Computer device for daratumumab resistance sub-grouping of multiple myeloma patients

Abstract

The application discloses a computer device for grouping daratumumab (Dara) resistance of multiple myeloma patients, and belongs to the field of medical care informatics. The technical problem to be solved by the application is how to group Dara resistance of multiple myeloma patients treated by Dara. To solve the technical problem, the application provides a computer device for grouping Dara resistance of multiple myeloma patients. The application first discloses the use of LacCer (d18:2 / 16:0) as a marker in distinguishing Dara resistance or Dara non-resistance of multiple myeloma patients. The application can provide a basis for personalized treatment of multiple myeloma patients: for patients with high drug resistance, a more potent or new drug combination can be selected to ensure treatment effect; the results of drug resistance grouping can provide a direction for new drug research and development.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of healthcare informatics, in particular to a computer device for Daratumumab (Dara) drug resistance subpopulation of multiple myeloma patients. BACKGROUND

[0002] Multiple Myeloma (MM) is a malignant plasma cell tumor, accounting for 1% of all cancers and about 10% of all hematological diseases. The diagnostic criteria for MM are more than 10% of clonal plasma cells in bone marrow or evidence of plasma cell tumor, and the presence of one of the end-organ damage such as CRAB symptoms of Hypercalcemia (C), Renal Insufficiency (R), Anemia (A), Lytic Bone Lesions (B). In recent years, the use of new drugs such as Protease Inhibitors (PIs), Immunomodulatory Drugs (IMIDs), monoclonal antibodies and autologous hematopoietic stem cell transplantation, the survival of patients has been significantly improved, but it is still incurable, drug resistance and recurrence are inevitable.

[0003] Bortezomib (BTZ, B or V) is the first generation of PI drugs, which is widely used in the treatment of MM. The VRD regimen of BTZ, lenalidomide (R) and dexamethasone (D) is the preferred standard targeted induction therapy for newly diagnosed multiple myeloma (NDMM). Daratumumab (Dara) is a human IgG kappa immunoglobulin that can bind to CD38 antigen. Dara treatment produces a remission rate of about 30% in patients with relapsed / refractory multiple myeloma (RRMM) who have received multiple lines of treatment. It is used to treat RRMM patients who have received at least 3 lines of treatment (including PIs and IMIDs), or are double resistant to PIs and immunomodulators. Real-world studies have found that the DRD regimen of Dara combined with lenalidomide and dexamethasone significantly reduces the risk of disease progression or death in NDMM patients who are not suitable for transplantation. CD38 monoclonal antibodies induce apoptosis and kill cells through various mechanisms such as antibody-dependent cell-mediated cytotoxicity (ADCC), antibody-dependent cellular phagocytosis (ADCP), direct cell apoptosis, complement-dependent cell cytotoxicity (CDC) and regulation of extracellular enzyme activity. The mechanisms of resistance to anti-CD38 immunotherapy include down-regulation of CD38 expression and related immune-mediated ADCC, ADCP or CDC failure.

[0004] Metabolic reprogramming is being used as a hallmark or potential feature of cancer for cancer treatment, not only to meet the increased energy needs of tumor cell proliferation, but also to change its cellular processes to better adapt to changes in the surrounding microenvironment, such as hypoxia, acidic microenvironment and nutrient deficiency. Anti-cancer treatments can further change these cellular processes and microenvironments. As tumors adapt and mutate to counteract the effects of treatment, gain resistance and allow continuous proliferation, it has been proven that cancer cells will undergo subsequent adjustments that are adapted to metabolism. Previous studies by our research group have found that the metabolism of MM tumor cells also changes compared to normal cells, but the changes in lipid metabolism during the development of MM cell resistance have not been clearly defined. SUMMARY

[0005] One technical problem to be solved by the present application is how to group the patients with multiple myeloma treated by Daratumumab (Dara) according to their Dara resistance. To solve the technical problem, the present application provides the following technical solutions.

[0006] The present application provides a computer device, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the following steps:

[0007] (A1) Data receiving and analysis processing: receiving the content data of LacCer(d18:2 / 16:0) in the patients with multiple myeloma treated by Daratumumab and known to be resistant to Daratumumab and the patients with multiple myeloma treated by Daratumumab and known to be non-resistant to Daratumumab, and determining the cut-off value for judging whether the patients with multiple myeloma treated by Daratumumab are resistant to Daratumumab according to the content data;

[0008] (A2) Data input: inputting sample data, wherein the sample data is the content data of LacCer(d18:2 / 16:0) of a subject, and the subject is a patient with multiple myeloma treated by Daratumumab;

[0009] (A3) Data comparison and result output: comparing the sample data with the cut-off value, and outputting the result of Daratumumab resistance of the subject according to the comparison result.

[0010] In the present application, the patient with multiple myeloma (MM) is diagnosed according to the 2014 International Myeloma Working Group (IMWG) diagnostic criteria. Specifically, the MM must meet two criteria: ① bone marrow clonal plasma cells (also known as bone marrow clonal plasma cells or clonal bone marrow plasma cells) ≥10% or biopsy-confirmed bone marrow / plasma cell tumor; ② any one or more of the following: one of the end-organ damage (C, hypercalcemia, R-kidney damage, A-anemia and B, bone damage, CRAB) symptoms attributable to underlying plasma cell proliferative disease; the percentage of clonal bone marrow plasma cells is ≥60%; the ratio of involved / uninvolved serum free light chain (FLC) is ≥100 (the involved free light chain level must be ≥100 mg / L); MRI shows >1 local lesion (≥5 mm).

[0011] In the present application, the patient with multiple myeloma can be a relapsed and refractory multiple myeloma patient (RRMM).

[0012] In the present application, the relapsed refractory multiple myeloma patient refers to a multiple myeloma patient who has a treatment response of progression after obtaining a treatment response of partial response (PR) or above, and who has no response to salvage therapy (does not obtain at least MR, M protein reduction < 25%).

[0013] In the present application, the molecular formula of the LacCer(d18:2 / 16:0) is C 46 H 85 NO 13 , and the structural formula is as formula (I),

[0014]

[0015] In the present application, the content of LacCer(d18:2 / 16:0) in the subject can be the content of LacCer(d18:2 / 16:0) in the fasting venous blood of the subject.

[0016] In the present application, the subject of (A2) is a multiple myeloma patient who has been treated with Daratumumab for 2-4 courses of treatment.

[0017] In the present application, the Daratumumab resistance result can be Dara resistance or Dara non-resistance, and the diagnostic criteria for Dara resistance and Dara non-resistance are as follows:

[0018] The diagnostic criteria for Dara non-resistance: the treatment received by the multiple myeloma patient includes Daratumumab administration for 2-4 courses of treatment, and the evaluation of treatment efficacy is PR or above, including stringent complete response (sCR), complete response (CR), very good partial response (VGPR) and PR; in the present application, it is represented by rMM-D.

[0019] The diagnostic criteria for Dara resistance: the treatment received by the multiple myeloma patient includes Daratumumab administration for 2-4 courses of treatment, and the evaluation of treatment efficacy is below PR, including MR, stable disease (SD) and progressive disease (PD), and in the present application, it is represented by nrMM-D.

