Methods, apparatus, and computer storage media for predicting drug resistance based on blood samples

Abstract

The application discloses a method for predicting drug resistance based on a blood sample, comprising the following steps: acquiring a concentration value a of CHIT1 protein in the blood sample, and determining an assignment value A corresponding to the value a; acquiring a concentration value b of IL6R protein in the blood sample, and determining an assignment value B corresponding to the value b; acquiring a concentration value c of NRCAM protein in the blood sample, and determining an assignment value C corresponding to the value c; acquiring a concentration value d of NT5E protein in the blood sample, and determining an assignment value D corresponding to the value d; determining a total sum E of the assignment values corresponding to the concentration values of the proteins, wherein E=A+B+C+D; and determining a representation value F of the drug resistance based on the total sum E of the assignment values corresponding to the concentration values of the proteins. The application can provide relevant data for doctors to help the doctors predict the curative effect of targeted drug treatment on a kidney cancer patient before the patient takes medicine. The application further provides a device for predicting drug resistance based on a blood sample. The application further provides a computer storage medium.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method, apparatus, and computer storage medium for predicting drug resistance based on blood samples. Background Technology

[0002] Renal cell carcinoma (RCC) is one of the most common malignant tumors of the urinary system, accounting for approximately 90% of kidney malignancies and 2%-3% of adult malignancies. In China, it ranks second among urinary tract tumors in incidence. RCC has an insidious onset, often lacking typical clinical symptoms in its early stages; therefore, 30%-40% of patients already have metastases at their first visit. For advanced RCC, traditional radiotherapy and chemotherapy have limited effectiveness. Cytokine therapy, such as interleukin-2 (IL-2) and interferon-alpha (IFN-α), was once the standard treatment, but it only proved effective in less than 20% of patients, with a median overall survival of only 13.3 months and significant side effects. The 5-year survival rate for patients with advanced or metastatic RCC is as low as 12.1%. With in-depth research into the development and progression of RCC, new small-molecule targeted drugs have greatly improved the treatment prospects for advanced RCC. The median progression-free survival (PFS) is twice that of patients previously treated with cytokine therapy, and the overall response rate has also significantly improved.

[0003] Kidney cancer TKIs, including sunitinib, pazopanib, sorafenib, and axitinib, are commonly used targeted therapies for advanced kidney cancer. Their main mechanism of action is to inhibit tumor cell growth and reduce tumor blood supply. However, approximately 22% of patients develop primary resistance upon starting treatment, and most develop secondary resistance or even disease progression within six months of treatment initiation. Due to a lack of data to predict resistance to targeted therapies for kidney cancer, physicians cannot predict the treatment efficacy of these drugs, and effective interventions are lacking after resistance develops. Summary of the Invention

[0004] The purpose of this invention is to address the current lack of relevant data to predict drug resistance in renal cell carcinoma patients, thus hindering the prediction of the therapeutic effects of such drugs. This invention provides a method, apparatus, and computer storage medium for predicting drug resistance based on blood samples. This data can provide doctors or researchers with the ability to predict the efficacy of targeted drug therapy in renal cell carcinoma patients before they begin treatment, enabling the development of individualized treatment plans.

[0005] To address the aforementioned technical problems, embodiments of the present invention disclose a method for predicting drug resistance based on blood samples, comprising:

[0006] Obtain the concentration value 'a' of CHIT1 protein in the blood sample, and assign the value A corresponding to the concentration value 'a' of CHIT1 protein.

[0007] Obtain the concentration value b of IL6R protein in the blood sample, and assign the value B to the concentration value b of IL6R protein.

[0008] Obtain the concentration value c of NRCAM protein in the blood sample, and determine the value C corresponding to the concentration value c of NRCAM protein;

[0009] Obtain the concentration value d of NT5E protein in the blood sample, and assign the value D corresponding to the concentration value d of NT5E protein;

[0010] Determine the sum of the values ​​assigned to each protein concentration, E, where E = A + B + C + D;

[0011] The characterization value F of drug resistance is determined based on the sum E of the values ​​assigned to the concentrations of each protein.

[0012] Using the above-mentioned technical solution, since there is currently no unified standard for clinicians to select targeted drugs, they all choose them based on experience. There is no relevant data or method that allows doctors or researchers to judge the drug resistance of patients with renal cell carcinoma before they take targeted drug therapy. Therefore, it is impossible to predict the therapeutic effect of this type of drug. In the applicant's previous experiments, the applicant used ELISA (Enzyme-Linked Immunosorbent Assay) to detect proteins in a large number of patients' blood samples and combined it with the patients' drug resistance to targeted drugs. It was found that the expression levels of four proteins in blood samples—CHIT1 (chitotriosidase-1), IL6R (Interleukin-6 receptor), NRCAM (Neuronal Cell Adhesion Molecule), and NT5E (5'-nucleotidaseecto extracellular-5'-nucleotidase)—are closely related to the patients' drug resistance to targeted drugs.

[0013] Furthermore, the expression levels of four proteins—CHIT1 (chitotriosidase-1), IL6R (interleukin-6 receptor), NRCAM (Neuronal Cell Adhesion Molecule), and NT5E (5'-nucleotidase ecto)—in blood samples can be determined to classify each protein. A higher total score indicates a higher level of resistance to targeted drugs, while a lower total score indicates a lower level of resistance.

