The prognostic value of combined CBC and immune cell profiles in patients with multiple myeloma treated with PAD sequential transplantation

The prognostic value of combined CBC and immune cell profiles in patients with multiple myeloma treated with PAD sequential transplantation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The prognostic value of combined CBC and immune cell profiles in patients with multiple myeloma treated with PAD sequential transplantation Jiang Zhang, Yao Chen, Yaoming Chen, Mengzhi Hong, Peisong Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4804600/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: Our study aimed to analyze the measurable residual disease (MRD), complete blood count (CBC), and immune cell profiles in multiple myeloma (MM) patients treated with bortezomib /adriamycin /dexamethasone (PAD) chemotherapy sequential autologous stem cell transplantation (ASCT) to determine their prognostic value and their interaction. Methods: CBC data from 93 MM patients were collected at diagnosis, before ASCT, and 3 months after ASCT. Immune cell profiles were detected by flow cytometry in fresh peripheral blood (PB) samples from 33 out of the 93 enrolled patients before ASCT and 3 months after ASCT. We then studied the relationship between MRD status and prognosis, the predictive value of CBC, and the changes in immune cell profiles before and after ASCT in multiple myeloma patients and their association with prognosis. Results: Early MRD-negative patients after ASCT had significantly longer progression-free survival (PFS) (median PFS was 36 months and 25 months, respectively, P < 0.05) and overall survival (OS) (median OS was 39 months and 33 months, respectively, P < 0.05) than MRD-positive patients. Three independent prognostic factors, neutrophil count (NEU), platelet count (PLT), and lymphocyte monocyte ratio (LMR) at diagnosis, were identified in our study group by LASSO regression. For the immune cell profiles, before ASCT, the negative immunomodulatory cell subsets (CD4/CD8 double-negative T cells (DNTs), regulatory T cells (Tregs), CD16 + CD56 high NK cells), PD1 + CD4 + central memory T cells (PD1 + T4CM), HLA-DR + CD8 + T cells were lower in MRD-negative or disease control patients than in MRD-positive or progressive disease patients ( P < 0.05). Otherwise, naive CD8 + T Cells (T8N) and CD28 + CD27 + naive CD8 + T Cells (CD28 + CD27 + T8N) were higher in MRD-negative or disease control patients than in MRD-positive or progressive disease patients ( P < 0.05). After ASCT, the levels of lymphocytes, marginal zone B cells, γδ T cells, and the ratio of (naive T cells plus central memory T cells to effector memory T cells plus effector T cells) were higher in disease control patients than in patients with progressive disease ( P < 0.05). Conclusion: CBC, MRD, and immune cell profile detection before and after ASCT have significant prognostic value in MM patients. Lower levels of NEU or PLT, higher levels of LMR at diagnosis, and a higher number of negative immunomodulatory cell subsets and activated T lymphocytes before ASCT were associated with poor prognosis. On the other hand, lower levels of depleted T lymphocytes, and higher levels of functional T cells and marginal zone B cells after ASCT predicted a good prognosis. Biological sciences/Cancer Biological sciences/Immunology Health sciences/Biomarkers Health sciences/Risk factors multiple myeloma minimal residual disease complete blood count immune cell profiles autologous stem cell transplantation Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Multiple myeloma (MM) is an incurable hematological disease [ 1 ]. Thanks to recent therapeutic advances, which include proteasome inhibitors and immunomodulatory drugs, many MM patients can be clinically complete response (CR). However, most of these CR patients would eventually relapse due to the persistence of myeloma cells [ 2 ]. As a new therapeutic endpoint, measurable residual disease (MRD) reflects the tumor burden of the disease, which can be timely and effectively treated to reduce the risk of disease progression. MRD is a prognostic factor independent of the clinical stages and genetic background of MM patients[ 3 – 6 ]. The immune system is also of great importance for understanding the control of MM. Immune cells such as lymphocytes, neutrophils, and monocytes are reported to be associated with the prognosis of MM patients [ 7 – 8 ]. It has been shown that analysis of immune cells before and after treatment in MM patients can identify the immune phenotype associated with disease improvement. It has been reported to correlate outcomes in multiple myeloma patients after ASCT has involved a small subset of immune cells [ 9 – 10 ]. Considering the large number of subsets of immune cells and the fine-grained nature of their function, a comprehensive immune cell profile assessment in MM patients is beneficial to analyze the disease progression and prognosis. In this study, we screened for parameters associated with complete blood count (CBC) that were associated with prognostic risk in multiple myeloma patients. At the same time, we focused on the simultaneous monitoring of immune cell profiles in MM patients before and after ASCT to explore indicators of a good prognosis. METHODS Patients and study design We collected 93 patients who were diagnosed with multiple myeloma in the first affiliated hospital of Sun Yat-sen University from April 2016 to December 2019. The treatment included bortezomib, adriamycin, and dexamethasone (PAD) induction followed by autologous transplant (ASCT), consolidation, and maintenance. All 93 patients underwent complete blood cell counting using BC-6800 Plus fully automated hematology analyzer (Mindray, Shenzhen, China). We collected CBC data at diagnosis, before ASCT, and 3 months after ASCT. MRD analysis was performed on BM aspirates that were collected at different time points: before ASCT, after ASCT at 3 to 12 months, and every 3 months at a time. Response to treatment was accessed according to the IMWG criteria, which classified as stringent complete response (sCR), complete response (CR), very good partial response (VGPR), partial response (PR), stable disease (SD), and progressive disease (PD). In this study, stringent complete response, complete response, very good partial response, partial response, stable disease are seemed disease control (DC). The patients were divided into the MRD positive group and MRD negative group according to the results of MRD analysis with multi-parameter flow cytometry three months after ASCT. The clinical data of patients including sex, age, revised international staging system (RISS) stage, Durie-Salmon (DS) stage, and time of progression were collected. Time to progression and time to death were obtained by hospital or telephone follow-up. Achieving MRD negativity at three months post-ASCT is considered early MRD negativity after ASCT. We also detected immune cells in fresh peripheral blood (PB) samples before ASCT and 3 months after ASCT from 33 out of the 93 enrolled patients. Flow cytometric immunophenotyping (FCI) Of MRD BM aspirate samples were collected in EDTA-anticoagulant and processed within 24 hr of collection. An optimized 8-color, 2-tube antibody panel including cKappa, cLambda, CD81, CD56, CD138, CD19, CD38, and CD45 was used for accurate identification of phenotypically aberrant, clonal plasma cells (PCs). The 2-tube strategy allows for the detection of MRD with specific confirmation of light-chain (mono)clonality on phenotypically aberrant PCs, identified by antigen underexpression (CD19, CD38, CD45, and CD81) or overexpression (CD56 and CD138), as compared with normal PCs. Data acquisition was performed on a FACSCanto plus flow cytometer (Becton Dickinson) which was standardized daily using CS&T beads. Data analysis was performed with Kaluza software (Beckman Coulter). At least 1,000,000 live events were acquired to achieve a potential sensitivity of at least 2×10 − 5 (0.002%). The number of viable nucleated cells was systematically registered, and the limit of detection (LOD) was determined in each sample according to the formula (20/viable nucleated cells) ×100%. Patients were thought to have detectable MRD whenever the percentage of phenotypically aberrant clonal PCs was equal to or greater than the LOD achieved in the corresponding sample. Conversely, patients were considered to have undetectable MRD when phenotypically aberrant clonal PCs were either absent or present at percentages below the LOD achieved in the corresponding sample. Instrument alignments, sensitivities, and spectral compensation were verified by standards, calibrators, procedural controls, and normal peripheral blood samples prior to processing of patient samples. FCI of Immune cells profile Fresh PB samples before ASCT mobilization and three months after ASCT (median of 101 days) were taken for flow cytometry analysis of immune cell subsets. We adopted the DURAClone IM immune function reagent (Beckman Headquarters, Brea, CA, USA) that included 50 antibodies distributed in six tubes to identify a total of 66 circulating immune cell subsets and detailed names of each cell subsets are shown in Online Resource Table 1 . All samples were measured with 10-color, three-laser Navios flow cytometers, as data files were analyzed with Kaluza software (Beckman Coulter). Flow cytometry analyzes the relative counts of immune cells, matches the data with the corresponding CBC indicators from the same blood sample, and calculates the absolute count of immune cells. Statistical methods SPSS 25.0 software and R-4.1.2 software were used for statistical analysis. Single Sample Kolmogorov-Smirnov (K-S) Test was used to judge the normal distribution. The measurement data of normal distribution were expressed as X ± SD , and the T-test was used to compare the two groups. The non-normal distribution data were expressed by M (IQR), the non-parametric rank sum test was used for comparison between two groups, the count data were expressed by case number and percentage, and the comparison among multiple groups was analyzed by X² test. Variables that can independently affect PFS were analyzed by the COX proportional hazard regression model. P -values in this text were obtained by the two-sided exact method, at the 5% significance level. The parameters of CBC were screened by LASSO regression with Glmnet. The indexes with a coefficient not equal to 0 were used to construct the Risk score model of prognosis, to evaluate the predictive value of CBC parameters for MRD status. The DxAI Intelligent Research Platform was used to perform statistical analysis of immune cell detection data, the subtyped variables were tested using the Fisher exact probability method and X² , and numeric variables were tested using independent sample t-test and Wilcoxon independent anecdotal test. P < 0.05 was considered statistically significant. RESULTS Patients’ characteristics Ninety-three patients were enrolled in our study, including 41 women and 52 men, with 37 MRD-positive patients and 56 MRD-negative patients after ASCT. According to the Revised International Staging System (RISS) at initial diagnosis, 23 patients (24.7%) were stage I, 53 patients (57.0%) were stage II, and 17 patients (18.3%) were stage III. According to the Durie-Salmon (DS) staging, 21 patients (22.6%) were stage IA, 12 patients (12.9%) were stage IB, 13 patients (14.0%) were stage IIA, 7 patients (7.5%) were stage IIB, 21 patients (22.6%) were stage IIIA, and 19 patients (20.4%) were stage IIIB. Age, sex, β 2 -microglobulin (β 2 MG), hemoglobin (Hb), creatinine (CREA), lactate dehydrogenase (LDH), serum calcium (Ca), serum albumin (ALB), RISS stage, and DS stage were not significantly associated with MRD status after ASCT ( P > 0.05) (Table 1 ). Table 1 Patient demographics and baseline characteristics. MRD+ MRD- P Demographics Age (years) 53.0 (49.5, 