[0020] In the above, the treatment received by the multiple myeloma patient includes Daratumumab administration for 2-4 courses of treatment, which can be Dara-based chemotherapy regimen treatment for 2-4 courses of treatment.

[0021] In the present application, the determination threshold is obtained by Spearman correlation analysis and Receiver Operating Characteristic curve (ROC) by SPSS 26.0.

[0022] In the present application, the multiple myeloma patients are all of Asian ethnicity.

[0023] In some embodiments of the present application, the multiple myeloma patients are all of Chinese ethnicity.

[0024] In some embodiments of the present application, the determination threshold is 0.1363 μmol / L.

[0025] Further, in the computer device, the Daratumumab drug resistance result of the subject is output according to the comparison result, and the Daratumumab drug resistance result of the subject is:

[0026] If the content of LacCer(d18:2 / 16:0) of the subject is lower than the determination threshold, the subject is or is a candidate for a Daratumumab drug-resistant individual;

[0027] If the content of LacCer(d18:2 / 16:0) of the subject is higher than the determination threshold, the subject is or is a candidate for a Daratumumab non-drug-resistant individual. SUMMARY

[0029] Further, the Daratumumab drug resistance result of the subject output according to the comparison result can be that the probability of the subject with the content of LacCer(d18:2 / 16:0) higher than the determination threshold being resistant to Daratumumab is lower than that of the subject with the content of LacCer(d18:2 / 16:0) lower than the determination threshold.

[0030] That is, compared with the subject with the content of LacCer(d18:2 / 16:0) higher than the determination threshold, the subject with the content of LacCer(d18:2 / 16:0) lower than the determination threshold has a higher probability of being resistant to Daratumumab.

[0031] In the present application, the purpose of providing the computer device is to group or assist in grouping multiple myeloma patients treated with Daratumumab according to the Daratumumab (Dara) drug resistance. By grouping or assisting in grouping, Dara drug-resistant and Dara non-drug-resistant patients can be effectively distinguished. Personalized treatment plans are developed for patients with different drug resistance, or more effective or new drug combinations are selected to ensure treatment effect and provide guidance.

[0032] In some embodiments of the present application, the determination threshold is 0.1363 μmol / L.

[0033] The present application also provides a computer program product comprising a computer program which, when executed by a processor, implements steps (A1) to (A3) as claimed in claim 1 or 2.

[0034] The present application also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements steps (A1) to (A3) as claimed in claim 1 or 2.

[0035] The present application also provides a device for Daratumumab resistance sub-grouping or auxiliary sub-grouping of a Daratumumab-treated multiple myeloma patient, comprising the following modules:

[0036] (B1) Data receiving and analysis processing module: for receiving the content data of LacCer(d18:2 / 16:0) in known Daratumumab-resistant multiple myeloma patients and known Daratumumab-non-resistant multiple myeloma patients, and determining the determination threshold (cut-off) of whether the multiple myeloma patient is resistant to Daratumumab according to the same;

[0037] (B2) Data input module: for inputting sample data, wherein the sample data is the content data of LacCer(d18:2 / 16:0) of a subject, and the subject is a Daratumumab-treated multiple myeloma patient;

[0038] (B3) Data comparison and result output module: for comparing the sample data with the determination threshold, outputting the Daratumumab resistance result of the subject according to the comparison result, and sub-grouping the subject according to the Daratumumab resistance result according to the following standard: if the content of LacCer(d18:2 / 16:0) of the subject is lower than the determination threshold, the subject belongs to the Daratumumab-resistant population;

[0039] If the content of LacCer(d18:2 / 16:0) of the subject is higher than the determination threshold, the subject belongs to the Daratumumab-non-resistant population.

[0040] The present application also provides a device for distinguishing the Daratumumab resistance of a Daratumumab-treated multiple myeloma patient, comprising the following modules:

[0041] (B1') data receiving and analyzing module: for receiving the content data of LacCer(d18:2 / 16:0) in known Daratumumab-resistant multiple myeloma patients and known Daratumumab-non-resistant multiple myeloma patients, and determining the cut-off of whether the multiple myeloma patients are resistant to Daratumumab according to the content data;

[0042] (B2') data input module: for inputting sample data, which is the content data of LacCer(d18:2 / 16:0) in a subject, the subject being a multiple myeloma patient treated with Daratumumab;

[0043] (B3') data comparison and result output module: for comparing the sample data with the cut-off, outputting the Daratumumab resistance result of the subject according to the comparison result, and classifying the resistance of the subject according to the Daratumumab resistance result according to the following standard:

[0044] if the content of LacCer(d18:2 / 16:0) in the subject is lower than the cut-off, the subject is or is a candidate for Daratumumab-resistant;

[0045] if the content of LacCer(d18:2 / 16:0) in the subject is higher than the cut-off, the subject is or is a candidate for Daratumumab-non-resistant.

[0046] The application also provides a method for grouping or assisting grouping of Daratumumab resistance of multiple myeloma patients treated with Daratumumab, which comprises the following steps: comparing sample data with a cut-off, and determining the Daratumumab resistance result of the subject according to the comparison result; the sample data is the content of LacCer(d18:2 / 16:0) in the subject, and the subject is a multiple myeloma patient treated with Daratumumab.

[0047] Further, in the method, the cut-off is obtained by receiving the content data of LacCer(d18:2 / 16:0) in known Daratumumab-resistant multiple myeloma patients treated with Daratumumab and known Daratumumab-non-resistant multiple myeloma patients treated with Daratumumab, and determining the cut-off of whether the multiple myeloma patients treated with Daratumumab are resistant to Daratumumab according to the content data.

[0048] The determination threshold is the content of LacCer(d18:2 / 16:0).

[0049] In some embodiments of the present application, the determination threshold is 0.1363 μmol / L.

[0050] The method only includes a data processing step, and does not include a step of processing an entity sample, such as a step of obtaining cells, tissues and / or organs from a living human or animal body, a step of processing an ex vivo sample from a living human or animal body, and a step of processing a living human or animal body.

[0051] Further, in the method, the Daratumumab resistance result of the subject is output according to the comparison result, and is determined according to the following standard:

[0052] If the content of LacCer(d18:2 / 16:0) of the subject is lower than the determination threshold, the subject is or is a candidate for a Daratumumab-resistant individual;

[0053] If the content of LacCer(d18:2 / 16:0) of the subject is higher than the determination threshold, the subject is or is a candidate for a Daratumumab non-resistant individual.

[0054] The present application also provides any one of the following applications:

[0055] (E1) Application of LacCer(d18:2 / 16:0) in screening or assisting in screening of multiple myeloma patients resistant to or non-resistant to Daratumumab;

[0056] (E2) Application of LacCer(d18:2 / 16:0) in the preparation of a product for screening or assisting in screening of multiple myeloma patients resistant to or non-resistant to Daratumumab;

[0057] (E3) Application of LacCer(d18:2 / 16:0) in the preparation of a product for diagnosing or assisting in diagnosing multiple myeloma patients resistant to or non-resistant to Daratumumab;

[0058] (E4) Application of LacCer(d18:2 / 16:0) in the preparation of a product for predicting or assisting in predicting the prognosis of multiple myeloma patients treated with Daratumumab.

