Computer device for carrying out damratumeumab resistance grouping on multiple myeloma patients

Abstract

The invention discloses a computer device for carrying out Darraumab (Dara) drug resistance grouping on multiple myeloma patients, and belongs to the field of medical care informatics. The technical problem to be solved in the invention is how to carry out Dara drug resistance grouping on multiple myeloma patients subjected to Dara treatment. In order to solve the technical problem, the invention provides a computer device for carrying out Dara drug resistance grouping on multiple myeloma patients. The invention discloses the application of LacCer (d18: 2 / 16: 0) as a marker in distinguishing Dara drug resistance or Dara non-drug resistance of multiple myeloma patients for the first time. According to the application, a basis can be provided for personalized treatment of patients with multiple myeloma: for patients with high drug resistance, a more powerful or novel drug combination can be selected to ensure the treatment effect; a drug resistance grouping result can provide a direction for research and development of new drugs.

Description

Technical Field

[0001] The present application relates to the field of medical care informatics, and in particular to a computer device for performing Daratumumab (Dara) resistance grouping on multiple myeloma patients. Background Art

[0002] Multiple Myeloma (MM) is a malignant plasma cell tumor, accounting for 1% of all cancers and about 10% of all blood diseases. The diagnostic criteria for MM are greater than 10% of clonal plasma cells in the bone marrow or evidence of plasmacytoma, as well as the presence of one of the end-organ damages such as hypercalcemia (C), renal insufficiency (R), anemia (A), lytic bone lesions (B), and CRAB symptoms. In recent years, the use of new drugs such as protease inhibitors (PIs), immunomodulatory drugs (IMIDs), monoclonal antibodies, and autologous hematopoietic stem cell transplantation has significantly improved the survival of patients, but it is still incurable, and drug resistance and relapse are inevitable.

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

[0004] Metabolic reprogramming is being used in cancer treatment as a hallmark or potential feature of cancer, not only to meet the increased energy demand for 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. Anticancer treatment can further change these cellular processes and microenvironment. As tumors adapt and mutate to counteract the effects of treatment, acquire drug resistance, and allow continued proliferation, cancer cells have been shown to undergo subsequent adjustments to their metabolism. Previous studies from our research group have found that the metabolism of MM tumor cells has also changed compared with normal cells, but the changes in lipid metabolism during the development of MM cell resistance have not been clearly defined. Summary of the invention

[0005] A technical problem to be solved by this application is how to perform Dara resistance grouping in a population of multiple myeloma patients treated with Daratumumab (Dara). To solve this technical problem, this application provides the following technical solutions:

[0006] The present application provides a computer device, including 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 reception and analysis and processing: receiving the LacCer (d18:2 / 16:0) content data of multiple myeloma patients with known Daratumumab resistance and multiple myeloma patients with known Daratumumab non-resistant who have been treated with Daratumumab, and determining the cut-off for determining whether the multiple myeloma patients treated with Daratumumab are Daratumumab resistant or not based on the data;

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

[0009] (A3) Data comparison and result output: The sample data is compared with the determination threshold, and the Daratumumab resistance result of the subject is output according to the comparison result.

[0010] In this application, the multiple myeloma patients (MM) are based on 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 clonal plasma cells in the bone marrow or clonal bone marrow plasma cells) ≥ 10% or biopsy-confirmed bone marrow / extramedullary plasmacytoma; ② Any one or more of the following: One of the symptoms of end-organ damage (C, hypercalcemia, R-renal damage, A-anemia and B, bone damage, CRAB) attributable to basic plasma cell proliferative diseases; Clonal bone marrow plasma cell percentage ≥ 60%; Involved / uninvolved serum free light chain (FLC) ratio ≥ 100 (involved free light chain level must be ≥ 100 mg / L); MRI suggests > 1 focal lesion (≥ 5 mm).

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

[0012] In the present application, the relapsed and refractory multiple myeloma patients refer to multiple myeloma patients whose disease progresses after obtaining a partial response (PR) or above and who are unresponsive to the salvage therapy (failure to obtain at least MR, M protein reduction <25%).

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

[0014]

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

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

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

[0018] Dara non-resistance diagnostic criteria: Multiple myeloma patients receive treatment including 2-4 courses of Daratumumab administration, and the treatment efficacy is evaluated as PR and above, including stringent complete response (sCR), complete response (CR), very good partial response (VGPR) and PR; represented by rMM-D in this application.

[0019] Diagnostic criteria for Dara resistance: Multiple myeloma patients receive treatment including 2-4 courses of Daratumumab administration, and the treatment efficacy is evaluated below PR, including MR, stable disease (SD) and progressive disease (PD), which is represented by nrMM-D in this application.

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

[0021] In the present application, the judgment threshold was obtained by performing Spearman correlation analysis and receiver operating characteristic curve (ROC) using SPSS26.0.