[0014] Therefore, the method for predicting drug resistance based on blood samples provided in this application enables doctors or researchers to predict a patient's resistance to targeted drug therapy before the patient takes medication, providing patients with personalized treatment plans and preventing drug resistance problems.

[0015] According to another specific embodiment of the present invention, an embodiment of the present invention discloses a method for predicting drug resistance based on blood samples. The method for determining the characterization value F of drug resistance based on the sum E of the assigned values ​​corresponding to the concentration values ​​of each protein includes: when E≤70, F≤0.05; when 70<E≤120, F=0.05+(E-70)*0.003; when 120<E≤156, F=0.2+(E-120)*0.2 / 36; when 156<E≤184, F=0.4+(E-156)*0.2 / 28; when 184<E≤220, F=0.6+(E-184)*0.2 / 36; when 220<E≤270, F=0.8+(E-220)*0.15 / 50; when E>270, F>0.95.

[0016] Using the above technical solution, a nomogram model was established using statistical methods, and a related detection kit was developed. This model can calculate the characterization value of drug resistance by the expression level of proteins, helping doctors or researchers to predict the efficacy in advance.

[0017] According to another specific embodiment of the present invention, an embodiment of the present invention discloses a method for predicting drug resistance based on blood samples. The step of obtaining the concentration value 'a' of CHIT1 protein in the blood sample and determining the assignment value A corresponding to the concentration value 'a' of CHIT1 protein includes: when the concentration value 'a' of CHIT1 protein is ≥ 1973.83 ng / L, A = 80; when the concentration value 'a' of CHIT1 protein is < 1973.83 ng / L, A = 0.

[0018] Using the above technical solution, the median function is used to determine whether the CHIT1 (chitotriosidase-1) protein in the patient's blood is highly or poorly expressed. High expression is assigned a value of 80, and low expression is assigned a value of 0.

[0019] According to another specific embodiment of the present invention, an embodiment of the present invention discloses a method for predicting drug resistance based on blood samples. The step of obtaining the concentration value b of IL6R protein in the blood sample and determining the assignment value B corresponding to the concentration value b of IL6R protein includes: when the concentration value b of IL6R protein is ≥ 17.0054 ng / mL, B = 96; when the concentration value b of IL-6R protein is < 17.0054 ng / mL, B = 0.

[0020] Using the above technical solution, the median function is used to determine whether the IL6R (Interleukin-6 receptor) protein in the patient's blood is highly expressed or poorly expressed. High expression is assigned a value of 96, and low expression is assigned a value of 0.

[0021] According to another specific embodiment of the present invention, an embodiment of the present invention discloses a method for predicting drug resistance based on blood samples. The step of obtaining the concentration value c of NRCAM protein in the blood sample and determining the assignment value C corresponding to the concentration value c of NRCAM protein includes: when the concentration value c of NRCAM protein is ≥ 2.4306 ng / mL, C = 100; when the concentration value c of NRCAM protein is < 2.4306 ng / mL, C = 0.

[0022] Using the above technical solution, the median function is used to determine whether the NRCAM (Neuronal Cell Adhesion Molecule) protein in the patient's blood is highly or poorly expressed. High expression is assigned a value of 100, and low expression is assigned a value of 0.

[0023] According to another specific embodiment of the present invention, an embodiment of the present invention discloses a method for predicting drug resistance based on blood samples. The step of obtaining the concentration value d of NT5E protein in the blood sample and determining the assignment value D corresponding to the concentration value d of NT5E protein includes: when the concentration value d of NT5E protein is ≥ 4.4277 ng / mL, D = 35; when the concentration value d of NT5E protein is < 4.4277 ng / mL, D = 0.

[0024] Using the above technical solution, the median function is used to determine whether the NT5E (5'-nucleotidase ecto extracellular-5'-nucleotidase) protein in the patient's blood is highly or poorly expressed. High expression is assigned a value of 35, and low expression is assigned a value of 0.

[0025] According to another specific embodiment of the present invention, an embodiment of the present invention discloses a method for predicting drug resistance based on blood samples, wherein the concentration values ​​of CHIT1 protein (a), IL6R protein (b), NRCAM protein (c), and NT5E protein (d) are detected and obtained by ELISA.

[0026] The present invention also discloses a device for predicting drug resistance based on blood samples, including an input interface; the input interface includes an input area, a calculation area, and a display area, wherein the input area is used to input the concentration values ​​of CHIT1 protein (a), IL6R protein (b), NRCAM protein (c), and NT5E protein (d); the calculation area includes a calculation button, clicking the calculation button calculates the sum E of the values ​​assigned to each protein concentration and obtains a characterization value F; the display area is used to display the characterization value F.

[0027] According to another specific embodiment of the present invention, an embodiment of the present invention discloses a computer storage medium including a memory and a processor, the memory being adapted to store computer instructions, and the processor being adapted to execute the method for predicting drug resistance based on blood samples as described in any of the above embodiments when running the computer instructions. Attached Figure Description

[0028] Figure 1 The flowchart illustrates a method for predicting drug resistance based on blood samples provided in an embodiment of the present invention.

[0029] Figure 2 This diagram illustrates the input interface of the device for predicting drug resistance based on blood samples provided in an embodiment of the present invention.