[0059] The prognosis includes the survival period of the multiple myeloma patient after treatment with Daratumumab, including but not limited to overall survival (OS) and progression-free survival (PFS).

[0060] The application discloses a computer device, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the following steps:

[0061] (F1) Data receiving and analysis processing: receiving the LacCer(d18:2 / 16:0) content data of known Daratumumab-treated multiple myeloma patients and the overall survival time or progression-free survival of the patients, and determining the Daratumumab prognosis determination threshold (cut-off) according to the LacCer(d18:2 / 16:0) content and the overall survival time and progression-free survival;

[0062] (F2) Data input: inputting sample data, wherein the sample data is the LacCer(d18:2 / 16:0) content data of a subject, and the subject is a Daratumumab-treated multiple myeloma patient;

[0063] (F3) Data comparison and result output: comparing the sample data with the determination threshold, and outputting the Daratumumab prognosis result of the subject according to the comparison result; the judgment rule for outputting the result information from the computer based on the sample data is that the prognosis of a subject with a LacCer(d18:2 / 16:0) content higher than the determination threshold is better than that of a subject with a LacCer(d18:2 / 16:0) content lower than the determination threshold.

[0064] In the application, the Kaplan-Meier survival analysis model is used to determine the relationship between the metabolic marker and disease progression. The overall survival (OS) refers to the time from diagnosis to death. The progression-free survival (PFS) refers to the time from chemotherapy to tumor progression or death.

[0065] In the application, the evaluation index of the "better prognosis" includes a longer overall survival time (OS) (month) and / or a longer progression-free survival time (PFS) (month).

[0066] In some embodiments of the application, the determination threshold is 0.1356 μmol / L. The prognosis of a subject higher than the determination threshold is better.

[0067] The application also provides a computer program product comprising a computer program, wherein the computer program is executed by a processor to implement the steps (F1) to (F3) described above.

[0068] The application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps (F1) to (F3).

[0069] In the application, the subject can be of Asian descent.

[0070] More specifically, in the application, the sub-species can be Chinese.

[0071] The application has the following beneficial technical effects:

[0072] The application first discloses the role of LacCer(d18:2 / 16:0) as a marker in distinguishing Dara-resistant or Dara-non-resistant multiple myeloma patients.

[0073] In the embodiments of the application, by determining the content of LacCer(d18:2 / 16:0) in multiple myeloma patients treated by a standard Dara treatment cycle, it can be grouped whether the patient is Dara-resistant, and it can also predict or assist in predicting the prognosis of the patient's Dara treatment.

[0074] The conclusion of the application can provide a basis for personalized treatment of multiple myeloma patients: for patients with high drug resistance, more potent or new drug combinations can be selected to ensure treatment effect, improve prognosis, and reduce or avoid unnecessary waste of money and medical resources.

[0075] The technical solution of the application has a reference effect on the drug research direction and clinical trial design of multiple myeloma: the results of drug resistance grouping can provide a direction for new drug research, helping researchers understand which types of drug resistance are more common, so as to develop new drugs targeting these drug resistances. In the clinical trial stage of new drugs, drug resistance grouping can help researchers select suitable subjects to ensure the accuracy and reliability of the test results. BRIEF DESCRIPTION OF DRAWINGS

[0076] Figure 1 A research workflow diagram for the analysis of Daratumumab drug resistance and prognosis-related lipids in the application;

[0077] Figure 2 A differential metabolite OPLS-DA analysis diagram; a is the distribution of differential metabolites of the HC group and the MM group (before treatment); b is the distribution of differential metabolites of the MM group (before treatment) and the rMM group; c is the distribution of differential metabolites of the rMM group and the nrMM group; d is the distribution of differential metabolites of the rMM-D group and the nrMM-D group.

[0078] Figure 3 32 kinds of differential metabolites and M protein correlation.

[0079] Figure 4 The 5 differential metabolites associated with MM symptoms.

[0080] Figure 5 ROC curve analysis of the 5 differential metabolites associated with MM symptoms.

[0081] Figure 6 The 15 differential metabolites significantly associated with M protein.

[0082] Figure 7 ROC curve of the 15 differential metabolites that can be used to evaluate the response to treatment. ROC analysis showed that the 15 differential metabolites had high sensitivity and specificity (AUC > 0.700, P < 0.05) in evaluating disease activity.

[0083] Figure 8 Heat map of serum lipid metabolites after treatment in the nrMM-D group and the rMM-D group.

[0084] Figure 9 The 13 differential metabolites expressed in the rMM, MM, and nrMM groups. There were statistically significant differences in the 13 differential metabolites between the MM group, the nrMM group, and the rMM group: HexCer (d16: 1 / 22: 0), LacCer (d18: 2 / 16: 0), LysoPE (18: 1), 6 PC, 2 SM, and 2 TG.

[0085] Figure 10 ROC curve of the 13 differential metabolites for evaluating the drug resistance of RRMM treatment. ROC analysis showed that the 13 differential metabolites had high sensitivity and specificity (AUC > 0.700, P < 0.05) in evaluating drug resistance.

[0086] Figure 11 The 5 differential metabolites for evaluating the drug resistance of Dara treatment for RRMM. The results showed that (1) there were statistically significant differences in the 5 differential metabolites between the nrMM-D group and the rMM-D group; (2) ROC analysis showed that LacCer (d18: 2 / 16: 0) had good sensitivity and specificity (P < 0.05).

[0087] Figure 12Correlation analysis of 13 differential metabolites with HIF-1α and clinical characteristics. Three differential metabolites have a negative correlation with HIF-1α, LacCer(d18:2 / 16:0), TG(18:0 / 18:1 / 22:4), TG(20:0 / 20:4 / 20:4); five differential metabolites have a negative correlation with Cr, HexCer(d16:1 / 22:0), SM(d18:2 / 14:0); nine differential metabolites have a positive correlation with Hb, HexCer(d16:1 / 22:0), LacCer(d18:2 / 16:0) LysoPE(18:1), four PCs, two SMs.

[0088] Figure 13 Correlation of differential metabolites and high-risk genetics.

[0089] Figure 14 Survival analysis graph of differential metabolites and OS.

[0090] Figure 15 Survival analysis graph of differential metabolites and PFS.

[0091] Figure 16 The device composition and workflow diagram for grouping RRMM patients with Dara resistance according to the level of LacCer(d18:2 / 16:0) metabolites of the subject.

[0092] Figure 17 The characteristic fragments used for detecting LacCer(d18:2 / 16:0) by LC-MS.

[0093] Figure 18 The quantitative ion pair used for detecting LacCer(d18:2 / 16:0) by LC-MS.