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

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

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

[0025] Furthermore, in the computer device, the Daratumumab resistance result of the subject is outputted according to the comparison result as follows:

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

[0027] If the subject's LacCer (d18:2 / 16:0) content is higher than the judgment threshold, the subject is or is a candidate for being a Daratumumab non-resistant individual. Summary of the invention:

[0029] Furthermore, the Daratumumab resistance result of the subject may be outputted based on the comparison result: the probability of the subject whose LacCer (d18:2 / 16:0) content is higher than the judgment threshold to be resistant to Daratumumab is lower than that of the subject whose LacCer (d18:2 / 16:0) content is lower than the judgment threshold.

[0030] That is, compared with the subjects whose LacCer (d18:2 / 16:0) content is higher than the judgment threshold, the subjects whose LacCer (d18:2 / 16:0) content is lower than the judgment threshold have 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 Daratumumab (Dara) resistance. Grouping or auxiliary grouping can effectively distinguish Dara-resistant and Dara-non-resistant patients. It provides guidance for formulating personalized treatment plans for patients with different drug resistance or selecting more potent or new drug combinations to ensure the treatment effect.

[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 implements steps (A1) to (A3) described in claim 1 or 2 when executed by a processor.

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

[0035] The present application also provides a device for performing Daratumumab resistance grouping or auxiliary grouping on multiple myeloma patients treated with Daratumumab, wherein the device comprises the following modules:

[0036] (B1) Data receiving and analysis processing module: used to receive the LacCer (d18:2 / 16:0) content data from known Daratumumab-resistant multiple myeloma patients and known Daratumumab-non-resistant multiple myeloma patients, and determine the cut-off value for determining whether the multiple myeloma patients are Daratumumab-resistant or not based on the data;

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

[0038] (B3) a data comparison and result output module: used to compare the sample data with the determination threshold, output the Daratumumab resistance result of the subject according to the comparison result, and group the subjects according to the Daratumumab resistance result according to the following criteria: if the LacCer (d18:2 / 16:0) content of the subject is lower than the determination threshold, the subject belongs to the Daratumumab resistance population;

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

[0040] The present application also provides a device for distinguishing Daratumumab resistance in multiple myeloma patients treated with Daratumumab, the device comprising the following modules:

[0041] (B1′) Data receiving and analysis processing module: used to receive the LacCer (d18:2 / 16:0) content data from known Daratumumab-resistant multiple myeloma patients and known Daratumumab-non-resistant multiple myeloma patients, and determine the judgment threshold (cut-off) of whether the multiple myeloma patient is Daratumumab-resistant or not based on the data;

[0042] (B2′) Data input module: used to input sample data, wherein the sample data is the content data of LacCer (d18:2 / 16:0) of a subject, and the subject is a multiple myeloma patient treated with Daratumumab;

[0043] (B3′) Data comparison and result output module: used to compare the sample data with the determination threshold, output the Daratumumab resistance result of the subject according to the comparison result, and distinguish the drug resistance of the subject according to the Daratumumab resistance result according to the following standards:

[0044] If the subject's LacCer (d18:2 / 16:0) content is lower than the judgment threshold, the subject is or is a candidate for Daratumumab resistance;

[0045] If the subject's LacCer (d18:2 / 16:0) content is higher than the judgment threshold, the subject is or is a candidate for Daratumumab non-resistant.

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

[0047] Furthermore, in the method described above, the determination threshold is obtained according to the following method: receiving LacCer (d18:2 / 16:0) content data in known Daratumumab-resistant multiple myeloma patients treated with Daratumumab and known Daratumumab-non-resistant multiple myeloma patients treated with Daratumumab, and determining a determination threshold (cut-off) for whether the multiple myeloma patients treated with Daratumumab are Daratumumab-resistant or not based on the data.

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

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

[0050] The method only includes data processing steps, and does not include steps for processing physical samples, such as steps for obtaining cells, tissues and / or organs from a living human or animal body, steps for processing in vitro samples from a living human or animal body, or steps for processing a living human or animal body.

[0051] Furthermore, in the method described above, the Daratumumab resistance result of the subject outputted according to the comparison result is determined according to the following criteria:

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

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

[0054] This application also provides any of the following applications:

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

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

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

[0058] (E4) Use 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 of multiple myeloma patients after treatment with Daratumumab, including but not limited to overall survival (OS) and progression-free survival (PFS).

[0060] The present application discloses a computer device, including 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 reception and analysis and processing: Receive the LacCer (d18:2 / 16:0) content data and the patients' overall survival or progression-free survival of multiple myeloma patients treated with Daratumumab, and determine the Daratumumab prognostic cut-off value based on the LacCer (d18:2 / 16:0) content and the overall survival and progression-free survival;

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

[0063] (F3) Data comparison and result output: comparing the sample data with the judgment 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: 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.

[0064] In this application, the Kaplan-Meier survival analysis model is used to determine the relationship between metabolic markers 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 present application, the evaluation indicators of "better prognosis" include: a longer (month) overall survival time (OS) and / or a longer (month) progression-free survival (PFS).

