Clonal Hematopoiesis Impacts Frailty of Newly Diagnosed Multiple Myeloma Patients: A Retrospective Multicentric Analysis

Clonal Hematopoiesis Impacts Frailty of Newly Diagnosed Multiple Myeloma Patients: A Retrospective Multicentric Analysis | 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 Clonal Hematopoiesis Impacts Frailty of Newly Diagnosed Multiple Myeloma Patients: A Retrospective Multicentric Analysis Elisa Gelli, Claudia Martinuzzi, Debora Soncini, Concetta Conticello, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4930569/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Somatic mutations of hematopoietic cells in peripheral blood of normal individuals refers to clonal hematopoiesis of indeterminate potential (CHIP) and is associated with a 0.5–1% risk of progression to hematological malignancies and cardiovascular diseases. CHIP has been reported also in Multiple Myeloma (MM) patients but its biological relevance remains still to be elucidated. Here, high-depth targeted sequencing on peripheral blood derived from 76 NDMM patients revealed CHIP in 46% of them with a variant allele frequency (VAF) between ~1% and 34%: the most frequently mutated gene was DNMT3A followed by TET2 . A more aggressive disease features were observed among CHIP carriers, which also exhibited more high-risk (ISS and R-ISS 3) stages than controls. Longitudinal analyses at diagnosis and during follow-up showed slight increase of VAFs (p=0.058) for epigenetic ( DNMT3A, TET2 , and ASXL1 ) and DNA repair ( TP53 ) genes (p=0.0123); a more stable frequency was observed among other genes, thus suggesting different temporal dynamics of CH clones. Adverse clinical outcomes, in term of overall and progression-free survivals, were observed among CHIP carriers, who also exhibited immune T-cells weakening and enhanced frailty status that predicted the greater risk of toxicity and consequent shorter event-free survival of this group. Finally, a correlogram analysis identified platelets count as biomarker for higher VAF among CHIP carriers, regardless of specific variant. Overall, our study, by highlighting specific biological and clinical features, paves the way for designing tailored strategies among MM patients carrying CHIP. Health sciences/Oncology/Cancer Health sciences/Oncology/Cancer/Cancer genetics Multiple Myeloma biomarkers CHIP toxicity frailty Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Multiple myeloma (MM) is an incurable malignancy of plasma cells that grow within a permissive bone marrow (BM) microenvironment, supporting tumor cells transformation, proliferation and drug resistance occurrence. 1 During recent years, available therapeutic approaches have shown promising results in clinical setting of MM patients, but unfortunately it still remains incurable due to frequent relapses. 2 In such a context, identification of disease-specific biologic features represents a valid strategy for improving MM clinical management. Genetic-molecular alterations including deletion 17p, t(4;14), t(14;16), t(14;20), amp/gain 1q, del 1p or TP53 mutations and high-risk gene expression profiling signatures are the most robust predictors of outcomes in MM with their combination conferring an even worse prognosis. 3 However, although the identification of these features is crucial for prognosis and selection of the most appropriate strategy, aging, defined as impaired organ function and reduced physiological reserves, adds greater complexity that makes patients stratification extremely challenging. 4 During recent years, several tools have been developed for a comprehensive assessment of MM patients’ frailty; however none of available biomarkers is currently able to effectively prevent relapse, reduce mortality, and ultimately cure this blood cancer. More recent studies based on next-generation sequencing (NGS) have shown the presence of recurrent somatic mutations in blood of healthy adults, a condition referred as clonal hematopoiesis of indeterminate potential (CHIP). 5 Remarkably, presence of somatic mosaicism in tissues during aging, is quite ubiquitous, but when the acquired variant confers growth advantage, mutant undergoes to clonal expansion. Somatic variants influencing cell fitness may occur in every tissue, but the wide availability of blood for serial genomic studies coupled with the blood circulation and interaction with all the other tissues, makes CHIP interesting for its clinical consequences. 6 Mutations driving the clonal expansion mostly involves leukemia-associated drivers, with DNMT3A, TET2 and ASXL1 being the most affected genes, followed by JAK2, TP53 and a large list of other genes less frequently mutated, but still attractive as study subjects for their contribution to fitness gain. 7 Currently, definition of CHIP requires the presence of a clone with a variant allele frequency (VAF) > 2% of the molecules, reported as the least clones having a clinical relevance, in a person without a hematologic malignancy. 5 , 8 Nevertheless, significance of these clones, remains to be assessed with further and long termed studies. 9 It has been demonstrated that CHIP is associated with aging, smoke and exposure to radiation and cytotoxic chemotherapy. 10 – 12 In addition, it is significantly associated with an increased risk of all-cause death that cannot be explained by the development of a myeloid malignancy, which incidence in elderly is less than 0,1%. 7,13,14 Among the overall causes of death, an augmented cardiovascular disease risk appears to be related to CHIP, with an increased incidence of coronary heart disease and ischemic stroke in mutation carriers. 7 , 15 Similarly, patients with CH are at higher risk of other inflammatory conditions including chronic obstructive pulmonary disease 16 and gout, 17 which are mediated by dysregulated inflammatory signaling of mutant macrophages. 18 Remarkably, CHIP is also detected in cancer patients, including those with hematologic malignancies. 19 – 22 Indeed, CHIP has been found also among MM patients with a prevalence of 21,6% at the time of ASCT (VAF of at least 1%); 23–25 importantly, its presence was associated with shorter OS and PFS as well, particularly in those who did not receive maintenance therapy with an IMiDs. Of note, the increased mortality in patients with CHIP was not related to the increased risk of developing therapy-related myeloid neoplasms (TMN), as observed in lymphomas patients following ASCT, 11 nor to an increase in cardiovascular events, but it was mainly due to disease progression, possibly related to a greater risk of developing toxicity during treatment, or to an inflammatory-prone bone marrow microenvironment supporting tumor cells growth. 25 Although these evidences, there is still little knowledge about the clinical impact of CHIP among MM patients, mainly in those ineligible for high-dose therapies. Here we show, through a retrospective and multicenter analysis, CHIP and its related mutations prevalence among NDMM patients, which results in more aggressive disease and poorer clinical outcomes. Notably, CHIP patients are at greater risk of developing toxicity during treatment, likely due to altered distribution of immune-cells and enhanced frailty, which anticipates shorter event-free survival. PATIENTS AND METHODS STUDY DESIGN A total of 76 patients diagnosed with MM according to the revised International Myeloma Working Group (IMWG) criteria, 26 from 2019 to September 2021, at three different hematologic Italian centers (Genoa, Catania and Cagliari) and whose peripheral blood mononuclear cells (PBMCs) were available at diagnosis and follow up for sequencing analyses were studied. Patients' characteristics are detailed in Table 1 . CD138 POS and CD138 NEG cells derived from Bone Marrow (BM) aspirates were isolated with an immune-magnetic bead-based strategy (MACS system, Mylteni biotech), as previously reported. 27 The study was conducted under all national and international ethical and legal recommendations, following approval by the local Ethics Review Committee, in accordance to the declaration of Helsinki. (CER Liguria: 626/2022 - DB id 12752, approved at 3th July 2023). All patients gave informed consent to the study. Table 1 Patients' characteristics. All cohort (n = 76) CHIP (n = 35) No CHIP (n = 41) p value Age at diagnosis Median (range) 71 (40–90) 72 (46–84) 71 (40–90) 0.52* 80 13 6 7 Gender Male 41 19 22 1*** Female 35 16 19 Myeloma subtype IgA kappa 9 4 5 IgA lambda 4 1 3 IgG kappa 29 15 14 0.84** IgG lambda 19 7 12 Kappa-light chain 9 4 5 Lambda-light chain 3 2 1 Missing 3 2 1 Biochemical markers Albumin (mg/dL) 35,9 35,69 36,09 0.461**** β-2-microglobulin (mg/L) 6,8 9,04 4,85 0.05**** Creatinine (mg/dL) 1,9 2,41 1,47 0.66**** Calcium (mmol/L) 9,44 9,53 9,37 0.075**** Hemoglobin (g/dL) 11,24 10,61 11,78 0.019**** Platelet count (x10^6) 229,3 224,9 233 0.609**** LDH (U/L) 226,1 237,6 216,3 0.335**** BM plasma cell ≥ 10%, median (1q;3q) 50 (18, 70) 55 (24, 80) 50 (15, 66) 0.2295**** ≥ 60%, median (1q;3q) 75 (70, 80) 80 (70, 81) 70 (65, 80) 0.0338* Disease stage ISS1 13 2 11 0.0051*** ISS2 26 12 14 ISS3 29 20 9 R-ISS1 7 1 6 0.0001*** R-ISS2 29 9 20 R-ISS3 29 23 6 EMD (Y/N) 48/27 24/11 24/16 0.58***** Induction therapy Anti-CD38 cont. regimens 33 15 18 PIs cont. regimens 25 13 12 Lent cont. Regimens 9 4 5 0.83** Other 9 9 6 LOT mean 1.43 (0–5) 1.53 (0–4) 1.35 (1–5) 0.1961**** *t-test **Fisher test ***chi^2 test ****Kolmgorov-Smirnov test *****binom. test SAMPLE PREPARATION, SEQUENCING AND DATA ANALYSIS DNA was isolated with QIAamp DNA Mini Kit (Qiagen) from whole peripheral blood and BM samples (using CD138 POS and CD138 NEG fractions as well), when available. Next Generation Sequencing of those samples was performed at the time of diagnosis and during follow up, by employing an Illumina Custom Enrichment panel of recurrently mutated genes in myeloid cells (N = 36). The genes list is detailed in Table 2 . Libraries were generated by the Illumina® DNA Prep with Enrichment workflow, following the manufacturer’s instruction; quality and size distribution were determined using Qubit fluorimeter and Agilent TapeStation system. 2 x 150 cycles, pair-end sequencing at 500 x median coverage depth was performed through the Genomics Core at IRCCS Istituto Giannina Gaslini on MiSeq platform (Illumina). The analysis of the data was performed with the BaseSpace® Software (Illumina) and the "Dragen Enrichment" pipeline with the “somatic” setting. Intronic and synonymous variants with no impact on splicing, missense and short ins/dels reported as “benign” or likely benign” in ClinVar were excluded. 28 Variants not reported in COSMIC, and with CADDphred score 1% was used to define CHIP. 30 , 31 Discovered variants with a VAF > 40% or those with a frequency in general population < 1% were filtered out. The selected somatic mutations and their VAFs were correlated with demographic and clinical parameters, including age, sex, ISS, R-ISS stage, outcomes, and occurrence of adverse events. Subsequent analyses at 12–24 months after therapy were also run and for those MM patients with available BM samples, the tumoral and non-tumoral fractions (CD138 POS and CD138 NEG cells, respectively) were analyzed in parallel. Table 2. Panel genes list used Gene Target region (exon) Gene2 Target region (exon)3 Gene4 Target region (exon)5 ASXL1 full GATA2 full PPM1D 5, 6 BCOR full GNAS 8, 9 PTEN full BCORL1 full GNB1 5-7 PTPN11 2-4, 8, 13 BRAF 11, 15 IDH1 4 RAD21 full BRCC3 full IDH2 4 RUNX1 full CBL 8, 9 IKZF1 full SF3B1 13-16 CREBBP full JAK2 12, 14 SMC3 full CUX1 full KRAS 2, 3 SRSF2 1 DNMT3A full MPL 10 STAG1 full EZH2 full MYD88 3-5 STAG2 full FLT3 14, 15, 20 NF1 full TET2 full GATA1 full NOTCH1 26-28, 34 TP53 full MULTIPARAMETER FLOW CYTOMETRY (MFC) ANALYSIS MFC analysis was performed at local laboratories on bone marrow (BM) samples collected at diagnosis or at any time before induction-therapy starting. EDTA blood (2 ml) was bulk lysed with 1 × BD Pharm LyseTM Lysing buffer (30 ml) for 5 min, centrifuged at 1,500 rpm for 7 min and washed once in Dulbecco's PBS. Cells (50 µl at 10–20 × 10 6 /ml) were stained for cell surface markers with 20 µl antibody combinations for 15 min at RT. Intracellular nuclear (n) and cytoplasmic (cy) staining were performed after cell fixation and permeabilization using Intrastain kit by DAKO (Milan, Italy). The following monoclonal antibodies (MoAbs) combinations were employed: 1) CD138FITC/CD56PE/CD20PerCp/CD117APC/ CD45APC-H7/CD38PE-Cy7 2) cyKappaFITC/cyLambdaPE/CD19PerCp/ CD56APC/CD45APC-H7/CD38PE-Cy7. From the first combination, we obtained plasma cells quantification; from the second combination, we evaluated plasma cells immunophenotype and clonality. Acquisition and analyses were performed using FACSCantoTM II (Becton Dickinson, Mountain View, CA), and DiVa software: a minimum of 1–2 x 10 4 of events for each sample were acquired. As controls, anti-isotype mouse antibodies were used. Importantly, CD38bright/SSC low population was representative for plasmacell fraction; cytofluorimetric data were analyzed when an abnormal fraction of plasmacell was detected. Based on expression levels of each cluster of differentiation (CD) measured, two categories were identified: bright-expressors and low(non)-expressors; dim level was considered as latter. Samples were considered as positive when at least 20% of MM cells expressed this antigenic profile, as previously described. 