Multiple Myeloma

Novel therapies for multiple myeloma.

Multiple myeloma (MM) is a hematological malignancy of aberrant clonal plasma cells that reside within the bone marrow (BM).1 The disease course differs from other BM malignancies essentially in 2 features. First, there are 2 precursor stages, monoclonal gammopathy of undetermined significance (MGUS) and smoldering MM (SMM), that can transition into MM over time.2 Second, once clinical MM develops, the disease remains largely incurable, and despite significant therapeutic improvements, relapse and refractoriness usually cannot be prevented. The interactions between MM cells and the BM microenvironment (BM-ME) are an area of particularly intense research, as tumor evasion and suppression of the host immune system constellate main factors of MM progression.3 Single-cell sequencing technologies that have emerged over the last years have the potential to significantly advance the field because they enable the evaluation of alterations in cell numbers and states as well as interactions between MM cells and the BM-ME. Recent studies have applied single-cell techniques at different precursor and MM stages to determine the comprehensive changes in the BM-ME and to identify mechanisms that foster oncogenesis.4–11 This article briefly summarizes these studies and proposes how the dissection of the BM-ME on a single-cell level can improve our understanding of MM pathogenesis, thereby advancing prognostication and the therapeutic landscape. We would like to emphasize that single-cell analyses are a rapidly expanding field and the number of published studies investigating the BM-ME in MM and its precursors is likely to grow exponentially in the next few years given the increased interest in and access to single-cell technologies. Thus, this perspective does not cover all available articles. THE IMMUNE MICROENVIRONMENT IN THE EARLY EVOLUTION OF MYELOMA Progression from precursor stages to clinical MM is highly heterogeneous, with some patients progressing quickly, while the others remain stable for decades.2 Treatment is hence not justified solely on the premise of a precursor diagnosis. Hence, recent research has focused on identifying SMM patients, who would benefit from early therapeutic interventions. Current risk classifiers are mainly based on clinical parameters and tumor aberrations (translocation t[4;14], t[14;16], gain1q and/or del13q), yet are only able to capture up to ~60% of patients who progress to MM within 2 years.12 Further molecular characterization of the BM-ME could improve the accuracy of prognostication and also pave the way for better therapeutic avenues. In that regard, Zavidij et al8 performed single-cell RNA sequencing (scRNA-seq) of the immune-ME from 5 MGUS, 11 SMM and 7 MM patients compared with 9 healthy individuals. The authors show that substantial alterations of the immune-ME are already present at the MGUS stage. These include increased populations of natural killer (NK) cells, T cells, particularly Tregs, and nonclassical CD16+ monocytes. CD14+ monocytes showed dysregulated expression of major histocompatibility complex (MHC) type II genes, which resulted in T-cell suppression in in vitro cultures. The progression from MGUS to SMM was associated with the loss of granzyme K+ memory cytotoxic T cells leading to reduced immunosurveillance in in vivo models. Symptomatic MM was characterized by an increased INF-alpha response across all immune cell types, which has been shown to promote immunosuppression, favoring expansion of MM cells. Similarly, using scRNA-seq and mass cytometry, Bailur et al11 showed that precursor stages already harbor BM-ME alterations compared with healthy donors, including early changes in NK and myeloid cells as well as increased terminal effector differentiation and enrichment of stem-like T cells in MGUS. Another scRNA-seq study with 8 MGUS, 7 SMM, and 10 MM patients showed substantial alterations in the immune-ME of MM compared with its precursor stages.5 Symptomatic MM was enriched for CD14+ and CD16+ monocyte populations, memory B and CD8 effector cells compared with early stages with a transitional decrease of CD4-positive T cells and MAIT cells. However, there was no differential expression of exhaustion markers such as PD1, TIGIT, and LAG-3 when comparing stages. Importantly, cell proportions were heterogeneous even across the same disease stage, emphasizing the need for larger datasets and also underscoring the existence of an individual immune phenotype, which is likely influenced by other factors than the aberrant plasma cells. THE IMMUNE MICROENVIRONMENT DURING MYELOMA THERAPY AND IN RELAPSING DISEASE Established therapeutic strategies yield high complete remission rates, but disease relapse will ensue in the vast majority of patients. Yet, the disease course remains very heterogeneous with some patients relapsing within months of treatment initiation and others experiencing deep remission for >10 years. This observation has led to the hypothesis that MM cells can enter a dormant state with regrowth over