[0030] Figure 3 This diagram illustrates the operation of the input interface of the device for predicting drug resistance based on blood samples provided in an embodiment of the present invention.

[0031] Figure 4 A nomogram of the computational model for the method of predicting drug resistance based on blood samples provided in an embodiment of the present invention is shown.

[0032] Figure 5 This diagram illustrates a device for predicting drug resistance based on blood samples, as provided in an embodiment of the present invention.

[0033] Figure 6 A block diagram of an electronic device provided in an embodiment of the present invention is shown.

[0034] Figure 7 A block diagram of a system-on-a-chip (SoC) provided by an embodiment of the present invention is shown. Detailed Implementation

[0035] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Although the description of the present invention is presented in conjunction with preferred embodiments, this does not mean that the features of the invention are limited to these embodiments. On the contrary, the purpose of describing the invention in conjunction with embodiments is to cover other options or modifications that may be derived based on the claims of the present invention. To provide a deep understanding of the invention, many specific details will be included in the following description. The invention may also be implemented without using these details. Furthermore, to avoid confusion or obscuring the focus of the invention, some specific details will be omitted in the description. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0036] It should be noted that in this specification, similar reference numerals and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0037] In the description of this embodiment, it should be noted that the terms "upper", "lower", "inner", "bottom", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship that the product of the invention is usually placed in during use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting the present invention.

[0038] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0039] Currently, there are no unified selection criteria for targeted therapy in clinical practice, and no effective and rapid method for predicting drug resistance. This makes drug resistance the most serious problem faced by patients with advanced renal cell carcinoma receiving targeted therapy, severely impacting their quality of life. Therefore, to overcome targeted drug resistance and improve patients' quality of life, a method based on blood samples to predict drug resistance is needed. This method would allow physicians or researchers to predict the likelihood of patients developing resistance to TKI targeted drugs such as sunitinib before medication, enabling the development of individualized treatment plans for patients.

[0040] Therefore, refer to Figure 1 This application provides a method for predicting drug resistance based on blood samples, such as... Figure 1 As shown in the embodiments of this application, the method for predicting drug resistance based on blood samples includes the following steps:

[0041] Step S10: Obtain the concentration value 'a' of CHIT1 protein in the blood sample, and assign the value A corresponding to the concentration value 'a' of CHIT1 protein.

[0042] For example, blood samples were taken from patients with advanced renal cell carcinoma before medication using EDTA (ethylenediaminetetraacetic acid) anticoagulant tubes. After separating the plasma, the plasma was diluted, and then CHIT1 (chitotriosidase-1) protein was detected in the patient's blood sample using ELISA (Enzyme-Linked Immunosorbent Assay). However, the method for protein detection in this application is not limited to this. Other commercially available products or self-made protein detection kits can also be used to detect protein in the patient's blood sample.

[0043] For example, the procedure for detecting blood proteins in patients using ELISA (Enzyme-Linked Immunosorbent Assay) is described in detail.

[0044] a. Allow the reagent kit to equilibrate at room temperature for half an hour before use;

[0045] b. No sample is added to the blank wells; only colorimetric reagents A and B and stop solution are added for zeroing.

[0046] c. Add 50 μl of diluted standard to each well of the standard sample, and 50 μl of standard / sample dilution solution to each well of the zero sample sample, and then add 50 μl of enzyme-labeled reagent.

[0047] d. Add 50 μl of sample to the sample well. Direct addition is recommended. If the concentration is high, it can be diluted 2-5 times with sample diluent, and then add μl of enzyme-labeled reagent.

[0048] e. Gently shake, cover with sealing film, and incubate in a 37°C incubator for 60 minutes.

[0049] f. Dilute the 25-fold concentrated washing solution with 25 times the amount of distilled water and set aside.

[0050] g. Carefully peel off the sealing film, discard the liquid, shake dry, fill each well with washing solution, let stand for 30 seconds, then discard. Repeat this process 5 times, then pat dry.

[0051] h. Add 50 μl of color developer A to each well, then add 50 μl of color developer B, gently shake to mix, and develop at 37°C in the dark for 10 min.

[0052] i. Add 50 μl of stop solution to each well to stop the reaction (at this point, the blue color will immediately turn yellow).

[0053] j. Using blank wells as a guide, measure the absorbance (OD value) of each well sequentially at a wavelength of 450 nm. The measurement should be performed within 10 minutes after adding the stop solution.

[0054] k. Calculate the regression equation for the standard curve based on the concentration and OD value. You can choose to use ELISAcalc for calculation, and select the logistic regression model for fitting.

[0055] For example, the CHIT1 (chitotriosidase-1) protein concentration is obtained according to the above detection method. For instance, the CHIT1 protein concentration obtained by the above detection method is 1900 ng / L. However, this application embodiment does not limit the specific value of the CHIT1 protein concentration, and the CHIT1 protein concentration may vary in different patients. This application embodiment will use a CHIT1 protein concentration of 1900 ng / L as an example for illustration.

[0056] After obtaining the concentration value 'a' of the CHIT1 protein, the corresponding value 'a' is assigned the score 'A'. This determines whether the CHIT1 protein concentration indicates high or low expression. For example, when the CHIT1 protein concentration 'a' ≥ 1973.83 ng / L, it is considered high expression with a score of 'high' and A = 80; when the CHIT1 protein concentration 'a' < 1973.83 ng / L, it is considered low expression with a score of 'low' and A = 0.

