KR20210010311A - Method for providing the information for prediction of multiple myeloma prognosis using PD-L1 expression - Google Patents

Abstract

The present invention relates to a method of providing information for predicting the prognosis of multiple myeloma using PD-L1 expression, and a method of providing information necessary to determine whether or not autologous hematopoietic stem cell transplantation can be performed in a patient with multiple myeloma. According to the method, it is possible to more reasonably predict the prognosis of multiple myeloma and determine a treatment method.

Description

A method for providing the information for prediction of multiple myeloma prognosis using PD-L1 expression}

The present invention relates to a method for providing information for predicting the prognosis of multiple myeloma using PD-L1 expression, and in more detail, determining the risk by measuring the expression level of PD-L1, and scores combined with other known risk factors It relates to a method of providing information that can more accurately predict the prognosis of multiple myeloma by a system.

PD-L1 is a representative immune evasion factor and has been studied as a prognostic factor and a target for treatment in various carcinomas. Among solid cancers, there are research results on the effect of PD-L1 expressed in cancer cells on the prognosis of patients in many carcinomas such as lung cancer, breast cancer, and colon cancer, and among hematologic cancers, a lot of related studies have been conducted, especially in lymphoma.

However, in multiple myeloma, there are existing studies that show that PD-L1 expression is associated with poor prognosis, but it has not been established as a prognostic factor, and a prognostic model using this has not been developed. Multiple myeloma is a blood disease in which plasma cells, a type of antibody-producing cells in the bone marrow, proliferate abnormally. In particular, it is characterized by infiltrating bones and is a fatal disease that causes immune disorders, hematopoietic disorders and kidney disorders.

In previous studies, PD-L1 expression was measured in plasma cells circulating or obtained from bone marrow-derived blood by flow cytometry or by measuring soluble PD-L1 in plasma. There are limited studies to analyze the prognosis by directly measuring PD-L1 expressed in plasma cells by immunohistochemistry (including immunofluorescence staining) in bone marrow samples from patients.

Methods for measuring the expression of specific proteins (markers) through quantitative immunofluorescence analysis in cancer tissues are currently being developed, and representatively, the AQUA (Automated Quantitative Analysis) method is a method that measures the expression of proteins (markers) in solid cancer. Is being used. However, multiple myeloma is a blood disease originating from the bone marrow, and it is not clear to distinguish the boundary between cancer and surrounding tissues, so the application of the AQUA method is limited.

In the present invention, referring to the AQUA method, a mask was created based on each plasma cell region to obtain the sum of MFI, and a score system was created using this to measure the expression of PD-L1. The analysis using immunohistochemical staining may differ in the interpretation of the results depending on the reader, but this technique has the advantage of enabling consistent prediction by using immunofluorescence staining to perform the analysis according to the protocol set by the image program.

The present inventors confirmed that the expression level of PD-L1 according to the MFI scoring system of PD-L1 measured in plasma cells in the bone marrow is a significant factor affecting the survival of patients through multivariable Cox regression analysis, and Contal and O`Quigley The optimal cut-off value (7.65) of PD-L1 expression was determined using the method. Based on this, expressions lower than the cut-off value (7.65) were classified as low-risk groups and high-risk groups, and it was confirmed that the high-risk group of PD-L1 expression had a poorer prognosis than the low-risk group. After that, a nomogram model was established by Cox regression analysis using least absolute shrinkage and selection operator (lasso) variable selection, and based on this, a new PD-L1-based multiple myeloma prognosis prediction model was developed, which classified into low-, medium-, and high-risk groups.

The new prognosis prediction model showed a high overall prognosis discrimination ability compared to the existing prognosis prediction model. In particular, it showed a remarkable advantage over the existing prognosis prediction model in predicting high-risk patients who died before 24 months after diagnosis. Based on this, it was found that high-risk patients who are expected to have low survival rates can be actively treated from the beginning to help improve the prognosis, and that hematopoietic stem cell transplantation in high-risk patients with a new prognosis prediction model can be a way to increase the survival rate. Confirmed.