time when conditions become permissive, particularly when immune surveillance falters.13 Although many MM therapies try to stimulate the immune-ME to fight the disease, it has been also shown that therapy causes immunosuppression with ensuing cytopenias and hypogammaglobulinemia, emphasizing that the balance of a stimulated or dysfunctioning immune-ME is very delicate. Deciphering the alterations within the immune-ME during therapy will hence be challenging. Tirier et al6 investigated 20 relapsed/refractory MM patients prior and post salvage therapy and compared them to 8 healthy donors. Applying scRNA-seq to the immune-ME and plasma cells, the authors analyzed both compositional changes and alterations in cell–cell interactions. In this population, which was heavily enriched for gain1q, the investigators found depletion of CD4+/CD8+ naïve and CD4+ memory cells with enrichment of CD14+/CD16+ monocytes, effector T-cell populations (CD8+ memory and cytotoxic cells) as well as gamma/delta T cells with an increase of inflammatory markers in the BM that were primarily secreted by CD14+ monocytes and MM cells. In a similar attempt to understand co-evolution of MM cells and their immune-ME, Liu et al4 described longitudinal alterations during disease progression in 14 MM patients. They report that genetic alterations tend to be associated with distinct immune cells clusters, for example, patients with translocation t(11;14) showed separate T-cell clusters with upregulation of lysine methyltransferase genes KMT2A and KMT2C in CD8+ T cells. Other studies addressed the role of the immune-ME in early relapse patients. Pilcher et al7 performed scRNA-seq on the immune-ME in patients with rapidly progressing disease (<18 months) compared with those without progression within 4 years of follow-up. Early relapse patients had significantly higher numbers of exhausted CD8+ T cells (GZMK+ and TIGIT) with decreased expression of cytotoxic markers (PRF1, GZMB, and GNLY). Similar to the study by Tirier et al, there were also alterations within the monocyte/macrophage compartment with an increase of M2 macrophages in rapid progressors. Yao et al combined scRNA-seq with an additional capture of surface markers (Cellular Indexing of Transcriptomes and Epitopes by Sequencing, CITE-Seq) and the mass cytometry approach cytometry by time of flight (CyTOF) to elucidate the biology of early relapse.9 Stratifying 18 MM patients by international staging system (ISS) and by time to progression (<6 months for rapid progressors or 6 months to 5 years for slow progressors), they found aggressive disease (ISS 3) to be associated with a decrease in CD4+ T cells and the CD4/CD8 ratio. Last but not least, recent studies investigated alterations of the BM-ME during immunotherapies, as those heavily rely on a functional immune system for optimal efficacy. Adams III et al determined the effect of the CD38-antibody Daratumumab on immune cells in the BM and peripheral blood (PB) in relapsed MM patients.10 Daratumumab depleted NK cells and reduced the amount of CD38+ basophils. As seen in the previous studies, immune profiling differed between responders and nonresponders with higher granzyme B expression in CD8+ T cells of responders, suggesting treatment induced T-cell activation with increased killing capacity. Friedrich et al determined the immunological single-cell mechanisms of resistance to bispecific BCMAxCD3 T-cell engager (TCE) therapy, a novel immunotherapy leading to unprecedented response rates in refractory MM.14 They show that TCEs lead to clonal expansion of immune cell subsets, particularly effector CD8+ T cells and that primary or acquired failure to TCE therapy is associated with exhaustion of CD8+ T cells. Importantly, treatment failure or loss of response was not solely associated with immune-ME alterations but also MM intrinsic adaptations, such as loss of the target epitope and MHC class I protein. Taken together, current research is rapidly progressing to identifying alterations within the immune-ME of particularly aggressive disease, as these patients are in most need of novel therapeutic approaches. Single-cell sequencing studies so far appear to uniformly show dysregulation of T cells, particularly cytotoxic/effector T cells with increase of exhaustion markers in progressing patients. Furthermore, there appear to be alterations leading to a dysfunctional state in the monocytes/macrophages compartment in more aggressive MM. CHALLENGES AND FUTURE PERSPECTIVES Single-cell techniques have the potential to comprehensively dissect disease mechanisms within the immune-ME and their relationship with MM cells, thereby offering a path to truly individualized medicine leading to improved prognostic measure and therapeutic avenues. Challenges remain in unifying cell subset annotations to better compare studies. The encountered heterogeneity between patients could be accounted for by increasing sample sizes and by stratifying patients based on the parameters such as age, genetic profiles of MM, and treatment received. Age has shown to have a significant