[0057] For example, refer to Figure 2 , Figure 2 An input interface for a device 2 for predicting drug resistance based on blood samples is shown. The input interface includes an input area 10, a calculation area 11, and a display area 12. The input area 10 is used to input the concentration values ​​of CHIT1 protein (a), IL6R protein (b), NRCAM protein (c), and NT5E protein (d). The calculation area 11 includes a calculation button 110. Clicking the calculation button 110 calculates the sum E of the values ​​assigned to each protein concentration and obtains the characterization value F. The display area 12 is used to display the characterization value F.

[0058] It should be noted that after obtaining the characterization value F, the concentration values ​​of CHIT1 protein a, IL6R protein b, NRCAM protein c, and NT5E protein d input into input area 10, as well as the corresponding assignments A for CHIT1 protein concentration value a, B for IL6R protein concentration value b, C for NRCAM protein concentration value c, D for NT5E protein concentration value d, and the sum of the corresponding assignments E, will all be stored in the storage module of the device 2 for predicting drug resistance based on blood samples. These can be printed out as forms as needed by doctors or researchers, allowing them to assess the patient's risk level for targeted drug therapy, predict the effectiveness of targeted drug therapy, and select a suitable treatment plan based on the prediction to avoid drug resistance issues later.

[0059] It should be noted that the specific content displayed in the form is not limited in this application embodiment. It may only display the concentration values ​​of CHIT1 protein (a), IL6R protein (b), NRCAM protein (c), and NT5E protein (d) and the characterization value (F). Alternatively, it may simultaneously display the corresponding assignments A, B, C, D, and E of the concentration values ​​of CHIT1 protein (a), IL6R protein (b), NRCAM protein (c), and NT5E protein (d).

[0060] It should be noted that this application embodiment does not impose specific restrictions on the type of the calculation button 110. It can be a touch button on a touch screen or a physical button on a keyboard. This application embodiment does not impose restrictions on the setting position of the specific area of ​​the input interface or the content displayed in the specific area. For example, the input area 10 can be set below the display area 12. At the same time, the input area 10 can also add input boxes for assigning values ​​A, B, C and D, and the display area 12 can also add an input box for E, etc.

[0061] For example, if the CHIT1 protein concentration is obtained as 1900 ng / L using the detection method described above, refer to... Figure 3 The value 1900 is input into input area 10 of the input interface of the device 2 for predicting drug resistance based on blood samples (i.e., the input box corresponding to CHIT1:). The device 2 for predicting drug resistance based on blood samples includes a storage module and a comparison module. The storage module stores the standard comparison value of the CHIT1 protein concentration (e.g., 1973.83). When 1900 is input into input area 10, the comparison module compares it with the standard comparison value (e.g., 1973.83) stored in the storage module. Since 1900 < 1973.83, the CHIT1 protein concentration is low. Figure 4When the CHIT1 protein concentration is low, the corresponding score (i.e. Figure 4 Points)A = 0.

[0062] It should be noted that after the calculation by the storage module and comparison module inside the device 2 for predicting drug resistance based on blood samples, the resulting value A will continue to be stored in the storage module inside the device 2 for predicting drug resistance based on blood samples. However, it is not limited to this. An input box for the value A can also be added to the input area 10. In this case, the specific value of A will be displayed in the input box.

[0063] Step S11: Obtain the concentration value b of IL6R protein in the blood sample, and assign the value B to the concentration value b of IL6R protein.

[0064] In this method, blood samples from patients with advanced renal cell carcinoma were collected before medication using EDTA (ethylenediaminetetraacetic acid) anticoagulant tubes. After separating the plasma, the plasma was diluted, and then IL6R (Interleukin-6 receptor) protein was detected in the patient's blood sample using ELISA (Enzyme-Linked Immunosorbent Assay). However, the method for protein detection in this application is not limited to this; other commercially available products or self-made protein detection kits can also be used to detect protein in the patient's blood sample.

[0065] The specific detection method is the same as the detection method for CHIT1 protein mentioned above, and will not be repeated here.

[0066] For example, the concentration of IL6R (Interleukin-6 receptor) protein is obtained according to the above detection method. For instance, the above detection method yields an IL6R protein concentration of 20 ng / mL. However, this application does not limit the specific value of the IL6R protein concentration, as different patients may have different IL6R protein concentrations. This application will use an IL6R protein concentration of 20 ng / mL as an example for illustration.

[0067] After obtaining the concentration value b of IL6R protein, the corresponding value b is assigned a score B. This determines whether the IL6R protein concentration indicates high or low expression. For example, when the IL6R protein concentration b ≥ 17.0054 ng / mL, it is considered high expression, with a score of "high" and B = 96; when the IL6R protein concentration b < 17.0054 ng / mL, it is considered low expression, with a score of "low" and B = 0.

[0068] For example, if the IL6R protein concentration obtained by the above detection method is 20 ng / mL, refer to Figure 3 The value 20 is input into input area 10 of the input interface of the device 2 for predicting drug resistance based on blood samples (i.e., the input box corresponding to IL6R:). The device 2 for predicting drug resistance based on blood samples includes a storage module and a comparison module. The storage module stores the standard comparison value of IL6R protein concentration (e.g., 17.0054). When 20 is input into input area 10, the comparison module compares it with the standard comparison value (e.g., 17.0054) stored in the storage module. Since 20 > 17.0054, the CHIT1 protein concentration is high. Figure 4 When the IL6R protein concentration is high, the corresponding score (i.e. Figure 4 Points in B = 96.