[0066] In some embodiments of the present application, the judgment threshold is 0.1356 μmol / L. Subjects with a value higher than the judgment threshold have a better prognosis.

[0067] The present application also provides a computer program product comprising a computer program, which implements the above steps (F1) to (F3) when executed by a processor.

[0068] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps (F1) to (F3) are implemented.

[0069] In the present application, the subject may be of Asian ethnicity.

[0070] More specifically, in the present application, the sub-race may be Chinese.

[0071] The beneficial technical effects achieved by this application are as follows:

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

[0073] In the embodiments of the present application, by measuring the LacCer (d18:2 / 16:0) content in multiple myeloma patients who have undergone a standard Dara treatment cycle, the patients can be grouped to determine whether they are Dara-resistant, and the prognosis of the patient's Dara treatment can be predicted or assisted in predicting.

[0074] The conclusions of this 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 efficacy, improve prognosis, and reduce or avoid unnecessary waste of money and medical resources.

[0075] The technical solution of this application has a reference role in the drug development direction and clinical trial design of multiple myeloma: the results of drug resistance clustering can provide direction for new drug development, help researchers understand which types of drug resistance are more common, and thus develop new drugs for these drug resistance. In the clinical trial stage of new drugs, drug resistance clustering can help researchers select suitable subjects and ensure the accuracy and reliability of test results. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0078] Figure 3 The correlation between 32 differential metabolites and M protein.

[0079] Figure 4 The correlation between the five differential metabolites and MM symptoms was analyzed.

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

[0081] Figure 6 Violin plots of 15 differential metabolites significantly associated with M protein.

[0082] Figure 7 The ROC curves of 15 differential metabolites that can evaluate treatment response were shown in Figure 2. ROC analysis showed that the 15 differential metabolites had high sensitivity and specificity in evaluating disease activity (AUC>0.700, P<0.05).

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

[0084] Fig. 9 Violin plots of the expression of 13 differential metabolites in rMM, MM, and nrMM groups; there were statistical differences in the 13 differential metabolites among the MM, nrMM, and rMM groups: HexCer (d16:1 / 22:0), LacCer (d18:2 / 16:0), LysoPE (18:1), 6 PCs, 2 SMs, and 2 TGs.

[0085] Fig.10 The ROC curves for evaluating the drug resistance of RRMM using 13 differential metabolites were plotted. ROC analysis showed that the 13 differential metabolites had high sensitivity and characteristics in evaluating drug resistance (AUC>0.700, P<0.05).

[0086] Fig.11 Violin plots and ROC curves for evaluating the drug resistance of Dara in the treatment of RRMM for the five differential metabolites (MM in the figure represents RRMM, rMM represents rMM-D, and nrMM represents nrMM-D). The results showed that (1) there were statistical differences in the five 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] Fig.12Correlation analysis of 13 differential metabolites with HIF-1α and clinical characteristics. Three differential metabolites were negatively correlated with HIF-1α, including LacCer (d18:2 / 16:0), TG (18:0 / 18:1 / 22:4), and TG (20:0 / 20:4 / 20:4); five differential metabolites were negatively correlated with Cr, including HexCer (d16:1 / 22:0), and SM (d18:2 / 14:0); and nine differential metabolites were positively correlated with Hb, including HexCer (d16:1 / 22:0), LacCer (d18:2 / 16:0), LysoPE (18:1), four PCs, and two SMs.

[0088] Fig.13 Correlation between differential metabolites and high-risk genetics.

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

[0090] Fig.15 This is a survival analysis diagram of differential metabolites and PFS.

[0091] Fig.16 The present invention provides a device composition and workflow diagram for grouping RRMM patients with Dara resistance based on the level of the subject's metabolite LacCer (d18:2 / 16:0).

[0092] Fig.17 Characteristic fragments used for detection of LacCer (d18:2 / 16:0) by ultra-high performance liquid chromatography triple quadrupole tandem mass spectrometry LC-MS.

[0093] Fig.18 It is a quantitative ion pair for detecting LacCer (d18:2 / 16:0) using ultra-high performance liquid chromatography triple quadrupole tandem mass spectrometry LC-MS.

[0094] Fig.19 is the retention time of LacCer (d18:2 / 16:0). DETAILED DESCRIPTION

[0095] In this application, targeted ultra-high performance liquid chromatography tandem mass spectrometry (UPLC-MS) was used to measure lipid metabolites in the serum of MM patients, and the levels of fatty acid translocase (FAT, also known as 6ZHONG) and hypoxia-inducible factor-1α (HIF-1α) were measured by enzyme-linked immunosorbent assay (ELISA). Serum lipid metabolomics analysis was performed on MM patients treated with BTZ / Dara basic regimen to study relevant biomarkers that can evaluate activity, treatment response and indicate drug resistance.

[0096] The present application is further described in detail below in conjunction with specific embodiments. The examples given are only for illustrating the present application, not for limiting the scope of the present application. The examples provided below can be used as a guide for further improvements by ordinary technicians in the technical field, and do not constitute a limitation of the present application in any way.

