Immune Profiling in Multiple Myeloma

Author(s):  
2019 ◽  
Vol 2019 ◽  
pp. 1-18
Author(s):  
Rachel E. Cooke ◽  
Rachel Koldej ◽  
David Ritchie

Multiple myeloma (MM) is usually diagnosed in older adults at the time of immunosenescence, a collection of age-related changes in the immune system that contribute to increased susceptibility to infection and cancer. The MM tumor microenvironment and cumulative chemotherapies also add to defects in immunity over the course of disease. In this review we discuss how mouse models have furthered our understanding of the immune defects caused by MM and enabled immunotherapeutics to progress to clinical trials, but also question the validity of using immunodeficient models for these purposes. Immunocompetent models, in particular the 5T series and Vk⁎MYC models, are increasingly being utilized in preclinical studies and are adding to our knowledge of not only the adaptive immune system but also how the innate system might be enhanced in anti-MM activity. Finally we discuss the concept of immune profiling to target patients who might benefit the most from immunotherapeutics, and the use of humanized mice and 3D culture systems for personalized medicine.


Blood ◽  
2016 ◽  
Vol 127 (25) ◽  
pp. 3165-3174 ◽  
Author(s):  
Bruno Paiva ◽  
Maria-Teresa Cedena ◽  
Noemi Puig ◽  
Paula Arana ◽  
Maria-Belen Vidriales ◽  
...  

Key Points MRD monitoring is one of the most relevant prognostic factors in elderly MM patients, irrespective of age or cytogenetic risk. Second-generation MFC immune profiling concomitant to MRD monitoring also helped to identify patients with different outcomes.


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 4461-4461 ◽  
Author(s):  
Manisha Bhutani ◽  
David M. Foureau ◽  
Nury M Steuerwald ◽  
Sally Trufan ◽  
Fei Guo ◽  
...  

Abstract INTRODUCTION: Progression from precursor states, MGUS and smoldering multiple myeloma (SMM), to multiple myeloma (MM) is dependent upon adaptive and innate immune contexture shaped by cross-talk between malignant plasma cells and bone marrow (BM) milieu. The complexity and heterogeneity of interactions between the immune system and plasma cells in BM triggers alterations in peripheral blood (PB) immune cell subsets. The advantage of using PB as a surrogate is that dynamic changes in the immune cells can be measured at various time points during disease progression or therapeutic intervention. Here, we performed a comprehensive analysis of immune repertoire to identify immune signatures in PB and BM associated with MM or its precursor states. We also performed T cell receptor (TCR) clonotyping to quantify clonal expansion specific to each immunotype. METHODS: Paired PB and BM specimens were collected from patients with MGUS/SMM (n=12) and MM (n=16) through an IRB-approved biospecimen protocol. PB mononuclear cells and BM mononuclear cells were isolated for immune profiling. A total of 59 immune variables were analyzed by flow cytometry surveying 6 cell lineages' [NK, NK-T, Th, CTL, Treg and ɣδ T cells] distribution and functional status [activation, differentiation and anergy]. In addition, ArcherDx Immunoverse TCR αδ-βɣ CDR3 targeted NGS assay was performed to study clonal distributions of Vα24Jα18 NK-T, βα and ɣδ T cell. Univariate analyses (ANOVA) were performed using p<0.15 cutoff. Each set of variables (PB or BM) was then validated by multivariate analyses (Wilk's lambda) and used for unsupervised hierarchical analysis by WPGMA methods. Innate (NK-T, ɣδ T) and adaptive (βα T) mobilization for each cluster were finally confirmed by calculating Shannon's TCR clonal diversity index (SI). RESULTS: PB immunotyping identified 1 marker of innate inflammation and 9 markers of adaptive T mobilization that differentiated precursor states and MM (p=0.005). This model generated 3 PB immune clusters (Figure): cluster #1 [8 precursor states, 1 MM] showed a lack of innate inflammation and low Th/CTL mobilization, cluster #2 [2MGUS, 5MM] showed low innate inflammation and, cluster #3 [2SMM, 10MM] showed strong innate inflammation (Vɣ9-Vδ2-NKG2D+), Th terminal differentiation (central memory phenotype) and CTL anergy (Tim3+). TCR clonotyping confirmed increased innate inflammation (TCRδ SI 3.99±0.3 vs 4.75±0.15, p<0.05) and T cell mobilization (TCRα SI 7.12±0.3 vs. 8.20± 0.2, p<0.05) in PB cluster #3 compared with PB cluster #1. BM immunotyping identified 3 markers of innate inflammation and 2 markers of adaptive T mobilization (p=0.0274) distinguishing precursor states from MM. This model generated 3 BM immune clusters: cluster #1 [6 precursor states, 6 MM] showed innate inflammation (ɣδ T) and CTL terminal differentiation (central memory phenotype); cluster #2 [4 SMM, 8 MM] showed innate inflammation (NK-T, ɣδ T) and CTL effector anergy; and cluster #3 [2 MGUS, 2 MM] showed low NK cell cytotoxicity (KIR3DL1+) and CTL terminal differentiation. TCR clonotyping confirmed qualitative differences in innate inflammation between BM cluster #1 and #2 with higher NK-T (%Vα24Jα18 p<0.01) but lower ɣδ T (TCRδ SI 3.36±0.2 vs 4.57±0.2, p<0.05). In addition, CTL mobilization whether resulting in terminal differentiation or anergy in BM cluster #1 and #2, respectively was associated with similar clonal expansion of T cells (TCRα SI 7.21±0.26 vs. 7.87± 0.4, ns). Comparisons showed associations between PB and BM ɣδ T cell involvement in 13/13 patients. High PB Th/CTL mobilization (terminal differentiation) was associated with high T cell anergy in BM in 9/12 patients; conversely low PB Th/CTL mobilization was associated with low BM T cell involvement in 6/7 patients. CONCLUSION: This pilot study shows immune clustering of MGUS, SMM and MM patients based on BM and PB immunotypes. This is the first study to demonstrate two very distinct MM immunotypes based on low vs. high inflammatory states. We also show a high correlation between innate immune inflammation status in both PB and BM, specifically pertaining to ɣδ T cell, conventional T cell mobilization or lack thereof. Additional studies including a larger cohort for validation and longer follow up to establish correlation with clinical outcomes are currently underway. Figure. Figure. Disclosures Foureau: Teneobio Inc.: Research Funding. Berlin:ArcherDx: Employment. Johnson:ArcherDx: Employment. Williams:ArcherDx: Employment. Voorhees:Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Other: served on an IRC; Amgen Inc.: Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: served on an IRC; Oncopeptides: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: served on an IRC; TeneoBio: Consultancy, Membership on an entity's Board of Directors or advisory committees. Usmani:Amgen, BMS, Celgene, Janssen, Merck, Pharmacyclics,Sanofi, Seattle Genetics, Takeda: Research Funding; Abbvie, Amgen, Celgene, Genmab, Merck, MundiPharma, Janssen, Seattle Genetics: Consultancy.