[0069] It should be noted that after the calculation by the storage module and comparison module inside the device 2 for predicting drug resistance based on blood samples, the resulting assigned value B will continue to be stored in the storage module inside the device 2 for predicting drug resistance based on blood samples. However, it is not limited to this. An input box for the assigned value B can also be added to the input area 10. In this case, the specific value of B will be displayed in the input box.

[0070] Step S12: Obtain the concentration value c of NRCAM protein in the blood sample, and assign the value C to the concentration value c of NRCAM protein.

[0071] In this study, blood samples were collected from patients with advanced renal cell carcinoma before medication using EDTA (ethylenediaminetetraacetic acid) anticoagulant tubes. After separating the plasma, the plasma was diluted, and then NRCAM (Neuronal Cell Adhesion Molecule) protein was detected in the patient's blood sample using ELISA (Enzyme-Linked Immunosorbent Assay). However, this application does not limit the specific value of NRCAM protein concentration. Protein detection in the patient's blood sample can also be performed using other commercial products or a self-made protein detection kit.

[0072] The specific detection method is the same as the detection method for CHIT1 protein mentioned above, and will not be repeated here.

[0073] For example, the NRCAM (Neuronal Cell Adhesion Molecule) protein concentration is obtained according to the above detection method. For instance, the NRCAM protein concentration obtained by the above detection method is 2.5 ng / mL. However, this application embodiment does not limit the specific value of the NRCAM protein concentration, and the NRCAM protein concentration may vary in different patients. This application embodiment will use an NRCAM protein concentration of 2.5 ng / mL as an example for illustration.

[0074] After obtaining the concentration value c of NRCAM protein, the corresponding value C is assigned to NRCAM protein concentration value c. This determines whether the NRCAM protein concentration value indicates high or low expression. For example, when the NRCAM protein concentration value c ≥ 2.4306 ng / mL, it is considered high expression with a score of "high" and C = 100; when the NRCAM protein concentration value c < 2.4306 ng / mL, it is considered low expression with a score of "low" and C = 0.

[0075] For example, if the NRCAM protein concentration obtained by the above detection method is 2.5 ng / mL, refer to... Figure 3 The value 2.5 is input into input area 10 of the input interface of the device 2 for predicting drug resistance based on blood samples (i.e., the input box corresponding to NRCAM:). The device 2 for predicting drug resistance based on blood samples includes a storage module and a comparison module. The storage module stores the standard comparison value of NRCAM protein concentration (e.g., 2.4306). When 2.5 is input into input area 10, the comparison module compares it with the standard comparison value (e.g., 2.4306) stored in the storage module. Since 2.5 > 2.4306, the NRCAM protein concentration is high. Figure 4 When the NRCAM protein concentration is high, the corresponding score (i.e. Figure 4 Points in the table) C = 100.

[0076] Step S13: Obtain the concentration value d of NT5E protein in the blood sample, and assign the value D corresponding to the concentration value d of NT5E protein.

[0077] In this method, blood samples from patients with advanced renal cell carcinoma were collected before medication using EDTA (ethylenediaminetetraacetic acid) anticoagulant tubes. After separating the plasma, the plasma was diluted, and then NT5E (5'-nucleotidase ecto) protein was detected in the patient's blood sample using ELISA (Enzyme-Linked Immunosorbent Assay). However, the specific value of NT5E protein concentration is not limited in this embodiment of the application. Protein detection in the patient's blood sample can also be performed using other commercial products or a self-made protein detection kit.

[0078] The specific detection method is the same as the detection method for CHIT1 protein mentioned above, and will not be repeated here.

[0079] For example, the concentration of NT5E (5'-nucleotidase ecto extracellular-5'-nucleotidase) protein is obtained according to the above detection method. For instance, the above detection method yields an NT5E protein concentration of 4 ng / mL. However, this application does not limit the specific value of the NT5E protein concentration, and the NT5E protein concentration may vary among different patients. This application will use an NT5E protein concentration of 4 ng / mL as an example for illustration.

[0080] After obtaining the NT5E protein concentration value 'd', the corresponding value 'D' is assigned to 'NT5E protein concentration value d'. This determines whether the NT5E protein concentration value indicates high or low expression. For example, when the NT5E protein concentration value 'd' ≥ 4.4277 ng / mL, it is considered high expression with a score of 'high' and 'D' = 35; when the NT5E protein concentration value 'd' < 4.4277 ng / mL, it is considered low expression with a score of 'low' and 'D' = 0.

[0081] For example, the above detection method yields an NT5E (5'-nucleotidase ecto extracellular-5'-nucleotidase) protein concentration of 4 ng / mL, as per the reference... Figure 3 The input 4 is entered into input area 10 of the input interface of the device 2 for predicting drug resistance based on blood samples (i.e., the input box corresponding to NT5E:). The device 2 for predicting drug resistance based on blood samples includes a storage module and a comparison module. The storage module stores the standard comparison value of NT5E protein concentration (e.g., 4.4277). When 4 is entered into input area 10, the comparison module compares it with the standard comparison value (e.g., 4.4277) stored in the storage module. Since 4 < 4.4277, the NT5E protein concentration is low expression. Figure 4When the NT5E protein concentration is low, the corresponding score (i.e. Figure 4 Points)D = 0.