Contal and O'Quigley, An application of changepoint methods in studying the effect of age on survival in breast cancer. Comput. Stat. Data An. 30, 253-270 (1999)

The present inventors confirmed that the model combining PD-L1 expression and clinical factors established by lasso variable selection and Cox regression analysis based on the score system using the MFI of PD-L1 has high prognostic discrimination.

Accordingly, an object of the present invention is to provide a method of providing information for predicting the prognosis of multiple myeloma based on the expression of PD-L1.

Another object of the present invention is to provide a method of providing information necessary to determine whether multiple myeloma is autologous hematopoietic stem cell transplantation.

The present invention relates to a method of providing information for predicting the prognosis of multiple myeloma using PD-L1 expression, and the method according to the present invention increases the accuracy of predicting the prognosis of multiple myeloma by measuring PD-L1 expression. It also shows significantly higher discriminant power compared to the predictive model of prognosis.

The present inventors confirmed that the model combining PD-L1 expression and clinical factors established by lasso variable selection and Cox regression analysis based on the scoring system using MFI (mean fluorescence intensity) of PD-L1 has high prognostic discrimination.

Hereinafter, the present invention will be described in more detail.

According to an aspect of the present invention, the present invention provides a method of providing information for predicting the prognosis of multiple myeloma, comprising the following steps:

Deriving normalized mean fluorescence intensity (MFI) of PD-L1 according to Equation 1 below by quantitatively measuring the expression levels of CD138 and PD-L1 in a sample containing bone marrow plasma cells:

Equation 1

Normalized MFI = (MFI in plasma cell compartments-Background intensity) / MFI from isotype-matched control;

Step of deriving the PD-L1 expression MFI measurement value according to Equation 2:

Equation 2

PD-L1 expression MFI = Sum of normalized MFIs in all plasma cell compartments / Total number of plasma cells; And

If the measured value of PD-L1 expression MFI according to Equation 2 is less than 7.65, classifying it as a low-risk group, and if it is 7.65 or more, classifying it as a high-risk group.

In one embodiment of the present invention, in the step of measuring the expression level, immunofluorescence staining is performed to confirm the expression level of PD-L1 in bone marrow plasma cells, and normalization of PD-L1 according to Equation 1 in the plasma cell region The resulting MFI is calculated, added all according to Equation 2, and then a value obtained by dividing it by the number of regions of the plasma cell is derived.

In the present specification, the "number of regions of plasma cells" refers to the number of plasma cells for measurement in a sample, and the number of plasma cells is the same.

In one embodiment of the present invention, the CD138 is a marker for detecting plasma cells. The normalized MFI of the PD-L1 represents the expression level of PD-L1 in the region inside the plasma cells due to the detection of the CD138.

According to another aspect of the present invention, the present invention provides a method of providing information for predicting the prognosis of multiple myeloma comprising the following steps:

i) PD-L1 expression MFI measured value (0~35) according to Equations 1 and 2 above, ii) Whether serum lactate dehydrogenase (LDH) exceeds the upper limit of normal (422 IU/L), iii) high risk cytogenetics ( In karyotype, t(4;14), t(14;16), 17p deletion, chromosome 1 abnormality or FISH in IGH/FGFR3 rearrangement, IGH/MAF rearrangement, TP53 mutation), iv) age 70 or older. A score scoring step of adding corresponding points (points, 0 to 100) from the nomogram; And

Calculating total points by summing each of the added scores, and then deriving a probability for a corresponding 1 year, 2 year, or 4 year total survival period from the nomogram.

In one embodiment of the present invention, the probability for the entire 1-year survival period is 0.9 to 0.6. In another embodiment of the present invention, the probability for the 2 year overall survival is 0.9 to 0.4. In another embodiment of the present invention, the probability for the total survival period of 4 years is 0.8 to 0.2.