27 STATISTICAL ANALYSIS Data were collected in spreadsheets and were analyzed using R statistical software (v. 4.0.5; RStudio) and SPSS (v. 25; IBM). Continuous variables were expressed as mean or median and compared with Wilcoxon rank-sum or student’s t-test. Categorical variables were expressed as counts and percentages and compared using Chi-square, Kolmgorov-Smirnov, binomial or Fisher’s exact test as appropriate. Log-rank (Mantel-Cox) test was used for survival analysis between group. A P value of < 0.05 was considered statistically significant. Correlation analysis between variables was performed using Pearson’s correlation method. RESULTS Prevalence of Clonal Hematopoiesis among NDMM patients A total of 76 MM patients whose peripheral blood (PB) samples were available at our institutions (Genoa, Catania and Cagliari) were screened for clonal hematopoiesis related mutations by using deep sequencing approaches. Table 1 summarizes demographic and clinical characteristics of the entire cohort. The median age was 71 years old (range, 40–90 years) with no gender (41 males vs. 35 females) or addictive behavior (smoking) prevalence as well; IgG was frequently observed as immunoglobulin isotype (48/76) with k and λ free lights chains occurring in 29 and 19 of these patients, respectively. The most represented induction regimens included MoAbs and PIs-based strategies with Len-based treatment used in 9 cases. Overall, next-generation sequencing (NGS) analyses revealed at least 1 CHIP variant in 46% of patients (35/76), with a median variant allele frequency (mVAF) of 0.022 (range 0.003–0.340). (Fig. 1 A-B) No significant differences were observed between the CHIP and the non-CHIP carriers in terms of gender (p = 1; chi-squared test), myeloma subtype (p = 0.84; Fisher's exact test) induction regimens (p = 0.83; Fisher's exact test) and lines of therapies (p = 0.1961; binomial test). Interestingly, differently from previously reported data, 25 advanced age was not associated with higher CHIP prevalence although greater VAF (> 0.1) occurred among patients older than 70 years. (Fig. 1 C) Nineteen patients (54.2%) had a single CHIP mutation, while six (17.1%) and ten (28.5%) had 2 or more than three mutations, respectively. (Fig. 1 D) Consistent with others reports, 32 – 34 the most commonly mutated gene was DNMT3A (54% of cases with mVAF of 9 %) folowed by TET2 (37% of cases with mVAF of 3.0%), ASXL1 and KRAS (both with mVAF of 11%), whereas mutations in splicing factors and JAK2 were rare. (Fig. 1 E and Table 3 ) Finally, CHIP-mutational spectrum analyses revealed majority of nonsynonymous followed by frameshift and synonymous mutations in affected genes. The gene-specific variant frequency across whole patients are summarized in Fig. 1 F. Collectively, these data acknowledge CHIP as a frequently observed event among MM patients at diagnosis, in line with reported data. 25 , 32 – 34 Table 3. List of variants observed in PB of CHIP carriers at diagnosis. CHIP is associated with greater aggressiveness and poorer clinical outcomes CHIP negatively impacts MM patient’s clinical outcomes with shorter PFS and OS after ASCT; importantly, these negativities are cleared by IMiDs maintenance. 24 , 25 , 34 Consistent with these findings, we first sought to assess the impact of CHIP on disease progression parameters of our cohort. Initially we investigated cellular origins of these abnormalities: as shown in Fig. 2 A, no significant differences were observed between CHIP and bone marrow plasma cells percentage, thus suggesting that myeloid somatic mutations are unlikely to derive from the malignant tumor cells. Indeed, the analysis of bone marrow CD138 NEG cells (i.e. fraction depleted of tumor plasma cells) showed correlation between BM and PB VAF values thus, confirming the non-tumor origin of screened myeloid mutations. (Pearson R = 0.97, p < 0.0001; Fig. 2 B) Next, we examined the impact of CHIP on disease aggressiveness markers: higher β2-microglobulin (9.04 vs 4.15 mg/dl; p = 0.05), 24 h urine protein output (1.44 vs 1.10 g/24hrs; p = 0.019), creatinine (2.41 vs 1.47 mg/dL; p = 0.05) and serum FLC k (557.2 vs 250 mg/dl; p = 0.031) levels in parallel with lower eGFR (46.57 vs 64.38; p = 0.024) and hemoglobin (10.61 vs 11.78 g/dL; p = 0.019) values were found among CHIP carriers than control. (Fig. 2 C) In line with these data, Mosaic plots analyses showed disease stages differences between these two groups: higher prevalence of CHIP carriers was found among high-risk patients, defined according to International Staging System (ISS) and Revised (R)-ISS staging systems score. (Fig. 2 D) Together, these data confirm CHIP role as disease-aggressiveness biomarker whose evaluation could therefore improve risk stratification analysis of MM patients. Taking into account these assumptions, we next examined clinical outcomes across our cohort. As shown in Fig. 3 A, C presence of CHIP was associated with significantly shorter PFS (mPFS of 493 days in those with CH vs. not reached at 5000 days in control; p < 0.0001) and OS (mOS of 925 days in CH carriers vs. not reached at 5000 days in those without CH, respectively; p = 0.025). A multivariate Cox-model CHIP focused on analysis, identified β-2 microglobulin high level as poorer clinical outcomes influencer for both PFS (HR, 1.20; 95% CI, 1.09–1.33) and OS (HR, 1.19; 95% CI, 1.05–1.34); remarkably, this parameter preserved its negative impact also among patients without CHIP. Furthermore, while platelets count significantly predicts both PFS (HR, 0.99; 95%CI, 0.98-1.00) and OS (HR, 0.98; 95%CI, 0.97–0.99) among CHIP carriers, no significant effects were observed among patients without CHIP; similarly, albumin (HR,0.85; 95%CI, 0.77–0.95) and creatinine levels (HR, 0.62; 95% CI, 0.43–0.90) significantly influence PFS of CHIP carriers.(Fig. 3 B,D) Dynamic changes of Clonal Hematopoiesis during MM progression To investigate clonal performance over time, we used serial VAF measurements as a surrogate for clone size. Although in general we found a slight increment of VAFs over time, it was no significant. (Fig. 4 A) However, by focusing on the most relevant genes for myeloid fitness, including DNMT3A, ASXL1, TET2 and TP53 , we observed significant increment in their VAF during follow -up, whilst a stable frequency affected all other genes. As result these data suggest coexistence of small clones with different clonal evolution (Fig. 4 B). To delineate the factors that determine each clone’s growth rate, we analyzed serial samples in 10 CHIP carriers. The median time between the first and second sample collection was 10 months (range: 3–12 months). While in general no significant increase in VAF was observed, we found that that clones bearing mutations in the fitness-related myeloid genes expanded most rapidly, with a significant increment in VAF over time. Moreover, backward tracking revealed that variants observed pre-existed at diagnosis, with either lower or similar VAF. Figure 4 C shows exemplary fish-plots of two representative patients with available clinical information: lenalidomide refractory patient (MM_032) with DNMT3A and TET2 mutant clones in BM (CD138 NEG cells) and PB at diagnosis, showed increased size of both variants during treatment (starting from VAF of 2.68 and 19.26, to 2.95 and 26.9, respectively); whilst patient (MM_066) exposed to daratumumab-based regimen showed stability of DNMT3A variant after 3 months, while PTPN11 mutant, observed at diagnosis in PB, CD138 NEG and CD138 POS BM cells, resulted almost undetectable after treatment. Collectively these data suggest that CHIP dynamics are not influenced by any specific treatment, but rather by the occurring mutant type: indeed, MDS/AML driving genes (i.e. DNMT3, TET2, TP53 e KRAS ) when involved, often increase their size even during treatment, regardless of response or disease status. CHIP presence enhances MM patients Frailty The overall safety was not significantly different by CHIP presence although incidence of adverse events (AEs) of any grade was slightly higher in CH (97%) versus no-CHIP patients (80%); similarly, the rates of grade ≥ 3 AEs were lower in non-CHIP (36.5%) compared with CHIP carriers’ patients (85.7%). (Table 4 ) The most common hematological event was neutropenia of any grade, which occurred in 20 patients (57%) among CHIP group and in 13 (31.7%) among those without this abnormality. A similar trend was observed for high grade (≥ 3) hematologic toxicities, although differences were not significant between two groups (p = 0.67). Among non-hematologic toxicities, peripheral neuropathy occurred more frequently among CHIP than no-CHIP carriers, even if differences were not significant (p = 0.20 and 0.19 for any grade or higher toxicities, respectively). The most notable difference between groups was about infections, including SarSCov2 and pneumonia, which occurred in 71.4% of CHIP carrying patients and in 23% of those in control group. Importantly, most serious (AEs ≥ 3 grade) infections occurred among CHIP; unfortunately, low censured cases did not make differences significant (p = 0.36 and p = 0.37 for any grade or grade ≥ 3 toxicity, respectively). Clonal hematopoiesis has been reported to be associated with cardiovascular disease also in MM patients, 32 thus we analyzed cardiovascular events rates (CVEs) in our cohort: while comparable events were observed between two groups, venous thromboembolism seemed to affect especially CHIP carriers (51.4 vs 17%), although the limited number of cases prevented significant conclusions. To support the greater infections occurrence between CHIP patient, we next screened bone marrow immune-phenotype of our cohort, where available. As shown in Fig. 5 , effector T-cells resulted significantly reduced in CHIP vs. no-CHIP carriers with both CD4 + and CD8 + cells equally compromised, whilst B (CD19 + ), monocytes (CD14 + ) and NK (CD16+/CD56+/CD3-) cells were almost unaffected by CHIP presence. T-cell fitness impairment made us to calculate frailty scores of our patients by using age (0 if ≤ 75 years, 1 if 76–80 years, 2 if > 80 years), Charlson Comobidity Index (CCI) (0 if ≤ 1, 1 if > 1), and ECOG PS (0 if 0, 1 if 1, 2 if ≥ 2). 35 Remarkably, this approach shown frail patients’ enrichment among CHIP carriers compared with no-CHIP patients (97.1% vs 56%; p < 0.0001) thus, supporting the enhanced AEs vulnerability observed in the former. (Table 5 ) It is supposed that AEs occurrence leads to treatment discontinuation thus, we examined the impact of clonal hematopoiesis on event free survival (EFS) of our cohort: CHIP negatively impacted prognosis with its presence associated with shorter EFS than its absence. (Fig. 6 A; p < 0.0001) A multivariate Cox-model indicates albumin level (HR, 0.92; 95%CI, 0.84-1) as the only parameter able to improve EFS among CHIP-carriers; by contrast, hemoglobin (HR, 2; 95%CI, 1.04–3.84) and β2-microblobulin (HR, 2.10; 95%CI, 1.18–3.74) values worsened outcome of patients without CHIP. (Fig. 6 B) Overall, our data suggest that CHIP impairs MM patient’s fitness making them more prone to the development of therapy-related toxicities, including infection; this in turn results in greater chance of treatment delays or dose reductions that limit patient’s ability to receive optimal therapies. Table 4. Drug-associated toxicities in the entire cohort Event All cohort CHIP No CHIP p value* Any grade Grade 3 or 4 Any grade Grade 3 or 4 Any adverse event, n (%) 60 (78.9) 34 (97) 30 (85.7) 33 (80) 15 (36.5) Hematologic adverse event Neutropenia 33 (43.4) 20 (57) 15 (42.8) 13 (31.7) 10 (24.3) Any grade: 0.19 Thrombocytopenia 12 (15.7) 8 (22.8) 7 (20) 4 (0.9) 0 Grade 3 or 4 : 0.67 Anemia 10 (13.1) 9 (25.7) 7 (20) 1 (0.2) 0 Non-hematologic adverse event GI 20 (26.3) 15 (42.8) 5 (14.2) 5 (12) 2 (4.8) Any grade: 0.20 PN 15 (19.7) 12 (34.2) 9 (25.7) 3 (0.7) 0 Grade 3 or 4 : 0.19 Rash 8 (10.5) 6 (17.1) 6 (17.1) 2 (0.5) 1 (2.4) Infections Coronavirus disease 2019 20 (26.3) 16 (45.7) 5 (14.2) 4 (9) 0 Any grade: 0,36 Pneumonia 15 (19.7) 9 (25.7) 5 (14.2) 6 (14) 3 (7.3) Grade 3 or 4 : 0.37 Cardio-vascular events VTE 25 (32.8) 18 (51.4) 10 (28.5) 7 (17) 5 (12.1) Any grade: 0,78 Ischemic events 5 (6.5) 4 (11.4) 1 (0.2) 1 (2.4) 0 Grade 3 or 4 : 0.62 Arrhythmias 8 (10.5) 5 (14.2) 2 (0.6) 3 (7.3) 2 (4.8) PN: peripheral neuropathy VTE: venous thromboembolism GI: gastrointestinal toxicity *ChiSq test Table 5 Frailty analysis of entire cohort according to Gordon Cook et al. Leukemia. 2020 All cohort CHIP No CHIP p value* ECOG PS, n (%) 0.0005 0 8 (10.5) 0 8 (19.5) 1 37 (48.6) 14 (40) 23 (56) ≥ 2 31 (40.7) 21 (60) 10 (24.3) Charlson Comorbidity Index, n 0.22 ≤ 1 6 (7.8) 1 (2.8) 5 (12.1) > 1 70 (92.1) 34 (97.1) 36 (87.8) Age at diagnosis, n 1 ≤ 75 54 (71) 25 (71.4) 29 (70.7) 76–80 9 (11.8) 4 (11.4) 5 (12.1) > 80 13 (17.1) 6 (17.1) 7 (17) Frailty definition < 0.0001 Nonfrail 19 (25) 1 (2.8) 18 (44) Frail 57 (75) 34 (97.1) 23 (56) *Fisher Test Platelets count positively correlates with VAF in MM patients To analyze the specific impact of clonal hematopoiesis among our cohort of MM patients, correlogram and principal component analyses (PCA) were performed based on available clinical data. As shown in Fig. S1 , CHIP and no-CHIP samples were projected onto the principal component (PC) space and two ellipses were calculated. Unfortunately, the first two PC1 to PC2, didn’t show a clear pattern thus suggesting that a clinical analysis-based approach fails to predict CHIP presence. CHIP refers to cancer-associated driver mutations present at mutational burden (VAF) ≥ 2% in subjects without hematologic abnormalities. 5 Based on this assumption, we built a model to screen VAF and clinical data in our cohort. The chosen mathematical model turned out more effective than previous approach, by identifying platelet count as clinical indicator for greater VAF in MM patients regardless of specific carried-variants; (Fig. 7 A) importantly, these results were also confirmed by focusing on the most frequent mutants DNMT3 A and TET2 . (Fig. 7 B-C and S2). Overall these results led us to speculate platelets count as surrogate biomarker for higher VAF rate among CHIP carriers. DISCUSSION Recent studies have identified, in the blood of aging people, the presence of somatic mutations in hematopoietic cells, a condition referred as clonal hematopoiesis of unknown significance (CHIP). 36 By providing a fitness advantage to HSCs, CHIP is associated with a 0.5–1% risk of progression to a non-plasma-cell hematologic neoplasm such as MDS and AML. 5 , 7 , 14 , 37 Moreover, CHIP is also associated with aging-related conditions linked to aberrant inflammatory response such as atherosclerosis and cardiovascular diseases. 18 In cancers, the prevalence of CHIP is higher compared to healthy population, especially for those patients exposed to cytotoxic chemotherapy or radiation. 10 Recent studies suggest the presence of these myeloid clones also among MM patients with an impact on clinical outcomes; 23 – 25 , 32 , 38 , 39 however, biological relevance of this abnormality still remains to be elucidated. Here, we report the prevalence of CHIP in a multicenter cohort of NDMM patients at time of diagnosis and after 12–24 months of treatment, and describe the association of CHIP with clinical characteristics and outcomes of these patients. Overall we found somatic mutations in 46% of our population which is quite higher than expected and that reported earlier (21.6%). 25 These finding could be related to the sequencing used method, which achieves greater reading depth and therefore allows recording lower VAF (> 1%). Remarkably, consistent with prior studies, we found that CHIP status correlates with higher disease aggressiveness but differences were observed in term of aging, since previous studies have demonstrated CHIP prevalence with increasing age. 24 , 25 Although these findings appear in contrast, greater burden (VAF > 0.1) occurred among patients older than 70 years which is consistent with other studies 40 as well as for the most frequently mutated genes ( DNMT3A followed by TET2) . Moreover, we confirm association between poorer clinical outcomes, including progression-free and overall survivals which in turn reflects disease biologic status, as in part reported. 