59.0) 54.0 (46.3, 60.0) 0.734 Men vs women (n, %) 24 (64.9) vs 13 (35.1) 28 (50.0) vs 28 (50.0) 0.158 Disease stage RISS (n, %) 0.874 Ⅰ 8 (21.6) 15 (26.8) Ⅱ 24 (64.9) 29 (51.8) Ⅲ 5 (13.5) 12 (21.4) DS (n, %) 0.450 ⅠA 12 (21.4) 9 (24.3) ⅠB 9 (16.1) 3 (8.1) ⅡA 10 (17.9) 3 (8.1) ⅡB 3 (5.4) 4 (10.9) ⅢA 12 (21.4) 9 (24.3) ⅢB 10 (17.8) 9 (24.3) Peripheral blood Hb (g/L) 102.0 (80.5, 115.0) 97.5 (80.8, 114.5) 0.371 PLT (×10 9 /L) 189.08 ± 69.58 221.00 ± 87.91 0.067 β 2 MG (mg/L) 3712.8 (2392.3, 6114.1) 4043.3 (2272.8, 8150.4) 0.672 CREA (µmol/L) 84.0 (64.5, 129.5) 81.0 (65.0, 165.5) 0.721 LDH (U/L) 186.0 (155.5, 230.5) 166.5 (121.5, 215.0) 0.110 Ca (mmol/L) 2.3 (2.1, 2.5) 2.3 (2.2, 2.4) 0.680 ALB (g/L) 35.9 ± 7.4 35.3 ± 6.9 0.725 Prognosis PFS (month) 25.2 (15.9, 43.8) 35.8 (27.4, 47.1) 0.007 OS (month) 32.5 (21.5, 45.2) 39.3 (29.4, 47.6) 0.013 DS, Durie-Salmon; RISS, Revised International Staging System; IQR, interquartile range. Impact of MRD status on OS and PFS The MM patients included in our study achieved very good partial response (VGPR) after PAD chemotherapy, and there was no statistical association between MRD status before ASCT transplantation and disease progression. OS and PFS were significantly associated with MRD status after ASCT ( P < 0.05) (Table 2 ). The OS of the MRD positive group (median 32.5 months, range 21.5–45.2) was significantly lower than that of the MRD negative group (median 39.3 months, range 29.4–47.6) ( P = 0.013) (Fig. 1 A). Likewise, the PFS of the MRD positive group (median 25.2 months, range 15.9–43.8) was significantly lower than that of the MRD negative group (median 35.8 months, range 27.4–47.1) ( P = 0.007) (Fig. 1 B). CBC Parameters closely related to MRD status LASSO regression analysis (Figure 2A) and ten-time cross-validation (Figure 2B) were performed on 25 variables including biochemical parameters, CBC parameters, and their derived parameters, and 3 variables were selected which were closely related to MRD status, that is, neutrophil count (NEU), platelet count (PLT), and lymphocyte monocyte ratio (LMR) at diagnosis. Lower levels of NEU or PLT, and higher levels of LMR within a certain range indicate an increased risk of MRD positivity. Construction and evaluation of CBC prognostic model for MM patients Based on the variables screened by LASSO regression, we constructed a predictive model for predicting the risk of MRD positivity in patients with MM (Fig. 3 ). According to the results of NEU, PLT, and LMR at diagnosis, the risk of early MRD positivity after ASCT can be calculated by Nomogram. The receiver operating characteristic (ROC) curve of the model predicting MRD positive risk had excellent AUC ( AUC = 0.699, P = 0.001) (Fig. 4 A). The results of the discriminant test showed that the predictive model had good prognostic accuracy with a C-index of 0.723. The results of model calibration evaluation show that the calibrated curve is in good agreement with the ideal curve (Fig. 4 B). Immune cell files before ASCT mobilization The counts of CD4/CD8 double-negative T cells (DNTs), regulatory T cells (Tregs), and CD16 + CD56 high NK cells in MRD-negative patients were significantly lower than those in MRD-positive patients before ASCT ( P < 0.05). The counts were (0.015 ± 0.014) ×10 9 /L vs. (0.032 ± 0.012) ×10 9 /L for DNTs, (0.012 ± 0.010) ×10 9 /L vs. (0.040 ± 0.019)×10 9 /L for Tregs and the percentages were 0.240% (0.122%, 0.502%) vs. 1.970% (0.660%, 3.790%) for CD16 + CD56 high NK cells, respectively. Disease progression was significantly correlated with a higher count of PD1 + T4CM and CD8 + HLA-DR + T cells in MM patients before ASCT. The count of PD1 + T4CM and CD8 + HLA-DR + T cells in the progressive group was significantly higher than that in the no-progression patient group (PD1 + T4CM: (48.966 ± 22.905)% vs. (28.126 ± 12.354)%, P < 0.05 ; CD8 + HLA-DR + T cells: (68.392 ± 13.164)% vs. (84.016 ± 11.822)%, P < 0.05). Additionally, the count of naive CD8 + T cells and naive CD8 + CD28 + CD27 + T cells were both lower in the disease progression group compared to those in the no-progression group (naive CD8 + T cells: 4.640% (2.390–5.750) % vs. 12.070% (7.000-23.200) %, P < 0.05; naive CD8 + CD28 + CD27 + T cells: (88.514 ± 8.544) % vs. (95.944 ± 2.787) %, P < 0.05). The immune cell files before ASCT are shown in Table 2 . Table 2 Most relevant immune cell subsets predicting MRD status or prognosis before ASCT in MM patients Immune cells before ASCT Prognosis P MRD status before ASCT P Disease control (n = 28) Progressive disease (n = 5) Negative (n = 9) Positive (n = 24) DN T cells 0.025 ± 0.016 0.025 ± 0.015 0.997 0.015 ± 0.014 0.032 ± 0.012 0.008 ** NK Cells 15.262 ± 5.949 13.814 ± 5.953 0.594 19.093 ± 5.826 12.013 ± 4.054 0.004 ** CD16 + CD56high NK 0.660 (0.260, 1.970) 1.795 (0.287, 4.645) 0.374 0.240 (0.122, 0.502) 1.970 (0.660, 3.790) 0.006 ** T4CM PD1+ 28.126 ± 12.354 48.966 ± 22.905 0.044 * 29.893 ± 9.672 42.482 ± 28.314 0.330 CD8 + CD57+' 0.122 ± 0.079 0.138 ± 0.066 0.633 0.079 ± 0.060 0.158 ± 0.065 0.011 * T8N 12.070 (7.000-23.200) 4.640 (2.390–5.750) 0.029 * 16.940 (8.090, 26.070) 5.865 (4.768, 6.835) 0.002 ** T8CM PD1+' 0.013 ± 0.010 0.016 ± 0.006 0.442 0.008 ± 0.006 0.018 ± 0.008 0.008 ** T8N CD28 + CD27+ 95.944 ± 2.787 88.514 ± 8.544 0.030 * 93.120 ± 2.608 96.200 ± 1.185 0.003 ** T8CM CD28 + CD27- 4.983 ± 2.894 2.712 ± 1.104 0.048 * 4.660 (3.027, 8.227) 3.300 (2.590, 3.730) 0.121 CD8 + HLA-DR+ 68.392 ± 13.164 84.016 ± 11.822 0.048 * 71.508 ± 13.292 72.260 ± 18.908 0.940 T8E CD28- CD27-' 0.089 ± 0.075 0.094 ± 0.054 0.853 0.044 (0.019, 0.061) 0.104 (0.063, 0.177) 0.016 * Treg' 0.029 ± 0.019 0.029 ± 0.026 0.964 0.012 ± 0.010 0.040 ± 0.019 0.001 ** Memory Treg' 0.025 ± 0.018 0.027 ± 0.024 0.831 0.009 ± 0.009 0.037 ± 0.017 0.001 ** pDCs 0.070 (0.050, 0.080) 0.120 (0.110, 0.155) 0.010 ** 0.090 (0.062, 0.150) 0.090 (0.080, 0.120) 0.913 * p < 0.05, **p < 0.01; mean ± standard deviation; median(P 25 -P 75 ); variable (%); variable'(×10 9 /L) Immune cell files after ASCT Higher levels of activated and depleted T lymphocytes were significantly associated with disease progression. HLA-DR + CD4 + T cell count in the progressive group was higher than that in the no-progression patient group ((76.610 ± 17.384) % vs. (59.667 ± 10.372) %, P < 0.05). The count of T4CM cells at 3 months after ASCT was significantly lower in the disease progression group ((38.204 ± 14.839) % vs. (53.315 ± 8.565) %, P < 0.05). Whereas, a higher level of CD4 + effector memory T cells (T4EMs) after ASCT was found in the disease progression group ((59.152 ± 15.792) % vs. (42.778 ± 6.859) %, P < 0.05). After ASCT, the ratio of (naive T cells + TCMs / TEMs + effector T cells) was higher in progression-free patients than in patients with advanced disease (0.489 ± 0.085 vs. 0.338 ± 0.176, P < 0.05). The levels of γδ T cells were higher in progression-free patients than in patients with advanced disease (0.052% (0.043%, 0.117%) vs. 0.019% (0.007%, 0.032%), P < 0.05). A lower level of marginal zone B cells was found in the disease progression group compared to that in the no-progression group ((1.586 ± 1.428) vs. (2.800 ± 0.760) %, P < 0.05). The immune cell files after ASCT are shown in Table 3 . Table 3 Most relevant immune cell subsets predicting MRD status or prognosis after ASCT in MM patients Immune cells after ASCT Prognosis P MRD status after ASCT P Disease control (n = 28) Progressive disease (n = 5) Negative (n = 24) Positive (n = 9) Granulocytes 46.776 ± 10.376 55.924 ± 22.530 0.291 44.381 ± 10.883 63.998 ± 19.213 0.030 * Lymphocytes 40.150 ± 12.252 31.456 ± 19.365 0.303 42.426 ± 11.987 22.210 ± 13.737 0.018 * transitional B cell 16.106 ± 8.103 28.606 ± 6.481 0.010 * 19.594 ± 10.284 24.367 ± 7.397 0.419 marginal zone B cell 2.800 ± 0.760 1.586 ± 1.428 0.048 * 2.661 ± 0.930 1.390 ± 1.137 0.050 * CD16 + CD56high NK 3.855 ± 4.755 3.786 ± 3.762 0.978 0.945 (0.665, 1.805) 7.285 (3.865, 10.928) 0.024 * CD3 + T cells' 1.533 ± 1.261 0.917 ± 0.701 0.333 1.225 (1.030, 1.913) 0.744 (0.471, 0.882) 0.014 * CD4 + HLA-DR+ 59.667 ± 10.372 76.610 ± 17.384 0.033 * 65.642 ± 15.168 67.875 ± 16.867 0.813 T4CM 53.315 ± 8.565 38.204 ± 14.839 0.025 * 47.048 ± 11.512 47.657 ± 17.072 0.939 T4CM CD28 + CD27-' 0.016 ± 0.008 0.006 ± 0.003 0.022 * 0.014 ± 0.009 0.008 ± 0.006 0.209 T8 CD28 + CD27+' 0.493 ± 0.847 0.181 ± 0.194 0.437 0.280 (0.189, 0.419) 0.100 (0.075, 0.113) 0.024 * T8EM CD28-CD27- 35.709 ± 13.534 44.704 ± 11.293 0.225 34.705 ± 12.886 50.180 ± 8.426 0.049 * CD3 + TCR γδ' 0.073 ± 0.076 0.065 ± 0.071 0.845 0.052 (0.043, 0.117) 0.019 (0.007, 0.032) 0.036 * TN + TCM/TEM + TE 0.489 ± 0.085 0.338 ± 0.176 0.040 * 0.441 ± 0.169 0.416 ± 0.119 0.751 CD8 + PD1+'/CD4 + PD1+' 4.144 ± 2.342 2.636 ± 2.129 0.248 4.197 (3.438, 5.245) 1.700 (1.264, 2.082) 0.004 ** * p < 0.05, **p < 0.01; mean ± standard deviation; median(P 25 -P 75 ); variable (%); variable'(×10 9 /L) DISCUSSION MRD has important applications in managing MM and has been unequivocally established as a strong prognostic marker [ 11 – 12 ]. In Gupta’s study, the median PFS of MRD-negative patients and positive patients after ASCT did not reach 100 days and 24 months, respectively ( P < 0.05) [ 13 ]. In our study, Early MRD-negative patients have longer PFS than that of MRD-positive patients (36 months vs. 25 months, P < 0.05). All these studies showed that MRD-negative patients had longer PFS than MRD-positive patients after ASCT, which illustrated the prognostic value of early MRD detection in MM patients. The MRD assessment has been suggested to improve the sensitivity of response evaluation and has been proposed as a surrogate for PFS in MM. Previous studies found that the absolute value of peripheral blood lymphocyte, neutrophil-lymphocyte ratio, LMR, and platelet-lymphocyte ratio could be used as prognostic factors for MM [ 14 – 17 ]. Our study showed that lower levels of NEU and PLT and higher levels of LMR at diagnosis had a higher MRD positive risk, which was associated with poor prognosis. Reduced NEU and PLT before starting treatment in MM patients have been shown to be a poor prognostic factor in MM patients. Reduced level of lymphocytes is considered a poor prognostic factor for malignant tumors [ 7 , 14 ]. In our research, the level of lymphocyte in the MRD-positive group was lower than that in the MRD-negative group after ASCT, while the level of NEU in the MRD-positive group was higher than that in the MRD-negative group. MRD positive group had higher NLR than MRD negative Group after ASCT, suggesting that increased NLR predicts poor clinical outcomes, which is consistent with the literature [ 14 , 15 , and 17 ]. Studies have shown that elevated LMR is associated with a good prognosis in MM patients [ 14 , 15 ]. Our study found that higher LMR at diagnosis was associated with MRD-positive risk and that this value decreased in the MRD-positive group after ASCT, suggesting that a certain number of monocytes are required before ASCT to perform the intrinsic phagocytic and cytotoxic effects, otherwise, the prognosis is poor. The immune system plays a key intermediate role in the balance between dormancy and progression of multiple myeloma. NK cells, or natural killer cells, function as a part of the innate immune system and play a critical role in immune surveillance and defense against infected or cancerous cells and are also involved in regulating the activities of T cells, macrophages, and dendritic cells. In addition to cytotoxic NK cells (CD16 + CD56 + NK cells, about 90% of the total NK), there is a subpopulation called secretors or immature NK cells (CD16 + CD56 high NK cells, about 10% of the total NK), which produce various cytokines and chemokines. These molecules help in limiting or exacerbating immune responses, recruiting other immune cells to the site of infection, and modulating the adaptive immune system. Studies have shown that the number of NK cells in the peripheral blood decreases as myeloma