[0094] Figure 19 The retention time of LacCer(d18:2 / 16:0). DETAILED DESCRIPTION

[0095] In the present application, targeted Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry (UPLC-MS) is used to determine lipid metabolites in serum of MM patients, and Enzyme-Linked Immunosorbent Assay (ELISA) method is used to determine the levels of Fatty Acid Translocase (FAT, also known as 6ZHONG) and Hypoxia Inducible Factor 1-alpha (HIF-1α). Serum lipid metabolomics analysis is performed on MM patients receiving BTZ / Dara-based regimen treatment to study related biomarkers that can evaluate activity, treatment response and suggest drug resistance.

[0096] The present application is further described in detail below with reference to the specific embodiments. The examples given are only for the purpose of illustrating the present application, and are not intended to limit the scope of the present application. The examples provided below can serve as a guide for further improvement by those skilled in the art, and do not in any way constitute a limitation on the present application.

[0097] In the following examples, the experimental methods are conventional methods, and are performed according to the techniques or conditions described in the literature in the art or according to the product instructions, unless otherwise specified. The materials, reagents, etc. used in the following examples can be obtained commercially, unless otherwise specified.

[0098] In the present application, the structural formula of LacCer(d18:2 / 16:0) is as formula (I),

[0099]

[0100] The detection method of LacCer(d18:2 / 16:0) in the present application is an internal standard method, and the internal standard is Ceramide / Sphingoid Internal Standard Mixture I from Avanti Research with the product number LM6002. The detection principle and confirmation process are as follows:

[0101] The equipment used for ceramide detection is ultra-high performance liquid chromatography triple quadrupole tandem mass spectrometry (LC-MS), commonly known as QQQ in mass spectrometry detection. Triple quadrupole mass spectrometry is commonly used for quantitative detection of low-concentration compounds due to its high sensitivity. QQQ refers to the fact that the mass spectrometer has three quadrupoles. The outer Q1 and Q3 typically have scanning functions, while the middle quadrupole Q2 does not have analysis functions but can be used as a collision cell. In quantitative detection, we usually select the parent ion and characteristic fragments of the compound as the ion pair. After separation by the front liquid phase, the compound is sprayed, charged by an electric field, and gasified at high temperature to remove the solvent to form a gas-phase ion, which enters Q1 for sorting. Only the specific mass-to-charge ratio of the gas-phase ion selected in advance can pass through Q1 and enter the Q2 collision cell. In the Q2 collision cell, high-purity inert gas collides to produce fragments that are sent to the third analysis quadrupole Q3. Like Q1, only the specific mass-to-charge ratio of the fragments set can pass through Q3, and finally be detected by the detector. That is, only compounds that pass through the two Q1 and Q3 screens can be detected, and if the mass-to-charge ratio is not set, the ion will be filtered out when it passes through Q1 or Q3.

[0102] The target compound to be detected is LacCer(d18:2 / 16:0), with a molecular formula of C 46 H 85 NO 13 The theoretical mass-to-charge ratio under positive ion mode is 860.6093, and the selected C18 alkyl acid chain with two double bonds is the characteristic fragment of LacCer(d18:2 / 16:0) ( Figure 17 ), so 860.6 and 262.3 are set as the quantitative ion pair of LacCer(d18:2 / 16:0) ( Figure 18 ).

[0103] We also use liquid chromatography-mass spectrometry detection, with ultra-high performance liquid chromatography for compound separation based on polarity. The smaller the polarity, the easier it is to elute from the reversed-phase chromatographic column with organic solvents in liquid chromatography. Conversely, the larger the polarity, the more difficult it is to elute, and the longer the compound retention time.

[0104] 4.60, 4.76 are the retention times of the compounds in minutes; looking at number 1, C12 is the standard peak time of d18:1 / 12:0 at 3.44; d18:1 / 16:0 in number 2 has a smaller polarity due to the increase in the alkyl chain, resulting in an increase in retention time, so it peaks at 4.06; and d18:2 / 16:0 in number 3 has an increased double bond compared to d18:1 / 16:0, resulting in an increase in unsaturation and polarity, so the retention time is advanced to 3.80 ( Figure 19).

[0105] Therefore, in the LC-MS detection and analysis process, the compound with a peak at 3.80 min is the LacCer(d18:2 / 16:0) based on the characteristic ion pair and retention time of LacCer(d18:2 / 16:0).

[0106] In the present application, the component concentration unit is μmol / L unless otherwise specified.

[0107] In the present application, the term:

[0108] BTZ: Bortezomib, a small molecule drug for treating multiple myeloma;

[0109] Dara: Daratumumab, a monoclonal antibody drug for treating multiple myeloma;

[0110] MM: multiple myeloma, Chinese name: multiple myeloma;

[0111] NDMM: Newly diagnosed multiple myeloma, Chinese name: newly diagnosed multiple myeloma;

[0112] RRMM: Relapsed refractory multiple myeloma, Chinese name: relapsed refractory multiple myeloma;

[0113] rMM: responding MM, Chinese name: responding multiple myeloma; the diagnostic criteria is that the patient receives 2-4 courses of treatment with a chemotherapy regimen based on BTZ or Dara, and the treatment efficacy is evaluated to be PR and above (≧PR), including Stringent Complete Response (sCR), Complete Response (CR), Very Good Partial Response (VGPR) and Partial Response (PR);

[0114] nrMM: no responding MM, Chinese name is no treatment response multiple myeloma; diagnostic criteria for patients receiving BTZ or Dara-based chemotherapy regimen for 2-4 courses of treatment to evaluate the efficacy of treatment below partial response PR (Partial Response), including minimal response (Minimal response, MR), stable disease (Stable Disease, SD) and disease progression (Progressive Disease, PD).

[0115] HC: Healthy Control, HC for short, Chinese name is healthy control or healthy control.

[0116] According to the 2014 International Myeloma Working Group (IMWG) diagnostic criteria, MM must meet two criteria: ① bone marrow clonal plasma cells ≥10% or biopsy confirmed bone marrow / myeloid plasma cell tumor; ② any one or more of the following end-organ damage symptoms: end-organ damage (CRAB) attributable to underlying plasma cell proliferative disease; clonal bone marrow plasma cell percentage ≥60%; affected / unaffected serum free light chain (FLC) ratio ≥100 (affected free light chain level must be ≥100 mg / L); MRI suggests >1 local lesion (>5mm)

[0117] According to the 2014 International Myeloma Working Group (IMWG) ISS and R-ISS staging criteria, the staging is as follows:

[0118]

[0119] According to the 2016 International Myeloma Working Group (IMWG) treatment response criteria, the evaluation is as follows:

[0120]

[0121]

[0122] The standard of relapsed and refractory myeloma is as follows:

[0123]

[0124] Example 1, drug resistance and prognosis evaluation of multiple myeloma patients and analysis of lipids related to drug resistance and prognosis

[0125] 1. Method

[0126] 1.1 Research object

[0127] 46 patients with MM (including 18 patients with NDMM and 28 patients with RRMM) before and after treatment and 12 HC were used as research objects. The above patients were diagnosed and received systematic anti-MM treatment in the Department of Hematology, Beijing ChaoYang Hospital, Capital Medical University, from October 2021 to September 2022, including induction remission, stem cell transplantation or consolidation and maintenance treatment. The flow chart of this study is shown in Figure 1 .