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

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

[0099]

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

[0101] The detection of ceramide uses an ultra-high performance liquid chromatography triple quadrupole tandem mass spectrometer LC-MS, which is commonly referred to as QQQ in general mass spectrometry. Triple quadrupole tandem mass spectrometry is often used for quantitative detection of low-concentration compounds because of its ultra-high sensitivity. QQQ refers to the fact that there are three quadrupoles in the mass spectrometer, usually the outer Q1 and Q3 have scanning functions, and the middle quadrupole Q2 has no analysis function, but can be used as a collision cell. In the quantitative detection process, we usually select the parent ion and characteristic fragments of the compound as ion pairs. After the previous liquid phase separation, the compound is sprayed, charged by the electric field, and vaporized at high temperature to desolvate to form gas phase ions, which enter Q1 for sorting. Only gas phase ions with a specific mass-to-charge ratio selected in advance can pass through Q1 and enter the Q2 collision cell. In the Q2 collision cell, high-purity inert gas collides, and the generated fragments are sent to the third analytical quadrupole Q3. Like Q1, only fragments with a set specific mass-to-charge ratio can pass through Q3, and finally the signal is detected by the detector. That is to say, only compounds that can pass the double screening of Q1 and Q3 can detect the corresponding signals. If the mass-to-charge ratio is not what we set, the ions will be screened out when passing through Q1 or Q3.

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

[0103] At the same time, we use liquid chromatography-mass spectrometry detection, with an ultra-high performance liquid chromatography at the front end to separate compounds based on their polarity. The smaller the polarity, the easier it is to be eluted from the reverse phase chromatography column by the organic solvent in the liquid phase. Conversely, the greater the polarity, the more difficult it is to elute, which corresponds to an increase in the retention time of the compound.

[0104] 4.60, 4.76 are the retention times of the compounds, in minutes. For example, C12 is the standard sample of d18:1 / 12:0, and its peak time is 3.44. For d18:1 / 16:0 in number 2, the peak is at 4.06 because of the increase of alkyl chain, the polarity decreases, and the retention time increases. For d18:2 / 16:0 in number 3, a double bond is added to d18:1 / 16:0, and its unsaturation increases, and its polarity increases, so its retention time is advanced to 3.80. Fig.19).

[0105] Therefore, during the LC-MS detection and analysis process, the characteristic ion pair and retention time of LacCer (d18:2 / 16:0) are used as the basis for two dimensions of information to confirm that the compound eluting at 3.80 min is the analyte LacCer (d18:2 / 16:0).

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

[0107] Terms used in this application:

[0108] BTZ: The generic name is Bortezomib, a small molecule drug used to treat multiple myeloma;

[0109] Dara: The generic name is Daratumumab, a monoclonal antibody drug used to treat multiple myeloma;

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

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

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

[0113] rMM: responding MM, Chinese name for multiple myeloma with response to treatment; the diagnostic criteria are that the patient receives 2-4 courses of BTZ- or Dara-based chemotherapy and the treatment efficacy is evaluated to be PR or above (≧PR), including stringent complete response (sCR), complete response (CR), very good partial response (VGPR) and partial response PR (Partial Response);

[0114] nrMM: No Responding MM, whose Chinese name is multiple myeloma with no treatment response. The diagnostic criteria are as follows: after the patient receives 2 - 4 cycles of chemotherapy regimens based on BTZ or Dara, the treatment efficacy is evaluated to be below partial response (PR) (<PR), including minimal response (MR), stable disease (SD), and progressive disease (PD).

[0115] HC: Healthy Control, abbreviated as HC, whose 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 The clonal plasma cells in the bone marrow are ≥ 10% or the bone marrow / extramedullary plasmacytoma confirmed by biopsy; ② Any one or more of the following terminal organ damage symptoms: terminal organ damage (CRAB) attributable to the underlying plasma cell proliferative disease; the percentage of clonal bone marrow plasma cells ≥ 60%; the ratio of involved / uninvolved serum free light chain (FLC) ≥ 100 (the level of involved free light chain must be ≥ 100 mg / L); MRI shows > 1 focal lesion (≥ 5 mm)

[0117] Staging is carried out according to the 2014 International Myeloma Working Group (IMWG) ISS and R-ISS staging criteria, 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 criteria for relapsed and refractory myeloma are as follows:

[0123]

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

[0125] 1. Method

[0126] 1.1 Research subjects

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

[0128] 1.2 Inclusion and Exclusion Criteria

[0129] 1.2.1 Inclusion criteria

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

[0131] (2) Diagnostic criteria for MM: The number of clonal plasma cells in the bone marrow is greater than 10%, which leads to one of the CRAB symptoms, such as increased blood calcium (Ca>11mg / dL), increased creatinine (Cr>2mg / dL), decreased hemoglobin (Hb<10g / dL) and bone damage (osteolytic bone destruction or pathological fracture). Primary refractory refers to MM patients who have not achieved minimal remission (MR) or partial remission (PR) after 4 cycles of standard treatment; relapsed / refractory refers to patients whose disease progresses after achieving a PR or above treatment response and who do not respond to salvage therapy (failure to achieve at least MR treatment response), etc.