[0082] Step S2: Determine the sum of the values ​​assigned to each protein concentration, E, where E = A + B + C + D.

[0083] For example, according to the above steps, the device 2 for predicting drug resistance based on blood samples has already obtained A=0, B=96, C=100 and D=0 based on the input values. Then, the calculation module inside the device 2 for predicting drug resistance based on blood samples will add the four values ​​A=0, B=96, C=100 and D=0 according to E=A+B+C+D to obtain E=0+96+100+0=196.

[0084] Step S3: Determine the characterization value F of drug resistance based on the sum E of the values ​​assigned to the concentrations of each protein.

[0085] For example, refer to Figure 4 This application uses statistical methods to establish a nomogram model and derives the following results: Figure 4 The nomogram shown illustrates how this model calculates a patient's sensitivity to targeted drug therapy based on protein expression levels, thus predicting treatment efficacy in advance. The sum of the assigned values ​​E for each protein concentration corresponds to... Figure 4 The Total Points (total score) and the resistance characterization value F correspond to... Figure 4 Risk (risk value) in the context.

[0086] Specifically, when E≤70, F≤0.05, refer to Figure 4 That is, when Total Points≤70, Risk≤0.05.

[0087] When 70 < E ≤ 120, F = 0.05 + (E - 70) * 0.003, which gives 0.05 < F ≤ 0.2. (Reference) Figure 4 That is, when 70 < Total Points ≤ 120, 0.05 < Risk ≤ 0.2.

[0088] When 120 < E ≤ 156, F = 0.2 + (E - 120) * 0.2 / 36, which gives 0.2 < F ≤ 0.4. (Reference) Figure 4 That is, when 120 < Total Points ≤ 156, 0.2 < Risk ≤ 0.4.

[0089] When 156 < E ≤ 184, F = 0.4 + (E - 156) * 0.2 / 28, which gives 0.4 < F ≤ 0.6; (Reference) Figure 4 That is, when 156 < Total Points ≤ 184, 0.4 < Risk ≤ 0.6.

[0090] When 184 < E ≤ 220, F = 0.6 + (E - 184) * 0.2 / 36; therefore, 0.6 < F ≤ 0.8; (Reference) Figure 4 That is, when 184 < Total Points ≤ 220, 0.6 < Risk ≤ 0.8.

[0091] When 220 < E ≤ 270, F = 0.8 + (E - 220) * 0.15 / 50; therefore, 0.8 < F ≤ 0.95; (Reference) Figure 4 That is, when 220 < Total Points ≤ 270, 0.8 < Risk ≤ 0.95.

[0092] When E > 270, F > 0.95, reference Figure 4 That is, when Total Points > 270, Risk > 0.95.

[0093] Therefore, a higher total score indicates greater resistance to targeted drugs, while a lower total score indicates lower resistance. The blood sample-based method for predicting drug resistance provided in this application allows doctors or researchers to predict a patient's resistance to targeted drug therapy before medication, enabling the development of individualized treatment plans and preventing drug resistance issues.

[0094] For example, according to the above steps, the prediction device 2 has obtained E = 0 + 96 + 100 + 0 = 196 based on the input value, referencing Figure 4 That is, Total Points = 196, which means 184 < Total Points ≤ 220. In this case, F = 0.6 + (E - 184) * 0.2 / 36 = 0.6 + (196 - 184) * 0.2 / 36 = 0.66666667, or Risk = 0.66666667. (See reference...) Figure 3 The display area shows 0.666667 (rounded to six decimal places).

[0095] For example, according to the above steps, doctors or researchers can obtain the following concentration values ​​for the patient's blood: CHIT1 protein (a) 1900 ng / L, IL6R protein (b) 20 ng / mL, NRCAM protein (c) 2.5 ng / mL (with a corresponding assignment C of 100), NT5E protein (d) 4 ng / mL, and characterization value F of 0.666667, based on the form or input interface. Doctors can use these data to assess the patient's response to targeted therapy and determine a personalized treatment plan accordingly. This solves the problem of existing technologies lacking relevant data to predict the resistance of renal cell carcinoma patients to targeted therapy and thus being unable to predict the therapeutic effects of such drugs.

[0096] In addition, refer to Figure 5 and combined Figure 2 The present invention also provides a device 2 for predicting drug resistance based on blood samples. This prediction device 2 can be a computer or a reagent kit, etc. The embodiments of this application do not impose specific limitations on the shape and type of the device 2 for predicting drug resistance based on blood samples; it can be, for example... Figure 5 The rectangular reagent kit shown.

[0097] For example, refer to Figure 5 and combined Figure 2 The prediction device 2 includes an input interface, which can be located at any position on the prediction device 2. The input interface includes an input area 10, a calculation area 11, and a display area 12. The input area 10 is used to input the concentration values ​​of CHIT1 protein (a), IL6R protein (b), NRCAM protein (c), and NT5E protein (d). The calculation area 11 includes a calculation button 110. Clicking the calculation button 110 calculates the sum E of the values ​​assigned to each protein concentration and obtains the characterization value F. The display area 12 is used to display the characterization value F.