[0128] 1.2 Inclusion criteria and exclusion criteria

[0129] 1.2.1 Inclusion criteria

[0130] (1) The diagnosis of MM meets the criteria of the International Myeloma Working Group in 2014 or 2016; the diagnosis, staging and treatment response criteria of MM follow the criteria of the International Myeloma Working Group (IMWG) in 2014 or 2016;

[0131] (2) The diagnostic criteria for MM: more than 10% of clonal plasma cells in bone marrow, which leads to one of the CRAB symptoms such as elevated blood Ca (Calcium, Ca>11mg / dL), elevated creatinine (Creatinine, Cr>2mg / dL), reduced hemoglobin (Hemoglobin, Hb<10g / dL) and bone damage (osteolytic bone destruction or pathological fracture). Primary refractory refers to MM patients who have not achieved minimal response (MR) or partial response (PR) after 4 cycles of standard treatment; relapsed / refractory refers to patients who have progressed after achieving PR or above treatment response, have no response to salvage therapy (have not achieved at least MR treatment response) and the like;

[0132] (3) The enrolled patients received at least 2-4 courses of BTZ or Dara-based chemotherapy regimen treatment.

[0133] 1.2.2 Exclusion criteria

[0134] (1) The treatment course of BTZ / Dara-based chemotherapy regimen is less than 2 courses;

[0135] (2) Patients with plasma cell leukemia or primary amyloidosis.

[0136] 1.3 Patient grouping

[0137] 1.3.1 Grouping according to disease activity

[0138] (1) MM group: symptomatic MM patients with more than 10% of clonal plasma cells in bone marrow and one of CRAB symptoms;

[0139] (2) HC group: healthy people matched with MM patients in age and gender as control group.

[0140] 1.3.2 Grouping according to the treatment response of MM patients

[0141] (1) rMM group: patients with PR and above, including Stringent Complete Response (sCR), Complete Response (CR), Very Good Partial Response (VGPR) and PR, after 2-4 courses of treatment with BTZ or Dara based chemotherapy regimen;

[0142] (2) nrMM group: patients with less than PR, including MR, Stable Disease (SD) and Progressive Disease (PD), after 2-4 courses of treatment with BTZ or Dara based chemotherapy regimen.

[0143] 1.3.3 Grouping of RRMM patients with no treatment response

[0144] (1) nrMM-B group: RRMM patients with no PR and above treatment response after 2-4 courses of treatment with BTZ based chemotherapy regimen on average;

[0145] (2) nrMM-D group: RRMM patients with no PR and above treatment response after 2-4 courses of treatment with Dara based chemotherapy regimen on average.

[0146] 1.3.3 Grouping of RRMM patients with treatment response

[0147] (1) rMM-B group: RRMM patients with PR and above treatment response after 2-4 courses of treatment with BTZ based chemotherapy regimen on average;

[0148] (2) rMM-D group: RRMM patients with PR and above treatment response after 2-4 courses of treatment with Dara based chemotherapy regimen on average.

[0149] 1.4 Specimen processing

[0150] MM patients were collected before and after treatment with bortezomib or daratumumab for an average of 2-4 cycles, and fasting venous blood was collected from 46 MM patients before and after treatment and HC, serum was isolated within 2 hours and stored at -80°C for testing. Routine serum tests were performed at the same time point as the serum sample collection, and serum protein electrophoresis (SPEP) and immunofixation electrophoresis (IFE) were performed.

[0151] 1.5 Metabolic determination based on UPLC-MS

[0152] In this study, ultra-high performance liquid chromatography (Waters ACQUITY UPLC I-CLASS) was used for chromatographic separation, and a non-target tandem triple quadrupole mass spectrometry system (Waters XEVO TQ-S Micro) was used to detect lipid and amino acid molecules, followed by targeted metabolomics mass spectrometry analysis using corresponding standard samples.

[0153] 1.6 Metabolic pathway enrichment analysis

[0154] Pathway enrichment analysis was performed using MetaboAnalyst 5.0.

[0155] 1.7 Determination of HIF-1α

[0156] The specific determination steps refer to the operation of the enzyme-linked immunosorbent assay (ELISA) kit instructions, and the enzyme label instrument is used to measure the optical density value (OD value) of each well. According to the standard curve, the concentration or amount of the test substance is calculated.

[0157] 1.8 Statistical method

[0158] TargetLynx quantitative software was used for peak area calculation of targeted data, and the retention time allowed an error of 15 s. The standard curve method was used to obtain quantitative results for concentration calculation. Simca software was used for orthogonal partial least-squares discrimination analysis (OPLS-DA) analysis, combined with variable important in projection (VIP) > 1 and t-test P < 0.050 to find differential metabolites. SPSS 26.0 was used for Spearman correlation analysis and receiver operating characteristic curve (ROC). Kolmogorov-Simirnov (K-S) test was used to assess normality, and normally distributed variables were expressed as mean ± standard deviation, and non-normally distributed variables were expressed as median and interquartile range (IQR). When comparing between groups, normally distributed continuous variables were analyzed by independent sample t-test or paired t-test, and non-normally distributed continuous variables were analyzed by Kruskal-Wallis test or Mann-Whitney U test. Kaplan-Meier survival analysis model was used to determine the relationship between metabolic markers and disease progression. Overall survival (OS) refers to the time from diagnosis to death, and progression-free survival (PFS) refers to the time from chemotherapy to tumor progression or death. Single-factor Cox regression and multi-factor Cox regression models were used to analyze factors affecting disease progression. P < 0.050 was considered statistically significant, P < 0.010 was considered significantly statistically significant, and P < 0.001 was considered very significantly statistically significant. Rstudio and Graphpad Prism 9 were used for picture drawing.

[0159] 2. Results:

[0160] The distribution of differential metabolites was compared according to sample source as follows: (a) MM group before treatment and HC group; (b) MM group before treatment and rMM group; (c) rMM group after treatment and nrMM group; (d) rMM-D group after treatment and nrMM-D group.

[0161] 2.1 Differential metabolites associated with MM onset and treatment response

[0162] A total of 77 neurotransmitters, 374 lipids and 37 carnitines were detected in this study. MM group (before treatment) and HC group had a good separation trend, which might be related to the pathogenesis of MM. For example Figure 2 as shown in formula a.

[0163] Among the 472 metabolites, neurotransmitters mainly include 15 amino acid neurotransmitters, 24 ceramides (Cer), 18 hexosylceramides (HexCer), and 4 lactosylceramides (LacCer); lipids include 5 cholesterol esters (CE), 36 fatty acids (FA), 127 phosphatidylcholines (PC), 32 LysoPC, 36 phosphatidylethanolamines (PE), 6 lysophosphatidylethanolamines (LysoPE), 36 sphingomyelins (SM), and 96 TGs (triglycerides). The results show that the differential metabolites are mainly enriched in sphingolipid metabolism, linoleic acid metabolism, and glycerophospholipid metabolism pathways.