35 , 41 , 42 During recent years, novel drugs have significantly improved MM patient’s prognosis, but unfortunately continuous treatments have often limitations due to the development of serious and unexpected toxicities leading their discontinuation. Consistent with this observation, we analyzed, among CHIP carriers, those patients who experienced AEs during treatment: the event-free survival is greatly impaired by presence of CHIP with AEs of any grades, including infections, occurring more frequently in this group; similarly, recurrent infections were censured among CHIP carriers. Although the small sample size prevents any firm conclusion, we might speculate that a pro-inflammatory microenvironment may have impaired patient’s fitness leading to increased susceptibility to therapeutic-toxicity. Indeed, a detailed Bone Marrow environment focused on analysis revealed a consistent T cells impairment in CHIP carriers which, together with their higher frailty scores, explains the greater vulnerability to infections of this group. Moreover, while our data support CHIP screening at disease onset to better profile patient fitness, they also suggest its presence may influence T-cells based therapies, thus making CHIP evaluation a mandatory strategy to select the most appropriate therapeutic strategy for each MM patient. 43 , 19 Interestingly, our study includes also longitudinal data for a subset of patients: a slight increase in VAF was recorded for MDS/AML drivers ( DNMT3A, TET2, ASXL1 and TP53 ) without appearance of additional mutations in other genes. This data demonstrates that micro-clones in the hematopoietic compartments of MM patients slightly increase in size but remain quite stable in terms of mutated genes type during disease evolution, thus suggesting that the mutational profile of myeloid cells may be largely unaffected by anti-MM therapies, including daratumumab-based regimens. On the other side, the presence of theses clones, as indicated by our data, correlates with a more aggressive disease and, worse outcome. As result, we speculate that the pro-inflammatory status, supported by these clones, facilitates tumor growth within BM microenvironment. Recent studies suggest an impaired regenerative potential of hematopoietic stem cell grafts in autologous stem cell transplant recipients harboring CHIP. Namely, reduced stem cell yields and delayed platelet count recovery following ASCT is observed in presence of DNMT3 A and PPM1D variants. 38 Consistent with these data, we found striking association between platelets count and CHIP burden: high platelets level indicates greater VAF in CHIP carriers, regardless of specific mutants. As reported, CHIP is associated with a pro-inflammatory status which lead to fourfold increase of cardiovascular illness incidence. 44 Biologic mechanisms of these dependencies are complex but, increased level of several cytokines (i.e. IL-6, IL-1 beta, IL-8 and NLRP3) are shared features. As result, inflammasome targeting agents (i.e. vitamin C and aspirin) are currently tested in ongoing clinical trials as preventive strategies to block CHIP-inflammation cascade and improve outcomes of individuals with CHIP. (ClinicalTrials.gov Identifier: NCT06097663, NCT03682029) Here, although the limited samples size of our cohort, we pinpoint relevance of platelets count, a well-known inflammatory indicator light, as promising biomarker of VAF among CHIP carriers. Importantly, by linking inflammatory-prone status and higher VAF rate, our study suggests the predictive role of platelets count to timely identify the greater tumor aggressiveness observed among CHIP carriers compared to other MM patients. However, the impaired fitness status observed among these patients, make us to speculate that less intensive therapies should be preferred in case of high platelets count to reduce severe adverse events occurrence, especially after high-dose regimens. However, larger prospective studies are needed to clarify the impact of platelet counts on those MM patients carrying CHIP and to support a cause-effect relationship with this association. In conclusion, our study confirms that CHIP is frequently observed among MM patients where it correlates with a more aggressive disease and poorer clinical outcome as well. Interestingly, the presence of CHIP is associated with the early development of toxic events while its burden seems to be related to platelets count. Consistent with our results, we propose a specific management of CHIP-carrying MM patients, especially for what concern therapy-related toxicity. Declarations ACKNOWLEDGMENTS This work was supported in part by the Associazione Italiana per la Ricerca sul Cancro (AIRC, IG #23438, to M.C.), International Myeloma Society (IMS) and Paula and Rodger Riney Foundation Translational Research Award 2023. AUTHORSHIP STATEMENT E.G. and M.C. designed the research and wrote the manuscript; C.M., D.S., G.G., and I.T. performed samples and libraries preparation and collaborated to sequencing analyses; D.T. and F.L. performed the statistical and bioinformatics analyses; C.C., F.G., M.M., A.L, S.A., A.C and D.D. provided patient samples. F.DR., D.C., and R.M.L. revised the final version of manuscript. DISCLOSURE OF CONFLICTS OF INTEREST No conflicts of interest to disclose. DATA AVAILABILITY The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Kumar, S. K. et al. Multiple myeloma. Nat. Rev. Dis. Primers . 3 , 17046 (2017). Rajkumar, S. V. Multiple myeloma: 2020 update on diagnosis, risk-stratification and management. Am. J. Hematol. 95 , 548–567 (2020). Marcon, C. et al. Experts’ consensus on the definition and management of high risk multiple myeloma. Front. Oncol. 12 , 1096852 (2022). Larocca, A. et al. Patient-centered practice in elderly myeloma patients: an overview and consensus from the European Myeloma Network (EMN). Leukemia . 32 , 1697–1712 (2018). Steensma, D. P. et al. Clonal hematopoiesis of indeterminate potential and its distinction from myelodysplastic syndromes. Blood . 126 , 9–16 (2015). Solís-Moruno, M., Batlle-Masó, L., Bonet, N., Aróstegui, J. I. & Casals, F. 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Leukemia . 34 , 2285–2294 (2020). Jaiswal, S. & Ebert, B. L. Clonal hematopoiesis in human aging and disease. Science . 366. 10.1126/science.aan4673 (2019). Genovese, G. et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N Engl. J. Med. 371 , 2477–2487 (2014). Stelmach, P. et al. Clonal hematopoiesis with DNMT3A and PPM1D mutations impairs regeneration in autologous stem cell transplant recipients. Haematologica . 108 , 3308–3320 (2023). Li, N. et al. Clonal haematopoiesis of indeterminate potential predicts delayed platelet engraftment after autologous stem cell transplantation for multiple myeloma. Br. J. Haematol. 201 , 577–580 (2023). Rodriguez, J. E., Micol, J. B. & Baldini, C. Exploring clonal hematopoiesis and its impact on aging, cancer, and patient care. Aging . 15 , 14507–14508 (2023). Zweegman, S. & Larocca, A. Frailty in multiple myeloma: the need for harmony to prevent doing harm. Lancet Haematol. 6 , e117–e118 (2019). Mian, H. et al. The prevalence and outcomes of frail older adults in clinical trials in multiple myeloma: A systematic review. Blood Cancer J. 13 , 6 (2023). Larocca, A., Cani, L., Bertuglia, G., Bruno, B. & Bringhen, S. New Strategies for the Treatment of Older Myeloma Patients. Cancers (Basel) . 15. 10.3390/cancers15102693 (2023). Libby, P. & Ebert, B. L. CHIP (Clonal Hematopoiesis of Indeterminate Potential): Potent and Newly Recognized Contributor to Cardiovascular Risk. Circulation . 138 , 666–668 (2018). Table Table 3 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table3.docx SupplementaryFigureslegend.docx SupplementaryFigures.pptx Cite Share Download PDF Status: Published Journal Publication published 26 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 23 Sep, 2024 Reviews received at journal 19 Sep, 2024 Reviews received at journal 10 Sep, 2024 Reviewers agreed at journal 30 Aug, 2024 Reviewers agreed at journal 28 Aug, 2024 Reviewers invited by journal 28 Aug, 2024 Editor assigned by journal 28 Aug, 2024 Editor invited by journal 22 Aug, 2024 Submission checks completed at journal 22 Aug, 2024 First submitted to journal 17 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4930569","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":356362569,"identity":"733ac8f8-637f-427b-a415-1e65614a3a50","order_by":0,"name":"Elisa Gelli","email":"","orcid":"","institution":"University of Genoa","correspondingAuthor":false,"prefix":"","firstName":"Elisa","middleName":"","lastName":"Gelli","suffix":""},{"id":356362570,"identity":"ca68ac51-6960-4b46-8915-ef6bea95809d","order_by":1,"name":"Claudia Martinuzzi","email":"","orcid":"","institution":"IRCCS Ospedale Policlinico San Martino, Clinic of 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Lemoli","email":"","orcid":"","institution":"University of Genoa","correspondingAuthor":false,"prefix":"","firstName":"Roberto","middleName":"M.","lastName":"Lemoli","suffix":""},{"id":356362592,"identity":"55950551-ce01-4f53-88ad-d3deaaa4dd5d","order_by":18,"name":"Michele Cea","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYDACZgY2BoYCMJPxAQODBIhuIEKLAYRpANXSSEAPQgubBFQEvzW67czPHnwwsGHgb29+Vs27w8Kun4G5/QE+LWaH2cwNZxikMUicOWZ2m/eMRPLMBgIOMzvMwybNY3CYwUAiAailTSLZ4AAxWv4Y/GcwkH/+rRikxZ4oLQwGB4C28JgxA7XYGRAKMaBfzCR7DJJ5JM7kFEvObZNIkDjM2DgDr5bzh59J/Kiwk+NvP77xw9u2Onv+9vYHH/BpgQEeGCOxgZkY9cjAnlQNo2AUjIJRMPwBAA/gQPHZgHXkAAAAAElFTkSuQmCC","orcid":"","institution":"University of Genoa","correspondingAuthor":true,"prefix":"","firstName":"Michele","middleName":"","lastName":"Cea","suffix":""}],"badges":[],"createdAt":"2024-08-17 16:01:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4930569/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4930569/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-79748-7","type":"published","date":"2024-11-26T15:57:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66674041,"identity":"f1afede9-63a9-467c-8ff5-960032705bf3","added_by":"auto","created_at":"2024-10-15 10:55:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":149357,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCHIP is frequently observed among NDMM patients. A) \u003c/strong\u003ePie chart showing percentage of CHIP vs no-CHIP patients in our cohort; \u003cstrong\u003eB) \u003c/strong\u003evariant allele frequency (VAF)\u003cstrong\u003e \u003c/strong\u003eamong CHIP carriers; \u003cstrong\u003eC)\u003c/strong\u003e percentage of MM patients stratified by the size of the clone, as measured by the VAF across the entire cohort (ALL) and within particular age groups. \u003cstrong\u003eD) \u003c/strong\u003eThe number of patients harboring mutations in 1, 2, and 3 different genes; \u003cstrong\u003eE) \u003c/strong\u003ethe number of mutations (y-axis) identified in each gene (x-axis) across the entire cohort. Mutation types are color-coded according to legend; \u003cstrong\u003eF)\u003c/strong\u003e Oncoplot showing mutation across the CHIP carrying cohort (N=36). Each column represents an individual patient while genes (listed according to their frequency) are indicated on the left. Max mutation VAF for each mutation is colored according to scale bar shown on the left; the VAF cutoff used to call mutations was 0.01. The right bar graph shows the number of patients carrying any mutants for indicated genes.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4930569/v1/8780ae17ff20229de7ccc1bf.png"},{"id":66674029,"identity":"ffb8a0a5-26b7-4c75-a029-23536feaaf54","added_by":"auto","created_at":"2024-10-15 10:55:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":120853,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCHIP is associated with greater biological aggressiveness of the disease. A) \u003c/strong\u003eHistogram plot showing BMPCs ≥ 60% in CHIP (red) carriers compared with those without abnormalities (blue). Data are mean ± SD; ns = not significant (unpaired t-test). \u003cstrong\u003eB) \u003c/strong\u003eRegression analysis to correlate\u003cstrong\u003e \u003c/strong\u003eVAF in BMSCs (CD138\u003csup\u003eNEG\u003c/sup\u003e) and PBMCs across entire cohort.\u0026nbsp;R = 0.97;\u0026nbsp;p \u0026lt; 0.0001(unpaired t-test).\u0026nbsp;\u003cstrong\u003eC)\u003c/strong\u003e Violin plot displaying β2-microglobulin, 24h protein urine, creatinine, serum FLC k, eGFR and hemoglobin levels between samples with CHIP (red) and those without (blue). Statistically significant differences are marked by asterisk. Data are mean ± SD; *p = 0.05, **p = 0.03, *** p= 0.02 (Kolmogorov-Smirnov test). \u003cstrong\u003eD) \u003c/strong\u003eMOSAIC plot related to Person’s chi-squared test. Analysis offers visual evidence of the relationship between CHIP and ISS and R-ISS stages (I-III).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4930569/v1/9feb9d59817d41421d72632b.png"},{"id":66675105,"identity":"3137f220-5390-417e-a312-f3a837e1d314","added_by":"auto","created_at":"2024-10-15 11:03:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":277593,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCHIP presence affects clinical outcomes of MM patients. \u003c/strong\u003eKaplan-Meyer curves of the overall survival (\u003cstrong\u003eA\u003c/strong\u003e) and PFS probability (\u003cstrong\u003eC\u003c/strong\u003e) among MM patients with (blue line) and without (red line) CHIP. Log-rank test is used to compute the p-value (Log-Rank test). Forest plot based on Cox proportional Hazard analysis of the indicated variables (albumin, BMPCs, creatinine, Hb, LDH, MCV, platelets count, white blood cells count and β2-microglobulin) for PFS (\u003cstrong\u003eB\u003c/strong\u003e) and OS (\u003cstrong\u003eD\u003c/strong\u003e) in our cohort. Squares represent hazard ratios, while bars represent 95% confidence intervals.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4930569/v1/7e20f7ca4af637727238ee06.png"},{"id":66674038,"identity":"449b4281-9175-4d29-b582-547c87cde733","added_by":"auto","created_at":"2024-10-15 10:55:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":257975,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDynamics changes of CHIP over time. \u003c/strong\u003eComparison of VAF between diagnosis and follow up of all (\u003cstrong\u003eA\u003c/strong\u003e) and most relevant (\u003cstrong\u003eB\u003c/strong\u003e) mutated genes, indicated in the legend. Data are mean ± SD; *p = 0.01, ns= not significant (unpaired t-test). \u003cstrong\u003eC) \u003c/strong\u003eFish plots showing acquisition of mutations in serial time points in BM (tumor and non-tumor fractions) and PB samples of two representative MM patients (MM_32 and MM_066). The phylogenetic trees visualize the estimated order of mutation acquisition and the proportion of subclones with a different combination of mutations at each time-point. Minor clones include cells with a different heterozygous combination of mutations. BM = Bone marrow, PB = Peripheral blood.