[0098] Furthermore, the present invention also provides a computer storage medium, including a memory and a processor, wherein the memory is adapted to store computer instructions, and the processor is adapted to execute the method for predicting drug resistance based on blood samples as described in any of the above embodiments when running the computer instructions.

[0099] Now for reference Figure 6The diagram shows a block diagram of an electronic device 600 according to one embodiment of this application. The electronic device 600 may include one or more processors 601 coupled to a controller hub 603. In at least one embodiment, the controller hub 603 communicates with the processor 601 via a multi-branch bus such as a Front Side Bus (FSB), a point-to-point interface such as a QuickPath Interconnect (QPI), or a similar connection 606. The processor 601 executes instructions controlling general types of data processing operations. In one embodiment, the controller hub 603 includes, but is not limited to, a Graphics & Memory Controller Hub (GMCH) (not shown) and an Input / Output Hub (IOH) (which may be on a separate chip) (not shown), wherein the GMCH includes memory and a graphics controller and is coupled to the IOH.

[0100] Electronic device 600 may also include a coprocessor 602 and a memory 604 coupled to a controller hub 603. Alternatively, one or both of the memory and GMCH may be integrated within the processor (as described in this application), with memory 604 and coprocessor 602 directly coupled to processor 601 and controller hub 603, controller hub 603 and IOH being on a single chip.

[0101] Memory 604 may be, for example, Dynamic Random Access Memory (DRAM), Phase Change Memory (PCM), or a combination of both. Memory 604 may include one or more tangible, non-transitory computer-readable media for storing data and / or instructions. The computer-readable storage medium stores instructions, specifically, temporary and permanent copies of those instructions. The instructions may include, when executed by at least one of the processors, causing the electronic device 600 to perform, as... Figure 1 The instructions for a method of predicting drug resistance based on blood samples are shown. When the instructions are executed on a computer, the computer performs the method disclosed in any or a combination of the above embodiments to obtain a final characterization value.

[0102] In one embodiment, the coprocessor 602 is a dedicated processor, such as, for example, a high-throughput MIC (Many Integrated Core) processor, a network or communication processor, a compression engine, a graphics processor, a GPGPU (General-purpose computing on graphics processing units), or an embedded processor, etc. Optional properties of the coprocessor 602 are indicated by dashed lines. Figure 6 middle.

[0103] In one embodiment, electronic device 600 may further include a network interface (NIC, Network Interface Controller) 606. Network interface 606 may include a transceiver for providing a radio interface for electronic device 600 to communicate with any other suitable device (such as a front-end module, antenna, etc.). In various embodiments, network interface 606 may be integrated with other components of electronic device 600. Network interface 606 can implement the functions of the communication unit in the above embodiments.

[0104] Electronic device 600 may further include input / output (I / O) devices 605. I / O 605 may include: a user interface designed to enable a user to interact with electronic device 600; a peripheral component interface designed to enable peripheral components to also interact with electronic device 600; and / or sensors designed to determine environmental conditions and / or location information related to electronic device 600.

[0105] It is worth noting that, Figure 6 This is merely an example. That is, although... Figure 6 The electronic device 600 shown includes multiple devices such as a processor 601, a controller hub 603, and a memory 604. However, in practical applications, devices using the methods of this application may include only a portion of the devices in the electronic device 600. For example, it may include only the processor 601 and the network interface 606. Figure 6 The properties of the optional devices are shown by dashed lines.

[0106] Now for reference Figure 7 The diagram shown is a block diagram of a SoC (System on Chip) 700 according to an embodiment of this application. Figure 7 Similar components share the same reference numerals. Additionally, dashed boxes are an optional feature for more advanced SoCs. Figure 7In this SoC 700, the following components are included: an interconnect unit 750 coupled to a processor 710; a system proxy unit 780; a bus controller unit 790; an integrated memory controller unit 740; a group or one or more coprocessors 720, which may include integrated graphics logic, an image processor, an audio processor, and a video processor; a static random-access memory (SRAM) unit 730; and a direct memory access (DMA) unit 760. In one embodiment, the coprocessor 720 includes a dedicated processor, such as, for example, a network or communication processor, a compression engine, a GPGPU (General-purpose computing on graphics processing units), a high-throughput MIC processor, or an embedded processor.

[0107] Static Random Access Memory (SRAM) cell 730 may include one or more tangible, non-transitory computer-readable media for storing data and / or instructions. The computer-readable storage medium stores instructions, specifically, temporary and permanent copies of those instructions. These instructions may include, when executed by at least one of the processors, causing the SoC implementation to... Figure 1 The instructions for a method of predicting drug resistance based on blood samples are shown. When the instructions are executed on a computer, the computer performs the method disclosed in the above embodiments.

[0108] All methods and implementations of this application can be implemented in the form of software, magnetic files, firmware, etc.

[0109] Program code can be applied to input instructions to perform the functions described herein and generate output information. The output information can be applied to one or more output devices in a known manner. For the purposes of this application, the processing system includes any system having a processor such as, for example, a digital signal processor (DSP), a microcontroller, an application-specific integrated circuit (ASIC), or a microprocessor.

[0110] The program code can be implemented using a high-level procedural language or an object-oriented programming language to communicate with the processing system. Assembly language or machine language can also be used when needed. In fact, the mechanisms described in this paper are not limited to any particular programming language. In either case, the language can be a compiled language or an interpreted language.