impact on immune function and is manifested at multiple levels including reduced production of B and T cells and diminished function of mature lymphocytes.15 It is hence not surprising that increased age has repeatedly shown to be an adverse risk factor for clinical outcome in MM, which is likely not only due to the higher prevalence of comorbidities in this population.16 The notion that molecular subgroups of MM can differ in the surrounding BM-ME has been reported long before the onset of single-cell technologies. For example, patients of the low bone disease molecular subgroup barely show osteolytic lesions, suggesting that this subset spares the stimulation of osteoclasts and the resulting bone resorption.17 The recently discovered associations of an immune-ME signature and +1q MM and the distinct clustering of immune cells in patients with translocation t(11;14) further corroborate the hypothesis that the MM genotype and phenotype influences its surrounding or vice versa. Hence, analyzing MM in its BM-ME on a single-cell level will offer the unique opportunity to associate alterations in the ME to intrinsic MM cell features, which could revolutionize our understanding of MM biology. Treatment has a significant effect on the immune-ME, and can lead to alterations in cell populations and expression patterns, which will need to be taken into account when analyzing single-cell data.10,18,19 These alterations are associated with immune cell cytotoxicity, exhaustion, and senescence, which can influence the further disease course.20 Conversely, comprehensive immune profiling before therapy could determine which treatment modalities would yield best responses. The use of single-cell techniques before and during therapy would enable us to dissect mechanisms of response versus resistance and could tailor therapeutic approaches based on the patients' individualized immune profile. Additional challenges to single-cell technologies are based on the current limitations of these techniques and are unlikely to be overcome by merely increasing the number of samples. This includes the limited number of analyzed cells due to the high costs. As an alternative or complementary approach, multidimensional flow cytometry allows for a cost-effective assessment of immune-phenotypes in the BM-ME and yields prognostic information in MM and its precursor stages.19,21,22 A particular hindrance to single-cell technologies is the lack of spatial resolution as they are performed on BM aspirates or PB. Although the current algorithms can predict interactions between cell populations based on the surface markers, the BM-ME architecture remains elusive. Furthermore, aspirates contain very small numbers of adherent cells, such as stromal cells, osteoblasts, and osteoclasts. Hence, to determine the structure and composition of the BM and its relationship with MM in great detail, it will be important to combine single-cell technologies with multiplex immunohistochemistry studies.23 Of note, BM-ME single-cell data are usually derived from a random BM site and might not be representative of the whole disease process. This is particularly important for MM, as the vast majority of patients present with focal lesions where genetic profiles of MM cells have been shown to be distinct from the random BM site.24,25 First reports suggest that this also holds true for the BM-ME at these sites.26,27 There is growing awareness that MM cell heterogeneity is, to at least some degree, reflected by circulating tumor cells, which are thought to originate from all affected MM sites and have prognostic impact.22 Thus, paired profiling of the BM and PB could unravel alterations that are not evident at the random BM site. A summary of the anticipated comprehensive analysis of MM and its BM-ME in the future is presented in Figure 1.Figure 1.: Summary of the anticipated future comprehensive analysis of myeloma and its microenvironment at a single-cell level.In conclusion, single-cell technologies have already influenced the MM field by identifying mechanisms underlying treatment resistance and they have the potential to further advance our current understanding of risk classification, prognostication, and individualized treatment approaches. While high costs currently limit their application, it is anticipated that technical advances and broader use will significantly reduce the costs and allow for larger sample collections. Hence, comprehensive and longitudinal profiling of the immune-ME using multiomic single-cell approaches with integration of the spatial BM architecture and genomic profiling of MM cells could soon be a reality for our MM patients and will eventually lead to improved clinical outcomes and cure. ACKNOWLEDGMENTS As a result of the space constraints, we apologize that we were unable to cite all our colleagues that have made an impact in defining our current knowledge on this topic. DISCLOSURES NW was supported by the Dietmar-Hopp Foundation. The authors declare no conflicts of interest. SOURCES OF FUNDING NW was supported by the Dietmar-Hopp Foundation.