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4930569/v1/e06432015a0d6fa93d17e6a1.png"},{"id":66674030,"identity":"f9598eee-231d-4596-818c-f5ebfa5f82ac","added_by":"auto","created_at":"2024-10-15 10:55:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":55830,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBone Marrow immune cells distribution in CHIP and no-CHIP carriers. \u003c/strong\u003eHistogram plots displaying the percentages of monocytes CD14+\u003cstrong\u003e (A)\u003c/strong\u003e, B-lymphocytes\u003cstrong\u003e \u003c/strong\u003eCD19+ \u003cstrong\u003e(B)\u003c/strong\u003e, T-lymphocytes CD3+ \u003cstrong\u003e(C)\u003c/strong\u003e, T-lymphocytes CD4+ \u003cstrong\u003e(D), \u003c/strong\u003eT-lymphocytes\u003cstrong\u003e \u003c/strong\u003eCD8+ \u003cstrong\u003e(E)\u003c/strong\u003e and NK CD16+/CD56+/CD3- cells \u003cstrong\u003e(F)\u003c/strong\u003e among BM mononuclear cells of samples with CHIP (red) and those without (blue). Statistically significant differences are marked by asterisk. Data are mean ± SD; *0.024\u0026lt;p\u0026lt;0.039, ns = not significant (unpaired t-test).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4930569/v1/18ab76c989e8263413b9d773.png"},{"id":66675106,"identity":"5bbc296f-8850-49ba-9018-29d6ee7cd5ef","added_by":"auto","created_at":"2024-10-15 11:03:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":199517,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCHIP impacts event free survival of MM patients. A) \u003c/strong\u003eKaplan-Meyer curves of the event free survival (EFS) among our cohort according to CHIP status. CHIP carriers and no are represented in blue and red, respectively. Log-rank test is used to compute the P-value (Log-Rank test). \u003cstrong\u003eB)\u003c/strong\u003e Forest plot based on Cox proportional Hazard analysis of the indicated variables (albumin, BMPCs, creatinine, Hb, LDH, MCV, platelets count, white blood cells count and β2-microglobulin) for CHIP carriers (top) and no-CHIP carriers (bottom). Squares represent hazard ratios, while bars represent 95% confidence intervals.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4930569/v1/6f94a7fd29c145497b098068.png"},{"id":66674035,"identity":"b06cb568-03c4-428d-817e-495309f4fd9a","added_by":"auto","created_at":"2024-10-15 10:55:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":207502,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlatelets count is a novel biomarker for high VAF rate among CHIP carriers. A-C) \u003c/strong\u003eOn the top correlogram between clinical parameters, including VAF or mVAF (median VAF) in CHIP carriers, carrying all (A) or specific variants (B and C): rows and columns indicate each feature. The colors in the boxes indicate correlation values: red corresponds to negative correlation whilst blue corresponds to positive correlation. The area covered in the square corresponds to the absolute value of the correlation. In the lower panel regression analysis with specific confidence intervals to link VAF and PLT count across CHIP carriers with all or indicated variants (specific p value is indicated).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4930569/v1/7732578a7ec3854de9f44bfe.png"},{"id":70381784,"identity":"e79747ca-efc7-44a2-a621-c6ad54c840ea","added_by":"auto","created_at":"2024-12-02 16:14:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2272154,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4930569/v1/ca64418e-5826-4093-8eb7-b02bfb91345b.pdf"},{"id":66676050,"identity":"21f22438-d9e3-499c-a1cb-1580f6477b41","added_by":"auto","created_at":"2024-10-15 11:11:54","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":27592,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-4930569/v1/5f76abb337d953fd448f84a9.docx"},{"id":66674032,"identity":"5f59409f-61f0-4eb8-adea-6cde587cbf70","added_by":"auto","created_at":"2024-10-15 10:55:54","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":12453,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureslegend.docx","url":"https://assets-eu.researchsquare.com/files/rs-4930569/v1/719d8f3d327282b1d6b79635.docx"},{"id":66674033,"identity":"bc125732-633c-4888-a1fe-e593ca749df3","added_by":"auto","created_at":"2024-10-15 10:55:54","extension":"pptx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":293865,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.pptx","url":"https://assets-eu.researchsquare.com/files/rs-4930569/v1/ed05fe33d858fd329aa16b72.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eClonal Hematopoiesis Impacts Frailty of Newly Diagnosed Multiple Myeloma Patients: A Retrospective Multicentric Analysis \u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMultiple myeloma (MM) is an incurable malignancy of plasma cells that grow within a permissive bone marrow (BM) microenvironment, supporting tumor cells transformation, proliferation and drug resistance occurrence.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e During recent years, available therapeutic approaches have shown promising results in clinical setting of MM patients, but unfortunately it still remains incurable due to frequent relapses.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e In such a context, identification of disease-specific biologic features represents a valid strategy for improving MM clinical management. Genetic-molecular alterations including deletion 17p, t(4;14), t(14;16), t(14;20), amp/gain 1q, del 1p or TP53 mutations and high-risk gene expression profiling signatures are the most robust predictors of outcomes in MM with their combination conferring an even worse prognosis.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e However, although the identification of these features is crucial for prognosis and selection of the most appropriate strategy, aging, defined as impaired organ function and reduced physiological reserves, adds greater complexity that makes patients stratification extremely challenging.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e During recent years, several tools have been developed for a comprehensive assessment of MM patients\u0026rsquo; frailty; however none of available biomarkers is currently able to effectively prevent relapse, reduce mortality, and ultimately cure this blood cancer.\u003c/p\u003e \u003cp\u003eMore recent studies based on next-generation sequencing (NGS) have shown the presence of recurrent somatic mutations in blood of healthy adults, a condition referred as clonal hematopoiesis of indeterminate potential (CHIP).\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Remarkably, presence of somatic mosaicism in tissues during aging, is quite ubiquitous, but when the acquired variant confers growth advantage, mutant undergoes to clonal expansion. Somatic variants influencing cell fitness may occur in every tissue, but the wide availability of blood for serial genomic studies coupled with the blood circulation and interaction with all the other tissues, makes CHIP interesting for its clinical consequences.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Mutations driving the clonal expansion mostly involves leukemia-associated drivers, with \u003cem\u003eDNMT3A, TET2\u003c/em\u003e and \u003cem\u003eASXL1\u003c/em\u003e being the most affected genes, followed by \u003cem\u003eJAK2, TP53\u003c/em\u003e and a large list of other genes less frequently mutated, but still attractive as study subjects for their contribution to fitness gain.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Currently, definition of CHIP requires the presence of a clone with a variant allele frequency (VAF)\u0026thinsp;\u0026gt;\u0026thinsp;2% of the molecules, reported as the least clones having a clinical relevance, in a person without a hematologic malignancy.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Nevertheless, significance of these clones, remains to be assessed with further and long termed studies.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e It has been demonstrated that CHIP is associated with aging, smoke and exposure to radiation and cytotoxic chemotherapy.\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e In addition, it is significantly associated with an increased risk of all-cause death that cannot be explained by the development of a myeloid malignancy, which incidence in elderly is less than 0,1%.\u003csup\u003e7,13,14\u003c/sup\u003e Among the overall causes of death, an augmented cardiovascular disease risk appears to be related to CHIP, with an increased incidence of coronary heart disease and ischemic stroke in mutation carriers.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Similarly, patients with CH are at higher risk of other inflammatory conditions including chronic obstructive pulmonary disease\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and gout,\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e which are mediated by dysregulated inflammatory signaling of mutant macrophages.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Remarkably, CHIP is also detected in cancer patients, including those with hematologic malignancies.\u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Indeed, CHIP has been found also among MM patients with a prevalence of 21,6% at the time of ASCT (VAF of at least 1%);\u003csup\u003e23\u0026ndash;25\u003c/sup\u003e importantly, its presence was associated with shorter OS and PFS as well, particularly in those who did not receive maintenance therapy with an IMiDs. Of note, the increased mortality in patients with CHIP was not related to the increased risk of developing therapy-related myeloid neoplasms (TMN), as observed in lymphomas patients following ASCT,\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e nor to an increase in cardiovascular events, but it was mainly due to disease progression, possibly related to a greater risk of developing toxicity during treatment, or to an inflammatory-prone bone marrow microenvironment supporting tumor cells growth.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Although these evidences, there is still little knowledge about the clinical impact of CHIP among MM patients, mainly in those ineligible for high-dose therapies.\u003c/p\u003e \u003cp\u003eHere we show, through a retrospective and multicenter analysis, CHIP and its related mutations prevalence among NDMM patients, which results in more aggressive disease and poorer clinical outcomes. Notably, CHIP patients are at greater risk of developing toxicity during treatment, likely due to altered distribution of immune-cells and enhanced frailty, which anticipates shorter event-free survival.\u003c/p\u003e"},{"header":"PATIENTS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eSTUDY DESIGN\u003c/h2\u003e\n \u003cp\u003eA total of 76 patients diagnosed with MM according to the revised International Myeloma Working Group (IMWG) criteria,\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e from 2019 to September 2021, at three different hematologic Italian centers (Genoa, Catania and Cagliari) and whose peripheral blood mononuclear cells (PBMCs) were available at diagnosis and follow up for sequencing analyses were studied. Patients\u0026apos; characteristics are detailed in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. CD138\u003csup\u003ePOS\u003c/sup\u003e and CD138\u003csup\u003eNEG\u003c/sup\u003e cells derived from Bone Marrow (BM) aspirates were isolated with an immune-magnetic bead-based strategy (MACS system, Mylteni biotech), as previously reported.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e The study was conducted under all national and international ethical and legal recommendations, following approval by the local Ethics Review Committee, in accordance to the declaration of Helsinki. (CER Liguria: 626/2022 - DB id 12752, approved at 3th July 2023). All patients gave informed consent to the study.\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePatients\u0026apos; 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\u003eAll cohort (n\u0026thinsp;=\u0026thinsp;76)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCHIP (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo CHIP (n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge at diagnosis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71 (40\u0026ndash;90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 (46\u0026ndash;84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71 (40\u0026ndash;90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\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\u003e50\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\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\u003e60\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u0026ndash;79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\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\u0026gt;\u0026thinsp;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\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\u003eGender\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\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\u003eMyeloma subtype\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIgA kappa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\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\u003eIgA lambda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\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\u003eIgG kappa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIgG lambda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\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\u003eKappa-light chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\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\u003eLambda-light chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\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\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\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\u003eBiochemical markers\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlbumin (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35,69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36,09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.461****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026beta;-2-microglobulin (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.075****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlatelet count (x10^6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e224,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.609****\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\u003e226,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e216,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.335****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBM plasma cell\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;10%, median (1q;3q)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (18, 70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55 (24, 80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (15, 66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2295****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;60%, median (1q;3q)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75 (70, 80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80 (70, 81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70 (65, 80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0338*\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eISS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0051***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eISS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\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\u003eISS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\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\u003eR-ISS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-ISS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\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\u003eR-ISS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\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\u003eEMD (Y/N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48/27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24/16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58*****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInduction therapy\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-CD38 cont. regimens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\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\u003ePIs cont. regimens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\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\u003eLent cont. Regimens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\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\u003eLOT mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43 (0\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.53 (0\u0026ndash;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35 (1\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1961****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e*t-test\u003c/p\u003e\n \u003cp\u003e**Fisher test\u003c/p\u003e\n \u003cp\u003e***chi^2 test\u003c/p\u003e\n \u003cp\u003e****Kolmgorov-Smirnov test\u003c/p\u003e\n \u003cp\u003e*****binom. test\u003c/p\u003e\n \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eSAMPLE PREPARATION, SEQUENCING AND DATA ANALYSIS\u003c/h2\u003e\n \u003cp\u003eDNA was isolated with QIAamp DNA Mini Kit (Qiagen) from whole peripheral blood and BM samples (using CD138\u003csup\u003ePOS\u003c/sup\u003e and CD138\u003csup\u003eNEG\u003c/sup\u003e fractions as well), when available. Next Generation Sequencing of those samples was performed at the time of diagnosis and during follow up, by employing an Illumina Custom Enrichment panel of recurrently mutated genes in myeloid cells (N\u0026thinsp;=\u0026thinsp;36). The genes list is detailed in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Libraries were generated by the Illumina\u0026reg; DNA Prep with Enrichment workflow, following the manufacturer\u0026rsquo;s instruction; quality and size distribution were determined using Qubit fluorimeter and Agilent TapeStation system. 2 x 150 cycles, pair-end sequencing at 500 x median coverage depth was performed through the Genomics Core at IRCCS Istituto Giannina Gaslini on MiSeq platform (Illumina). The analysis of the data was performed with the BaseSpace\u0026reg; Software (Illumina) and the \u0026quot;Dragen Enrichment\u0026quot; pipeline with the \u0026ldquo;somatic\u0026rdquo; setting. Intronic and synonymous variants with no impact on splicing, missense and short ins/dels reported as \u0026ldquo;benign\u0026rdquo; or likely benign\u0026rdquo; in ClinVar were excluded.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Variants not reported in COSMIC, and with CADDphred score\u0026thinsp;\u0026lt;\u0026thinsp;25, were filtered out as well as ones reported with neutral/uncertain impact on protein function.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e The Variant allele frequency (VAF) cut-off \u0026gt;\u0026thinsp;1% was used to define CHIP.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Discovered variants with a VAF\u0026thinsp;\u0026gt;\u0026thinsp;40% or those with a frequency in general population\u0026thinsp;\u0026lt;\u0026thinsp;1% were filtered out. The selected somatic mutations and their VAFs were correlated with demographic and clinical parameters, including age, sex, ISS, R-ISS stage, outcomes, and occurrence of adverse events. Subsequent analyses at 12\u0026ndash;24 months after therapy were also run and for those MM patients with available BM samples, the tumoral and non-tumoral fractions (CD138\u003csup\u003ePOS\u003c/sup\u003e and CD138\u003csup\u003eNEG\u003c/sup\u003e cells, respectively) were analyzed in parallel.\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003ePanel genes list used\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.642262895174708%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.136439267886857%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget region (exon)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget region (exon)3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.312811980033278%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29783693843594%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget region (exon)5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.642262895174708%\" valign=\"top\"\u003e\n \u003cp\u003eASXL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.136439267886857%\" valign=\"top\"\u003e\n \u003cp\u003efull\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003eGATA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003efull\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.312811980033278%\" valign=\"top\"\u003e\n \u003cp\u003ePPM1D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29783693843594%\" valign=\"top\"\u003e\n \u003cp\u003e5, 6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.642262895174708%\" valign=\"top\"\u003e\n \u003cp\u003eBCOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.136439267886857%\" valign=\"top\"\u003e\n \u003cp\u003efull\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003eGNAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e8, 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.312811980033278%\" valign=\"top\"\u003e\n \u003cp\u003ePTEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29783693843594%\" valign=\"top\"\u003e\n \u003cp\u003efull\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.642262895174708%\" valign=\"top\"\u003e\n \u003cp\u003eBCORL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.136439267886857%\" valign=\"top\"\u003e\n \u003cp\u003efull\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003eGNB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e5-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.312811980033278%\" valign=\"top\"\u003e\n \u003cp\u003ePTPN11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29783693843594%\" valign=\"top\"\u003e\n \u003cp\u003e2-4, 8, 13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.642262895174708%\" valign=\"top\"\u003e\n \u003cp\u003eBRAF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.136439267886857%\" valign=\"top\"\u003e\n \u003cp\u003e11, 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003eIDH1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.312811980033278%\" valign=\"top\"\u003e\n \u003cp\u003eRAD21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29783693843594%\" valign=\"top\"\u003e\n \u003cp\u003efull\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.642262895174708%\" valign=\"top\"\u003e\n \u003cp\u003eBRCC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.136439267886857%\" valign=\"top\"\u003e\n \u003cp\u003efull\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003eIDH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.312811980033278%\" valign=\"top\"\u003e\n \u003cp\u003eRUNX1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29783693843594%\" valign=\"top\"\u003e\n \u003cp\u003efull\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.642262895174708%\" valign=\"top\"\u003e\n \u003cp\u003eCBL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.136439267886857%\" valign=\"top\"\u003e\n \u003cp\u003e8, 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003eIKZF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003efull\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.312811980033278%\" valign=\"top\"\u003e\n \u003cp\u003eSF3B1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29783693843594%\" valign=\"top\"\u003e\n \u003cp\u003e13-16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.642262895174708%\" valign=\"top\"\u003e\n \u003cp\u003eCREBBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.136439267886857%\" valign=\"top\"\u003e\n \u003cp\u003efull\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003eJAK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e12, 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.312811980033278%\" valign=\"top\"\u003e\n \u003cp\u003eSMC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29783693843594%\" valign=\"top\"\u003e\n \u003cp\u003efull\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.642262895174708%\" valign=\"top\"\u003e\n \u003cp\u003eCUX1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.136439267886857%\" valign=\"top\"\u003e\n \u003cp\u003efull\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003eKRAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e2, 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.312811980033278%\" valign=\"top\"\u003e\n \u003cp\u003eSRSF2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29783693843594%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.642262895174708%\" valign=\"top\"\u003e\n \u003cp\u003eDNMT3A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.136439267886857%\" valign=\"top\"\u003e\n \u003cp\u003efull\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003eMPL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.312811980033278%\" valign=\"top\"\u003e\n \u003cp\u003eSTAG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29783693843594%\" valign=\"top\"\u003e\n \u003cp\u003efull\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.642262895174708%\" valign=\"top\"\u003e\n \u003cp\u003eEZH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.136439267886857%\" valign=\"top\"\u003e\n \u003cp\u003efull\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003eMYD88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e3-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.312811980033278%\" valign=\"top\"\u003e\n \u003cp\u003eSTAG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29783693843594%\" valign=\"top\"\u003e\n \u003cp\u003efull\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.642262895174708%\" valign=\"top\"\u003e\n \u003cp\u003eFLT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.136439267886857%\" valign=\"top\"\u003e\n \u003cp\u003e14, 15, 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003eNF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003efull\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.312811980033278%\" valign=\"top\"\u003e\n \u003cp\u003eTET2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29783693843594%\" valign=\"top\"\u003e\n \u003cp\u003efull\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.642262895174708%\" valign=\"top\"\u003e\n \u003cp\u003eGATA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.136439267886857%\" valign=\"top\"\u003e\n \u003cp\u003efull\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.309484193011647%\" valign=\"top\"\u003e\n \u003cp\u003eNOTCH1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e26-28, 34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.312811980033278%\" valign=\"top\"\u003e\n \u003cp\u003eTP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29783693843594%\" valign=\"top\"\u003e\n \u003cp\u003efull\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eMULTIPARAMETER FLOW CYTOMETRY (MFC) ANALYSIS\u003c/h2\u003e\n \u003cp\u003eMFC analysis was performed at local laboratories on bone marrow (BM) samples collected at diagnosis or at any time before induction-therapy starting. EDTA blood (2 ml) was bulk lysed with 1 \u0026times; BD Pharm LyseTM Lysing buffer (30 ml) for 5 min, centrifuged at 1,500 rpm for 7 min and washed once in Dulbecco\u0026apos;s PBS. Cells (50 \u0026micro;l at 10\u0026ndash;20 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e/ml) were stained for cell surface markers with 20 \u0026micro;l antibody combinations for 15 min at RT. Intracellular nuclear (n) and cytoplasmic (cy) staining were performed after cell fixation and permeabilization using Intrastain kit by DAKO (Milan, Italy). The following monoclonal antibodies (MoAbs) combinations were employed: 1) CD138FITC/CD56PE/CD20PerCp/CD117APC/ CD45APC-H7/CD38PE-Cy7 2) cyKappaFITC/cyLambdaPE/CD19PerCp/ CD56APC/CD45APC-H7/CD38PE-Cy7. From the first combination, we obtained plasma cells quantification; from the second combination, we evaluated plasma cells immunophenotype and clonality. Acquisition and analyses were performed using FACSCantoTM II (Becton Dickinson, Mountain View, CA), and DiVa software: a minimum of 1\u0026ndash;2 x 10\u003csup\u003e4\u003c/sup\u003e of events for each sample were acquired. As controls, anti-isotype mouse antibodies were used. Importantly, CD38bright/SSC low population was representative for plasmacell fraction; cytofluorimetric data were analyzed when an abnormal fraction of plasmacell was detected. Based on expression levels of each cluster of differentiation (CD) measured, two categories were identified: bright-expressors and low(non)-expressors; dim level was considered as latter. Samples were considered as positive when at least 20% of MM cells expressed this antigenic profile, as previously described.