Prognostic markers for multiple myeloma and their applications

Abstract

The present invention belongs to the field of biotechnology and specifically relates to prognostic markers for multiple myeloma and their applications. In one aspect, the present invention provides the use of a reagent for detecting ATF4 in the preparation of a product for diagnosing multiple myeloma or predicting the prognosis of multiple myeloma patients. Preferably, the reagent for detecting ATF4 includes reagents required for detecting ATF4 protein expression levels, RPF content, or translation efficiency. In another aspect, the present invention also provides a system / device for achieving diagnosis and prognosis prediction.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology, and in particular relates to a prognostic marker for multiple myeloma and an application thereof. Background Art

[0002] Multiple myeloma (MM) is a highly heterogeneous hematologic malignancy characterized by the abnormal clonal expansion of plasma cells in the bone marrow. Current large-scale genomic and transcriptomic studies of MM have identified numerous driver mutations and prognostic gene signatures. The most common mutations in MM include KRAS, NRAS, BRAF, and FGFR3, which are involved in MEK / ERK signaling, as well as mutations in NF-κB signaling, cell cycle transitions, and epigenetic regulators. At the transcriptional level, studies using gene expression profiling (GEP) to stratify patient risk have led to the development of several GEP signatures, such as GEP-70 and GEP-15, by various groups.

[0003] Treatment options for multiple myeloma include chemotherapy, targeted therapy, and stem cell transplantation. These options are evolving with the emergence of numerous new immunotherapies, including immunomodulatory drugs, monoclonal antibodies, and CAR-T, achieving remarkable success. Despite these advances, multiple myeloma remains incurable and carries a high risk of relapse and progression. This is because early-stage multiple myeloma may be asymptomatic, making it difficult to diagnose. Furthermore, due to the high genetic heterogeneity of multiple myeloma, current international staging systems, including RISS, have limitations in stratifying patients for multiple myeloma and are difficult to guarantee prognostic accuracy. This poses challenges in developing treatment strategies for individual patients, and treatment resistance can easily develop. Many treatments also have long-term side effects that can impact patients' quality of life. Further research is needed to identify additional therapeutic targets and develop tailored treatments for patients with specific genetic mutations or subtypes.

[0004] While some scientists have focused on translational dysregulation in multiple myeloma, such as the aberrant activation of the PI3K / mTOR pathway, which is closely associated with translational regulation, and overexpression of the translational regulator MYC, a comprehensive understanding of translational regulation in multiple myeloma remains lacking. Translonomics is a relatively new research field. By capturing information about mRNAs being bound and translated by ribosomes and identifying translated open reading frames (ORFs), it provides a global analysis of mRNA translation within cells. Furthermore, translation efficiency, derived by comparison with the transcriptome, can reflect the state of translational regulation. Therefore, translatomics provides a comprehensive view of protein expression and translational regulation that is unavailable from traditional gene expression analysis. Comprehensive analysis of the multiple myeloma translatome will lay the foundation for a comprehensive understanding of the complex molecular mechanisms of multiple myeloma and the development of new therapeutic approaches targeting translational regulation. Summary of the Invention

[0005] This study collects translationome, transcriptome, and gene-targeted sequencing data from purified plasma cells from NDMM patients for comprehensive analysis. Using translationomics technology, the study systematically describes the translational status of MM cells, explores the correlation between translational regulation and the occurrence and development of MM, uncovers key translational dysregulation events and their precise molecular regulatory mechanisms, and explores new strategies for MM prognosis targeting key molecules.

[0006] Specifically, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides use of a reagent for detecting ATF4 in the preparation of a product for diagnosing multiple myeloma or predicting the prognosis of a multiple myeloma patient.

[0008] Preferably, the reagents for detecting ATF4 include reagents required for detecting the protein expression level, RPF content or translation efficiency (TE) of ATF4.

[0009] Preferably, the protein expression level of ATF4 can be detected by reagents used in any of the following methods: Western blotting, enzyme-linked immunosorbent assay, radioimmunoassay, sandwich assay, immunohistochemistry staining, mass spectrometry, immunoprecipitation analysis, complement fixation analysis, flow cytometry fluorescence analysis technology and protein chip method.

[0010] Preferably, the reagents for detecting the RPF content of ATF4 include reagents required for ribosome blot sequencing.