In one embodiment of the present invention, the upper limit value of the normal value of LDH in blood is a value that may vary depending on the testing institution and method, and is the upper limit value of the reference value (238-422 IU/L) of the institution to which the present inventor belongs. 422 IU/L was applied as a standard.

According to another aspect of the present invention, the present invention provides a method of providing information necessary to determine whether or not to autologous hematopoietic stem cell transplantation of multiple myeloma comprising the following steps:

i) PD-L1 expression MFI measured value (0~35) according to Equations 1 and 2 above, ii) Whether serum lactate dehydrogenase (LDH) exceeds the upper limit of normal (422 IU/L), iii) high risk cytogenetics ( t(4;14), t(14;16), 17p deletion, chromosome 1 abnormality in karyotype, IGH/FGFR3 rearrangement in FISH, IGH/MAF rearrangement, and TP53 mutation) iv) a score distribution step of adding a corresponding score (points, 0-100) from the nomogram based on whether the age is 70 years or older;

A classification step of classifying each of the added scores into a low-risk group if it is less than 50 points, a medium-risk group if it is 50 to 100 points, and a high-risk group if it exceeds 100 points; And

Providing information that the survival rate is improved when autologous hematopoietic stem cells are transplanted to patients classified as high-risk groups with a total score exceeding 100 points.

The present invention relates to a method for providing information for predicting the prognosis of multiple myeloma using PD-L1 expression, and to a method for providing information necessary for determining whether or not autologous hematopoietic stem cell transplantation in a patient with multiple myeloma, according to the method, predicts the prognosis of multiple myeloma. And the determination of the treatment method can be performed more reasonably.

1 is a confocal micrograph confirming that PD-L1 is expressed in plasma cells in the bone marrow of a patient with multiple myeloma.
2 is a photograph showing a method of determining a region of plasma cells and measuring MFI to measure the expression of PD-L1.
Figure 3a is a photo representatively selected to show the PD-L1 MFI (mean fluorescence intensity) measurement value and the expression level of PD-L1 according to the risk group.
3B is a graph showing the distribution of PD-L1 expression according to the measured value of PD-L1 MFI (mean fluorescence intensity).
4A is a graph showing overall survival according to risk groups based on PD-L1 expression based on PD-L1 MFI measurements in all patient groups.
Figure 4b is a graph showing the overall survival rate according to the risk group based on the expression of PD-L1 by the measured value of PD-L1 MFI in the patient group undergoing autologous stem cell transplantation (ASCT).
Figure 4c is a graph showing the overall survival rate according to the risk group based on PD-L1 expression by the PD-L1 MFI measurement value in the patient group not receiving ASCT.
Figure 4d is the overall risk group based on PD-L1 expression based on PD-L1 MFI measurements in patients treated with VTD (bortezomib, thalidomide and dexamethasone), TD (thalidomide and dexamethasone), and RD (lenalidomide and dexamethasone) therapy. It is a graph showing the survival rate.
Figure 4e is a graph showing the overall survival rate according to the risk group based on the PD-L1 expression by the PD-L1 MFI measurement value in the patient group treated with the VMP (bortezomib (Velcade) plus melphalan and prednisone) therapy.
FIG. 4F is a graph showing progression-free survival according to risk groups based on PD-L1 expression based on PD-L1 MFI measurements in the patient group receiving ASCT.
Figure 4g is a graph showing the progression-free survival rate according to the risk group based on PD-L1 expression by the measured value of PD-L1 MFI in the patient group who did not receive ASCT.
4H is a graph showing progression-free survival rates according to risk groups based on PD-L1 expression by PD-L1 MFI measurements in patient groups treated with VTD, TD, and RD therapy.
Figure 4i is a graph showing the progression-free survival rate according to the risk group based on the expression of PD-L1 by the measured value of PD-L1 MFI in the patient group treated with VMP therapy.
5 is a table showing significant risk factors obtained through multivariable Cox regression. The risk factor for PD-L1 expression was defined as a binary variable using the optimal cut-off value for the PD-L1 MFI measurement value.
6A is a nomogram model in which PD-L1 expression and clinical factors established by lasso variable selection and Cox regression analysis are combined. Risk factors for PD-L1 expression were defined as continuous variables using PD-L1 MFI measurements.
Figure 6b is a calibration graph for the established nomogram model.
6C is a graph comparing the survival of each group with a Kaplan-Meier survival curve divided into three groups based on the total score of the established nomogram.
6D is a graph of time-dependent AUC analysis for evaluating the established nomogram model.
Figure 6e is a graph calibrated by applying to the test cohort to test the established nomogram model.
6F is a graph comparing the survival of each group by applying a test cohort to test the established nomogram model with a Kaplan-Meier survival curve.
6G is a graph showing a time-dependent AUC analysis applied to a test cohort to test the established nomogram model.
7A is a graph comparing the survival of each group with a Kaplan-Meier survival curve in a new prognosis prediction model divided into three groups based on a nomogram total score.
Figure 7b is a graph comparing the survival of each group based on the existing R-ISS Kaplan-Meier survival curve.
7C is a graph comparing the performance of a new prognosis prediction model and R-ISS using time-dependent AUC analysis.
8A to 8F are graphs showing the overall survival rate and progression-free survival rate for treatments by risk group in a new prognostic prediction model.