progresses, and that expression of immature NK cells increases in patients with relapsed/refractory MM [ 18 – 19 ]. In our study, the count of NK cells was lower in the MRD positive group than that in the MRD negative group before ASCT, but higher levels of immature NK cells before and after ASCT were indicative of poor prognosis in MM patients, consistent with existing findings. Dendritic cells (DC) are a kind of antigen-presenting cells, which play an important role in the treatment of MM. As a protease inhibitor drug, bortezomib induces immunogenic cell death by activating DC through increased contact with tumor antigens. DC deficiency has been reported in patients with MM [ 20 ]. In our study, MM progression was significantly associated with a higher level of plasmacytoid DC in MM patients before ASCT. The effects of different types of DC on the prognosis of MM need to be further studied. In our study, the number of γδ T cells in MRD-negative patients increased at 3 months after ASCT, which was consistent with existing research findings. Studies have shown that high-frequency γδ T cells were associated with long-term disease-free survival in children and adults [ 21 ]. γδ T cells showed rapid early reconstruction 2 months after ASCT, and most of the γδ T cells recovered in the first few weeks are derived from the graft's γδ 1 and γδ 2 cell subsets, and all have CD27pos/CD45RAneg central memory phenotype, which helps ensure early protection against viruses, bacteria, and surviving residual tumor cells. In addition to γδ T cells, CD4 + T cell subsets also play a major role in their involvement in anti-tumor effects, such as central memory CD4 + T cells (T4CM) associated with lymph node homing, DC stimulation, and differentiation into CD4 + effector cells. In our study, T4CM cells with higher accounts after ASCT were shown to predict a good prognosis for patients. However, pre-ASCT results showed that some T4CM cell subsets increased in the MRD-positive and disease progression groups, such as the CD28 − CD27 − T4CM cells and the PD-1 + T4CM cells. In our study, Tregs increased in the MRD-positive group before ASCT. The main function of Tregs is to suppress the immune response in the functional homeostasis of the immune system and induce immune tolerance [ 22 ]. As an inhibitory molecule, the expression upregulation of PD-1 is related to T cell depletion, which can hinder the normal differentiation of T cells and prevent cytotoxic effects, thus negatively affecting the prognosis of patients. At the same time, studies have found that the dynamic changes of immune cells caused by treatment, such as ASCT, have an impact on T cell heterogeneity in MM patients, inducing the production of depleted or senescent T cells [ 23 ]. In the CD8 + effector memory T cell (T8EM) subsets, only CD28 − CD27 − T8EM cell count was higher in the post-ASCT MRD-positive group, and other subsets (CD28 − CD27 + T8EM and CD28 + CD27 + T8EM) were both lower in post-ASCT MRD positive patients. It is worth noting that there are also depleted, aging-related subsets in the T8EM population (PD1 + T8EM and CD28 − CD27 + T8EM), which makes sense with post-transplant MRD status; we speculate that treatment results in T cell heterogeneity. Sustained anti-tumor immunity requires long-term survival of memory CD8 + T cells to maintain, which may also be the reason for the higher account of T8CM cells in the group of MRD-negative patients after ASCT. The relative atrophy of naive T cell and central memory T cell populations and the increased differentiation of effector memory T cells versus effector T cell populations are termed T cell exhaustion; The exhaustion status of the T cell population was reflected by calculating the ratio of the sum of naive T cells and central memory T cells to the sum of effector memory T cells to effector T cells ((TN + TCM) / (TEM + TE)). The low expression of this ratio after ASCT in patients with progressive disease indicates that T-cell depletion may result in a poor prognosis [ 24 – 25 ]. Our study did not conclude that disease progression is associated with the total number of CD19 + B cells; however, in the analysis of various subsets of B cells, it was found that the lower account of marginal zone B cells after ASCT was found in MM patients with poor prognosis. The latest studies have shown that marginal B cells engulf DC cells to obtain MHC II molecules bound to complement C3 through cell pulverization and present them to CD4 + T cells as antigens [ 26 ]. At the same time, MM is essentially a kind of plasma cell clonal proliferation Plasma cells are differentiated from B cells. The mechanism of B cells in the marginal region affecting the prognosis outcome of MM needs to be further studied. The diversity of the results of our study showed that MM has the characteristics of significant heterogeneity in immune reconstitution and prognosis. The immune cell status before ASCT after chemotherapy, the immune cell reconstruction, and the MRD status at three months after ASCT have a certain relationship with the prognosis of MM patients. Therefore, regular detection of CBC, MRD along with immune cell profiles before and after ASCT is of great importance to the management of MM patients. Declarations CONTRIBUTIONS JZ and YC acquired data, interpreted results, and drafted, and revised the manuscript. YMC, MZH, and PSC provided feedback on data analysis and the manuscript. JOY and JXL designed the study, interpreted the results, revised the manuscript, and approved the final manuscript. Ethical Approval In accordance with the Declaration of Helsinki, our study was approved by the Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University, and the research group was allowed to participate in the experiment and publish the results. Funding No funding was received for conducting this study. Competing Interests The authors have no competing interests. Patient Consent Statement Informed consent was obtained from all subjects. Data Availability Statement The authors declare that the data supporting the findings of this study are available within the paper and its supplementary information files. References Röllig C, Knop S, Bornhäuser M (2015) Multiple myeloma. Lancet. 385:2197–2208. https://doi.org/10.1016/S0140-6736(14)60493-1 . Anderson KC, Auclair D, Adam SJ, et al (2021) Minimal Residual Disease in Myeloma: Application for Clinical Care and New Drug Registration. Clin Cancer Res. 27:5195–5212. https://doi.org/10.1158/1078-0432.CCR-21-1059 . Avet-Loiseau H, Ludwig H, Landgren O, et al (2020) Minimal residual disease status as a surrogate endpoint for progression-free survival in newly diagnosed multiple myeloma studies: a meta-analysis. Clin Lymphoma Myeloma Leuk. 20:e30–e37. https://doi.org/10.1016/j.clml.2019.09.622 . Martinez-Lopez J, Wong SW, Shah N, et al (2020) Clinical value of measurable residual disease testing for assessing depth, duration, and direction of response in multiple myeloma. Blood Adv. 4:3295–3301. https://doi.org/10.1182/bloodadvances.2020002037 . Wallington-Beddoe CT, Mynott RL (2021) Prognostic and predictive biomarker developments in multiple myeloma. J Hematol Oncol. 14:151. https://doi.org/10.1186/s13045-021-01162-7 . Hillengass J, Usmani S, Rajkumar SV, et al (2019) International myeloma working group consensus recommendations on imaging in monoclonal plasma cell disorders. Lancet Oncol. 20: e302–e312. https://doi.org/10.1016/S1470-2045(19)30309-2 . Yang Y, Liu Z, Wang H (2020) Peripheral Absolute Lymphocyte Count: An Economical and Clinical Available Immune-Related Prognostic Marker for Newly Diagnosed Multiple Myeloma. Med Sci Monit. 26:e923716. https://doi.org/10.12659/MSM.923716 . Zahran AM, Nafady-hego H, Moeen S M, et al (2021) Higher Proportion of Non-Classical and Intermediate Monocytes in Newly Diagnosed Multiple Myeloma Patients in Egypt: A Possible Prognostic Marker. Afr J Lab Med. 10:129. https://doi.org/10.4102/ajlm.v10i1.1296 . Wang Y, Feng W, Liu P (2020) Genotype-immunophenotype analysis reveals the immunogenomic subtype and prognosis of multiple myeloma. Carcinogenesis. 41:1746–1754. https://doi.org/10.1093/carcin/bgaa037 . Ho M, McCarthy L, Wallace K, et al (2017) Immune signatures associated with improved progression-free and overall survival for myeloma patients treated with AHSCT. Blood Adv. 1: 1056–1066. https://doi.org/10.1182/bloodadvances.2017005447 . Munshi NC, Avet-Loiseau H, Anderson KC, et al (2020) A large meta-analysis establishes the role of MRD negativity in long-term survival outcomes in patients with multiple myeloma. Blood Adv. 4:5988–5999. https://doi.org/10.1182/bloodadvances.2020002827 . Costa LJ, Derman BA, Bal S, et al (2021) International harmonization in performing and reporting minimal residual disease assessment in multiple myeloma trials. Leukemia. 35:18–30. https://doi.org/10.1038/s41375-020-01012-4 . Gupta R, Kumar L, Dahiya M, et al (2017) Minimal residual disease evaluation in autologous stem cell transplantation recipients with multiple myeloma. Leuk Lymphoma. 58:1234–1237. https://doi.org/10.1080/10428194.2016.1228930 . Shi L, Qin X, Wang H, et al (2017) Elevated neutrophil-to-lymphocyte ratio and monocyte-to-lymphocyte ratio and decreased platelet-to-lymphocyte ratio are associated with poor prognosis in multiple myeloma. Oncotarget. 8:18792–18801. https://doi.org/10.18632/oncotarget.13320 . Solmaz Medeni S, Acar C, Olgun A, et al (2018) Can Neutrophil-to-Lymphocyte Ratio, Monocyte-to-Lymphocyte Ratio, and Platelet-to-Lymphocyte Ratio at Day + 100 be used as a prognostic marker in Multiple Myeloma patients with autologous transplantation? Clin Transplant. 32: e13359. https://doi.org/10.1111/ctr.13359 . Bakeer M, Zubair AC, Roy V (2020) Low baseline platelet count predicts poor response to plerixafor in patients with multiple myeloma undergoing autologous stem cell mobilization. Cytotherapy. 22: 16–20. https://doi.org/10.1016/j.jcyt.2019.10.008 . Szudy-Szczyrek A, Mlak R, Mielnik M, et al (2020) Prognostic value of pretreatment neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios in multiple myeloma patients treated with thalidomide-based regimen. Ann Hematol. 99: 2881–2891. https://doi.org/10.1007/s00277-020-04092-5 . Tirier S M, Mallm J-P, Steiger S, et al (2021) Subclone-Specific Microenvironmental Impact and Drug Response in Refractory Multiple Myeloma Revealed by Single-cell Transcriptomics. Nat Commun. 12: 6960. https://doi.org/10.1038/s41467-021-26951-z . Zaghi E, Calvi M, Di Vito C, et al (2019) Innate Immune Responses in the Outcome of Haploidentical Hematopoietic Stem Cell Transplantation to Cure Hematologic Malignancies. Front Immunol. 10: 2794. https://doi.org/10.3389/fimmu.2019.02794 . Verheye E, Bravo Melgar J, Deschoemaeker S, et al (2022) Dendritic Cell-Based Immunotherapy in Multiple Myeloma: Challenges, Opportunities, and Future Directions. Int J Mol Sci. 23: 904. https://doi.org/10.3390/ijms23020904 . Lee H W, Chung Y S, Kim T J (2020) Heterogeneity of Human γδ T Cells and Their Role in Cancer Immunity. Immune Netw. 20:e5. https://doi.org/10.4110/in.2020.20.e5 . Suzuki K, Nishiwaki K, Yano S (2021) Treatment Strategy for Multiple Myeloma to Improve Immunological Environment and Maintain MRD Negativity. Cancers (Basel). 13: 4867. https://doi.org/10.3390/cancers13194867 . Chung D J, Pronschinske K B, Shyer J A, et al (2016) T-Cell Exhaustion in Multiple Myeloma Relapse after Autotransplant: Optimal Timing of Immunotherapy. Cancer Immunol Res. 4: 61–71. https://doi.org/10.1158/2326-6066.CIR-15-0055 . Duchemann B, Naigeon M, Auclin E, et al (2022) CD8 + PD-1 + to CD4 + PD-1 + Ratio (PERLS) Is Associated with Prognosis of Patients with Advanced NSCLC Treated with PD(L)1 Blockers. J Immunother Cancer. 10: e004012. https://doi.org/10.1136/jitc-2021-004012 . Zhaoyun L, Rong F (2021) Predictive Role of Immune Profiling for Survival of Multiple Myeloma Patients. Front Immunol. 