[0164] There are 32 common differential metabolites related to IgG and IgA type M protein, which may be related to the activity of MM Figure 3 ). The 32 metabolites include 3 FAs, 4 CEs, Cer (d18:1 / 20:0), Cer (d18:1 / 21:0), HexCer (d16:1 / 22:0), HexCer (d18:2 / 22:0), LacCer (d18:2 / 16:0), LysoPC (20:4), LysoPE (18:1), PE (O-16:0 / 22:6), 8 PCs, SM (d18:1 / 20:1), SM (d18:2 / 14:0), and 7 TGs.

[0165] Among the 32 metabolites, 5 metabolites are related to more than one typical symptom (elevated blood calcium, kidney damage, and anemia) (P < 0.050): CE (20:3), LacCer (d18:2 / 16:0), LysoPC (20:4), PE (O-16:0 / 22:6), and TG (16:0 / 18:1 / 18:1), as shown in Figure 4 . ROC analysis shows that Figure 5 the 5 differential metabolites have good sensitivity and specificity in evaluating the activity of MM (AUC > 0.700, P < 0.050).

[0166] Among the 32 metabolites related to the activity of MM, 15 metabolites were statistically different between the MM group (before treatment) and the rMM group (P < 0.050), see Table 2. Figure 6 Compared with the HC group, the expression levels of 15 metabolites were reduced in the MM group (P < 0.050), and the expression levels of the metabolites in the rMM group after treatment were higher than those in the MM group (P < 0.050), suggesting the possibility of being a marker for judging the treatment response of MM, see Table 2. Figure 7

[0167] Further analysis showed that the levels of 6 metabolites had no statistical difference between the HC group and the rMM group (P > 0.050), which were CE (22:6), LacCer (d18:2 / 16:0), LysoPE (18:1), PC (O-20:0 / 22:4), PC (O-18:0 / 22:4), and PE (O-16:0 / 22:6).

[0168] The OPLS-DA score scatter plot showed that the nrMM group and the rMM group had a good separation trend, see Figure 2. Figure 2 c.

[0169] Combined with Vip > 1 and P < 0.050, there were 106 differential metabolites. They included CE (20:4), Cer (d18:1 / 14:0), 4 LacCer, 1 FA (pentadecanoic acid), 16 LysoPC, 48 PC, 7 PE, 4 carnitine, 6 SM, 17 TG, and 5-hydroxytryptamine. The expression of 7 PE, 3 SM, 17 TG, pentadecanoic acid, 4 carnitine, 5-hydroxytryptamine was increased in the nrMM group, and the expression of the remaining 72 differential metabolites was reduced in the nrMM group (see Table 2c). Further KEGG pathway analysis suggested that the differential metabolites in the nrMM group and the rMM group were mainly enriched in glycerophospholipid metabolism, glycosylphosphatidylinositol anchor biosynthesis, glycerolipid metabolism, and tryptophan metabolism. Figure 2

[0170] 2.2 Differential metabolites related to drug resistance

[0171] In RRMM, we performed OPLS-DA analysis on the serum lipid metabolites of the nrMM-D group and the rMM-D group after treatment, and the scatter plot showed that the two groups had a good separation trend, see Figure 3. Figure 2 ​​Metabolites with VIP > 1 and P < 0.050, a total of 64 differential metabolites were identified. They included 2 LacCer, 22 LysoPC, LysoPE(18:1), 23 PC, SM(d18:2 / 14:0), 14 TG and octenoylcarnitine. Among the 64 differential metabolites, except for 14 TG with increased expression level (FC > 1) in nrMM-D group, the expression levels of the remaining differential metabolites decreased (FC > 1) in nrMM-D group. The heatmap is shown in Figure 8 .

[0172] 2.3 Differential metabolites evaluation of RRMM treatment resistance

[0173] In the first part of this study, we screened 32 differential metabolites that may be associated with disease activity. To further explore the changes of the 32 differential metabolites in RRMM during the treatment resistance process, the analysis showed that 13 differential metabolites: HexCer(d16:1 / 22:0), LacCer(d18:2 / 16:0), LysoPE(18:1), PC(O-18:0 / 18:1), PC(O-18:0 / 22:1), PC(O-18:0 / 22:4), PC(O-20:0 / 22:4), PC(P-16:0 / 16:1), PC(17:0 / 18:0), SM(d18:1 / 20:1), SM(d18:2 / 14:0), TG(18:0 / 18:1 / 22:4), TG(20:0 / 20:4 / 20:4) had statistical differences between rMM and nrMM groups (P < 0.050).

[0174] The violin plot shows the changes of the above 13 differential metabolites in MM, rMM and nrMM groups, as shown in Figure 9 LacCer(d18:2 / 16:0), LysoPE(18:1), PC(O-18:0 / 22:4), PC(O-20:0 / 22:4), PC(P-16:0 / 16:1), PC(17:0 / 18:0) had statistical differences between MM and rMM groups (P < 0.050), and no statistical differences between MM and nrMM groups (P > 0.050); SM(d18:1 / 20:1), TG(18:0 / 18:1 / 22:4), TG(20:0 / 20:4 / 20:4) had statistical differences among the three groups (P > 0.050); HexCer(d16:1 / 22:0), PC(O-18:0 / 22:1), SM(d18:2 / 14:0) had no statistical differences between MM and rMM groups (P > 0.050), and no statistical differences between MM and nrMM groups (P > 0.050).

[0175] ROC analysis showed that the above 13 differentially expressed metabolites had high sensitivity and specificity in distinguishing between the nrMM and rMM groups in RRMM (AUC > 0.7, P < 0.05). (See [link to ROC analysis]). Figure 10 .

[0176] like Figure 11 As shown in Tables 1 and 2, among the 13 differentially expressed metabolites in the nrMM-D and rMM-D groups, 5 showed statistically significant differences (P<0.050): LacCer (d18:2 / 16:0), LysoPE (18:1), PC (O-18:0 / 22:4), TG (18:0 / 18:1 / 22:4), and TG (20:0 / 20:4 / 20:4). ROC analysis showed that among the 5 differentially expressed metabolites, 2, LacCer (d18:2 / 16:0) and TG (20:0 / 20:4 / 20:4), had high sensitivity and specificity in distinguishing between the nrMM-D and rMM-D groups (AUC>0.700, P<0.050). Among RRMM patients treated with Dara, the cut-off threshold for determining drug resistance and non-resistance based on the LacCer(d18:2 / 16:0) content in the subjects was 0.1363 μmol / L.

[0177] Table 1. LacCer(d18:2 / 16:0) content (μmol / L) in subjects.

[0178]

[0179]

[0180] Receiver operating characteristic (ROC) reflects the balance between sensitivity and specificity. The area under the ROC curve is an important indicator of test accuracy; the larger the area under the ROC curve, the greater the diagnostic value of the test.

[0181] Sensitivity (true positive rate): The percentage of actual drug resistance that is correctly judged as drug resistance according to the test standards. The higher the sensitivity, the better. The ideal sensitivity is 100%.