Hereinafter, the present invention will be described in more detail by the following examples. However, these examples are for illustrative purposes only, and the scope of the present invention is not limited by these examples.

Throughout this specification, "%" used to indicate the concentration of a specific substance is (weight/weight)% for solids/solids, (weight/volume)% for solids/liquids, and Liquid/liquid is (vol/vol)%.

Example 1: Preparation of bone marrow sample

Samples of patients used in the experiment were obtained from 126 patients (83 retrospective, 43 prospective) diagnosed as multiple myeloma after bone marrow examination at the Department of Hematology and Oncology at Korea University Anam Hospital from January 2011 to October 2019. . All samples were prepared according to the protocol after IRB approval as a bone marrow paraffin cell block used for diagnosis, and at least two or more sections were prepared and confirmed for each sample.

Example 2: Immunofluorescence staining Confirmation of PD-L1 expression through

The prepared bone marrow paraffin cell block was cut from a 4-5 μm thick section to make a slide, dried at 60° C. for 1 hour, 3 times for 15 minutes with xylene, 100%, 95%, 70% ethanol Deparaffinization was performed by washing with running water after treatment twice for 5 minutes each. After that, it was boiled for 10 minutes in a pH 6.0 buffer (sodium citrate buffer) using a pressure cooker, and then cooled with flowing water to restore antigenicity, and the cell membrane permeation process was performed using 0.5% Triton X-100. I did.

After blocking with 5% serum (normal donkey serum) for 1 hour at room temperature, washing with PBS, primary antibody (goat CD138 Ab (R&D Systems), mouse anti-PD-L1 mAb [ABM4E54] (Abcam)) It was incubated at 4° C. overnight. After washing 3 times with PBS for 5 minutes and incubating for 1 hour at room temperature using secondary antibodies (alexa flour 488 conjugated donkey anti-goat IgG (Invitrogen), alexa flour 647 conjugated donkey anti-mouse IgG (Invitrogen)), then PBS Washed three times for 5 minutes and treated with a mountant containing DAPI (ProLong™ Diamond Antifade Mountant with DAPI (Invitrogen)).

As can be seen in FIG. 1, it was confirmed that PD-L1 was expressed in plasma cells in the bone marrow of patients with multiple myeloma using a confocal microscope. Images of CD138 and PD-L1 (8 bit raw) were analyzed using an image analysis program (Celleste® Image Analysis Software (Invitrogen)).