Age 40 Years and Under Does Not Confer Superior Prognosis in Patients with Multiple Myeloma Undergoing Upfront Autologous Stem Cell Transmplant

Immunomodulatory drugs lenalidomide and pomalidomide inhibit multiple myeloma-induced osteoclast formation and the RANKL/OPG ratio in the myeloma microenvironment targeting the expression of adhesion molecules

Due to prolonged survival and increasing treatment costs per patient, cancer treatment imposes an increasing burden on total healthcare expenditures. Healthcare expenditures in cancer will approximately increase from 5.6 billion euros in 2015 to 61 billion in 2060 in the Netherlands, of which 49 billion will be hospital costs.1 Previous research showed that treatment costs in cancer patients are highest in the first and last year of life.2,3 Health economics research demonstrated that proximity to death is a better predictor of increasing health expenditures than age, especially in cancer.4,5 Furthermore, aggressive treatment and higher costs of treatment at the end of life are associated with worse quality of life.6–8 Therefore, critically reviewing the use of intensive treatment at the end of life may provide valuable insights to improve the quality of life while reducing costs. Although there is an increasing interest in end-of-life care in cancer patients, research on the costs of end-of-life care in hematological malignancies is limited. Patients with hematological malignancies, and multiple myeloma (MM) patients in particular, are more likely to undergo intensive hospital care compared with other cancer patients in the last month before death.9–13 Nevertheless, analyses addressing the burden and costs of end-of-life care in MM are lacking. Given the limited research on the use and costs of end-of-life care,14 we aimed to describe the hospital-related care in the last month before death in patients diagnosed with MM and to provide a comparison with other malignancies in our hospital. In this single-site study, anonymized data from electronic health records were used. All MM patients deceased between 2017 and 2021 were included in our analyses. Hospital care activities were defined as all clinical and outpatient diagnostic, treatment (anticancer treatments excluded), follow-up, and aftercare activities by medical professionals performed in the Dutch hospital setting and were obtained from claims data, which are standardized throughout the Netherlands. We used standardized cost estimates for the year 2021 of Amphia Hospital, which corresponded with costs in comparable nonacademic Dutch teaching hospitals (Suppl. Table S1). The costs of anticancer treatments, that is, medication treatments to stop the progression of cancer, were analyzed separately. Furthermore, subanalyses were performed by age, using the cutoff between old age and very old age of 80 years as is used by the World Health Organization,15 and by time since diagnosis, in which diagnosis could mean time of diagnosis or time of start of oncological treatment (after initial diagnosis by other medical departments). The total costs presented in this study are accumulated over 5 years (2017–2021) and mean costs per patient were calculated over the total group of deceased patients. To put the outcomes of the MM group in perspective, a cohort with reference data from patients with other malignancies (including 500 patients with hematological malignancies other than MM) was collected similarly. These patients were diagnosed and treated in Amphia Hospital with at least 1 hospital care activity in the last year before death (to only include patients who were recently actively treated or received follow-up for malignant disease) (Suppl. Table S2). Descriptive statistics for end-of-life care, defined as costs of hospital-related care in the last 30 days before death, were performed for the MM and the reference group. Because the comparison of groups was primarily intended to interpret the MM data, no inferential statistical analyses were performed.16 Between 2017 and 2021, a total of 131 deceased patients with MM were identified, with a median age at death of 76. In total 44 (33.6%) patients had anticancer treatments and 106 (81%) patients had hospital care activities in the last 30 days before death. In the reference group, 4841 deceased patients were identified with a median age at death of 73 years. In total 733 (15.1%) received anticancer treatments and 3582 (74%) patients had hospital care activities in the last 30 days before death (Suppl. Table S3). The mean costs per patient of anticancer treatment were €1614 per patient in MM in the last 30 days before death between 2017 and 2021, which was higher than in the reference group, €452 per patient (Figure 1A). Also, the proportion