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eSTATISTICAL ANALYSIS\u003c/h2\u003e\n \u003cp\u003eData were collected in spreadsheets and were analyzed using R statistical software (v. 4.0.5; RStudio) and SPSS (v. 25; IBM). Continuous variables were expressed as mean or median and compared with Wilcoxon rank-sum or student\u0026rsquo;s t-test. Categorical variables were expressed as counts and percentages and compared using Chi-square, Kolmgorov-Smirnov, binomial or Fisher\u0026rsquo;s exact test as appropriate. Log-rank (Mantel-Cox) test was used for survival analysis between group. A P value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. Correlation analysis between variables was performed using Pearson\u0026rsquo;s correlation method.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003ePrevalence of Clonal Hematopoiesis among NDMM patients\u003c/h2\u003e\n \u003cp\u003eA total of 76 MM patients whose peripheral blood (PB) samples were available at our institutions (Genoa, Catania and Cagliari) were screened for clonal hematopoiesis related mutations by using deep sequencing approaches. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes demographic and clinical characteristics of the entire cohort. The median age was 71 years old (range, 40\u0026ndash;90 years) with no gender (41 males vs. 35 females) or addictive behavior (smoking) prevalence as well; IgG was frequently observed as immunoglobulin isotype (48/76) with k and \u0026lambda; free lights chains occurring in 29 and 19 of these patients, respectively. The most represented induction regimens included MoAbs and PIs-based strategies with Len-based treatment used in 9 cases. Overall, next-generation sequencing (NGS) analyses revealed at least 1 CHIP variant in 46% of patients (35/76), with a median variant allele frequency (mVAF) of 0.022 (range 0.003\u0026ndash;0.340). (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA-B) No significant differences were observed between the CHIP and the non-CHIP carriers in terms of gender (p\u0026thinsp;=\u0026thinsp;1; chi-squared test), myeloma subtype (p\u0026thinsp;=\u0026thinsp;0.84; Fisher\u0026apos;s exact test) induction regimens (p\u0026thinsp;=\u0026thinsp;0.83; Fisher\u0026apos;s exact test) and lines of therapies (p\u0026thinsp;=\u0026thinsp;0.1961; binomial test). Interestingly, differently from previously reported data,\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e advanced age was not associated with higher CHIP prevalence although greater VAF (\u0026gt;\u0026thinsp;0.1) occurred among patients older than 70 years. (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC) Nineteen patients (54.2%) had a single CHIP mutation, while six (17.1%) and ten (28.5%) had 2 or more than three mutations, respectively. (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD) Consistent with others reports,\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e the most commonly mutated gene was \u003cem\u003eDNMT3A\u003c/em\u003e (54% of cases with mVAF of 9 %) folowed by \u003cem\u003eTET2\u003c/em\u003e (37% of cases with mVAF of 3.0%), \u003cem\u003eASXL1\u003c/em\u003e and \u003cem\u003eKRAS\u003c/em\u003e (both with mVAF of 11%), whereas mutations in splicing factors and \u003cem\u003eJAK2\u003c/em\u003e were rare. (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE and Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) Finally, CHIP-mutational spectrum analyses revealed majority of nonsynonymous followed by frameshift and synonymous mutations in affected genes. The gene-specific variant frequency across whole patients are summarized in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF. Collectively, these data acknowledge CHIP as a frequently observed event among MM patients at diagnosis, in line with reported data.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e List of variants observed in PB of CHIP carriers at diagnosis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eCHIP is associated with greater aggressiveness and poorer clinical outcomes\u003c/h2\u003e\n \u003cp\u003eCHIP negatively impacts MM patient\u0026rsquo;s clinical outcomes with shorter PFS and OS after ASCT; importantly, these negativities are cleared by IMiDs maintenance.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Consistent with these findings, we first sought to assess the impact of CHIP on disease progression parameters of our cohort. Initially we investigated cellular origins of these abnormalities: as shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA, no significant differences were observed between CHIP and bone marrow plasma cells percentage, thus suggesting that myeloid somatic mutations are unlikely to derive from the malignant tumor cells. Indeed, the analysis of bone marrow CD138\u003csup\u003eNEG\u003c/sup\u003e cells (i.e. fraction depleted of tumor plasma cells) showed correlation between BM and PB VAF values thus, confirming the non-tumor origin of screened myeloid mutations. (Pearson R\u0026thinsp;=\u0026thinsp;0.97, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB) Next, we examined the impact of CHIP on disease aggressiveness markers: higher \u0026beta;2-microglobulin (9.04 vs 4.15 mg/dl; p\u0026thinsp;=\u0026thinsp;0.05), 24 h urine protein output (1.44 vs 1.10 g/24hrs; p\u0026thinsp;=\u0026thinsp;0.019), creatinine (2.41 vs 1.47 mg/dL; p\u0026thinsp;=\u0026thinsp;0.05) and serum FLC k (557.2 vs 250 mg/dl; p\u0026thinsp;=\u0026thinsp;0.031) levels in parallel with lower eGFR (46.57 vs 64.38; p\u0026thinsp;=\u0026thinsp;0.024) and hemoglobin (10.61 vs 11.78 g/dL; p\u0026thinsp;=\u0026thinsp;0.019) values were found among CHIP carriers than control. (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC) In line with these data, Mosaic plots analyses showed disease stages differences between these two groups: higher prevalence of CHIP carriers was found among high-risk patients, defined according to International Staging System (ISS) and Revised (R)-ISS staging systems score. (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD) Together, these data confirm CHIP role as disease-aggressiveness biomarker whose evaluation could therefore improve risk stratification analysis of MM patients. Taking into account these assumptions, we next examined clinical outcomes across our cohort. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, C presence of CHIP was associated with significantly shorter PFS (mPFS of 493 days in those with CH vs. not reached at 5000 days in control; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and OS (mOS of 925 days in CH carriers vs. not reached at 5000 days in those without CH, respectively; p\u0026thinsp;=\u0026thinsp;0.025). A multivariate Cox-model CHIP focused on analysis, identified \u0026beta;-2 microglobulin high level as poorer clinical outcomes influencer for both PFS (HR, 1.20; 95% CI, 1.09\u0026ndash;1.33) and OS (HR, 1.19; 95% CI, 1.05\u0026ndash;1.34); remarkably, this parameter preserved its negative impact also among patients without CHIP. Furthermore, while platelets count significantly predicts both PFS (HR, 0.99; 95%CI, 0.98-1.00) and OS (HR, 0.98; 95%CI, 0.97\u0026ndash;0.99) among CHIP carriers, no significant effects were observed among patients without CHIP; similarly, albumin (HR,0.85; 95%CI, 0.77\u0026ndash;0.95) and creatinine levels (HR, 0.62; 95% CI, 0.43\u0026ndash;0.90) significantly influence PFS of CHIP carriers.(Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB,D)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eDynamic changes of Clonal Hematopoiesis during MM progression\u003c/h2\u003e\n \u003cp\u003eTo investigate clonal performance over time, we used serial VAF measurements as a surrogate for clone size. Although in general we found a slight increment of VAFs over time, it was no significant. (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA) However, by focusing on the most relevant genes for myeloid fitness, including \u003cem\u003eDNMT3A, ASXL1, TET2\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e, we observed significant increment in their VAF during follow -up, whilst a stable frequency affected all other genes. As result these data suggest coexistence of small clones with different clonal evolution (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). To delineate the factors that determine each clone\u0026rsquo;s growth rate, we analyzed serial samples in 10 CHIP carriers. The median time between the first and second sample collection was 10 months (range: 3\u0026ndash;12 months). While in general no significant increase in VAF was observed, we found that that clones bearing mutations in the fitness-related myeloid genes expanded most rapidly, with a significant increment in VAF over time. Moreover, backward tracking revealed that variants observed pre-existed at diagnosis, with either lower or similar VAF. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC shows exemplary fish-plots of two representative patients with available clinical information: lenalidomide refractory patient (MM_032) with \u003cem\u003eDNMT3A\u003c/em\u003e and \u003cem\u003eTET2\u003c/em\u003e mutant clones in BM (CD138\u003csup\u003eNEG\u003c/sup\u003e cells) and PB at diagnosis, showed increased size of both variants during treatment (starting from VAF of 2.68 and 19.26, to 2.95 and 26.9, respectively); whilst patient (MM_066) exposed to daratumumab-based regimen showed stability of \u003cem\u003eDNMT3A\u003c/em\u003e variant after 3 months, while \u003cem\u003ePTPN11\u003c/em\u003e mutant, observed at diagnosis in PB, CD138\u003csup\u003eNEG\u003c/sup\u003e and CD138\u003csup\u003ePOS\u003c/sup\u003e BM cells, resulted almost undetectable after treatment.\u003c/p\u003e\n \u003cp\u003eCollectively these data suggest that CHIP dynamics are not influenced by any specific treatment, but rather by the occurring mutant type: indeed, MDS/AML driving genes (i.e. \u003cem\u003eDNMT3, TET2, TP53\u003c/em\u003e e \u003cem\u003eKRAS\u003c/em\u003e) when involved, often increase their size even during treatment, regardless of response or disease status.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eCHIP presence enhances MM patients Frailty\u003c/h2\u003e\n \u003cp\u003eThe overall safety was not significantly different by CHIP presence although incidence of adverse events (AEs) of any grade was slightly higher in CH (97%) versus no-CHIP patients (80%); similarly, the rates of grade\u0026thinsp;\u0026ge;\u0026thinsp;3 AEs were lower in non-CHIP (36.5%) compared with CHIP carriers\u0026rsquo; patients (85.7%). (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) The most common hematological event was neutropenia of any grade, which occurred in 20 patients (57%) among CHIP group and in 13 (31.7%) among those without this abnormality. A similar trend was observed for high grade (\u0026ge;\u0026thinsp;3) hematologic toxicities, although differences were not significant between two groups (p\u0026thinsp;=\u0026thinsp;0.67). Among non-hematologic toxicities, peripheral neuropathy occurred more frequently among CHIP than no-CHIP carriers, even if differences were not significant (p\u0026thinsp;=\u0026thinsp;0.20 and 0.19 for any grade or higher toxicities, respectively). The most notable difference between groups was about infections, including SarSCov2 and pneumonia, which occurred in 71.4% of CHIP carrying patients and in 23% of those in control group. Importantly, most serious (AEs\u0026thinsp;\u0026ge;\u0026thinsp;3 grade) infections occurred among CHIP; unfortunately, low censured cases did not make differences significant (p\u0026thinsp;=\u0026thinsp;0.36 and p\u0026thinsp;=\u0026thinsp;0.37 for any grade or grade\u0026thinsp;\u0026ge;\u0026thinsp;3 toxicity, respectively). Clonal hematopoiesis has been reported to be associated with cardiovascular disease also in MM patients,\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e thus we analyzed cardiovascular events rates (CVEs) in our cohort: while comparable events were observed between two groups, venous thromboembolism seemed to affect especially CHIP carriers (51.4 vs 17%), although the limited number of cases prevented significant conclusions. To support the greater infections occurrence between CHIP patient, we next screened bone marrow immune-phenotype of our cohort, where available. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, effector T-cells resulted significantly reduced in CHIP vs. no-CHIP carriers with both CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e cells equally compromised, whilst B (CD19\u003csup\u003e+\u003c/sup\u003e), monocytes (CD14\u003csup\u003e+\u003c/sup\u003e) and NK (CD16+/CD56+/CD3-) cells were almost unaffected by CHIP presence. T-cell fitness impairment made us to calculate frailty scores of our patients by using age (0 if\u0026thinsp;\u0026le;\u0026thinsp;75 years, 1 if 76\u0026ndash;80 years, 2 if\u0026thinsp;\u0026gt;\u0026thinsp;80 years), Charlson Comobidity Index (CCI) (0 if\u0026thinsp;\u0026le;\u0026thinsp;1, 1 if\u0026thinsp;\u0026gt;\u0026thinsp;1), and ECOG PS (0 if 0, 1 if 1, 2 if\u0026thinsp;\u0026ge;\u0026thinsp;2).\u003csup\u003e35\u003c/sup\u003e Remarkably, this approach shown frail patients\u0026rsquo; enrichment among CHIP carriers compared with no-CHIP patients (97.1% vs 56%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) thus, supporting the enhanced AEs vulnerability observed in the former. (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) It is supposed that AEs occurrence leads to treatment discontinuation thus, we examined the impact of clonal hematopoiesis on event free survival (EFS) of our cohort: CHIP negatively impacted prognosis with its presence associated with shorter EFS than its absence. (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) A multivariate Cox-model indicates albumin level (HR, 0.92; 95%CI, 0.84-1) as the only parameter able to improve EFS among CHIP-carriers; by contrast, hemoglobin (HR, 2; 95%CI, 1.04\u0026ndash;3.84) and \u0026beta;2-microblobulin (HR, 2.10; 95%CI, 1.18\u0026ndash;3.74) values worsened outcome of patients without CHIP. (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB) Overall, our data suggest that CHIP impairs MM patient\u0026rsquo;s fitness making them more prone to the development of therapy-related toxicities, including infection; this in turn results in greater chance of treatment delays or dose reductions that limit patient\u0026rsquo;s ability to receive optimal therapies.