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

[0133] 1.2.2 Exclusion criteria

[0134] (1) Less than 2 courses of BTZ / Dara-based chemotherapy;

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

[0136] 1.3 Patient groups

[0137] 1.3.1 Patients were grouped according to disease activity

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

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

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

[0141] (1) rMM group: patients received 2-4 courses of BTZ- or Dara-based chemotherapy and were evaluated to have a therapeutic efficacy of PR or above, including strict complete response (sCR), complete response (CR), very good partial response (VGPR) and PR;

[0142] (2) nrMM group: patients received BTZ- or Dara-based chemotherapy for 2-4 courses and the treatment efficacy was evaluated below PR, including MR, stable disease (SD) and progressive disease (PD).

[0143] 1.3.3 Grouping of RRMM patients who do not respond to treatment

[0144] (1) nrMM-B group: RRMM patients who received BTZ-based chemotherapy for an average of 2-4 courses but did not achieve a response above PR;

[0145] (2) nrMM-D group: RRMM patients received Dara-based chemotherapy for an average of 2-4 courses but did not achieve a response above PR.

[0146] 1.3.3 Grouping of RRMM patients who responded to treatment

[0147] (1) rMM-B group: RRMM patients received BTZ-based chemotherapy for an average of 2-4 courses and achieved a response of PR or above;

[0148] (2) rMM-D group: RRMM patients received Dara-based chemotherapy for an average of 2-4 courses and achieved a therapeutic response of PR or above.

[0149] 1.4 Specimen processing

[0150] Venous blood samples were collected from MM patients before and after 2-4 cycles of bortezomib or daratumumab treatment to evaluate the treatment response. Fasting venous blood samples were collected from 46 MM patients before and after treatment and HC, and serum was separated within 2 hours and frozen at -80 degrees for testing. Routine serum tests were performed at the same time point when serum samples were collected, and serum protein electrophoresis (SPEP) and immunofixation electrophoresis (IFE) were performed.

[0151] 1.5 Metabolic assay based on UPLC-MS

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

[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 measurement steps refer to the instructions of the Enzyme-Linked Immunosorbent Assay (ELISA) kit, use a microplate reader to measure the optical density value (OD value) of each well, and calculate the concentration or amount of the test substance based on the standard curve.

[0157] 1.8 Statistical methods

[0158] TargetLynx quantitative software was used to calculate the peak area of ​​targeted data, and the retention time allowed an error of 15 seconds. The standard curve method was used to obtain quantitative results for concentration calculation. Orthogonal partial least squares discrimination analysis (OPLS-DA) was performed using Simca software, and differential metabolites were found by combining variable weight values ​​(VIP)>1 and t-test P<0.050. SPSS26.0 was used for Spearman correlation analysis and receiver operating characteristic curve (ROC). Kolmogorov-Simirnov (KS) test was used to evaluate normality. 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, independent sample t-test or paired t-test were used for continuous variables that met normal distribution, and Kruskal-Wallis test or Mann-Whitney U test was used for continuous variables that did not meet normal distribution. The 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, while progression-free survival (PFS) refers to the time from chemotherapy to tumor progression or death. Univariate Cox regression and multivariate Cox regression models were used to analyze factors affecting disease progression. P < 0.050 was considered to be statistically different, P < 0.010 was considered to be statistically significant, and P < 0.001 was considered to be very statistically significant. Rstudio and Graphpad Prism 9 were used to draw the figures.

[0159] 2. Results:

[0160] The differential metabolite distribution was compared according to the sample sources in the following groups: (a) MM group and HC group before treatment; (b) MM group and rMM group before treatment; (c) rMM group and nrMM group after treatment; (d) rMM-D group and nrMM-D group after treatment.

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

[0162] In this study, 77 neurotransmitters, 374 lipids and 37 carnitines, a total of 472 metabolites, were detected. There was a good separation trend between the MM group (before treatment) and the HC group, which may be related to the onset of MM. Figure 2 As shown in 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 phosphatidylcholine (PC), 32 LysoPC, 36 phosphatidylethanolamine (PE), 6 lysophosphatidylethanolamine (LysoPE), 36 sphingomyelin (SM) and 96 TG (triglycerides). The results showed that the differential metabolites were mainly enriched in sphingolipid metabolism, linoleic acid metabolism and glycerophospholipid metabolism pathways.

[0164] There are 32 common differential metabolites related to IgG and IgA M proteins, which may be related to the activity of MM ( Figure 3 The 32 metabolites included 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, there were 5 substances that were simultaneously associated with more than one typical symptom (increased blood calcium, renal damage, and anemia) (P < 0.050): CE (20:3), LacCer (d18:2 / 16:0), LysoPC (20:4), PE (O-16:0 / 22:6), TG (16:0 / 18:1 / 18:1), see Figure 4 . ROC analysis showed that ( Figure 5 ), the five differentially metabolites had good sensitivity and specificity in assessing MM activity (AUC>0.700, P<0.050).