of MM patients receiving anticancer treatment was higher than in the reference group (33.6% versus 15.1%). The higher costs per patient of anticancer treatments in MM may be caused by the fact that MM treatments are more expensive. The higher proportion of patients treated with anticancer treatment may be caused by sudden health deteriorations leading to death in actively treated patients. The mean costs per patient of hospital care activities in MM patients were €8367 per patient, which was considerably higher than in the reference group (€5414/patient) (Figure 1) and also higher than the mean anticancer treatment costs per patient. The total accumulated costs of anticancer treatment and hospital care activities in the last 30 days of life of patients with MM were €1,307,546 in 2017–2021 (see Suppl. Table S4 for more details on total costs).Figure 1.: Costs per patients 30 days before death in MM (N=131) and the reference group (N=4841). (A) Cost per patient of ACT and hospital care. (B) Cost per patient of hospital and ICU admissions. (C) Cost per patient of treatment*, diagnostics, outpatient/ED visits. (D) Costs per patient by age group. (E) Cost per patient by time since diagnosis. *ACT excluded. The scales in figures (A)–(E) are different. MM = multiple myeloma; ACT = anticancer treatment; ICU = intensive care unit; ED = emergency department.Analyses by type of hospital activity showed that hospital admissions in the last 30 days before death were observed in 57% (N=75) of the MM patients and caused the highest total end-of-life costs and €3424 per patient. Intensive care unit (ICU) admissions caused €1666 per patient (N=17, 13.0%). In the reference cohort, the proportion of patients with hospital and ICU admissions was lower (49% and 6.9%, respectively), and also mean costs per patient were lower (€2678 and €628, respectively), which means that relatively more MM patients were admitted to the hospital and ICU, with longer length of stay, compared with the reference group (Figure 1B). Costs of treatments, which included (blood)transfusion therapy, dialysis, surgery, paramedical, and rehabilitation treatments (excluding anticancer treatments), in the last 30 days before death were observed in 65% (N=85) MM patients, with mean treatment costs per patient of €1696 per patient of which costs of blood transfusions and dialysis were the largest cost components (Suppl. Table S4). These (supportive) treatment costs were higher in MM than in the reference cohort and were equivalent to the costs of anticancer treatments in MM (Figure 1C). Furthermore, diagnostics costs (N=92), including radiology and laboratory tests, were €1111 per patient, and outpatient and emergency department consultations costs were €461 per patient (N=90), which were both higher than in the reference group (Figure 1C). Subanalysis by age groups showed that hospital care activities and anticancer treatment costs in the last 30 days before death were lower in MM patients ≥80 years (N=43) compared with younger MM patients (N=88). Hospital care activity costs and anticancer treatment costs were also considerably higher in MM patients <80 years compared with patients <80 in the reference group (Figure 1D). In the subanalysis by time since diagnosis, we found €10,276 per patient hospital care activity costs in the last 30 days before death in MM patients who died within 1 year after diagnosis (N=39) and €10,798 per patient in MM patients who died between 1 and 5 years after diagnosis (N=45). In MM patients who survived >5 years after diagnosis, the total costs of hospital care activities were considerably lower at €4456 per patient (N=47). End-of-life anticancer treatment costs in MM patients based on the time since diagnosis showed the following: €1377 per patient in patients who died within 1 year, €2336 per patient in patients who died between 1 and 5 years, and €1119 per patient in patients who lived >5 years after diagnosis. The median survival since diagnosis in the MM group was 1039 days (2.9 years). In the reference group, all costs per subgroup were lower except in patients with >5 years since diagnosis (hospital care activity costs €5080 per patient), and the median survival time was 432 days (1.2 years) (Figure 1E). The percentages of patients receiving intensive end-of-life treatment and costs per patient at the end of life are higher in MM compared with patients with other malignancies. This may be explained by the fact that more treatment lines are available in MM compared with other malignancies, which may explain the higher proportion of MM patients being treated near the end of life. Furthermore, unforeseen health deteriorations caused by infections or other adverse events leading to unexpected death are