[0011] Preferably, the reagents for detecting the translation efficiency of ATF4 include reagents required for ribosome blot sequencing and transcriptome sequencing, or include reagents for detecting protein expression levels and mRNA expression levels.

[0012] In this invention, the term "RPF" refers to ribosome footprints (RFP, or ribosome-protected fragments, RPF), also known as ribosome-enclosed mRNA (ribosome footprints, RPFs), and is detected by Ribo-seq. Ribo-seq (ribosome profiling sequencing) is the most commonly used translationome sequencing technology. This technology uses RNase to digest RNA in cells to obtain ribosome-protected RNA fragments that are currently translating (the RPFs described in this invention). These RNA fragments are then enriched, sequenced, and analyzed.

[0013] In this invention, the term "translation efficiency (TE)" refers to the proportion of the total mRNA of a gene in a sample that is bound to ribosomes and translated. When TE is determined by sequencing, the calculation formula is: TE = Ribo-seq counts / RNA-seq counts. When the relative change in TE of MM samples compared to normal control (HC) samples is determined experimentally, the calculation formula is: TE relative variable = relative protein grayscale (MM sample protein grayscale / HC sample protein grayscale) / relative mRNA level (MM sample mRNA level / HC sample mRNA level). The density of ribosome footprints (RPFs) on transcripts can quantitatively reflect the rate of protein synthesis, but the abundance of mRNA for a transcript directly affects the probability of ribosome occupancy. Therefore, transcriptional regulation and the translation efficiency (TE) of each transcript jointly influence protein expression.

[0014] Preferably, the prognostic indicators include objective response rate, overall survival (OS), progression-free survival (PFS), objective response rate (ORR), time to progression (TTP), disease-free survival (DFS), time to treatment failure (TTF), response rate (RR), complete response (CR), and partial response (PR).

[0015] Preferably, the prognosis is overall survival or progression-free survival.

[0016] Preferably, the prognosis is 1-3 year overall survival rate.

[0017] Preferably, the prognosis is 1-year overall survival rate, 2-year overall survival rate, 3-year overall survival rate or longer overall survival rate.

[0018] Preferably, the prognosis is 1-3 years progression-free survival.

[0019] Preferably, the prognosis is 1-year progression-free survival, 2-year progression-free survival, 3-year progression-free survival or longer progression-free survival.

[0020] Preferably, the detection is performed by collecting samples from the subject for detection.

[0021] Preferably, the sample comprises bone marrow, peripheral blood, tissue, blood, serum, plasma, urine, saliva, semen, milk, cerebrospinal fluid, tears, sputum, mucus, lymph, cytosol, ascites, pleural effusion, amniotic fluid, bladder washing fluid and bronchoalveolar lavage fluid.

[0022] Preferably, the sample is bone marrow.

[0023] In a specific embodiment, the sample is plasma cells purified from bone marrow.

[0024] Preferably, the bone marrow collection method is conventionally used in the art, and the plasma cell purification method is conventionally used in the art.

[0025] The term "multiple myeloma (MM)," also referred to as "myeloma," is a disease in which plasma cells (a type of white blood cell responsible for antibody production) in the bone marrow transform into cancer cells and undergo clonal proliferation.

[0026] Preferably, the multiple myeloma can be divided into new diagnosis MM (NDMM), complete remission (CRMM) and relapse and refractory (RRMM) according to the patient's progression stage.

[0027] Preferably, the multiple myeloma is de novo multiple myeloma.

[0028] Preferably, the product comprises a chip, a kit or a nucleic acid membrane strip.

[0029] The term "prognosis" is recognized in the art and includes predictions about the likely course of a disease or disease progression, particularly about the likelihood of disease remission, disease recurrence, tumor recurrence, metastasis, and death. "Good prognosis" refers to the likelihood that a patient with cancer, particularly pancreatic cancer, will remain disease-free (i.e., cancer-free). "Poor prognosis" refers to the likelihood of recurrence or recurrence of the underlying cancer or tumor, metastasis, or death.

[0030] In specific embodiments, the time frame used to evaluate prognosis and outcome is, for example, less than 1 year, 1 year, 2 years, 3 years, 4 years, 5 years, 6 years, 7 years, 8 years, 9 years, 10 years, 15 years, 20 years or more.