[0166] Among the 32 differential metabolites related to MM activity, 15 differential metabolites were statistically different between the MM group (before treatment) and the rMM group (P < 0.050). Figure 6 Compared with the HC group, the levels of 15 differential metabolites were decreased in the MM group (P<0.050), and the expression levels of differential metabolites in the rMM group after treatment were higher than those in the MM group (P<0.050), suggesting that they may be used as markers for judging MM treatment response. Figure 7 .

[0167] Further analysis showed that there was no statistical difference in the levels of 6 differential metabolites between the HC group and the rMM group (P>0.050), namely 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 Middle c.

[0169] Combined with Vip>1 and P<0.050, there were 106 differential metabolites, including 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-HT. 7 PE, 3 SM, 17 TG, pentadecanoic acid, 4 carnitine, 5-HT and were up-regulated in the nrMM group, and the remaining 72 differential metabolites were down-regulated in the nrMM group ( Figure 2 (c) Further KEGG pathway analysis showed that the differential metabolites in the nrMM group and rMM group were mainly enriched in glycerophospholipid metabolism, glycosylphosphatidylinositol anchor biosynthesis, glyceride metabolism, and tryptophan metabolism.

[0170] 2.2 Differential metabolites are associated with drug resistance

[0171] In RRMM, we performed OPLS-DA analysis on serum lipid metabolites in the nrMM-D and rMM-D groups after treatment. The scatter plots showed a good separation trend between the two groups, see Figure 2d. Combined with VIP>1 and P<0.050, there were 64 differential metabolites. Including 2 LacCer, 22 LysoPC, LysoPE (18:1), 23 PC, SM (d18:2 / 14:0), 14 TG and octenylcarnitine. Among the 64 differential metabolites, except for 14 TG with increased expression levels in the nrMM-D group (FC>1), the expression levels of the remaining differential metabolites were decreased in the nrMM-D group (FC>1), as shown in the heat map. Figure 8 .

[0172] 2.3 Evaluation of RRMM treatment resistance by differential metabolites

[0173] In the first part of this study, we screened 32 differential metabolites that might be associated with disease activity. To further explore the changes of these 32 differential metabolites in RRMM during the treatment resistance process, 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) were statistically different between the rMM group and the nrMM group (P < 0.050).

[0174] The violin plots show the changes of the above 13 differential metabolites in MM, rMM and nrMM groups, see Fig. 9 There were statistically significant differences in 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), and PC (17:0 / 18:0) between the MM group and the rMM group (P < 0.050), but no statistically significant differences between the MM group and the nrMM group (P > 0.050). There were statistical differences in G (18:0 / 18:1 / 22:4) and TG (20:0 / 20:4 / 20:4) among the three groups (P>0.050). There were no statistical differences in HexCer (d16:1 / 22:0), PC (O-18:0 / 22:1), and SM (d18:2 / 14:0) between the MM group and the rMM group (P>0.050), and there were no statistical differences between the MM group and the nrMM group (P>0.050).

[0175] ROC analysis showed that the above 13 differential metabolites had high sensitivity and specificity in distinguishing nrMM group and rMM group in RRMM (AUC>0.7, P<0.05). Fig.10 .

[0176] like Fig.11 As shown in Table 1 and Table 2, there were statistical differences in 5 of the 13 differential metabolites between the nrMM-D group and the rMM-D group (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 differential metabolites, 2 differential metabolites: LacCer (d18:2 / 16:0) and TG (20:0 / 20:4 / 20:4) had high sensitivity and specificity in distinguishing the nrMM-D group from the rMM-D group (AUC>0.700, P<0.050). Among the RRMM patients treated with Dara, the cut-off 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] The 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 samples that are actually resistant but are correctly judged as resistant according to the test standards. The higher the sensitivity, the better, and the ideal sensitivity is 100%.

[0182] Specificity (true negative rate): The percentage of actually drug-intolerant samples that are correctly judged as drug-intolerant according to the test standards. The higher the specificity, the better. The ideal specificity is 100%.

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

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

[0185] Threshold (μmol / L) Sensitivity 1-Specificity Specificity Youden 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 presents with extramedullary disease (EMD) or plasma cell leukemia in the terminal 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 based solely on the M protein level. When RRMM progresses and does not respond to treatment, there is often a persistent decrease in Hb levels and an increase in blood Ca and Cr levels. HIF-1α is currently recognized to be related to the drug resistance mechanism of RRMM. Therefore, when screening for differential metabolites related to RRMM resistance, we conducted a correlation analysis between the above 13 differential metabolites and HIF-1α, Ca, Cr, and Hb. The results showed that LacCer (d18:2 / 16:0) was negatively correlated with HIF-1α ( Fig.12 ).