2020 ◽  
Vol 42 ◽  
pp. 24
Author(s):  
K. Papadimitriou ◽  
I. Ntanasis-Stathopoulos ◽  
N. Tsakirakis ◽  
M. Gavriatopoulou ◽  
I. Kostopoulos ◽  
...  

2017 ◽  
Vol 8 ◽  
Author(s):  
Benjamin W. Teh ◽  
Simon J. Harrison ◽  
Cody Charles Allison ◽  
Monica A. Slavin ◽  
Tim Spelman ◽  
...  

Leukemia ◽  
2021 ◽  
Author(s):  
Jooeun Bae ◽  
Fabrizio Accardi ◽  
Teru Hideshima ◽  
Yu-Tzu Tai ◽  
Rao Prabhala ◽  
...  

AbstractImmune profiling in patients with monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM), and multiple myeloma (MM) provides the framework for developing novel immunotherapeutic strategies. Here, we demonstrate decreased CD4+ Th cells, increased Treg and G-type MDSC, and upregulation of immune checkpoints on effector/regulatory and CD138+ cells in MM patients, compared MGUS/SMM patients or healthy individuals. Among the checkpoints profiled, LAG3 was most highly expressed on proliferating CD4+ Th and CD8+ Tc cells in MM patients BMMC and PBMC. Treatment with antibody targeting LAG3 significantly enhanced T cells proliferation and activities against MM. XBP1/CD138/CS1-specific CTL generated in vitro displayed anti-MM activity, which was further enhanced following anti-LAG3 treatment, within the antigen-specific memory T cells. Treg and G-type MDSC weakly express LAG3 and were minimally impacted by anti-LAG3. CD138+ MM cells express GAL-3, a ligand for LAG3, and anti-GAL-3 treatment increased MM-specific responses, as observed for anti-LAG3. Finally, we demonstrate checkpoint inhibitor treatment evokes non-targeted checkpoints as a cause of resistance and propose combination therapeutic strategies to overcome this resistance. These studies identify and validate blockade of LAG3/GAL-3, alone or in combination with immune strategies including XBP1/CD138/CS1 multipeptide vaccination, to enhance anti-tumor responses and improve patient outcome in MM.


Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 721-721 ◽  
Author(s):  
Paula Arana ◽  
Bruno Paiva ◽  
Noemi Puig ◽  
Teresa Cedena ◽  
Lourdes Cordon ◽  
...  