[0031] On the other hand, the present invention provides a system / device for predicting the prognosis of a multiple myeloma patient, wherein the system / device includes a processing unit for determining the patient's prognosis based on any one of the following: ATF4 protein expression level, RPF content, or translation efficiency.

[0032] On the other hand, the present invention provides a system / device for diagnosing multiple myeloma, wherein the system / device comprises a processing unit for determining whether a subject suffers from multiple myeloma based on any one of the following: ATF4 protein expression level, RPF content, or translation efficiency.

[0033] Specifically, the processing unit (processor) may include one or more microprocessors or digital processors. The processor can invoke program code stored in memory to execute related functions. The processor, also known as a central processing unit (CPU), can be a very large-scale integrated circuit (VLSI) that serves as both the computing core and the control unit.

[0034] More specifically, the protein expression level, RPF content or translation efficiency of ATF4 is elevated in samples from multiple myeloma patients, and multiple myeloma patients with high ATF4 translation efficiency have a poor prognosis. More preferably, the samples are from newly diagnosed multiple myeloma.

[0035] Preferably, the system / device may further comprise a detection unit, which may be used to perform one or more of protein expression detection, polysome qPCR, ribosome blot sequencing or transcriptome sequencing.

[0036] In a specific embodiment, the detection unit may be a sequencer.

[0037] Preferably, the system / device may further include an information acquisition unit, which is used to perform an operation of acquiring detection information of the subject, wherein the detection information includes one or more of the protein expression level of ATF4, RPF content or translation efficiency.

[0038] Preferably, the system / device may further include a result display unit, and the result display unit is used to display the conclusion obtained by the evaluation unit.

[0039] Preferably, the result display unit displays the result by screen display, voice broadcast or printing.

[0040] It should be understood that the "system", "device" and "unit" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0041] Those skilled in the art will appreciate that the present invention may be implemented as a device, method, or computer program product. Therefore, the present disclosure may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software, generally referred to herein as a "unit" or "system." Furthermore, in some embodiments, the present invention may be implemented as a computer program product embodied in one or more computer-readable media containing computer-readable program code.

[0042] In another aspect, the present invention provides a method for diagnosing multiple myeloma, comprising determining whether a subject is ill based on the protein expression level of ATF4 and the RPF content. Specifically, the protein expression level of ATF4 and the RPF content are elevated in a sample from a patient.

[0043] In another aspect, the present invention provides a method for predicting the prognosis of multiple myeloma, the method comprising determining the prognosis of a multiple myeloma patient based on the translation efficiency of ATF4. Specifically, patients with low translation efficiency have a better prognosis, while patients with high translation efficiency have a poorer prognosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a summary graph of the sequencing results.

[0045] FIG2 is a summary diagram of differentially transcribed genes and differentially translated efficiency genes between NDMM patients and healthy controls.

[0046] Figure 3This is the result diagram of four types of differentially expressed genes identified using delta TE. In the figure, F stands for Forward, E stands for Exclusive, I stands for Intensified, and B stands for Buffered.

[0047] Figure 4 The figure shows the results of comparing RNA, TE and RPF between NDMM patients and healthy controls.

[0048] Figure 5 This is the result of testing the patient's protein level.

[0049] Figure 6 This is the test result of the translation efficiency of 4 genes in MM samples.

[0050] Figure 7 This is the result of polysome qPCR detection, showing that the translation efficiency of four genes in MM samples was significantly upregulated.

[0051] FIG8 is a survival curve showing the correlation between ATF4 translation efficiency and OS and PFS, A: OS, B: PFS.

[0052] FIG9 is a survival curve diagram showing the correlation between ATF4 and OS and PFS in the training set, A: OS, B: PFS.

[0053] FIG10 is a survival curve diagram showing the correlation between ATF4 and OS and PFS in the validation set, A: OS, B: PFS.

[0054] Figure 11 This is the ROC curve drawn for OS.

[0055] Figure 12 This is the ROC curve drawn for PFS. DETAILED DESCRIPTION

[0056] The present invention will be further described below with reference to the following embodiments. The following description is merely a preferred embodiment of the present invention and does not limit the present invention in any other form. Any person skilled in the art may utilize the above disclosed technical content to make equivalent embodiments with equivalent variations. Any simple modification or equivalent variation of the following embodiments made in accordance with the technical essence of the present invention without departing from the content of the present invention shall fall within the scope of protection of the present invention.