[0111] One or more aspects of at least one embodiment can be implemented by representational instructions stored on a computer-readable storage medium, the instructions representing various logics in a processor, which, when read by a machine, cause the machine to create logic for performing the techniques described herein. These representations, referred to as “IP (Intellectual Property) cores,” can be stored on a tangible computer-readable storage medium and provided to multiple customers or production facilities for loading into manufacturing machines that actually manufacture the logic or processor.

[0112] In some cases, an instruction translator can be used to translate instructions from a source instruction set to a target instruction set. For example, an instruction translator can transform (e.g., using static binary transformation, including dynamically compiled dynamic binary transformation), morph, emulate, or otherwise translate instructions into one or more other instructions that will be processed by the core. Instruction translators can be implemented in software, hardware, firmware, or a combination thereof. Instruction translators can be on the processor, off the processor, or partially on and partially off the processor.

[0113] While the present invention has been illustrated and described with reference to certain preferred embodiments, those skilled in the art should understand that the above description is a further detailed explanation of the invention in conjunction with specific embodiments, and should not be construed as limiting the specific implementation of the invention to these descriptions. Various changes in form and detail can be made by those skilled in the art, including several simple deductions or substitutions, without departing from the spirit and scope of the invention.

Claims

1. A method for predicting drug resistance based on blood samples, characterized in that, include: Obtain the concentration value 'a' of CHIT1 protein in the blood sample, and assign the value A corresponding to the concentration value 'a' of CHIT1 protein. Obtain the concentration value b of IL6R protein in the blood sample, and assign the value B to the concentration value b of IL6R protein. Obtain the concentration value c of NRCAM protein in the blood sample, and determine the value C corresponding to the concentration value c of NRCAM protein; Obtain the concentration value d of NT5E protein in the blood sample, and assign the value D corresponding to the concentration value d of NT5E protein; Determine the sum of the values ​​assigned to each protein concentration, E, where E = A + B + C + D; The characterization value F of drug resistance is determined based on the sum E of the values ​​assigned to the concentrations of each protein.

2. The method for predicting drug resistance based on blood samples as described in claim 1, characterized in that, The determination of the characterization value F of drug resistance based on the sum E of the values ​​assigned to each protein's concentration includes: When E≤70, F≤0.05; When 70 < E ≤ 120 ; When 120 < E ≤ 156 ; When 156 < E ≤ 184 ; When 184 < E ≤ 220 ; When 220 < E ≤ 270 ; When E > 270, F > 0.

95.

3. The method for predicting drug resistance based on blood samples as described in claim 1, characterized in that, The step of obtaining the concentration value 'a' of CHIT1 protein in the blood sample and determining the value A corresponding to the concentration value 'a' of CHIT1 protein includes: When the concentration of the CHIT1 protein, a, is ≥ 1973.83 ng / L, A = 80; When the concentration value a of the CHIT1 protein is less than 1973.83 ng / L, A = 0.

4. The method for predicting drug resistance based on blood samples as described in claim 1, characterized in that, The process of obtaining the concentration value b of IL6R protein in a blood sample and determining the value B corresponding to the concentration value b of IL6R protein includes: When the concentration of the IL6R protein, b, is ≥ 17.0054 ng / mL, B = 96; When the concentration of the IL6R protein, b, is less than 17.0054 ng / mL, B = 0.

5. The method for predicting drug resistance based on blood samples as described in claim 1, characterized in that, The step of obtaining the concentration value c of NRCAM protein in the blood sample and determining the value C corresponding to the concentration value c of NRCAM protein includes: When the concentration of the NRCAM protein is c≥2.4306ng / mL, C=100; When the concentration of the NRCAM protein, c, is less than 2.4306 ng / mL, then C = 0.

6. The method for predicting drug resistance based on blood samples as described in claim 1, characterized in that, The process of obtaining the concentration value d of NT5E protein in a blood sample and determining the value D corresponding to the concentration value d of NT5E protein includes: When the concentration of the NT5E protein, d, is ≥ 4.4277 ng / mL, then D = 35; When the concentration of the NT5E protein, d, is less than 4.4277 ng / mL, then D = 0.

7. The method for predicting drug resistance based on blood samples as described in claim 1, characterized in that, The concentration values ​​of the CHIT1 protein (a), IL6R protein (b), NRCAM protein (c), and NT5E protein (d) were obtained by ELISA detection.

8. A device for predicting drug resistance based on blood samples, characterized in that, The method for predicting drug resistance based on blood samples according to any one of claims 1-7, wherein the apparatus for predicting drug resistance based on blood samples includes an input interface: The input interface includes an input area, a calculation area, and a display area. The input area is used to input the concentration values ​​of the CHIT1 protein (a), the IL6R protein (b), the NRCAM protein (c), and the NT5E protein (d). The calculation area includes a calculation button. Clicking the calculation button calculates the sum E of the values ​​assigned to the concentration values ​​of each protein and obtains the characterization value F. The display area is used to display the characterization value F.

9. A computer storage medium, characterized in that, The method includes a memory and a processor, the memory being adapted to store computer instructions, and the processor being adapted to execute, when running the computer instructions, the method for predicting drug resistance based on blood samples as described in any one of claims 1 to 7.

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