more common in MM. Also, MM patients are more often in need of (intensive) supportive treatments such as blood transfusions and dialysis treatments, compared with patients with other malignancies (Figure 2A and 2B).Figure 2.: Type of costs in the last 30 days before death in percentages of total costs including anticancer treatments. (A) MM. (B) Reference cohort. MM = multiple myeloma; ACT = anticancer treatment; ICU = intensive care unit.This study aimed to contribute to the scarce research on end-of-life costs of hematological malignancies and particularly in MM. However, the generalizability of the findings needs to be addressed with caution due to the small sample size. Data collection and analyses were relatively easy to perform and repeat with updated data. Nevertheless, hospital, hospice, or home care policies may differ between countries, which should be considered when comparing these outcomes to findings in other countries. Also, the high costs of new anticancer treatments versus off-patent medications need to be considered when monitoring end-of-life costs over time. Further research preferably in a larger population of MM patients, addressing the effect of end-of-life care on both quality of life and costs is necessary. In conclusion, costs per patient in the last 30 days of life were higher in MM patients compared with patients with other malignancies, predominantly caused by hospitalizations and high costs of ICU admissions, especially in patients deceased within 5 years from diagnosis. Supportive treatment costs, primarily caused by blood transfusions and dialysis, were equal to the costs of anticancer treatment in MM patients’ last 30 days of life. More awareness about the high intensity and potential burden of treatment at the end of life in MM may improve the quality and efficiency of care, although further research to validate our results in other settings is necessary. AUTHOR CONTRIBUTIONS CB, MK, and HW presented the idea. All authors actively participated in the study design. CB, DS, and FB executed the data capture. CB, DS, FB, MK, HW, and HB performed the analyses and drafted the article. All authors critically revised the article and approved the final version. DISCLOSURES PS: Received Research grants from Amgen, Celgene, Janssen, Skyline Dx. Honoraria from Amgen, Celgene, Janssen, Karyopharm, Seagen, Chairman of European Myeloma Network, Co-chairman of HOVON Myeloma Working Group. HW: travel expenses Astellas and Ipsen; Honoraria Astellas, Roche, Merck. HB: Reports consulting or advisory role for Pfizer (paid to institute) and research funding from BMS-Celgene (paid to institute). All the other authors have no conflicts of interest to disclose. SOURCES OF FUNDING The authors declare no sources of funding for this manuscript.

Clonal hematopoiesis of indeterminate potential (CHIP) is defined by the age-dependent accumulation of somatic leukemia-associated driver mutations in hematopoietic stem cells, in individuals with normal blood counts and with absence of an underlying myeloid neoplasm (MN).1, 2 CHIP is associated with an increased risk of developing MN and an increased all-cause mortality, largely due to cardiovascular disease.3 The presence of CHIP prior to receiving chemotherapy and radiation has been associated with therapy related MN (T-MN), such as myelodysplastic syndromes (MDS) and acute myeloid leukemia.4 Autologous stem cell transplantation (ASCT) is an effective treatment modality for managing higher-risk patients with non-Hodgkin's lymphoma (NHL) and multiple myeloma (MM). In a seminal NHL study, 30% of patients were found to have CHIP at the time of ASCT, with the presence of CHIP being associated with an increased rate of T-MN (10-year cumulative incidence of 14.1% vs 4.3%) and an inferior overall survival (10 year OS 30.4% vs 60.9%).5 In MM, targeted sequencing of 629 patients prior to ASCT detected CHIP in 21.6% of patients, with the presence of CHIP strongly associating with inferior OS (HR 1.34, p = .02) and an inferior progression-free survival (PFS, HR 1.45, p < .001). Interestingly, in this study, adverse CHIP-associations were apparently overcome by lenalidomide maintenance therapy.6 Unlike in NHL, CHIP in MM was not associated with T-MN; while lenalidomide maintenance therapy, independent of the presence or absence of CHIP, was associated with T-MN (p = .047) and second primary malignancies (SPM).6 We carried out this study to assess the prevalence and prognostic impact of CHIP in a relatively uniform cohort of MM patients at the time of ASCT, with all patients going on to receive lenalidomide maintenance therapy. Successive MM patients who consented to have their pre-ASCT bone marrow (BM) sample collected and who underwent first ASCT at