12:663748. https://doi.org/10.3389/fimmu.2021.663748 . Schriek P, Ching AC, Moily NS, et al (2022) Marginal zone B cells acquire dendritic cell functions by trogocytosis. Science. 375: eabf7470. https://doi.org/10.1126/science.abf7470 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4804600","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":347782269,"identity":"36f0a185-54b9-48ae-88dc-dc7a11669bb1","order_by":0,"name":"Jiang Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Jiang","middleName":"","lastName":"Zhang","suffix":""},{"id":347782270,"identity":"3a748aed-0e80-4220-be85-0fb85184c7a5","order_by":1,"name":"Yao Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Chen","suffix":""},{"id":347782271,"identity":"8c2d97cf-2e50-4cc4-9266-3d4a71d23c28","order_by":2,"name":"Yaoming Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yaoming","middleName":"","lastName":"Chen","suffix":""},{"id":347782272,"identity":"1fc5bc45-54e5-4d21-ab06-222bf641f5a1","order_by":3,"name":"Mengzhi Hong","email":"","orcid":"","institution":"The First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Mengzhi","middleName":"","lastName":"Hong","suffix":""},{"id":347782273,"identity":"ae52f27a-152d-4de2-8440-984acb8b8dc2","order_by":4,"name":"Peisong Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Peisong","middleName":"","lastName":"Chen","suffix":""},{"id":347782274,"identity":"63a7e345-4374-4b73-bb1f-814732399cbc","order_by":5,"name":"Junxun Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Junxun","middleName":"","lastName":"Li","suffix":""},{"id":347782275,"identity":"a543aec4-5b7d-427a-b883-3ca04490999d","order_by":6,"name":"Juan Ouyang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYPACNjl+IHngAYzPQ4QWY8kGoJYEErQwJG44ACSJ0sIvkWP4ueAXH+Pma4cfAm2pS5w/I4Hxwds2BnlzHFokZ+QYS8/sY2M2u51mANRyOHHDjQRmw7ltDIY7G7BrMbiRYyDN28PGZnY7AaTlQOIGiQQ2ad42BiAXpxbj30AtPMaz0z/AHMb+m4AWM2meH2wSBtI5IFuYExtuJLAx49Mi2fOszJq3gc1A4nZOwYEEg8PGG848bJacc07CcAMOLfzsyZtv8/w5Vt8/O33zhw8VdbLz25MPfnhTZiOPyxYGBg4DBsa2YzB3Mjg2MDA2AFkSuNQDAfsDBoY/NXCuPR6lo2AUjIJRMEIBAMVfXovrVJnHAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Juan","middleName":"","lastName":"Ouyang","suffix":""}],"badges":[],"createdAt":"2024-07-26 01:21:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4804600/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4804600/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64308837,"identity":"04065716-8a22-4e40-82ed-e747ec349db3","added_by":"auto","created_at":"2024-09-11 13:08:51","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38029,"visible":true,"origin":"","legend":"\u003cp\u003e(A)\u003cstrong\u003e \u003c/strong\u003eThe correlation of MRD status with OS in MM patients. (B) The correlation of MRD status with PFS in MM patients\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4804600/v1/b91621e22004cfc9523bb8fd.jpg"},{"id":64307432,"identity":"63b26a56-c8ec-47d1-8cc6-cd1e067b215a","added_by":"auto","created_at":"2024-09-11 13:00:51","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60948,"visible":true,"origin":"","legend":"\u003cp\u003e(A) LASSO coefficient profiles of the 25 variables. (B) Ten-time cross-validation for tuning parameter selection in the LASSO model\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Using LASSO regression analysis and ten-time cross-validation, we screened out 3 variables (NEU, PLT, and LMR before ASCT) that were closely related to MRD positive risk from 25 recorded variables.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4804600/v1/e366040f69e31d1c6fda98a1.jpg"},{"id":64308836,"identity":"da0dce93-b28d-4744-9460-78105e31169f","added_by":"auto","created_at":"2024-09-11 13:08:51","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":27400,"visible":true,"origin":"","legend":"\u003cp\u003eNomograms predicting the risk of MRD positivity in MM patients\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4804600/v1/5d347767a4df679c232803ef.jpg"},{"id":64307428,"identity":"184a98f9-940d-4a09-ab24-4fc566491eee","added_by":"auto","created_at":"2024-09-11 13:00:51","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":45349,"visible":true,"origin":"","legend":"\u003cp\u003e(A) ROC curve of this prognostic model. (B) The calibration curve depicts the calibration of each model in terms of agreement between predicted and observed probability\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4804600/v1/7ff78dedfe3f9d708b1140bd.jpg"},{"id":64309541,"identity":"77e1778e-0e31-4520-858d-51fb4ba21675","added_by":"auto","created_at":"2024-09-11 13:16:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1026910,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4804600/v1/f9620f49-0244-4788-8026-486258faf1e0.pdf"},{"id":64307430,"identity":"58d75d78-d9a6-4da6-9ac6-58a3f7448cfc","added_by":"auto","created_at":"2024-09-11 13:00:51","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":38552,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-4804600/v1/7e77f1ed67daf35cf5ae1ba7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The prognostic value of combined CBC and immune cell profiles in patients with multiple myeloma treated with PAD sequential transplantation","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMultiple myeloma (MM) is an incurable hematological disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Thanks to recent therapeutic advances, which include proteasome inhibitors and immunomodulatory drugs, many MM patients can be clinically complete response (CR). However, most of these CR patients would eventually relapse due to the persistence of myeloma cells [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As a new therapeutic endpoint, measurable residual disease (MRD) reflects the tumor burden of the disease, which can be timely and effectively treated to reduce the risk of disease progression. MRD is a prognostic factor independent of the clinical stages and genetic background of MM patients[\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The immune system is also of great importance for understanding the control of MM. Immune cells such as lymphocytes, neutrophils, and monocytes are reported to be associated with the prognosis of MM patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It has been shown that analysis of immune cells before and after treatment in MM patients can identify the immune phenotype associated with disease improvement. It has been reported to correlate outcomes in multiple myeloma patients after ASCT has involved a small subset of immune cells [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Considering the large number of subsets of immune cells and the fine-grained nature of their function, a comprehensive immune cell profile assessment in MM patients is beneficial to analyze the disease progression and prognosis.\u003c/p\u003e \u003cp\u003eIn this study, we screened for parameters associated with complete blood count (CBC) that were associated with prognostic risk in multiple myeloma patients. At the same time, we focused on the simultaneous monitoring of immune cell profiles in MM patients before and after ASCT to explore indicators of a good prognosis.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and study design\u003c/h2\u003e \u003cp\u003eWe collected 93 patients who were diagnosed with multiple myeloma in the first affiliated hospital of Sun Yat-sen University from April 2016 to December 2019. The treatment included bortezomib, adriamycin, and dexamethasone (PAD) induction followed by autologous transplant (ASCT), consolidation, and maintenance. All 93 patients underwent complete blood cell counting using BC-6800 Plus fully automated hematology analyzer (Mindray, Shenzhen, China). We collected CBC data at diagnosis, before ASCT, and 3 months after ASCT. MRD analysis was performed on BM aspirates that were collected at different time points: before ASCT, after ASCT at 3 to 12 months, and every 3 months at a time. Response to treatment was accessed according to the IMWG criteria, which classified as stringent complete response (sCR), complete response (CR), very good partial response (VGPR), partial response (PR), stable disease (SD), and progressive disease (PD). In this study, stringent complete response, complete response, very good partial response, partial response, stable disease are seemed disease control (DC).\u003c/p\u003e \u003cp\u003eThe patients were divided into the MRD positive group and MRD negative group according to the results of MRD analysis with multi-parameter flow cytometry three months after ASCT. The clinical data of patients including sex, age, revised international staging system (RISS) stage, Durie-Salmon (DS) stage, and time of progression were collected. Time to progression and time to death were obtained by hospital or telephone follow-up. Achieving MRD negativity at three months post-ASCT is considered early MRD negativity after ASCT.\u003c/p\u003e \u003cp\u003eWe also detected immune cells in fresh peripheral blood (PB) samples before ASCT and 3 months after ASCT from 33 out of the 93 enrolled patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eFlow cytometric immunophenotyping (FCI) Of MRD\u003c/h2\u003e \u003cp\u003eBM aspirate samples were collected in EDTA-anticoagulant and processed within 24 hr of collection. An optimized 8-color, 2-tube antibody panel including cKappa, cLambda, CD81, CD56, CD138, CD19, CD38, and CD45 was used for accurate identification of phenotypically aberrant, clonal plasma cells (PCs). The 2-tube strategy allows for the detection of MRD with specific confirmation of light-chain (mono)clonality on phenotypically aberrant PCs, identified by antigen underexpression (CD19, CD38, CD45, and CD81) or overexpression (CD56 and CD138), as compared with normal PCs. Data acquisition was performed on a FACSCanto plus flow cytometer (Becton Dickinson) which was standardized daily using CS\u0026amp;T beads. Data analysis was performed with Kaluza software (Beckman Coulter). At least 1,000,000 live events were acquired to achieve a potential sensitivity of at least 2\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e (0.002%). The number of viable nucleated cells was systematically registered, and the limit of detection (LOD) was determined in each sample according to the formula (20/viable nucleated cells) \u0026times;100%. Patients were thought to have detectable MRD whenever the percentage of phenotypically aberrant clonal PCs was equal to or greater than the LOD achieved in the corresponding sample. Conversely, patients were considered to have undetectable MRD when phenotypically aberrant clonal PCs were either absent or present at percentages below the LOD achieved in the corresponding sample. Instrument alignments, sensitivities, and spectral compensation were verified by standards, calibrators, procedural controls, and normal peripheral blood samples prior to processing of patient samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFCI of Immune cells profile\u003c/h2\u003e \u003cp\u003eFresh PB samples before ASCT mobilization and three months after ASCT (median of 101 days) were taken for flow cytometry analysis of immune cell subsets. We adopted the DURAClone IM immune function reagent (Beckman Headquarters, Brea, CA, USA) that included 50 antibodies distributed in six tubes to identify a total of 66 circulating immune cell subsets and detailed names of each cell subsets are shown in Online Resource Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAll samples were measured with 10-color, three-laser Navios flow cytometers, as data files were analyzed with Kaluza software (Beckman Coulter).