA) was chosen to present metabolic profile trends on a multivariate dataset. Supervised orthogonal partial least squares discriminant analysis (OPLS-DA) was then used to further screen for different metabolites between groups. The model quality is quantified by variance R0. 2 To evaluate predictive ability, Q is used. 2 To make a judgment.

[0033] Metabolite data were imported into MetaboAnalyst 5.0 (https: / / www.metaboanalyst.ca / ) for multivariate analysis, and VIP values ​​were used to assess the contribution of each metabolite. Heatmap analysis also visually illustrated the differences in metabolites between groups. Metabolites with VIP > 1 and P < 0.05 were considered significant. Relevant metabolic pathways were manually plotted according to KEGG pathways using CorelDRAW (Corel Corporation, Canada). All calculations were performed using GraphPad Prism (8.0.1, China) and SPSS (IBM Corporation, New York, USA).

[0034] 5. ROC Analysis

[0035] ROC analysis is used to assess the diagnostic ability of metabolites. This study selected differentially expressed metabolites with VIP>1 and P<0.05 to evaluate their predictive ability for breast cancer and chemotherapy efficacy. ROCs were generated using GraphPadPrism (8.0.1, China), and the corresponding AUC, specificity, and sensitivity were calculated.

[0036] Experimental results

[0037] 1. Clinical characteristics of the selected subjects

[0038] This study enrolled 112 participants, including 51 healthy controls and 61 breast cancer patients. Of the 61 breast cancer patients, 46 received postoperative chemotherapy. The mean age of the 61 breast cancer patients was significantly higher than that of the control group (Table 1). There was a significant difference in menopausal status between the control group and the breast cancer patients. The levels of CEA and CA15-3 in breast cancer patients before chemotherapy were significantly different from those after chemotherapy. The significant decrease in CEA and CA15-3 levels after chemotherapy indicates that adjuvant chemotherapy is effective for breast cancer patients. Details regarding tumor size, lymph node status, clinical stage, and molecular type in breast cancer patients are shown in Table 1.

[0039] Table 1. Clinical data (mean ± SEM) of the healthy control group and breast cancer patients before and after chemotherapy in the study.

[0040]

[0041] Note: For the control group and before chemotherapy, *p<0.05, **p<0.01, ***p<0.001; for the post-chemotherapy and before chemotherapy, #p<0.05, ##p<0.01.

[0042] 2. Serum 1 H NMR spectrum

[0043] Figure 1 Serum samples from healthy controls and breast cancer patients before and after chemotherapy 1 ¹H NMR spectra. The differences in metabolites between the two groups were concentrated in the δ 0.5–9.0 peak region. A total of 23 endogenous metabolites were identified by assigning the peaks.

[0044] 3. Multivariate analysis

[0045] Figure 2 PCA plots of metabolites before and after chemotherapy were displayed for healthy controls and breast cancer patients. Results showed a clear clustering between the control group and breast cancer patients before chemotherapy, while the metabolites of breast cancer patients after chemotherapy fell between the two groups. The results indicated that the serum metabolite profile of breast cancer patients before chemotherapy differed from that of the healthy control group. Adjuvant chemotherapy induced significant metabolic changes in breast cancer patients compared to their pre-chemotherapy metabolites. The model quality was assessed by cross-validation parameter R0. 2 and Q 2 For evaluation.

[0046] An OPLS-DA model was constructed to determine the metabolic changes induced by chemotherapy and between the two groups. Results showed significant differences in the metabolite profiles of breast cancer patients before and after chemotherapy. Figure 3 (A, B). Based on the VIP values ​​obtained from the analysis, it was found that compared with pre-chemotherapy levels, the following metabolites in the serum of breast cancer patients showed VIP>1: formic acid, alanine, acetic acid, 1-methylhistidine, pyruvate, glycoprotein, and lactate. Further t-tests were performed on the VIP>1 metabolites (Table 2). The results showed that the four VIP>1 metabolites (lactate, pyruvate, 1-methylhistidine, and formic acid) in breast cancer patients were significantly negatively regulated by chemotherapy after chemotherapy, and showed a significant regression of metabolic disturbances compared with the control group.

[0047] Table 2. Statistical analysis of potential biochemical markers in serum before and after chemotherapy in the control group and BC patients.

[0048]

[0049] Note: Fold value is the ratio of the normalized integral area. *P<0.05, **P<0.01 and ***P<0.001.

[0050] We assessed the correlation between chemotherapy-related metabolic markers (lactic acid, pyruvate, 1-methylhistidine, and formic acid) and clinical indicators (lymph node metastasis, clinical stage, and tumor size). By comparing changes in metabolite concentrations, we found no significant correlation between the four chemotherapy-related metabolites and clinical indicators, indicating that chemotherapy metabolic markers are not affected by clinical indicators (Table 3).

[0051] Table 3. Changes in four biomarkers based on clinical indicators

[0052]

[0053] Note: *p < 0.05.

[0054] 4. Diagnostic value of relevant metabolic markers

[0055] The predictive value of four chemotherapy-regulated metabolites in predicting chemotherapy efficacy. Figure 4 The four metabolites were lactate (AUC = 0.595, sensitivity = 95.08%, specificity = 21.74%), pyruvate (AUC = 0.731, sensitivity = 57.38%, specificity = 89.13%), 1-methylhistidine (AUC = 0.843, sensitivity = 83.61%, specificity = 71.74%), and formic acid (AUC = 0.851, sensitivity = 98.36%, specificity = 76.09%). The AUC of the combination of the four metabolites was 0.958, with sensitivity and specificity of 98.36% and 91.30%, respectively. Figure 4 E), the combination of these four metabolites has high application value in predicting the efficacy of chemotherapy.

[0056] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. Use of a combination marker for the manufacture of a test kit for evaluating the efficacy of chemotherapy of breast cancer, characterized in that, The combination marker consists of lactate, pyruvate, 1-methylnicotinic acid and formate.

2. Use according to claim 1, characterized in that, The lactate, pyruvate, 1-methylnicotinic acid and formate are derived from serum.

3. Use according to claim 2, characterized in that, The content of lactic acid, pyruvic acid, 1-methy lhistidine and formic acid is detected by nuclear magnetic resonance 1 H spectrum detection.

4. Use according to claim 1, characterized in that, The breast cancer is stage I breast cancer, stage II breast cancer, stage III breast cancer and stage IV breast cancer.

5. The use according to claim 1, characterized in that, The breast cancer is Luminal A molecular subtype breast cancer, Luminal B molecular subtype breast cancer, HER2+ molecular subtype breast cancer and TNBC molecular subtype breast cancer.

6. A combination marker for use in any of the applications of claims 1-5, wherein, The combination marker consists of lactate, pyruvate, 1-methylnicotinic acid and formate. The combination marker consists of lactate, pyruvate, 1-methylnicotinic acid and formate.

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