Analysis was carried out in at least 3 fields per sample (average 5). As shown in Figure 2, the region of plasma cells was determined based on the expression of CD138, and using an image analysis program (Celleste?? Image Analysis Software (Invitrogen)). PD-L1 expression MFI (PD-L1 expression) is semi-quantitative based on the value divided by the total number of plasma cells after calculating and adding the normalized mean fluorescence intensity (MFI) of PD-L1 in each plasma cell region. MFI) was measured.

The equation for calculating the normalized mean fluorescence intensity (MFI) is the same as Equation 1, and the equation for calculating the PD-L1 expression MFI from this is the same as Equation 2.

Equation 1

Normalized MFI = (MFI in plasma cell compartments-Background intensity) / MFI from isotype-matched control

Equation 2

PD-L1 expression (MFI) = Sum of normalized MFIs in all plasma cell compartments / Total number of plasma cells

The MFI from isotype-matched control in Equation 1 does not target PD-L1 as the MFI observation value of the isotype control group, but a control using an antibody having the same isotype and subclass as the antibody used to measure the MFI of PD-L1 Says MFI.

As can be seen in Figures 3a and 3b, if the MFI measured value for PD-L1 expression was less than 7.65, it was classified as a low-risk group, and if it was 7.65 or more, it was classified as a high-risk group (Reference: Contal and O'Quigley, An application of changepoint methods in studying the effect of age on survival in breast cancer . Comput. Stat. Data An. 30, 253-270 (1999)).

Example 3: PD-L1 Survival analysis for expression

All patients were i) patients who received autologous hematopoietic stem cell transplant (ASCT) and ii) patients who did not, iii) bortezomib, thalidomide, and VTDs containing the immunomodulators thalidomide and lenalidomide as a frontline. dexamethasone), TD (thalidomide and dexamethasone), RD (lenalidomide and dexamethasone) therapy, and iv) VMP (bortezomib(Velcade) plus melphalan and prednisone) therapy without immunomodulators. Analysis of overall survival and progression-free survival were performed, and are shown in FIGS. 4A to 4I.

The survival curves of FIGS. 4A to 4I were prepared using Kaplan-Meier survival analysis, and the survival rates of the two groups were compared with a log-rank test.

As can be seen from FIGS. 4A to 4I, as a result of the analysis, in the group receiving ASCT, there was no difference in survival between the high-risk group and the low-risk group of PD-L1 expression (p>0.05), but all other patients, the group receiving ASCT, the group receiving the immunomodulatory agent In all treatment groups without immunomodulators, the overall survival rate and progression-free survival rate were poor in the high-risk group (p<0.05).

Example 4: PD-L1 based new prognostic prediction model

5 and 6A, a nomogram model was established in which PD-L1 expression and clinical factors were combined by lasso (least absolute shrinkage and selection operator) variable selection and Cox regression analysis, and to avoid overfitting in this process, leave- Lasso's lambda value was determined by one-out cross validation (LOOCV).

Specifically, the clinical factors are 70 years of age or older, ECOG Performance Status of 2 or more, serum M-protein of 3 mg/dL or more, non-IgG isotype, serum free light chain ratio of 100 or more, BM plasma cell of 60% or more, 5.5 mg/d. L or higher b2-microglobulin, 3.5 g/dL or higher albumin, LDH above the upper limit of normal (422 IU/L), cytogenetic high risk group (chromosome 1 abnormality), and PD-L1 high risk group were analyzed.

In univariate analysis, clinical factors such as LDH above the upper limit of normal, high cytogenetic risk, and high risk of PD-L1 were found to be significant, and in multivariate analysis using Cox regression analysis, the elderly aged 70 years or older, LDH above the upper limit of normal. , Cytogenetic high risk group (1 type from the group consisting of t(4;14), t(14;16), 17p deletion, chromosome 1 abnormality in karyotype, IGH/FGFR3 rearrangement, IGH/MAF rearrangement, and TP53 mutation in FISH If abnormality is positive), and the clinical factors of the PD-L1 high risk group were analyzed to be significant (Fig. 5).