[0187] In addition, we conducted a correlation analysis between the five metabolites with statistically significant differences between the nrMM-D group and the rMM-D group and HIF-1α, Ca, Cr, and Hb. The results showed that LacCer (d18:2 / 16:0) was negatively correlated with HIF-1α, and TG (18:0 / 18:1 / 22:4) and TG (20:0 / 20:4 / 20:4) were positively correlated with HIF-1α.

[0188] The 13 differential metabolites were correlated with extramedullary lesions (EMD) with poor prognosis and high-risk gene FISH (with 2 genetic abnormalities / 3 genetic abnormalities). The results 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 was found with EMD ( Fig.13 ).

[0189] 2.4 Survival analysis

[0190] Among the 13 differential metabolites that can be used to evaluate RRMM resistance, 7 differential metabolites are associated with at least 2 clinical characteristics (HIF-1α level, Ca, Cr, Hb, RISS stage and high-risk genetics). To further evaluate the impact of these 7 differential metabolites on the survival and progression of RRMM patients, we performed Kaplan-Meier survival analysis. Fig.14 shown.

[0191] Kaplan-Meier survival analysis was performed on the OS of RRMM patients. 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 when the cut-off values ​​were lower than this, the overall survival (OS) of RRMM patients was significantly shortened, with OS of (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 PFS in RRMM patients. Fig.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 when the cut-off value was lower than this, the PFS of RRMM patients was significantly shortened, and their PFS were (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 suggested that the decrease of LacCer (d18:2 / 16:0), SM (d18:2 / 14:0), and SM (d18:1 / 20:1) was associated with shortened OS (P<0.050); 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), and SM (d18:1 / 20:1) was associated with shortened PFS (P<0.050).

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

[0195] Example 2: Decreased levels of metabolite LacCer (d18:2 / 16:0) for Dara resistance clustering and prognostic evaluation in RRMM patients

[0196] 1Diagnostic criteria

[0197] Diagnostic criteria for non-resistance: After 2-4 courses of Dara-based chemotherapy, the patient is evaluated to have a treatment efficacy of PR or above, including strict complete response (sCR), complete response (CR), very good partial response (VGPR) and PR; abbreviated as rMM-D.

[0198] Diagnostic criteria for drug resistance: After patients receive 2-4 courses of Dara-based chemotherapy, the treatment efficacy is evaluated to be below PR, including MR, stable disease (SD) and progressive disease (PD), referred to as nrMM-D.

[0199] 2Device for clustering RRMM patients with Dara resistance based on the level of LacCer (d18:2 / 16:0) metabolites

[0200] The device comprises the following modules: Fig.16 It is a component module of the device for clustering Dara resistance of RRMM patients according to the level of metabolite LacCer (d18:2 / 16:0) in the subjects of the present invention.

[0201] 2.1 Data reception and analysis processing module

[0202] 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 determine the cut-off for determining whether the multiple myeloma patients are Daratumumab-resistant or not based on the data; the multiple myeloma patients are RRMM patients. The specific processing steps can refer to implementation 1.

[0203] 2.2 Data Input Module

[0204] Used to input sample data, wherein the sample data is the content data of LacCer (d18:2 / 16:0) of the subject, and the subject is a multiple myeloma patient treated with Daratumumab.

[0205] 2.3 Data comparison and result output module

[0206] The sample data is used to compare with the judgment threshold, and output the Daratumumab resistance 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:

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

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

[0209] 3Device for predicting the prognosis of Dara treatment in RRMM patients based on the level of LacCer (d18:2 / 16:0) metabolites in subjects

[0210] The device comprises the following modules

[0211] 3.1 Data reception and analysis processing module

[0212] It is used to receive the LacCer (d18:2 / 16:0) content data of multiple myeloma patients treated with Daratumumab and the patients' overall survival time or progression-free survival time, and determine the Daratumumab prognosis judgment threshold (cut-off) according to the LacCer (d18:2 / 16:0) content and the overall survival time and progression-free survival time; the specific processing steps can refer to implementation 1. The LacCer (d18:2 / 16:0) judgment threshold (cut-off) is 0.1356 μmol / L.

[0213] 3.2 Data Input Module

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

[0215] 3.3 Data comparison and result output module

[0216] Used to compare the sample data with the judgment threshold, and output the Daratumumab prognosis result of the subject according to the comparison result; the judgment rule based on the sample data outputting the result information from the computer 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.

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

[0218] The present application has been described in detail above. For those skilled in the art, without departing from the purpose and scope of the present application, and without the need to carry out unnecessary experimental conditions, the present application can be implemented in a wide range under equivalent parameters, concentrations and conditions. Although the present application provides specific embodiments, it should be understood that further improvements can be made to the present application. In a word, according to the principles of the present application, the present application is intended to include any changes, uses or improvements to the present application, including departure from the disclosed scope in the present application and changes made with conventional techniques known in the art.