\u003c/p\u003e \u003cp\u003eFlow cytometry analyzes the relative counts of immune cells, matches the data with the corresponding CBC indicators from the same blood sample, and calculates the absolute count of immune cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical methods\u003c/h2\u003e \u003cp\u003eSPSS 25.0 software and R-4.1.2 software were used for statistical analysis. Single Sample Kolmogorov-Smirnov (K-S) Test was used to judge the normal distribution. The measurement data of normal distribution were expressed as \u003cem\u003eX\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e, and the T-test was used to compare the two groups. The non-normal distribution data were expressed by \u003cem\u003eM\u003c/em\u003e (IQR), the non-parametric rank sum test was used for comparison between two groups, the count data were expressed by case number and percentage, and the comparison among multiple groups was analyzed by \u003cem\u003eX\u0026sup2;\u003c/em\u003e test. Variables that can independently affect PFS were analyzed by the COX proportional hazard regression model. \u003cem\u003eP\u003c/em\u003e-values in this text were obtained by the two-sided exact method, at the 5% significance level. The parameters of CBC were screened by LASSO regression with Glmnet. The indexes with a coefficient not equal to 0 were used to construct the Risk score model of prognosis, to evaluate the predictive value of CBC parameters for MRD status. The DxAI Intelligent Research Platform was used to perform statistical analysis of immune cell detection data, the subtyped variables were tested using the Fisher exact probability method and \u003cem\u003eX\u0026sup2;\u003c/em\u003e, and numeric variables were tested using independent sample t-test and Wilcoxon independent anecdotal test. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003ePatients\u0026rsquo; characteristics\u003c/h2\u003e\n\u003cp\u003eNinety-three patients were enrolled in our study, including 41 women and 52 men, with 37 MRD-positive patients and 56 MRD-negative patients after ASCT. According to the Revised International Staging System (RISS) at initial diagnosis, 23 patients (24.7%) were stage I, 53 patients (57.0%) were stage II, and 17 patients (18.3%) were stage III. According to the Durie-Salmon (DS) staging, 21 patients (22.6%) were stage IA, 12 patients (12.9%) were stage IB, 13 patients (14.0%) were stage IIA, 7 patients (7.5%) were stage IIB, 21 patients (22.6%) were stage IIIA, and 19 patients (20.4%) were stage IIIB. Age, sex, \u0026beta;\u003csub\u003e2\u003c/sub\u003e-microglobulin (\u0026beta;\u003csub\u003e2\u003c/sub\u003eMG), hemoglobin (Hb), creatinine (CREA), lactate dehydrogenase (LDH), serum calcium (Ca), serum albumin (ALB), RISS stage, and DS stage were not significantly associated with MRD status after ASCT ( \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePatient demographics and baseline characteristics.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMRD+\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMRD-\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.0 (49.5, 59.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.0 (46.3, 60.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.734\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMen vs women (n, %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24 (64.9) vs 13 (35.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (50.0) vs 28 (50.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.158\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDisease stage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRISS (n, %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.874\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eⅠ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (21.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (26.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eⅡ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24 (64.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29 (51.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eⅢ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (13.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (21.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDS (n, %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.450\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eⅠA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (21.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (24.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eⅠB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (16.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (8.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eⅡA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (17.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (8.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eⅡB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (5.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (10.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eⅢA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (21.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (24.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eⅢB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (17.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (24.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePeripheral blood\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHb (g/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102.0 (80.5, 115.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97.5 (80.8, 114.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.371\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePLT (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e189.08\u0026thinsp;\u0026plusmn;\u0026thinsp;69.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e221.00\u0026thinsp;\u0026plusmn;\u0026thinsp;87.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.067\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026beta;\u003csub\u003e2\u003c/sub\u003eMG (mg/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3712.8 (2392.3, 6114.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4043.3 (2272.8, 8150.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.672\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCREA (\u0026micro;mol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84.0 (64.5, 129.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81.0 (65.0, 165.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.721\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLDH (U/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e186.0 (155.5, 230.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e166.5 (121.5, 215.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.110\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCa (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.3 (2.1, 2.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.3 (2.2, 2.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.680\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALB (g/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.725\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePrognosis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePFS (month)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.2 (15.9, 43.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35.8 (27.4, 47.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOS (month)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.5 (21.5, 45.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.3 (29.4, 47.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eDS, Durie-Salmon; RISS, Revised International Staging System; IQR, interquartile range.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eImpact of MRD status on OS and PFS\u003c/h2\u003e\n\u003cp\u003eThe MM patients included in our study achieved very good partial response (VGPR) after PAD chemotherapy, and there was no statistical association between MRD status before ASCT transplantation and disease progression.\u003c/p\u003e\n\u003cp\u003eOS and PFS were significantly associated with MRD status after ASCT (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The OS of the MRD positive group (median 32.5 months, range 21.5\u0026ndash;45.2) was significantly lower than that of the MRD negative group (median 39.3 months, range 29.4\u0026ndash;47.6) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Likewise, the PFS of the MRD positive group (median 25.2 months, range 15.9\u0026ndash;43.8) was significantly lower than that of the MRD negative group (median 35.8 months, range 27.4\u0026ndash;47.1) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eCBC Parameters closely related to MRD status\u003c/h2\u003e\n\u003cp\u003eLASSO regression analysis (Figure 2A) and ten-time cross-validation (Figure 2B) were performed on 25 variables including biochemical parameters, CBC parameters, and their derived parameters, and 3 variables were selected which were closely related to MRD status, that is, neutrophil count (NEU), platelet count (PLT), and lymphocyte monocyte ratio (LMR) at diagnosis. Lower levels of NEU or PLT, and higher levels of LMR within a certain range indicate an increased risk of MRD positivity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eConstruction and evaluation of CBC prognostic model for MM patients\u003c/h2\u003e\n\u003cp\u003eBased on the variables screened by LASSO regression, we constructed a predictive model for predicting the risk of MRD positivity in patients with MM (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). According to the results of NEU, PLT, and LMR at diagnosis, the risk of early MRD positivity after ASCT can be calculated by Nomogram. The receiver operating characteristic (ROC) curve of the model predicting MRD positive risk had excellent \u003cem\u003eAUC\u003c/em\u003e (\u003cem\u003eAUC\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.699, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). The results of the discriminant test showed that the predictive model had good prognostic accuracy with a C-index of 0.723. The results of model calibration evaluation show that the calibrated curve is in good agreement with the ideal curve (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eImmune cell files before ASCT mobilization\u003c/h2\u003e\n\u003cp\u003eThe counts of CD4/CD8 double-negative T cells (DNTs), regulatory T cells (Tregs), and CD16\u003csup\u003e+\u003c/sup\u003e CD56\u003csup\u003ehigh\u003c/sup\u003e NK cells in MRD-negative patients were significantly lower than those in MRD-positive patients before ASCT (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The counts were (0.015\u0026thinsp;\u0026plusmn;\u0026thinsp;0.014) \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L vs. (0.032\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012) \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L for DNTs, (0.012\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010) \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L vs. (0.040\u0026thinsp;\u0026plusmn;\u0026thinsp;0.019)\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L for Tregs and the percentages were 0.240% (0.122%, 0.502%) vs. 1.970% (0.660%, 3.790%) for CD16\u003csup\u003e+\u003c/sup\u003e CD56\u003csup\u003ehigh\u003c/sup\u003e NK cells, respectively.