The points obtained by scoring the influence of the clinical factor selected by the above analysis and the presence of high PD-L1 expression, total points, the cumulative total of each number, and the probability for the 1-year overall survival rate, 2-year overall survival rate, and 4-year overall survival rate were calculated. A nomogram was created (FIG. 6A).

Next, as shown in Fig. 6b, calibration was performed using bootstrap 1000 times for the overall survival rates of 1, 2, and 4 years, and it was confirmed that the calibration was successful. Based on the total score of the nomogram established as in Fig. 6c, each group is divided into three groups with a low risk group of less than 50, a medium risk group of 50 to 100, and a high risk group of more than 100 as a Kaplan-Meier survival curve. The survival of the three groups was compared, and as a result of log ranking analysis, it was confirmed that the overall survival rates of the three groups were statistically significantly classified as P <0.001.

To evaluate the nomogram model established as in FIG. 6D, time-dependent AUC analysis was performed using 1000 bootstraps, and the 1-4 year AUC value was 0.6 or more, and the 1 year and 2 year AUC was 0.8 or more. It was confirmed that the model performance was good and was particularly excellent in the 1-year and 2-year survival evaluation (0.9-1.0 = excellent; 0.8-0.9 = very good; 0.7-0.8 = good; 0.6-0.7= sufficient; 0.5-0.6 = bad; <0.5 = not useful (Muller et al., 2005)).

The nomogram model established as in FIG. 6E was applied to the prospective (test) cohort and tested, and it was confirmed that the predicted survival probability and the observed survival probability by the nomogram were well calibrated. The nomogram model established as in FIG. 6f was applied to the prospective (test) cohort to compare the survival of each group with a Kaplan-Meier survival curve, and the overall survival rate of the three groups was statistically significant with P = 0.029. It was confirmed that it was distinguished. In order to evaluate the performance of the nomogram model established as in FIG. 6f, time-dependent AUC was analyzed by applying it to a prospective (test) cohort, and a 6-month AUC of 0.724 and a 12-month AUC of 0.740 were measured, so that the nomogram of the present invention was obtained in the test set. It was confirmed to be excellent in determining the prognosis of multiple myeloma.

The sample of the cohort patient was obtained from the results of 126 patients (83 retrospective, 43 prospective) diagnosed as multiple myeloma after bone marrow examination at the Department of Hematology and Oncology at Korea University Anam Hospital from January 2011 to October 2019. . The results were from patients diagnosed retrospectively from January 2011 to April 2018 and 43 prospectively from May 2018 to October 2019.

Example 5: Comparative analysis with existing prognostic models

The prognostic discrimination ability and degree of agreement between the novel multiple myeloma prognosis prediction model of the present invention and the conventional prognostic prediction model R-ISS (Revised multiple myeloma International Staging System) were compared.

As can be seen in Figures 7a and 7b, the statistical significance of the survival curve for each group that can be derived from the new prognosis prediction model of the present invention compared to the R-ISS was confirmed. In addition, as in Table 1, Harrell's c-index (Reference: Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat. Med . 15, 361-387 (1996).) It was confirmed that the new prognostic prediction model showed higher discriminant power in predicting prognosis than the existing R-ISS model, and in the time-dependent AUC analysis as shown in FIG. I confirmed what was seen.

division Harrell's C 95% CI Difference 95% CI P Prognosis prediction model of the present invention 0.775 0.717-0.833 0.125 0.037-0.213 0.005 R-ISS 0.650 0.568-0.732 - - -

CI, Confidence Interval; R-ISS, Revised International Staging System

As can be seen in Table 2, 21.4% of R-ISS stage I patients were in the middle-risk group in the new model, 32.9% and 32.9% of stage II patients in the R-ISS were in the low-risk group and high-risk group, respectively, and R -48.5% of ISS stage III patients were reclassified into the intermediate risk group of the new model, and this difference was statistically significant. The NRI (Net Reclassification Improvement) calculated by reclassification was 0.337, which was judged to be superior to the R-ISS in terms of sensitivity and specificity.