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eDrug-associated toxicities in the entire cohort\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"937\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.921108742004265%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.83368869936034%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll cohort\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.733475479744136%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;CHIP\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.174840085287848%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; No CHIP\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.33688699360341%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAny grade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade 3 or 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAny grade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade 3 or 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\"\u003e\n \u003cp\u003eAny adverse event, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\"\u003e\n \u003cp\u003e60 (78.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\"\u003e\n \u003cp\u003e34 (97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\"\u003e\n \u003cp\u003e30 (85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\"\u003e\n \u003cp\u003e33 (80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\"\u003e\n \u003cp\u003e15 (36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\"\u003e\n \u003cp\u003eHematologic adverse event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\"\u003e\n \u003cp\u003eNeutropenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\"\u003e\n \u003cp\u003e33 (43.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\"\u003e\n \u003cp\u003e20 (57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\"\u003e\n \u003cp\u003e15 (42.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\"\u003e\n \u003cp\u003e13 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\"\u003e\n \u003cp\u003e10 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\" valign=\"bottom\"\u003e\n \u003cp\u003eAny grade: 0.19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\"\u003e\n \u003cp\u003eThrombocytopenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\"\u003e\n \u003cp\u003e12 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\"\u003e\n \u003cp\u003e8 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\"\u003e\n \u003cp\u003e7 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\"\u003e\n \u003cp\u003e4 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\" valign=\"bottom\"\u003e\n \u003cp\u003eGrade 3 or 4 : 0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\"\u003e\n \u003cp\u003eAnemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\"\u003e\n \u003cp\u003e10 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\"\u003e\n \u003cp\u003e9 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\"\u003e\n \u003cp\u003e7 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\"\u003e\n \u003cp\u003e1 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\"\u003e\n \u003cp\u003eNon-hematologic adverse event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\"\u003e\n \u003cp\u003eGI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\"\u003e\n \u003cp\u003e20 (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\"\u003e\n \u003cp\u003e15 (42.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\"\u003e\n \u003cp\u003e5 (14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\"\u003e\n \u003cp\u003e5 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\"\u003e\n \u003cp\u003e2 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\" valign=\"bottom\"\u003e\n \u003cp\u003eAny grade: 0.20\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\"\u003e\n \u003cp\u003ePN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\"\u003e\n \u003cp\u003e15 (19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\"\u003e\n \u003cp\u003e12 (34.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\"\u003e\n \u003cp\u003e9 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\"\u003e\n \u003cp\u003e3 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\" valign=\"bottom\"\u003e\n \u003cp\u003eGrade 3 or 4 : 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\"\u003e\n \u003cp\u003eRash\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\"\u003e\n \u003cp\u003e8 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\"\u003e\n \u003cp\u003e6 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\"\u003e\n \u003cp\u003e6 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\"\u003e\n \u003cp\u003e2 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\"\u003e\n \u003cp\u003e1 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\"\u003e\n \u003cp\u003eInfections\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\"\u003e\n \u003cp\u003eCoronavirus disease 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\"\u003e\n \u003cp\u003e20 (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\"\u003e\n \u003cp\u003e16 (45.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\"\u003e\n \u003cp\u003e5 (14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\"\u003e\n \u003cp\u003e4 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\"\u003e\n \u003cp\u003eAny grade: 0,36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\"\u003e\n \u003cp\u003e15 (19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\"\u003e\n \u003cp\u003e9 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\"\u003e\n \u003cp\u003e5 (14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\"\u003e\n \u003cp\u003e6 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\"\u003e\n \u003cp\u003e3 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\" valign=\"bottom\"\u003e\n \u003cp\u003eGrade 3 or 4 : 0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\"\u003e\n \u003cp\u003eCardio-vascular events\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\"\u003e\n \u003cp\u003eVTE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\"\u003e\n \u003cp\u003e25 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\"\u003e\n \u003cp\u003e18 (51.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\"\u003e\n \u003cp\u003e10 (28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\"\u003e\n \u003cp\u003e7 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\"\u003e\n \u003cp\u003e5 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\" valign=\"bottom\"\u003e\n \u003cp\u003eAny grade: 0,78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\"\u003e\n \u003cp\u003eIschemic events\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\"\u003e\n \u003cp\u003e5 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\"\u003e\n \u003cp\u003e4 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\"\u003e\n \u003cp\u003e1 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\"\u003e\n \u003cp\u003e1 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\" valign=\"bottom\"\u003e\n \u003cp\u003eGrade 3 or 4 : 0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\"\u003e\n \u003cp\u003eArrhythmias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\"\u003e\n \u003cp\u003e8 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\"\u003e\n \u003cp\u003e5 (14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\"\u003e\n \u003cp\u003e2 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\"\u003e\n \u003cp\u003e3 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\"\u003e\n \u003cp\u003e2 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\" valign=\"bottom\"\u003e\n \u003cp\u003ePN: peripheral neuropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\" valign=\"bottom\"\u003e\n \u003cp\u003eVTE: venous thromboembolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.945570971184633%\" valign=\"bottom\"\u003e\n \u003cp\u003eGI: gastrointestinal toxicity\u003c/p\u003e\n \u003cp\u003e*ChiSq test\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.84631803628602%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.901814300960512%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.858057630736393%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059765208110992%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.032017075773746%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356456776947706%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\" style=\"margin-right: calc(42%); width: 58%;\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFrailty analysis of entire cohort according to Gordon Cook et al. Leukemia. 2020\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"12\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 25.8921%;\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 11.4523%;\"\u003e\n \u003cp\u003eAll cohort\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 10.1245%;\"\u003e\n \u003cp\u003eCHIP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 10.4564%;\"\u003e\n \u003cp\u003eNo CHIP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 21.2448%;\"\u003e\n \u003cp\u003ep value*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 25.8921%;\"\u003e\n \u003cp\u003eECOG PS, n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 11.4523%;\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 10.1245%;\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 10.4564%;\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 21.2448%;\"\u003e\n \u003cp\u003e0.0005\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\" style=\"width: 25.8921%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.4523%;\"\u003e\n \u003cp\u003e8 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.1245%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.4564%;\"\u003e\n \u003cp\u003e8 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.2448%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 25.8921%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.4523%;\"\u003e\n \u003cp\u003e37 (48.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.1245%;\"\u003e\n \u003cp\u003e14 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.4564%;\"\u003e\n \u003cp\u003e23 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.2448%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 25.8921%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;\u0026thinsp;2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.4523%;\"\u003e\n \u003cp\u003e31 (40.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.1245%;\"\u003e\n \u003cp\u003e21 (60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.4564%;\"\u003e\n \u003cp\u003e10 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.2448%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 25.8921%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharlson Comorbidity Index, n\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.4523%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.1245%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.4564%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.2448%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 25.8921%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;\u0026thinsp;1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.4523%;\"\u003e\n \u003cp\u003e6 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.1245%;\"\u003e\n \u003cp\u003e1 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.4564%;\"\u003e\n \u003cp\u003e5 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.2448%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 25.8921%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;\u0026thinsp;1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.4523%;\"\u003e\n \u003cp\u003e70 (92.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.1245%;\"\u003e\n \u003cp\u003e34 (97.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.4564%;\"\u003e\n \u003cp\u003e36 (87.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.2448%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 25.8921%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at diagnosis, n\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.4523%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.1245%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.4564%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.2448%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 25.8921%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;\u0026thinsp;75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.4523%;\"\u003e\n \u003cp\u003e54 (71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.1245%;\"\u003e\n \u003cp\u003e25 (71.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.4564%;\"\u003e\n \u003cp\u003e29 (70.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.2448%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 25.8921%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e76\u0026ndash;80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.4523%;\"\u003e\n \u003cp\u003e9 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.1245%;\"\u003e\n \u003cp\u003e4 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.4564%;\"\u003e\n \u003cp\u003e5 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.2448%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 25.8921%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;\u0026thinsp;80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.4523%;\"\u003e\n \u003cp\u003e13 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.1245%;\"\u003e\n \u003cp\u003e6 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.4564%;\"\u003e\n \u003cp\u003e7 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.2448%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 25.8921%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrailty definition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.4523%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.1245%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.4564%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.2448%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 25.8921%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonfrail\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.4523%;\"\u003e\n \u003cp\u003e19 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.1245%;\"\u003e\n \u003cp\u003e1 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.4564%;\"\u003e\n \u003cp\u003e18 (44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.2448%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 25.8921%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrail\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.4523%;\"\u003e\n \u003cp\u003e57 (75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.1245%;\"\u003e\n \u003cp\u003e34 (97.