\u003c/p\u003e\n\u003cp\u003eDisease progression was significantly correlated with a higher count of PD1\u003csup\u003e+\u003c/sup\u003eT4CM and CD8\u003csup\u003e+\u003c/sup\u003e HLA-DR\u003csup\u003e+\u003c/sup\u003e T cells in MM patients before ASCT. The count of PD1\u003csup\u003e+\u003c/sup\u003eT4CM and CD8\u003csup\u003e+\u003c/sup\u003e HLA-DR\u003csup\u003e+\u003c/sup\u003e T cells in the progressive group was significantly higher than that in the no-progression patient group (PD1\u003csup\u003e+\u003c/sup\u003eT4CM: (48.966\u0026thinsp;\u0026plusmn;\u0026thinsp;22.905)% vs. (28.126\u0026thinsp;\u0026plusmn;\u0026thinsp;12.354)%, \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e; CD8\u003csup\u003e+\u003c/sup\u003e HLA-DR\u003csup\u003e+\u003c/sup\u003e T cells: (68.392\u0026thinsp;\u0026plusmn;\u0026thinsp;13.164)% vs. (84.016\u0026thinsp;\u0026plusmn;\u0026thinsp;11.822)%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, the count of naive CD8\u003csup\u003e+\u003c/sup\u003e T cells and naive CD8\u003csup\u003e+\u003c/sup\u003e CD28\u003csup\u003e+\u003c/sup\u003e CD27\u003csup\u003e+\u003c/sup\u003e T cells were both lower in the disease progression group compared to those in the no-progression group (naive CD8\u003csup\u003e+\u003c/sup\u003e T cells: 4.640% (2.390\u0026ndash;5.750) % vs. 12.070% (7.000-23.200) %, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; naive CD8\u003csup\u003e+\u003c/sup\u003e CD28\u003csup\u003e+\u003c/sup\u003e CD27\u003csup\u003e+\u003c/sup\u003e T cells: (88.514\u0026thinsp;\u0026plusmn;\u0026thinsp;8.544) % vs. (95.944\u0026thinsp;\u0026plusmn;\u0026thinsp;2.787) %, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The immune cell files before ASCT are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMost relevant immune cell subsets predicting MRD status or prognosis before ASCT in MM patients\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eImmune cells before ASCT\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePrognosis\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMRD status before ASCT\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDisease control\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eProgressive disease\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDN T cells\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.025\u0026thinsp;\u0026plusmn;\u0026thinsp;0.016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.025\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.997\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015\u0026thinsp;\u0026plusmn;\u0026thinsp;0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.032\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008 **\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNK Cells\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.262\u0026thinsp;\u0026plusmn;\u0026thinsp;5.949\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.814\u0026thinsp;\u0026plusmn;\u0026thinsp;5.953\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.093\u0026thinsp;\u0026plusmn;\u0026thinsp;5.826\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.013\u0026thinsp;\u0026plusmn;\u0026thinsp;4.054\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004 **\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD16\u0026thinsp;+\u0026thinsp;CD56high NK\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.660\u003c/p\u003e\n\u003cp\u003e(0.260, 1.970)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.795\u003c/p\u003e\n\u003cp\u003e(0.287, 4.645)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.374\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.240\u003c/p\u003e\n\u003cp\u003e(0.122, 0.502)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.970\u003c/p\u003e\n\u003cp\u003e(0.660, 3.790)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006 **\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT4CM PD1+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.126\u0026thinsp;\u0026plusmn;\u0026thinsp;12.354\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.966\u0026thinsp;\u0026plusmn;\u0026thinsp;22.905\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.044 *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.893\u0026thinsp;\u0026plusmn;\u0026thinsp;9.672\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42.482\u0026thinsp;\u0026plusmn;\u0026thinsp;28.314\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.330\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD8\u0026thinsp;+\u0026thinsp;CD57+'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.122\u0026thinsp;\u0026plusmn;\u0026thinsp;0.079\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.138\u0026thinsp;\u0026plusmn;\u0026thinsp;0.066\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.079\u0026thinsp;\u0026plusmn;\u0026thinsp;0.060\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.158\u0026thinsp;\u0026plusmn;\u0026thinsp;0.065\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011 *\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT8N\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.070\u003c/p\u003e\n\u003cp\u003e(7.000-23.200)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.640\u003c/p\u003e\n\u003cp\u003e(2.390\u0026ndash;5.750)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.029 *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.940\u003c/p\u003e\n\u003cp\u003e(8.090, 26.070)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.865\u003c/p\u003e\n\u003cp\u003e(4.768, 6.835)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002 **\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT8CM PD1+'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.016\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.442\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.018\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008 **\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT8N CD28\u0026thinsp;+\u0026thinsp;CD27+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95.944\u0026thinsp;\u0026plusmn;\u0026thinsp;2.787\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88.514\u0026thinsp;\u0026plusmn;\u0026thinsp;8.544\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.030 *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93.120\u0026thinsp;\u0026plusmn;\u0026thinsp;2.608\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96.200\u0026thinsp;\u0026plusmn;\u0026thinsp;1.185\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003 **\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT8CM CD28\u0026thinsp;+\u0026thinsp;CD27-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.983\u0026thinsp;\u0026plusmn;\u0026thinsp;2.894\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.712\u0026thinsp;\u0026plusmn;\u0026thinsp;1.104\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.048 *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.660\u003c/p\u003e\n\u003cp\u003e(3.027, 8.227)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.300\u003c/p\u003e\n\u003cp\u003e(2.590, 3.730)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.121\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD8\u0026thinsp;+\u0026thinsp;HLA-DR+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68.392\u0026thinsp;\u0026plusmn;\u0026thinsp;13.164\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84.016\u0026thinsp;\u0026plusmn;\u0026thinsp;11.822\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.048 *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.508\u0026thinsp;\u0026plusmn;\u0026thinsp;13.292\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.260\u0026thinsp;\u0026plusmn;\u0026thinsp;18.908\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.940\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT8E CD28- CD27-'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.089\u0026thinsp;\u0026plusmn;\u0026thinsp;0.075\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.094\u0026thinsp;\u0026plusmn;\u0026thinsp;0.054\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.853\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.044\u003c/p\u003e\n\u003cp\u003e(0.019, 0.061)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.104\u003c/p\u003e\n\u003cp\u003e(0.063, 0.177)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.016 *\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTreg'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.029\u0026thinsp;\u0026plusmn;\u0026thinsp;0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.029\u0026thinsp;\u0026plusmn;\u0026thinsp;0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.964\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.012\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.040\u0026thinsp;\u0026plusmn;\u0026thinsp;0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001 **\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMemory Treg'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.025\u0026thinsp;\u0026plusmn;\u0026thinsp;0.018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.027\u0026thinsp;\u0026plusmn;\u0026thinsp;0.024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.831\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.037\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001 **\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epDCs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.070\u003c/p\u003e\n\u003cp\u003e(0.050, 0.080)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.120\u003c/p\u003e\n\u003cp\u003e(0.110, 0.155)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.010 **\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.090\u003c/p\u003e\n\u003cp\u003e(0.062, 0.150)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.090\u003c/p\u003e\n\u003cp\u003e(0.080, 0.120)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.913\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation; median(P\u003csub\u003e25\u003c/sub\u003e-P\u003csub\u003e75\u003c/sub\u003e); variable (%); variable'(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eImmune cell files after ASCT\u003c/h2\u003e\n\u003cp\u003eHigher levels of activated and depleted T lymphocytes were significantly associated with disease progression. HLA-DR\u003csup\u003e+\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T cell count in the progressive group was higher than that in the no-progression patient group ((76.610\u0026thinsp;\u0026plusmn;\u0026thinsp;17.384) % vs. (59.667\u0026thinsp;\u0026plusmn;\u0026thinsp;10.372) %, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The count of T4CM cells at 3 months after ASCT was significantly lower in the disease progression group ((38.204\u0026thinsp;\u0026plusmn;\u0026thinsp;14.839) % vs. (53.315\u0026thinsp;\u0026plusmn;\u0026thinsp;8.565) %, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Whereas, a higher level of CD4\u003csup\u003e+\u003c/sup\u003e effector memory T cells (T4EMs) after ASCT was found in the disease progression group ((59.152\u0026thinsp;\u0026plusmn;\u0026thinsp;15.792) % vs. (42.778\u0026thinsp;\u0026plusmn;\u0026thinsp;6.859) %, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). After ASCT, the ratio of (naive T cells\u0026thinsp;+\u0026thinsp;TCMs / TEMs\u0026thinsp;+\u0026thinsp;effector T cells) was higher in progression-free patients than in patients with advanced disease (0.489\u0026thinsp;\u0026plusmn;\u0026thinsp;0.085 vs. 0.338\u0026thinsp;\u0026plusmn;\u0026thinsp;0.176, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eThe levels of \u0026gamma;\u0026delta; T cells were higher in progression-free patients than in patients with advanced disease (0.052% (0.043%, 0.117%) vs. 0.019% (0.007%, 0.032%), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eA lower level of marginal zone B cells was found in the disease progression group compared to that in the no-progression group ((1.586\u0026thinsp;\u0026plusmn;\u0026thinsp;1.428) vs. (2.800\u0026thinsp;\u0026plusmn;\u0026thinsp;0.760) %, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The immune cell files after ASCT are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMost relevant immune cell subsets predicting MRD status or prognosis after ASCT in MM patients\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eImmune cells after ASCT\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePrognosis\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMRD status after ASCT\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDisease control\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eProgressive