Total R-ISS - - Prognosis prediction model of the present invention Stage I Stage II Stage III total P Low risk group 11 (78.6%) 26 (32.9%) 0 (0.0%) 37 (29.4%) <0.001 Medium risk 3 (21.4%) 27 (34.2%) 16 (48.5%) 46 (36.5%) - High risk group 0 26 (32.9%) 17 (51.5%) 43 (34.1%) -

Example 6: New prognosis prediction model For each group Analysis of the effectiveness of the treatment

Kaplan-Meier survival curves and multivariate Cox regression analysis were performed in the low-, medium-, and high-risk groups of the new prognostic prediction model to determine the effect of autologous hematopoietic stem cell transplantation and administration of frontline immunomodulators on survival. The impact was analyzed. As can be seen in FIG. 8 and Table 3, administration of frontline immunomodulators and autologous hematopoietic stem cell transplantation did not affect survival in the low-risk group and the middle-risk group, but in the high-risk group, autologous hematopoietic stem cell transplantation was associated with high overall survival and progression-free survival. Was confirmed.

Therefore, it is believed that the use of the new prognostic prediction model of the present invention may help to predict the prognosis of multiple myeloma as well as determine whether to autologous hematopoietic stem cell transplantation in high-risk patients.

Claims (3)

Information providing method for predicting the prognosis of multiple myeloma, including the following steps:
Deriving normalized mean fluorescence intensity (MFI) of PD-L1 according to Equation 1 below by quantitatively measuring the expression levels of CD138 and PD-L1 in a sample containing bone marrow plasma cells:
Equation 1
Normalized MFI = (MFI in plasma cell compartments-Background intensity) / MFI from isotype-matched control;
Step of deriving the PD-L1 expression MFI measurement value according to Equation 2:
Equation 2
PD-L1 expression MFI = Sum of normalized MFIs in all plasma cell compartments / Total number of plasma cells; And
If the measured value of PD-L1 expression MFI according to Equation 2 is less than 7.65, classifying it as a low-risk group, and if it is 7.65 or more, classifying it as a high-risk group.
A method of providing information for predicting the prognosis of multiple myeloma comprising the following steps:

i) PD-L1 expression MFI measured value (0~35) according to Equation 1 and Equation 2 in Clause 1, ii) Serum lactate dehydrogenase (LDH) in blood above the upper limit of normal, iii) high risk cytogenetics (t(in karyotype) 4;14), t(14;16), 17p deletion, chromosome 1 abnormality, FISH in the case of at least one positive from the group consisting of IGH/FGFR3 rearrangement, IGH/MAF rearrangement, and TP53 mutation), iv) age 70 A score distribution step of adding a corresponding score (points, 0 to 100) from the nomogram based on whether or not they are aged or older; And
Calculating total points by summing each of the added scores, and then deriving a probability for a corresponding 1 year, 2 year, or 4 year total survival period from the nomogram.
A method of providing the information necessary to determine whether a patient with multiple myeloma has autologous hematopoietic stem cell transplantation, including the following steps:

i) PD-L1 expression MFI measured value (0~35) according to Equation 1 and Equation 2 in Clause 1, ii) Serum lactate dehydrogenase (LDH) in blood above the upper limit of normal, iii) high risk cytogenetics (t(in karyotype) 4;14), t(14;16), 17p deletion, chromosome 1 abnormality, FISH in the case of at least one positive from the group consisting of IGH/FGFR3 rearrangement, IGH/MAF rearrangement, and TP53 mutation), iv) age 70 A score distribution step of adding a corresponding score (points, 0 to 100) from the nomogram based on whether or not they are aged or older;
A classification step of classifying each of the added scores into a low-risk group if it is less than 50 points, a medium-risk group if it is 50 to 100 points, and a high-risk group if it exceeds 100 points; And
Providing information that the survival rate is improved when autologous hematopoietic stem cells are transplanted to patients classified as high-risk groups with a total score exceeding 100 points.

Images