[0182] Specificity (true negative rate): The percentage of actual non-resistant individuals who are correctly judged as non-resistant according to the test standards. The higher the specificity, the better, with an ideal specificity of 100%.

[0183] The test standard is that if the content of LacCer(d18:2 / 16:0) of the subject is lower than the determination threshold, the subject is a Daratumumab-resistant individual (positive); if the content of LacCer(d18:2 / 16:0) of the subject is higher than the determination threshold, the subject is a Daratumumab non-resistant individual (negative). The determination threshold is that the content of LacCer(d18:2 / 16:0) is 0.1363 μmol / L.

[0184] Table 2 Original data of LacCer(d18:2 / 16:0) ROC curve

[0185] Threshold (pmol / L) Sensitivity 1 - Specificity Specificity Youden's Index 0.1363 0.667 0 1 0.667 0.0871 1 0.375 0.625 0.625 0.0946 0.833 0.25 0.75 0.583 0.1146 0.667 0.125 0.875 0.542 0.0746 1 0.5 0.5 0.5 0.1622 0.5 0 1 0.5 0.0930 0.833 0.375 0.625 0.458 0.1014 0.667 0.25 0.75 0.417 0.0636 1 0.625 0.375 0.375 0.1880 0.333 0 1 0.333 0.0556 1 0.75 0.25 0.25 0.2050 0.167 0 1 0.167 0.0499 1 0.875 0.125 0.125 0.0000 1 1 0 0 1.0000 0 0 1 0

[0186] Since RRMM often appears extramedullary disease (EMD) or plasma cell leukemia at the end stage of the disease, and malignant plasma cells may be more primitive and do not secrete M protein, it is not reliable to evaluate the remission status of the disease only by M protein level. RRMM often has persistent Hb level reduction and blood Ca and Cr level increase when the disease progresses and does not respond to treatment, and HIF-1a is currently recognized as related to the drug resistance mechanism of RRMM, so when screening differential metabolites related to RRMM drug resistance, we analyzed the correlation of the above 13 differential metabolites with HIF-1a, Ca, Cr, and Hb. The results show that LacCer(d18:2 / 16:0) has a negative correlation with HIF-1a Figure 12

[0187] In addition, we analyzed the correlation of the 5 metabolites that have statistical differences between the nrMM-D group and the rMM-D group with HIF-1a, Ca, Cr, and Hb, and the results show that LacCer(d18:2 / 16:0) has a negative correlation with HIF-1a, TG(18:0 / 18:1 / 22:4) and TG(20:0 / 20:4 / 20:4) have a positive correlation with HIF-1a.

[0188] Correlation analysis of 13 differential metabolites with poor prognosis extramedullary disease (EMD) and high-risk gene FISH (with 2 genetic abnormalities / 3 genetic abnormalities) showed that LacCer(d18:2 / 16:0), LysoPE(18:1), PC(P-16:0 / 16:1), SM(d18:2 / 14:0), and SM(d18:1 / 20:1) were all negatively correlated with FISH (double hit or triple hit), and no correlation with EMD was found Figure 13

[0189] 2.4 Survival analysis ​​

[0190] Among the 13 differential metabolites that can be used to evaluate the drug resistance of RRMM, 7 differential metabolites have correlation with at least 2 or more clinical characteristics (HIF-1a level, Ca, Cr, Hb, RISS stage and high-risk genetics). In order to further evaluate the influence of the 7 differential metabolites on the survival and progression of RRMM patients, we performed Kaplan-Meier survival analysis, and the results are shown in Figure 14 .

[0191] Kaplan-Meier survival analysis was performed on the OS of RRMM patients, and the cut-off values of LacCer(d18:2 / 16:0), SM(d18:1 / 20:1) and SM(d18:2 / 14:0) were 0.1356, 0.3565 and 0.1915 (μmol / L), respectively. The results showed that the overall survival time (OS) of RRMM patients was significantly shortened when less than the cut-off value, and the OS was (37.8 vs. 10.9) months, (37.8 vs. 10.2) months and (37.8 vs. 10.5) months, respectively.

[0192] Kaplan-Meier survival analysis was performed on the PFS of RRMM patients, and the results are shown in Figure 15 . The cut-off values of HexCer(d16:1 / 22:0), LacCer(d18:2 / 16:0), LysoPE(18:1), PC(O-18:0 / 18:1), PC(P-16:0 / 16:1), SM(d18:1 / 20:1) and SM(d18:2 / 14:0) were 0.0275, 0.1356, 0.3430, 1.7886, 0.3565 and 0.1915 (μmol / L), respectively. The results showed that the PFS of RRMM patients was significantly shortened when less than the cut-off value, and the PFS was (8.7 vs. 4.0), (7.9 vs. 4.0), (13.45 vs. 4.15), (7.9 vs. 3.5), (7.0 vs. 3.5) and (6.9 vs. 3.7 months), respectively.

[0193] Kaplan-Meier survival analysis showed that the decrease of LacCer(d18:2 / 16:0), SM(d18:2 / 14:0), SM(d18:1 / 20:1) was associated with shorter OS(P<0.050), and the decrease of HexCer(d16:1 / 22:0), LacCer(d18:2 / 16:0), LysoPE(18:1), PC(O-18:0 / 18:1), PC(P-16:0 / 16:1), SM(d18:2 / 14:0), SM(d18:1 / 20:1) was associated with shorter PFS(P<0.050).

[0194] From the above results, it can be seen that the decrease of the level of metabolite LacCer(d18:2 / 16:0) is significantly different between MM patients and HC, is highly related to MM disease progression, drug resistance and poor prognosis, and is involved in the whole process of MM pathology.

[0195] Example 2 The decrease of the level of metabolite LacCer(d18:2 / 16:0) for Dara drug resistance subgroups and prognosis evaluation of RRMM patients

[0196] 1 Diagnosis criteria

[0197] Non-drug diagnosis criteria: patients who received Dara-based chemotherapy regimen for 2-4 courses of treatment and evaluated the treatment efficacy as PR and above, including Stringent Complete Response (sCR), Complete Response (CR), Very Good Partial Response (VGPR) and PR; abbreviated as rMM-D.

[0198] Drug diagnosis criteria: patients who received Dara-based chemotherapy regimen for 2-4 courses of treatment and evaluated the treatment efficacy below PR, including MR, Stable Disease (SD) and Progressive Disease (PD), abbreviated as nrMM-D.

[0199] 2 Device for grouping RRMM patients according to the level of metabolite LacCer(d18:2 / 16:0) in the subject

[0200] The device comprises the following modules, Figure 16 The device comprises the following modules,

[0201] 2.1 Data receiving and analysis processing module

[0202] The content data of LacCer(d18:2 / 16:0) in known Daratumumab-resistant multiple myeloma patients and known Daratumumab-non-resistant multiple myeloma patients are received, and a determination threshold (cut-off) for determining whether the multiple myeloma patients, i.e., RRMM patients, are resistant to Daratumumab is determined according to the content data. The specific processing steps can refer to Implementation 1.