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 implement the following steps: (A1) Data reception and analysis and processing: receiving the LacCer (d18:2 / 16:0) content data of multiple myeloma patients with known Daratumumab resistance and multiple myeloma patients with known Daratumumab non-resistant who have been treated with Daratumumab, and determining the cut-off for determining whether the multiple myeloma patients treated with Daratumumab are Daratumumab resistant or not based on the data; (A2) Data input: input sample data, wherein the sample data is the content data of LacCer (d18:2 / 16:0) of a 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 determination threshold, and the Daratumumab resistance result of the subject is output according to the comparison result.

2. The computer device according to claim 1, wherein: According to the comparison results, the Daratumumab resistance results of the subjects are output as follows: If the subject's LacCer (d18:2 / 16:0) content is lower than the judgment threshold, the subject is or is a candidate for being a Daratumumab-resistant individual; If the subject's LacCer (d18:2 / 16:0) content is higher than the judgment threshold, the subject is or is a candidate for being a Daratumumab non-resistant individual.

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

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

5. A device for performing Daratumumab resistance grouping or auxiliary grouping of multiple myeloma patients treated with Daratumumab, characterized in that: The device comprises the following modules: (B1) Data receiving and analysis processing module: used to receive the LacCer (d18:2 / 16:0) content data from known Daratumumab-resistant multiple myeloma patients and known Daratumumab-non-resistant multiple myeloma patients, and determine the cut-off value for determining whether the multiple myeloma patients are Daratumumab-resistant or not based on the data; (B2) Data input module: used to input sample data, wherein the sample data is the content data of LacCer (d18:2 / 16:0) of a subject, and the subject is a multiple myeloma patient treated with Daratumumab; (B3) a data comparison and result output module: used to compare the sample data with the determination threshold, output the Daratumumab resistance result of the subject according to the comparison result, and group the subjects according to the Daratumumab resistance result according to the following criteria: if the LacCer (d18:2 / 16:0) content of the subject is lower than the determination threshold, the subject belongs to the Daratumumab resistance population; If the subject's LacCer (d18:2 / 16:0) content is higher than the judgment threshold, the subject belongs to the Daratumumab non-resistant population.

6. A device for distinguishing Daratumumab resistance in multiple myeloma patients treated with Daratumumab, characterized in that: The device comprises the following modules: (B1′) Data receiving and analysis processing module: used to receive the LacCer (d18:2 / 16:0) content data from known Daratumumab-resistant multiple myeloma patients and known Daratumumab-non-resistant multiple myeloma patients, and determine the judgment threshold (cut-off) of whether the multiple myeloma patient is Daratumumab-resistant or not based on the data; (B2′) Data input module: used to input sample data, wherein the sample data is the content data of LacCer (d18:2 / 16:0) of a 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 determination threshold, output the Daratumumab resistance result of the subject according to the comparison result, and distinguish the drug resistance of the subject according to the Daratumumab resistance result according to the following standards: If the subject's LacCer (d18:2 / 16:0) content is lower than the judgment threshold, the subject is or is a candidate for Daratumumab resistance; If the subject's LacCer (d18:2 / 16:0) content is higher than the judgment threshold, the subject is or is a candidate for Daratumumab non-resistant.

7. A method for Daratumumab resistance grouping or auxiliary grouping of multiple myeloma patients treated with Daratumumab, the method comprising the following steps: comparing sample data with a determination threshold, and determining the Daratumumab resistance result of the subject based on the comparison result; the sample data is the LacCer (d18:2 / 16:0) content of the subject, and the subject is a multiple myeloma patient treated with Daratumumab.

8. The method according to claim 7, characterized in that: The determination threshold is obtained according to the following method: receiving LacCer (d18:2 / 16:0) content data in known Daratumumab-resistant multiple myeloma patients treated with Daratumumab and known Daratumumab-non-resistant multiple myeloma patients treated with Daratumumab, and determining the determination threshold (cut-off) of whether the multiple myeloma patients treated with Daratumumab are Daratumumab-resistant or not based on the data.

9. The method according to claim 7 or 8, characterized in that: Based on the comparison results, the Daratumumab resistance results of the subjects are determined according to the following criteria: If the subject's LacCer (d18:2 / 16:0) content is lower than the judgment threshold, the subject is or is a candidate for being a Daratumumab-resistant individual; If the subject's LacCer (d18:2 / 16:0) content is higher than the judgment threshold, the subject is or is a candidate for being a Daratumumab non-resistant individual.

10. 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 implement the following steps: (F1) Data reception and analysis and processing: Receive the LacCer (d18:2 / 16:0) content data and the patients' overall survival or progression-free survival of multiple myeloma patients treated with Daratumumab, and determine the Daratumumab prognostic cut-off value based on the LacCer (d18:2 / 16:0) content and the overall survival and progression-free survival; (F2) Data input: input sample data, wherein the sample data is the content data of LacCer (d18:2 / 16:0) of a subject, and the subject is a multiple myeloma patient treated with Daratumumab; (F3) Data comparison and result output: comparing the sample data with the judgment 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: 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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