Mayo Clinic, followed by lenalidomide maintenance therapy, were included in the study. The BM mononuclear cell DNA from pre-ASCT samples was extracted after excluding CD38/CD138+ (negative selection) plasma cells and then subjected to targeted NGS testing (42-myeloid related genes) by previously described methods.7 All patients were closely followed for the development of T-MN as defined by the 2016 WHO criteria, arterial and venous thromboembolism (VTE) and SPM.8, 9 Response to therapy was assessed using the international myeloma working group (IMWG) consensus criteria 2016.10 Statistical methods are highlighted in the supplemental material. Clonal hematopoiesis was detectable in 23 (23%) of 101 MM patients assessed in the study (Table S1, Figure S1). Clinical characteristics, MM risk stratification, median number of prior therapies, response to therapy, ASCT conditioning regimens, engraftment data, day +100 post ASCT outcomes and median duration of lenalidomide maintenance are outlined in Table 1. Except for a higher median age at MM diagnosis in MM patient with CHIP (p = .002), there were no other significant differences between the two groups (Figure S2). Ten (43.5%) patients in the MM CHIP group and 30 (38.0%) in the MM no CHIP group, received alkylatingagent-based induction therapy prior to ASCT (p = .66). Melphalan 200 mg/m2 conditioning was used in 87.1% of patients (95.7% in the CHIP vs 84.6% in the no CHIP group, p = .45), while the remainder received melphalan 140 mg/m2 conditioning. The median duration of lenalidomide maintenance therapy was 21 months (10–36); 16 months in MM patients with CHIP and 22 months in MM patients without CHIP (p = .76), with the median lenalidomide dose being 15 mg (range 10–15 mg; 10 mg in the CHIP group and 15 mg in the no CHIP group, p = .08). The most frequent CHIP mutations encountered included DNMT3A [52%; median variant allele frequency (VAF) 9.0%, range 2.0%–29.2%], TET2 (26%; median VAF 3.0%, range 2.0%–4.8%), followed by TP53, PPM1D and BRAF (10.0% each), respectively (Figures 1(A) and S1). Sixteen patients (69.6%) had one mutation, while seven (30.4%) had >1 mutation and two patients had four mutations each (Figure 1(B)). Eight (66.6%) of 12 patients with DNMT3A mutations had nonsynonymous missense mutations, while four had deletion variants: with no patient harboring the commonly mutated DNMT3A R882 hot spot. There were no statistically significant differences in CHIP mutation distribution, including TP53 and PPM1D mutations, between MM CHIP patients that received alkylating-agent based induction therapy prior to ASCT, vs MM CHIP patients that did not (Figure 1(C)). At last follow up, 70 (69.3%) relapses after ASCT and 41 (40.6%) deaths were documented. Twenty-nine patients (28.7%) were on salvage therapy whereas 13 (12.9%) were on lenalidomide maintenance and 14 (13.8%) were on observation alone, with no statistically significant differences between MM patients with CHIP vs MM without CHIP. Rates of VTE were similar between MM patients with CHIP (30%) and those without CHIP (24%), with similar rates of provoked thromboses (33% vs 29%, p = .4). Thromboses were diagnosed in typical locations in individuals with CHIP including seven lower extremity deep vein thromboses (DVT) and two pulmonary emboli (PE). In contrast, a variety of VTE locations were diagnosed in those without CHIP, including 10 lower extremity DVT, three PE, one DVT with PE, two upper extremity DVT and one portal vein thrombosis. There was a distinction in VTE timing with regards to lenalidomide use between MM patients with CHIP and those without CHIP. For MM patients with CHIP, 2/9 (22.2%) VTE occurred while on lenalidomide, 2/9 (22.2%) occurred prior to lenalidomide and 4/9 (44.4%) occurred at least 3 months after discontinuation of lenalidomide therapy. For MM patients without CHIP, majority (13/17, 76.5%) of VTE occurred while on lenalidomide, with a minority (11%) occurring either before or at least 3 months after discontinuation of lenalidomide. While lenalidomide is a known risk factor for thrombosis in MM, the fact that 44% of VTE in MM with CHIP occurred >3 months after discontinuing lenalidomide, suggests that CHIP might increase VTE risk in this setting (P = .04). Median OS from the time of diagnosis of the entire cohort was 124.6 months (95%CI 97.5-N/A months) with a corresponding 5 year OS of 82.0% (95% CI 74.8%–89.9%). There was no difference in median OS between MM with CHIP vs those without CHIP (100.2 months; 95%CI 76.2-NA months vs 135.6 months; 95%CI 106.3-N/A months, p = .27) (Figure 1(D)), including assessments with individual CHIP-mutations. The median EFS after ASCT was 36.4 months (95%CI 30.5–48.5 months), with there being no difference in median PFS