disease\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGranulocytes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.776\u0026thinsp;\u0026plusmn;\u0026thinsp;10.376\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55.924\u0026thinsp;\u0026plusmn;\u0026thinsp;22.530\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.291\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44.381\u0026thinsp;\u0026plusmn;\u0026thinsp;10.883\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.998\u0026thinsp;\u0026plusmn;\u0026thinsp;19.213\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.030 *\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLymphocytes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40.150\u0026thinsp;\u0026plusmn;\u0026thinsp;12.252\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.456\u0026thinsp;\u0026plusmn;\u0026thinsp;19.365\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.303\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42.426\u0026thinsp;\u0026plusmn;\u0026thinsp;11.987\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.210\u0026thinsp;\u0026plusmn;\u0026thinsp;13.737\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.018 *\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003etransitional B cell\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.106\u0026thinsp;\u0026plusmn;\u0026thinsp;8.103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.606\u0026thinsp;\u0026plusmn;\u0026thinsp;6.481\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.010 *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.594\u0026thinsp;\u0026plusmn;\u0026thinsp;10.284\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.367\u0026thinsp;\u0026plusmn;\u0026thinsp;7.397\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.419\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emarginal zone B cell\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.800\u0026thinsp;\u0026plusmn;\u0026thinsp;0.760\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.586\u0026thinsp;\u0026plusmn;\u0026thinsp;1.428\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.048 *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.661\u0026thinsp;\u0026plusmn;\u0026thinsp;0.930\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.390\u0026thinsp;\u0026plusmn;\u0026thinsp;1.137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.050 *\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD16\u0026thinsp;+\u0026thinsp;CD56high NK\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.855\u0026thinsp;\u0026plusmn;\u0026thinsp;4.755\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.786\u0026thinsp;\u0026plusmn;\u0026thinsp;3.762\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.978\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.945\u003c/p\u003e\n\u003cp\u003e(0.665, 1.805)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.285\u003c/p\u003e\n\u003cp\u003e(3.865, 10.928)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.024 *\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD3\u0026thinsp;+\u0026thinsp;T cells'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.533\u0026thinsp;\u0026plusmn;\u0026thinsp;1.261\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.917\u0026thinsp;\u0026plusmn;\u0026thinsp;0.701\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.333\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.225\u003c/p\u003e\n\u003cp\u003e(1.030, 1.913)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.744\u003c/p\u003e\n\u003cp\u003e(0.471, 0.882)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.014 *\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD4\u0026thinsp;+\u0026thinsp;HLA-DR+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.667\u0026thinsp;\u0026plusmn;\u0026thinsp;10.372\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76.610\u0026thinsp;\u0026plusmn;\u0026thinsp;17.384\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.033 *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65.642\u0026thinsp;\u0026plusmn;\u0026thinsp;15.168\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67.875\u0026thinsp;\u0026plusmn;\u0026thinsp;16.867\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.813\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT4CM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.315\u0026thinsp;\u0026plusmn;\u0026thinsp;8.565\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38.204\u0026thinsp;\u0026plusmn;\u0026thinsp;14.839\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.025 *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47.048\u0026thinsp;\u0026plusmn;\u0026thinsp;11.512\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47.657\u0026thinsp;\u0026plusmn;\u0026thinsp;17.072\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.939\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT4CM CD28\u0026thinsp;+\u0026thinsp;CD27-'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.016\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.022 *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.014\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.209\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT8 CD28\u0026thinsp;+\u0026thinsp;CD27+'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.493\u0026thinsp;\u0026plusmn;\u0026thinsp;0.847\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.181\u0026thinsp;\u0026plusmn;\u0026thinsp;0.194\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.437\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.280\u003c/p\u003e\n\u003cp\u003e(0.189, 0.419)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.100\u003c/p\u003e\n\u003cp\u003e(0.075, 0.113)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.024 *\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT8EM CD28-CD27-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35.709\u0026thinsp;\u0026plusmn;\u0026thinsp;13.534\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44.704\u0026thinsp;\u0026plusmn;\u0026thinsp;11.293\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.225\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.705\u0026thinsp;\u0026plusmn;\u0026thinsp;12.886\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50.180\u0026thinsp;\u0026plusmn;\u0026thinsp;8.426\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.049 *\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD3\u0026thinsp;+\u0026thinsp;TCR \u0026gamma;\u0026delta;'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.073\u0026thinsp;\u0026plusmn;\u0026thinsp;0.076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.065\u0026thinsp;\u0026plusmn;\u0026thinsp;0.071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.845\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.052\u003c/p\u003e\n\u003cp\u003e(0.043, 0.117)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003cp\u003e(0.007, 0.032)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.036 *\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTN\u0026thinsp;+\u0026thinsp;TCM/TEM\u0026thinsp;+\u0026thinsp;TE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.489\u0026thinsp;\u0026plusmn;\u0026thinsp;0.085\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.338\u0026thinsp;\u0026plusmn;\u0026thinsp;0.176\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.040 *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.441\u0026thinsp;\u0026plusmn;\u0026thinsp;0.169\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.416\u0026thinsp;\u0026plusmn;\u0026thinsp;0.119\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.751\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD8\u0026thinsp;+\u0026thinsp;PD1+'/CD4\u0026thinsp;+\u0026thinsp;PD1+'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.144\u0026thinsp;\u0026plusmn;\u0026thinsp;2.342\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.636\u0026thinsp;\u0026plusmn;\u0026thinsp;2.129\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.248\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.197\u003c/p\u003e\n\u003cp\u003e(3.438, 5.245)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.700\u003c/p\u003e\n\u003cp\u003e(1.264, 2.082)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004 **\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\" align=\"left\"\u003e\n\u003cp\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation; median(P\u003csub\u003e25\u003c/sub\u003e-P\u003csub\u003e75\u003c/sub\u003e); variable (%); variable'(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eMRD has important applications in managing MM and has been unequivocally established as a strong prognostic marker [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In Gupta\u0026rsquo;s study, the median PFS of MRD-negative patients and positive patients after ASCT did not reach 100 days and 24 months, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In our study, Early MRD-negative patients have longer PFS than that of MRD-positive patients (36 months vs. 25 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). All these studies showed that MRD-negative patients had longer PFS than MRD-positive patients after ASCT, which illustrated the prognostic value of early MRD detection in MM patients. The MRD assessment has been suggested to improve the sensitivity of response evaluation and has been proposed as a surrogate for PFS in MM.\u003c/p\u003e \u003cp\u003ePrevious studies found that the absolute value of peripheral blood lymphocyte, neutrophil-lymphocyte ratio, LMR, and platelet-lymphocyte ratio could be used as prognostic factors for MM [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Our study showed that lower levels of NEU and PLT and higher levels of LMR at diagnosis had a higher MRD positive risk, which was associated with poor prognosis. Reduced NEU and PLT before starting treatment in MM patients have been shown to be a poor prognostic factor in MM patients. Reduced level of lymphocytes is considered a poor prognostic factor for malignant tumors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In our research, the level of lymphocyte in the MRD-positive group was lower than that in the MRD-negative group after ASCT, while the level of NEU in the MRD-positive group was higher than that in the MRD-negative group. MRD positive group had higher NLR than MRD negative Group after ASCT, suggesting that increased NLR predicts poor clinical outcomes, which is consistent with the literature [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, and \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Studies have shown that elevated LMR is associated with a good prognosis in MM patients [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Our study found that higher LMR at diagnosis was associated with MRD-positive risk and that this value decreased in the MRD-positive group after ASCT, suggesting that a certain number of monocytes are required before ASCT to perform the intrinsic phagocytic and cytotoxic effects, otherwise, the prognosis is poor.\u003c/p\u003e \u003cp\u003eThe immune system plays a key intermediate role in the balance between dormancy and progression of multiple myeloma. NK cells, or natural killer cells, function as a part of the innate immune system and play a critical role in immune surveillance and defense against infected or cancerous cells and are also involved in regulating the activities of T cells, macrophages, and dendritic cells. In addition to cytotoxic NK cells (CD16\u003csup\u003e+\u003c/sup\u003e CD56\u003csup\u003e+\u003c/sup\u003e NK cells, about 90% of the total NK), there is a subpopulation called secretors or immature NK cells (CD16\u003csup\u003e+\u003c/sup\u003e CD56\u003csup\u003ehigh\u003c/sup\u003e NK cells, about 10% of the total NK), which produce various cytokines and chemokines. These molecules help in limiting or exacerbating immune responses, recruiting other immune cells to the site of infection, and modulating the adaptive immune system. Studies have shown that the number of NK cells in the peripheral blood decreases as myeloma progresses, and that expression of immature NK cells increases in patients with relapsed/refractory MM [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In our study, the count of NK cells was lower in the MRD positive group than that in the MRD negative group before ASCT, but higher levels of immature NK cells before and after ASCT were indicative of poor prognosis in MM patients, consistent with existing findings.