[0203] 2.2 Data input module

[0204] The sample data, i.e., the content data of LacCer(d18:2 / 16:0) in a subject, i.e., a multiple myeloma patient treated with Daratumumab, are input.

[0205] 2.3 Data comparison and result output module

[0206] The sample data are compared with the determination threshold, and the Daratumumab resistance result of the subject is output according to the comparison result. The determination rule for outputting the result information from the computer based on the sample data is as follows:

[0207] If the content of LacCer(d18:2 / 16:0) in the subject is lower than the determination threshold, the subject is or is a candidate for a Daratumumab-resistant individual;

[0208] If the content of LacCer(d18:2 / 16:0) in the subject is higher than the determination threshold, the subject is or is a candidate for a Daratumumab-non-resistant individual.

[0209] 3 Device for predicting the Dara treatment prognosis of a RRMM patient according to the level of the metabolite LacCer(d18:2 / 16:0) in the subject

[0210] The device comprises the following modules

[0211] 3.1 Data receiving and analysis processing module

[0212] The content data of LacCer(d18:2 / 16:0) in known Daratumumab-treated multiple myeloma patients and the overall survival time or progression-free survival of the patients are received, and a determination threshold (cut-off) for the prognosis of Daratumumab is determined according to the content of LacCer(d18:2 / 16:0) and the overall survival time and progression-free survival. The specific processing steps can refer to Implementation 1. The determination threshold (cut-off) of LacCer(d18:2 / 16:0) is 0.1356 μmol / L.

[0213] 3.2 data input module

[0214] For inputting sample data, the sample data is the content data of LacCer(d18:2 / 16:0) of a subject, the subject is a multiple myeloma patient treated with Daratumumab.

[0215] 3.3 data comparison and result output module

[0216] For comparing the sample data with the determination threshold, and outputting the Daratumumab prognosis result of the subject according to the comparison result; the determination rule for outputting the result information from the computer based on the sample data is that the prognosis of the subject with the content of LacCer(d18:2 / 16:0) higher than the determination threshold is better than that of the subject with the content of LacCer(d18:2 / 16:0) lower than the determination threshold.

[0217] The better prognosis includes longer overall survival time (OS) in months and / or longer progression-free survival (PFS) in months.

[0218] The above has been described in detail. For those skilled in the art, the present application can be implemented in a wider range under the same parameters, concentrations and conditions without departing from the purpose and scope of the present application, and without unnecessary experiments. Although the present application gives a special example, it should be understood that the present application can be further improved. In summary, according to the principle of the present application, the present application is intended to include any change, use or improvement of the present application, including changes made by conventional techniques known in the art, which deviates from the scope disclosed in the present application.

Claims

1. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to perform the following steps: (A1) Data reception and analysis: Receive data on the content of LacCer (d18:2 / 16:0) in known daratumumab-resistant multiple myeloma patients treated with daratumumab and known daratumumab-non-resistant multiple myeloma patients treated with daratumumab, and determine the threshold for determining whether daratumumab-resistant multiple myeloma patients treated with daratumumab are daratumab-resistant based on the data. (A2) Data input: Input sample data, which is the content data of LacCer (d18:2 / 16:0) in the subject, and the subject is a multiple myeloma patient treated with Daratumumab; (A3) Data comparison and result output: The sample data is compared with the judgment threshold, and the Daratumumab resistance result of the subject is output according to the comparison result.

2. The computer device as claimed in claim 1, characterized in that: Based on the comparison results, the Daratumumab resistance results for the subjects are as follows: If the level of LacCer(d18:2 / 16:0) in a subject is below the threshold, the subject is or is a candidate for Daratumumab resistance. If the level of LacCer(d18:2 / 16:0) in a subject is higher than the threshold, the subject is or a candidate for being a non-Daratumumab resistant individual.

3. A computer program product, comprising a computer program, characterized in that: When the computer program is executed by a processor, it performs steps (A1) to (A3) as described in claim 1 or 2.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it performs steps (A1) to (A3) as described in claim 1 or 2.

5. A device for daratumumab resistance grouping or adjunctive grouping of multiple myeloma patients treated with daratumumab, characterized in that: The device includes the following modules: (B1) Data receiving and analysis processing module: used to receive the content data of LacCer (d18:2 / 16:0) in known Daratumumab-resistant multiple myeloma patients and known Daratumumab-non-resistant multiple myeloma patients, and to determine the threshold for judging whether multiple myeloma patients are Daratumumab-resistant based on the data. (B2) Data input module: used to input sample data, which is the content data of LacCer (d18:2 / 16:0) in the subject, and the subject is a multiple myeloma patient treated with Daratumumab; (B3) Data comparison and result output module: used to compare the sample data with the judgment threshold, output the Daratumumab resistance result of the subject according to the comparison result, and group the subject according to the Daratumumab resistance result according to the following criteria: if the subject's LacCer(d18:2 / 16:0) content is lower than the judgment threshold, the subject belongs to the Daratumumab resistant population. If a subject's LacCer(d18:2 / 16:0) level is higher than the threshold, the subject is considered to be in the daratumumab non-resistant population.

6. A device for differentiating daratumab resistance in multiple myeloma patients treated with daratumab, characterized in that: The device includes the following modules: (B1′) Data receiving and analysis processing module: used to receive the content data of LacCer (d18:2 / 16:0) in known Daratumumab-resistant multiple myeloma patients and known Daratumumab-non-resistant multiple myeloma patients, and to determine the threshold for judging whether multiple myeloma patients are Daratumumab-resistant based on the data. (B2′) Data input module: used to input sample data, which is the content data of LacCer (d18:2 / 16:0) in the subject, and the subject is a multiple myeloma patient treated with Daratumumab; (B3′) Data Comparison and Result Output Module: Used to compare the sample data with the judgment threshold, output the Daratumumab resistance result of the subject based on the comparison result, and distinguish the drug resistance of the subject according to the following criteria based on the Daratumumab resistance result: If the level of LacCer(d18:2 / 16:0) in a subject is below the threshold, the subject is considered or a candidate for Daratumumab resistance. If a subject's LacCer(d18:2 / 16:0) level is higher than the threshold, the subject is considered or a candidate for Daratumumab non-resistant.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to perform the following steps: (F1) Data reception and analysis: Receive known LacCer(d18:2 / 16:0) content data and overall survival or progression-free survival data of multiple myeloma patients treated with Daratumumab, and determine the prognostic threshold of Daratumumab based on LacCer(d18:2 / 16:0) content, overall survival and progression-free survival. (F2) Data Input: Input sample data, which is the content data of LacCer (d18:2 / 16:0) in the subject, and the subject is a multiple myeloma patient treated with Daratumumab; (F3) Data Comparison and Result Output: The sample data is compared with the judgment threshold, and the Daratumumab prognosis result of the subject is output according to the comparison result; the judgment rule for outputting the result information from the computer based on the sample data is: compared with the subjects whose LacCer(d18:2 / 16:0) content is lower than the judgment threshold, the subjects whose LacCer(d18:2 / 16:0) content is higher than the judgment threshold have a better prognosis.

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