Abstract Introduction: Although in multiple myeloma (MM) failure to attain deep remissions after therapy typically limits the chances of long-term survival, such paradigm does not apply to all patients since a fraction of patients with persistent disease may be progression-free for more than 10-years even without continuous treatment. Accordingly, it could be hypothesized that prolonged survival in such patients is related to their immune surveillance in controlling detectable (MRD-positive) or undetectable (MRD-negative) residual disease. Unfortunately, while the immune impairment in newly-diagnosed MM as well as the presence of unique immune profiles among patients attaining long-term disease control have been described, no studies have been performed after therapy, during MRD monitoring, to develop an immune signature capable to predict patients' outcome. Methods: We have investigated the immune signature of 146 elderly patients enrolled in the GEM2010MAS65 clinical trial after therapy, during MRD monitoring. Briefly, patients were treated with sequential chemotherapy with 9 cycles of bortezomib-melphalan-prednisone (VMP) followed by 9 cycles of lenalidomide-low dose dexamethasone (Rd) (n=72), or alternating cycles of VMP and Rd up to 18 cycles (n=74). A single 8-color antibody combination (CD45-PacB/CD138-OC515/CD38-FITC/CD56-PE/CD27-PerCPCy5.5/CD19-PECy7/CD117-APC/CD81-APCH7) was used to monitor MRD, and allowed for the enumeration of not only normal and clonal plasma cells, but also erythroid and myeloid hematopoietic progenitors, erythroblasts, mast cells, eosinophils, basophils, monocytes, neutrophils, B-cells and their respective precursor, naïve and memory subsets, as well as T-cells plus TNK- and NK-cells. Median follow-up of the series was 3-years; time-to-progression (TTP) and overall survival (OS) were measured from diagnosis. Results: Principal component analysis (PCA) based on the bone marrow distribution of the 13 immune cell populations revealed the presence of 3 clusters (Panel 1): A (n=16), B (n=117) and C (n=13). When comparing cluster A with clusters B and C, there was a decrease in mean values of erythroblasts (25%, 15% and 13%; P=.03) combined with a trend for increased neutrophils (52%, 59% and 60%; P=.07). The distribution of different maturation subsets within the B-cell compartment was also significantly altered between clusters C and B vs. A, with decreased numbers of B-cell precursors (4%, 0.6% and 1%; P<.001) but increased frequencies of naïve (0.1%, 0.09% and 0.5%; P<.001) and antigen-experienced memory (0.05%, 0.03% and 0.3%; P=.006) B-cells (Panel 2). There were no significant differences in cluster frequency according to treatment schema, nor according to baseline ISS or FISH risk-stratification. Most interestingly though, particularly when compared to cluster C, patients clustering in group A had a trend toward superior TTP (median of 44 vs 35 months, respectively; P=.08) and significantly superior OS (3-year rates of 77 vs 100%, respectively; P=.02); patients belonging to cluster B had intermediate outcome (median TTP of 37 months and 82% 3-year OS rate) (Panels 3 and 4). Noteworthy, there were no significant differences according to patients' MRD status across the different clusters; accordingly, even among MRD-positive patients immune profiling continued to impact patients survival with 3-year OS rates of 62%, 77% and 100% for clusters C, B and A, respectively (P=.02). Conclusions: We showed for the first time that immune profiling in MM after therapy during MRD monitoring is prognostically relevant and allows the identification of patients with either poor survival or sustained disease control. Accordingly, flow-based MRD monitoring offers complementary information to quantification of MRD levels, and may contribute to identify patients that albeit being MRD-positive can still experience prolonged survival due to a unique immune signature particularly characterized by increased peripheral B-cell maturation. Figure 1. Figure 1. Figure 2. Figure 2. Figure 3. Figure 3. Disclosures Paiva: BD Bioscience: Consultancy; Binding Site: Consultancy; Onyx: Consultancy; Millenium: Consultancy; Janssen: Consultancy; Celgene: Consultancy; Sanofi: Consultancy; EngMab AG: Research Funding. Puig:Janssen: Consultancy; The Binding Site: Consultancy. Gironella:Celgene Corporation: Consultancy, Honoraria. Mateos:Janssen-Cilag: Consultancy, Honoraria; Takeda: Consultancy; Celgene: Consultancy, Honoraria; Onyx: Consultancy. San Miguel:Bristol-Myers Squibb: Honoraria; Celgene: Honoraria; Janssen-Cilag: Honoraria; Millennium: Honoraria; Novartis: Honoraria; Sanofi-Aventis: Honoraria; Onyx: Honoraria.


2019 ◽  
Vol 19 (10) ◽  
pp. e87
Author(s):  
Gregorio Barilà ◽  
Laura Pavan ◽  
Susanna Vedovato ◽  
Tamara Berno ◽  
Antonio Branca ◽  
...  

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