[0057] Example 1: Analysis of translational dysregulation events

[0058] In this study, bone marrow samples were collected from untreated Chinese NDMM patients and 9 healthy controls (Table 1 shows some of the patients' clinical characteristics). Figure 1 ), gene targeted sequencing, and analyzed the differential gene expression patterns and translation regulation characteristics of different prognoses.

[0059] Table 1. Clinical information of patients

[0060]

[0061]

[0062] Note:

[0063] 1.1: IgG; 2: IgA; 3: IgD; 4: non-secretory; 5: IgM

[0064] 2. 1: Kappa; 2: Lambda; 3: Non-secretory

[0065] 3. 1: Issue 1; 2: Issue 2; 3: Issue 3

[0066] 4. 1: Issue 1; 2: Issue 2; 3: Issue 3

[0067] 5. 1: Issue 1; 2: Issue 2; 3: Issue 3

[0068] 6. 1: A (normal); 2: B (incomplete)

[0069] To distinguish the fraction of genes encoding specific transcriptional and translational regulation, we applied the delta-TE algorithm to integrate the Ribo-seq and RNA-seq data of NDMM patients and healthy controls (HC). We identified 7507 differentially transcribed genes (DTGs) with transcriptional regulation and 1875 differentially translated efficiency genes (DTEGs) with translational regulation ( Figure 2A , B).

[0070] We further used delta TE to identify four categories of differentially expressed genes (DEGs). Among them, "Forward" DEGs were mainly regulated at the transcriptional level, that is, RPF changed with changes in mRNA but TE remained unchanged; "Exclusive" DEGs were only regulated at the translational level, that is, mRNA levels remained unchanged but RPF changed significantly; "Intensified" and "Buffered" DEGs were regulated by both transcription and translation. In the former, the changes in mRNA and TE were in the same direction, and TE enhanced the effect of transcription, while in the latter, the opposite was true, and the changes in TE offset the changes in transcription ( Figure 3 ).

[0071] Enrichment analysis of "Exclusive" DEGs, which are solely regulated by translation, revealed that pathways such as the DNA damage stress response, ER stress, cell cycle, MAPK cascade, and DNA repair were significantly enriched among translationally upregulated genes. For example, ATF4, a key effector of the ER stress response, has been reported in MM to mediate Mcl-1 upregulation, thereby conferring bortezomib resistance to MM cells. Other research systems have reported that ATF4 translation is regulated by m6A methyltransferases. We first discovered translational upregulation of ATF4 in NDMM patient data. Other important oncogenes, such as MTDH and MAPKAPK2, have been reported to promote tumor progression through translational dysregulation. We also found translational upregulation of these genes in the "Exclusive" DEGs. This finding is consistent with phenomena reported in other tumor types, suggesting that similar molecular mechanisms may play a role in MM. Genes such as MARCHF9 have not been previously reported to be overexpressed in MM, but studies have shown that they can mediate the ubiquitination degradation of MHCI, which may be related to tumor immune escape in MM. The CTSS gene has been reported to regulate antigen processing and presentation, and showed translational downregulation in our data, which may also contribute to MM immune escape.

[0072] We further verified whether the four "Exclusive" DEGs MTDH, ATF4, MAPKAPK2, and MARCHF9 were translational dysregulation targets. Sequencing data showed that the RNA levels of these genes did not change significantly in MM compared with HC, while the levels of TE and RPF were significantly increased ( Figure 4 ). Relative mRNA quantification and protein expression level identification were performed on samples from three normal controls and five MM patients. It was found that, except for MTDH, the mRNA expression levels of the other three genes in MM did not change significantly compared with HC, but the protein levels of the four genes were significantly increased in MM ( Figure 5 ), the translation efficiency (TE) was estimated by calculating the ratio of protein density to mRNA level. It was found that the relative TE of the four genes in MM samples were greater than 1 compared with HC, indicating that TE was upregulated ( Figure 6 The calculation formula of TE relative variable is relative protein gray level (MM sample protein gray level / HC sample protein gray level) / relative mRNA level (MM sample mRNA level / HC sample mRNA level)

[0073] Next, we performed polysome qPCR. Utilizing the different sedimentation coefficients of ribosome components, we used sucrose density gradient centrifugation to separate ribosomal subunits and polysomes. We then purified RNA bound to ribosomal subunits and polysomes and analyzed mRNA translation using qRT-PCR. The polysome fraction of mRNAs with upregulated translation was significantly higher than that of the control group (*, P < 0.05; **, P < 0.01; ***, P <0.001 [Student t tests ])( Figure 7 ATF4 high TE was associated with poor prognosis in our cohort (log rank test, p < 0.05) ( Figure 8 ).