between MM CHIP (36.4 months, 95%CI 24.1–58.5 months) patients vs MM patients without CHIP (36.4 months, 95%CI 29.9–52.4 months) (p = .34) (Figure 1(E)), including TP53 mutations (Figure S3). There were also no differences between the two groups with regards to non-relapse mortality (Figure S4), cumulative incidence of relapse after ASCT (Figure S5) and time to next treatment. Nineteen (18.8%) SPM were documented, 7 (30.4%) in MM CHIP group vs 12 (15.3%) in the MM no CHIP group (Figure 1(F), p = .13), with corresponding 5-year cumulative incidence rates of 22% and 13%, respectively. These SPM included five (4.9%) hematological malignancies (two in MM CHIP vs three in MM no CHIP), seven (6.9%) skin cancers (three in MM CHIP vs four in MM no CHIP) and seven visceral malignancies (two in MM CHIP vs five in MM no CHIP) (Table S2, Figure S6). The two MM CHIP patients who developed T-MN/MDS had TP53 and PPM1D mutations, respectively. In the MM no CHIP group, there was one patient with B-acute lymphoblastic leukemia and two patients with T-MDS with monosomal karyotypes. In summary, we define the CHIP landscape in MM patients at the time of ASCT, with mutations in epigenetic regulator genes being most common (57%), followed by tumor suppressor genes (17%). Unlike in NHL, presence of CHIP at time of ASCT in MM did not impact OS, PFS and incidence of T-MN; a finding potentially attributable to immunomodulatory properties of lenalidomide, or to the use of maintenance therapy in general.6, 11 While the presence of CHIP and lenalidomide therapy have individually been associated with increased risk of thromboses,1-3, 9 we did not see synergy in MM patients with CHIP, although the timing and patterns of thromboses suggest that CHIP might negatively influence thrombotic risk. While SPM have been well described with lenalidomide maintenance therapy,9 we did not see any differences in SPM and hematological malignancies between the two groups. The findings of this study independently confirm a prior observation on the potential ability of lenalidomide maintenance to mitigate the expected adverse effects of CHIP on OS and PFS in MM patients' post-ASCT6; an important consideration given that approximately 13 000 MM patients undergo ASCT in the US annually, with lenalidomide maintenance considered as standard of care.12, 13 Given the smaller sample size and the inherent flaws of a retrospective analysis, future clinical trials evaluating therapies in MM patients' post-ASCT should consider accounting for the presence of CHIP and its impact on outcomes. The authors would like to acknowledge the “Henry Predolin Leukemia Foundation”, Mayo Clinic, Rochester, MN, USA. Mrinal Patnaik has served on the advisory board of Kura Oncology. A Keith Stewart has served on the advisory board for Celgene. Rafael Fonseca has the following disclosures: Consulting: Amgen, BMS, Celgene, Takeda, Bayer, Janssen, Novartis, Pharmacyclics, Sanofi, Karyopharm, Merck, Juno, Kite, Aduro, OncoTracker, Oncopeptides, GSK, AbbVie. Scientific Advisory Board: Adaptive Biotechnologies, Caris Life Sciences and OncoTracker. Gene mutations annotated in the study have been provided in the supplementary material. Raw sequencing data can be made available on request. Appendix S1 Supporting Information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Sprouty2 regulates proliferation and survival of multiple myeloma byinhibiting activation of the ERK1/2 pathway invitro and invivo.

Macrophage Migration Inhibitory Factor Regulates Multiple Myeloma Bone Marrow Homing

Understanding multiple myeloma

Treatment of Newly Diagnosed Multiple Myeloma Based on Mayo Stratification of Myeloma and Risk-Adapted Therapy (mSMART): Consensus Statement

Targeted therapy of multiple myeloma based upon tumor-microenvironmental interactions

Stroma-Derived Exosomes Mediate Oncogenesis in Multiple Myeloma

Trends in Incidence and Survival of Multiple Myeloma with Second Primary Malignancy,

Multiple myeloma (MM) cells are the malignant counterpart of post-germinal centre (GC) long-lived plasma cells (PCs), characterized by strong bone marrow (BM) dependence, somatic hypermutation of immunoglobulin (Ig) genes and isotype class-switch resulting in absence of IgM expression in all but 1% of tumours. However, MM cells differ from healthy PCs because they retain the potential for a low rate of proliferation (1–3% of cycling cells). Virtually every case of MM is preceded by a premalignant PC tumour called monoclonal gammopathy of undetermined significance (MGUS) that, like MM, produces a typical M-spike (almost always non-IgM) by serum protein electrophoresis (SPEP) or free light chain in the urine [1].

Suppression of Multiple Myeloma Via Inhibition of EFNB2 Reverse Signaling