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.4564%;\"\u003e\n \u003cp\u003e23 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.2448%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 82.4896%;\"\u003e*Fisher Test\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003ePlatelets count positively correlates with VAF in MM patients\u003c/h2\u003e\n \u003cp\u003eTo analyze the specific impact of clonal hematopoiesis among our cohort of MM patients, correlogram and principal component analyses (PCA) were performed based on available clinical data. As shown in \u003cstrong\u003eFig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e, CHIP and no-CHIP samples were projected onto the principal component (PC) space and two ellipses were calculated. Unfortunately, the first two PC1 to PC2, didn\u0026rsquo;t show a clear pattern thus suggesting that a clinical analysis-based approach fails to predict CHIP presence. CHIP refers to cancer-associated driver mutations present at mutational burden (VAF)\u0026thinsp;\u0026ge;\u0026thinsp;2% in subjects without hematologic abnormalities.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Based on this assumption, we built a model to screen VAF and clinical data in our cohort. The chosen mathematical model turned out more effective than previous approach, by identifying platelet count as clinical indicator for greater VAF in MM patients regardless of specific carried-variants; (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA) importantly, these results were also confirmed by focusing on the most frequent mutants \u003cem\u003eDNMT3\u003c/em\u003eA and \u003cem\u003eTET2\u003c/em\u003e. (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB-C and S2). Overall these results led us to speculate platelets count as surrogate biomarker for higher VAF rate among CHIP carriers.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eRecent studies have identified, in the blood of aging people, the presence of somatic mutations in hematopoietic cells, a condition referred as clonal hematopoiesis of unknown significance (CHIP).\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e By providing a fitness advantage to HSCs, CHIP is associated with a 0.5\u0026ndash;1% risk of progression to a non-plasma-cell hematologic neoplasm such as MDS and AML.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Moreover, CHIP is also associated with aging-related conditions linked to aberrant inflammatory response such as atherosclerosis and cardiovascular diseases.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e In cancers, the prevalence of CHIP is higher compared to healthy population, especially for those patients exposed to cytotoxic chemotherapy or radiation.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Recent studies suggest the presence of these myeloid clones also among MM patients with an impact on clinical outcomes;\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e however, biological relevance of this abnormality still remains to be elucidated.\u003c/p\u003e \u003cp\u003eHere, we report the prevalence of CHIP in a multicenter cohort of NDMM patients at time of diagnosis and after 12\u0026ndash;24 months of treatment, and describe the association of CHIP with clinical characteristics and outcomes of these patients. Overall we found somatic mutations in 46% of our population which is quite higher than expected and that reported earlier (21.6%).\u003csup\u003e25\u003c/sup\u003e These finding could be related to the sequencing used method, which achieves greater reading depth and therefore allows recording lower VAF (\u0026gt;\u0026thinsp;1%). Remarkably, consistent with prior studies, we found that CHIP status correlates with higher disease aggressiveness but differences were observed in term of aging, since previous studies have demonstrated CHIP prevalence with increasing age.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Although these findings appear in contrast, greater burden (VAF\u0026thinsp;\u0026gt;\u0026thinsp;0.1) occurred among patients older than 70 years which is consistent with other studies\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e as well as for the most frequently mutated genes (\u003cem\u003eDNMT3A\u003c/em\u003e followed by \u003cem\u003eTET2)\u003c/em\u003e. Moreover, we confirm association between poorer clinical outcomes, including progression-free and overall survivals which in turn reflects disease biologic status, as in part reported.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDuring recent years, novel drugs have significantly improved MM patient\u0026rsquo;s prognosis, but unfortunately continuous treatments have often limitations due to the development of serious and unexpected toxicities leading their discontinuation. Consistent with this observation, we analyzed, among CHIP carriers, those patients who experienced AEs during treatment: the event-free survival is greatly impaired by presence of CHIP with AEs of any grades, including infections, occurring more frequently in this group; similarly, recurrent infections were censured among CHIP carriers. Although the small sample size prevents any firm conclusion, we might speculate that a pro-inflammatory microenvironment may have impaired patient\u0026rsquo;s fitness leading to increased susceptibility to therapeutic-toxicity. Indeed, a detailed Bone Marrow environment focused on analysis revealed a consistent T cells impairment in CHIP carriers which, together with their higher frailty scores, explains the greater vulnerability to infections of this group. Moreover, while our data support CHIP screening at disease onset to better profile patient fitness, they also suggest its presence may influence T-cells based therapies, thus making CHIP evaluation a mandatory strategy to select the most appropriate therapeutic strategy for each MM patient.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eInterestingly, our study includes also longitudinal data for a subset of patients: a slight increase in VAF was recorded for MDS/AML drivers (\u003cem\u003eDNMT3A, TET2, ASXL1\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e) without appearance of additional mutations in other genes. This data demonstrates that micro-clones in the hematopoietic compartments of MM patients slightly increase in size but remain quite stable in terms of mutated genes type during disease evolution, thus suggesting that the mutational profile of myeloid cells may be largely unaffected by anti-MM therapies, including daratumumab-based regimens. On the other side, the presence of theses clones, as indicated by our data, correlates with a more aggressive disease and, worse outcome. As result, we speculate that the pro-inflammatory status, supported by these clones, facilitates tumor growth within BM microenvironment.\u003c/p\u003e \u003cp\u003eRecent studies suggest an impaired regenerative potential of hematopoietic stem cell grafts in autologous stem cell transplant recipients harboring CHIP. Namely, reduced stem cell yields and delayed platelet count recovery following ASCT is observed in presence of \u003cem\u003eDNMT3\u003c/em\u003eA and \u003cem\u003ePPM1D\u003c/em\u003e variants.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e Consistent with these data, we found striking association between platelets count and CHIP burden: high platelets level indicates greater VAF in CHIP carriers, regardless of specific mutants. As reported, CHIP is associated with a pro-inflammatory status which lead to fourfold increase of cardiovascular illness incidence.\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e Biologic mechanisms of these dependencies are complex but, increased level of several cytokines (i.e. IL-6, IL-1 beta, IL-8 and NLRP3) are shared features. As result, inflammasome targeting agents (i.e. vitamin C and aspirin) are currently tested in ongoing clinical trials as preventive strategies to block CHIP-inflammation cascade and improve outcomes of individuals with CHIP. (ClinicalTrials.gov Identifier: NCT06097663, NCT03682029) Here, although the limited samples size of our cohort, we pinpoint relevance of platelets count, a well-known inflammatory indicator light, as promising biomarker of VAF among CHIP carriers. Importantly, by linking inflammatory-prone status and higher VAF rate, our study suggests the predictive role of platelets count to timely identify the greater tumor aggressiveness observed among CHIP carriers compared to other MM patients. However, the impaired fitness status observed among these patients, make us to speculate that less intensive therapies should be preferred in case of high platelets count to reduce severe adverse events occurrence, especially after high-dose regimens. However, larger prospective studies are needed to clarify the impact of platelet counts on those MM patients carrying CHIP and to support a cause-effect relationship with this association.\u003c/p\u003e \u003cp\u003eIn conclusion, our study confirms that CHIP is frequently observed among MM patients where it correlates with a more aggressive disease and poorer clinical outcome as well. Interestingly, the presence of CHIP is associated with the early development of toxic events while its burden seems to be related to platelets count. Consistent with our results, we propose a specific management of CHIP-carrying MM patients, especially for what concern therapy-related toxicity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported in part by the Associazione Italiana per la Ricerca sul Cancro (AIRC, IG #23438, to M.C.), International Myeloma Society (IMS) and Paula and Rodger Riney Foundation Translational Research Award 2023.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHORSHIP STATEMENT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eE.G. and M.C. designed the research and wrote the manuscript; C.M., D.S., G.G., and I.T. performed samples and libraries preparation and collaborated to sequencing analyses; D.T. and F.L. performed the statistical and bioinformatics analyses; C.C., F.G., M.M., A.L, S.A., A.C and D.D. provided patient samples. F.DR., D.C., and R.M.L. revised the final version of manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDISCLOSURE OF CONFLICTS OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKumar, S. K. et al. Multiple myeloma. \u003cem\u003eNat. Rev. Dis. Primers\u003c/em\u003e. \u003cb\u003e3\u003c/b\u003e, 17046 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajkumar, S. V. Multiple myeloma: 2020 update on diagnosis, risk-stratification and management. \u003cem\u003eAm. J. 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CHIP (Clonal Hematopoiesis of Indeterminate Potential): Potent and Newly Recognized Contributor to Cardiovascular Risk. \u003cem\u003eCirculation\u003c/em\u003e. \u003cb\u003e138\u003c/b\u003e, 666\u0026ndash;668 (2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 3 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Multiple Myeloma, biomarkers, CHIP, toxicity, frailty","lastPublishedDoi":"10.21203/rs.3.rs-4930569/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4930569/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSomatic mutations of hematopoietic cells in peripheral blood of normal individuals refers to clonal hematopoiesis of indeterminate potential (CHIP) and is associated with a 0.5–1% risk of progression to hematological malignancies and cardiovascular diseases. CHIP has been reported also in Multiple Myeloma (MM) patients but its biological relevance remains still to be elucidated. Here, high-depth targeted sequencing on peripheral blood derived from 76 NDMM patients revealed CHIP in 46% of them with a variant allele frequency (VAF) between ~1% and 34%: the most frequently mutated gene was \u003cem\u003eDNMT3A\u003c/em\u003e followed by \u003cem\u003eTET2\u003c/em\u003e. A more aggressive disease features were observed among CHIP carriers, which also exhibited more high-risk (ISS and R-ISS 3) stages than controls. Longitudinal analyses at diagnosis and during follow-up showed slight increase of VAFs (p=0.058) for epigenetic (\u003cem\u003eDNMT3A, TET2\u003c/em\u003e, and \u003cem\u003eASXL1\u003c/em\u003e) and DNA repair (\u003cem\u003eTP53\u003c/em\u003e) genes (p=0.0123); a more stable frequency was observed among other genes, thus suggesting different temporal dynamics of CH clones. Adverse clinical outcomes, in term of overall and progression-free survivals, were observed among CHIP carriers, who also exhibited immune T-cells weakening and enhanced frailty status that predicted the greater risk of toxicity and consequent shorter event-free survival of this group. Finally, a correlogram analysis identified platelets count as biomarker for higher VAF among CHIP carriers, regardless of specific variant. Overall, our study, by highlighting specific biological and clinical features, paves the way for designing tailored strategies among MM patients carrying CHIP.\u003c/p\u003e","manuscriptTitle":"Clonal Hematopoiesis Impacts Frailty of Newly Diagnosed Multiple Myeloma Patients: A Retrospective Multicentric Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-15 10:55:49","doi":"10.21203/rs.3.rs-4930569/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-23T07:31:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-19T14:23:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-10T08:44:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336523070007312050467899523890099993899","date":"2024-08-30T06:58:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120646570858399606864635524656611648768","date":"2024-08-28T15:54:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-28T07:04:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-28T07:02:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-22T10:01:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-22T09:59:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-08-17T15:58:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9f77706e-811b-44bb-b10a-9e8ee07473e0","owner":[],"postedDate":"October 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":37950266,"name":"Health sciences/Oncology/Cancer"},{"id":37950267,"name":"Health sciences/Oncology/Cancer/Cancer genetics"}],"tags":[],"updatedAt":"2024-12-02T15:59:37+00:00","versionOfRecord":{"articleIdentity":"rs-4930569","link":"https://doi.org/10.1038/s41598-024-79748-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-11-26 15:57:00","publishedOnDateReadable":"November 26th, 2024"},"versionCreatedAt":"2024-10-15 10:55:49","video":"","vorDoi":"10.1038/s41598-024-79748-7","vorDoiUrl":"https://doi.org/10.1038/s41598-024-79748-7","workflowStages":[]},"version":"v1","identity":"rs-4930569","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4930569","identity":"rs-4930569","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}