[0074] These results collectively indicate that MTDH, ATF4, MAPKAPK2, and MARCHF9 are potential targets of translational regulation, and further investigation of the functions of these genes in MM and their contributions to disease etiology may provide valuable insights into the development of targeted translational therapeutics.

[0075] Example 2: Verification of the prognostic function of ATF4 translation efficiency

[0076] The data set was split using the "sample" function in R language, with 70% as a training set and 30% as a validation set. 35 patients were then used as a test set, which was then validated against the 15 patients used as a test set. The survival status of all 50 subjects and the translation efficiency of ATF4 are shown in Table 2.

[0077] Survival curves and ROC curves were plotted using maxstat.

[0078] Table 2. Survival of subjects and translation efficiency of ATF4

[0079]

[0080]

[0081] Note: 0 represents no and 1 represents yes. No: no event occurred, yes: event occurred

[0082] In the training set, the relationship between ATF4 translation efficiency and patient OS was analyzed using survival curves (P = 0.019). The relationship between ATF4 translation efficiency and patient PFS was (P = 7e-04). The differences were statistically significant, indicating that ATF4 translation efficiency can be used as a prognostic marker for MM ( Figure 9 ).

[0083] In the validation set, after grouping by ATF4 translation efficiency, there were differences in OS and PFS between the groups, and those with higher ATF4 had a worse prognosis (Figure 10). Figure 11 , draw the ROC curve for PFS as Figure 12 As shown in the figure, as confirmed by the ROC curve, the AUC values ​​were all far higher than the standard of 0.7, indicating that the translation efficiency of ATF4 can accurately predict the prognosis of MM.

Claims

1. Use of a reagent for detecting ATF4 in the preparation of a product for predicting the prognosis of a patient with multiple myeloma, characterized in that: The multiple myeloma patients were not receiving treatment; The reagents for detecting ATF4 include reagents required for detecting the protein expression level, RPF content and translation efficiency of ATF4.

2. The use according to claim 1, characterized in that The reagents required for detecting the protein expression level of ATF4 are reagents used in any of the following methods: Western blotting, radioimmunoassay, immunohistochemical staining, mass spectrometry, immunoprecipitation analysis, and complement fixation analysis.

3. The use according to claim 1, characterized in that The reagent required for detecting the protein expression level of ATF4 is a reagent used in enzyme-linked immunosorbent assay.

4. The use according to claim 1, characterized in that The reagents required for detecting the protein expression level of ATF4 are the reagents used in the sandwich assay.

5. The use according to claim 1, characterized in that The reagent required for detecting the protein expression level of ATF4 is a reagent used in flow cytometry fluorescence analysis technology.

6. The use according to claim 1, characterized in that The reagents required for detecting the protein expression level of ATF4 are the reagents used in the protein chip method.

7. The use according to claim 1, characterized in that The reagents required for detecting the RPF content of ATF4 include reagents required for ribosome imprint sequencing.

8. The use according to claim 1, characterized in that The reagents required for detecting the translation efficiency of ATF4 include reagents required for ribosome imprint sequencing and transcriptome sequencing, or include reagents for detecting protein expression levels and mRNA expression levels.

9. The use according to claim 1, characterized in that The prognostic indicators include objective response rate, overall survival rate, progression-free survival, objective response rate, time to disease progression, disease-free survival, and time to treatment failure.

10. The use according to claim 1, characterized in that The prognostic indicator is the response rate.

11. The use according to claim 1, characterized in that The prognostic indicators include complete response and partial response.

12. A system for predicting the prognosis of a multiple myeloma patient, characterized in that: The system includes a processing unit for determining the patient's prognosis based on the following data: ATF4 protein expression level, RPF content, and translation efficiency, and the multiple myeloma patient has not received treatment.

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