\u003c/p\u003e \u003cp\u003eDendritic cells (DC) are a kind of antigen-presenting cells, which play an important role in the treatment of MM. As a protease inhibitor drug, bortezomib induces immunogenic cell death by activating DC through increased contact with tumor antigens. DC deficiency has been reported in patients with MM [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In our study, MM progression was significantly associated with a higher level of plasmacytoid DC in MM patients before ASCT. The effects of different types of DC on the prognosis of MM need to be further studied.\u003c/p\u003e \u003cp\u003eIn our study, the number of γδ T cells in MRD-negative patients increased at 3 months after ASCT, which was consistent with existing research findings. Studies have shown that high-frequency γδ T cells were associated with long-term disease-free survival in children and adults [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. γδ T cells showed rapid early reconstruction 2 months after ASCT, and most of the γδ T cells recovered in the first few weeks are derived from the graft's γδ 1 and γδ 2 cell subsets, and all have CD27pos/CD45RAneg central memory phenotype, which helps ensure early protection against viruses, bacteria, and surviving residual tumor cells.\u003c/p\u003e \u003cp\u003eIn addition to γδ T cells, CD4\u003csup\u003e+\u003c/sup\u003e T cell subsets also play a major role in their involvement in anti-tumor effects, such as central memory CD4\u003csup\u003e+\u003c/sup\u003e T cells (T4CM) associated with lymph node homing, DC stimulation, and differentiation into CD4\u0026thinsp;+\u0026thinsp;effector cells. In our study, T4CM cells with higher accounts after ASCT were shown to predict a good prognosis for patients. However, pre-ASCT results showed that some T4CM cell subsets increased in the MRD-positive and disease progression groups, such as the CD28\u003csup\u003e\u0026minus;\u003c/sup\u003eCD27\u003csup\u003e\u0026minus;\u003c/sup\u003eT4CM cells and the PD-1\u003csup\u003e+\u003c/sup\u003eT4CM cells. In our study, Tregs increased in the MRD-positive group before ASCT. The main function of Tregs is to suppress the immune response in the functional homeostasis of the immune system and induce immune tolerance [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs an inhibitory molecule, the expression upregulation of PD-1 is related to T cell depletion, which can hinder the normal differentiation of T cells and prevent cytotoxic effects, thus negatively affecting the prognosis of patients. At the same time, studies have found that the dynamic changes of immune cells caused by treatment, such as ASCT, have an impact on T cell heterogeneity in MM patients, inducing the production of depleted or senescent T cells [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In the CD8\u003csup\u003e+\u003c/sup\u003e effector memory T cell (T8EM) subsets, only CD28\u003csup\u003e\u0026minus;\u003c/sup\u003eCD27\u003csup\u003e\u0026minus;\u003c/sup\u003e T8EM cell count was higher in the post-ASCT MRD-positive group, and other subsets (CD28\u003csup\u003e\u0026minus;\u003c/sup\u003eCD27\u003csup\u003e+\u003c/sup\u003eT8EM and CD28\u003csup\u003e+\u003c/sup\u003e CD27\u003csup\u003e+\u003c/sup\u003eT8EM) were both lower in post-ASCT MRD positive patients. It is worth noting that there are also depleted, aging-related subsets in the T8EM population (PD1\u003csup\u003e+\u003c/sup\u003eT8EM and CD28\u003csup\u003e\u0026minus;\u003c/sup\u003eCD27\u003csup\u003e+\u003c/sup\u003e T8EM), which makes sense with post-transplant MRD status; we speculate that treatment results in T cell heterogeneity. Sustained anti-tumor immunity requires long-term survival of memory CD8\u003csup\u003e+\u003c/sup\u003e T cells to maintain, which may also be the reason for the higher account of T8CM cells in the group of MRD-negative patients after ASCT. The relative atrophy of naive T cell and central memory T cell populations and the increased differentiation of effector memory T cells versus effector T cell populations are termed T cell exhaustion; The exhaustion status of the T cell population was reflected by calculating the ratio of the sum of naive T cells and central memory T cells to the sum of effector memory T cells to effector T cells ((TN\u0026thinsp;+\u0026thinsp;TCM) / (TEM\u0026thinsp;+\u0026thinsp;TE)). The low expression of this ratio after ASCT in patients with progressive disease indicates that T-cell depletion may result in a poor prognosis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study did not conclude that disease progression is associated with the total number of CD19\u003csup\u003e+\u003c/sup\u003e B cells; however, in the analysis of various subsets of B cells, it was found that the lower account of marginal zone B cells after ASCT was found in MM patients with poor prognosis. The latest studies have shown that marginal B cells engulf DC cells to obtain MHC II molecules bound to complement C3 through cell pulverization and present them to CD4\u003csup\u003e+\u003c/sup\u003e T cells as antigens [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. At the same time, MM is essentially a kind of plasma cell clonal proliferation Plasma cells are differentiated from B cells. The mechanism of B cells in the marginal region affecting the prognosis outcome of MM needs to be further studied.\u003c/p\u003e \u003cp\u003eThe diversity of the results of our study showed that MM has the characteristics of significant heterogeneity in immune reconstitution and prognosis. The immune cell status before ASCT after chemotherapy, the immune cell reconstruction, and the MRD status at three months after ASCT have a certain relationship with the prognosis of MM patients. Therefore, regular detection of CBC, MRD along with immune cell profiles before and after ASCT is of great importance to the management of MM patients.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJZ and YC acquired data, interpreted results, and drafted, and revised the manuscript. YMC, MZH, and PSC provided feedback on data analysis and the manuscript. JOY and JXL designed the study, interpreted the results, revised the manuscript, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn accordance with the Declaration of Helsinki, our study was approved by the Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University, and the research group was allowed to participate in the experiment and publish the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the data supporting the findings of this study are available within the paper and its supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eR\u0026ouml;llig C, Knop S, Bornh\u0026auml;user M (2015) Multiple myeloma. 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Science. 375: eabf7470. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.abf7470\u003c/span\u003e\u003cspan address=\"10.1126/science.abf7470\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"multiple myeloma, minimal residual disease, complete blood count, immune cell profiles, autologous stem cell transplantation","lastPublishedDoi":"10.21203/rs.3.rs-4804600/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4804600/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eOur study aimed to analyze the measurable residual disease (MRD), complete blood count (CBC), and immune cell profiles in multiple myeloma (MM) patients treated with bortezomib /adriamycin /dexamethasone (PAD) chemotherapy sequential autologous stem cell transplantation (ASCT) to determine their prognostic value and their interaction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e CBC data from 93 MM patients were collected at diagnosis, before ASCT, and 3 months after ASCT. Immune cell profiles were detected by flow cytometry in fresh peripheral blood (PB) samples from 33 out of the 93 enrolled patients before ASCT and 3 months after ASCT. We then studied the relationship between MRD status and prognosis, the predictive value of CBC, and the changes in immune cell profiles before and after ASCT in multiple myeloma patients and their association with prognosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Early MRD-negative patients after ASCT had significantly longer progression-free survival (PFS) (median PFS was 36 months and 25 months, respectively, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) and overall survival (OS) (median OS was 39 months and 33 months, respectively, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) than MRD-positive patients. Three independent prognostic factors, neutrophil count (NEU), platelet count (PLT), and lymphocyte monocyte ratio (LMR) at diagnosis, were identified in our study group by LASSO regression. For the immune cell profiles, before ASCT, the negative immunomodulatory cell subsets (CD4/CD8 double-negative T cells (DNTs), regulatory T cells (Tregs), CD16\u003csup\u003e+\u003c/sup\u003e CD56\u003csup\u003ehigh\u003c/sup\u003e NK cells), PD1\u003csup\u003e+\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e central memory T cells (PD1\u003csup\u003e+\u003c/sup\u003eT4CM), HLA-DR\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003eT cells were lower in MRD-negative or disease control patients than in MRD-positive or progressive disease patients (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Otherwise, naive CD8\u003csup\u003e+\u003c/sup\u003e T Cells (T8N) and CD28\u003csup\u003e+\u003c/sup\u003e CD27\u003csup\u003e+\u003c/sup\u003e naive CD8\u003csup\u003e+\u003c/sup\u003eT Cells (CD28\u003csup\u003e+\u003c/sup\u003e CD27\u003csup\u003e+ \u003c/sup\u003eT8N) were higher in MRD-negative or disease control patients than in MRD-positive or progressive disease patients (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). After ASCT, the levels of lymphocytes, marginal zone B cells, γδ T cells, and the ratio of (naive T cells plus central memory T cells to effector memory T cells plus effector T cells) were higher in disease control patients than in patients with progressive disease (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e CBC, MRD, and immune cell profile detection before and after ASCT have significant prognostic value in MM patients. Lower levels of NEU or PLT, higher levels of LMR at diagnosis, and a higher number of negative immunomodulatory cell subsets and activated T lymphocytes before ASCT were associated with poor prognosis. On the other hand, lower levels of depleted T lymphocytes, and higher levels of functional T cells and marginal zone B cells after ASCT predicted a good prognosis.\u003c/p\u003e","manuscriptTitle":"The prognostic value of combined CBC and immune cell profiles in patients with multiple myeloma treated with PAD sequential transplantation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-11 13:00:46","doi":"10.21203/rs.3.rs-4804600/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ce4fd6ca-dd0e-4682-984a-136ae8e6b26f","owner":[],"postedDate":"September 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":36897755,"name":"Biological sciences/Cancer"},{"id":36897756,"name":"Biological sciences/Immunology"},{"id":36897757,"name":"Health sciences/Biomarkers"},{"id":36897758,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-09-11T13:00:48+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-11 13:00:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4804600","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4804600","identity":"rs-4804600","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}