Next Article in Journal
Hemodynamic Imaging in Cerebral Diffuse Glioma—Part A: Concept, Differential Diagnosis and Tumor Grading
Previous Article in Journal
Successful Pregnancies, Births, and Children Development Following Oocyte Cryostorage in Female Cancer Patients During 25 Years of Fertility Preservation
Previous Article in Special Issue
Single-Cells Isolation and Molecular Analysis: Focus on HER2-Low CTCs in Metastatic Breast Cancer
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Cancers
Volume 14
Issue 6
10.3390/cancers14061430
cancers-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Perspective

Biological Characterization and Clinical Relevance of Circulating Tumor Cells: Opening the Pandora’s Box of Multiple Myeloma

by
Juan-José Garcés
*,
Jesús San-Miguel
and
Bruno Paiva
*
Clinica Universidad de Navarra, Cancer Center Universidad de Navarra (CCUN), Centro de Investigacion Medica Aplicada (CIMA), Centro de Investigacion Biomedica en Red—Oncologia (CIBERONC), Instituto de Investigacion Sanitaria de Navarra (IDISNA), 31008 Navarra, Spain
*
Authors to whom correspondence should be addressed.
Cancers 2022, 14(6), 1430; https://doi.org/10.3390/cancers14061430
Submission received: 14 February 2022 / Revised: 28 February 2022 / Accepted: 9 March 2022 / Published: 10 March 2022
(This article belongs to the Special Issue Strategies for Isolation and Phenotypic, Genetic, and Functional Characterization of Circulating Tumor Cells)
Browse Figure
Versions Notes

Abstract

:

Simple Summary

Bone marrow (BM) aspirates are mandatory for diagnosis and follow-up of patients with multiple myeloma (MM). However, they present two important caveats: Their invasiveness and limited scope to capture the broad tumor heterogeneity. Conversely, circulating tumor cells (CTCs) are detectable in the peripheral blood of patients with precursor and malignant disease states and have strong prognostic value. Moreover, the high genetic and transcriptomic overlap between both plasma cell compartments suggests that CTCs might reflect with notable precision the medullar clone. Furthermore, the study of CTCs could be used as a model to identify mechanisms favoring BM egression and disease spreading. Here, we summarize the state of the art on MM CTCs and provide insights on what they may offer in research and clinical scenarios.

Abstract

Bone marrow (BM) aspirates are the gold standard for patient prognostication and genetic characterization in multiple myeloma (MM). However, they represent an important limitation for periodic disease monitoring because they entail an aggressive procedure. Moreover, recent findings show that a single BM aspirate is unable to reflect the complex MM heterogeneity. Recent advances in flow cytometry, microfluidics, and “omics” technologies have opened Pandora’s box of MM: The detection and isolation of circulating tumor cells (CTCs) offer a promising and minimally invasive alternative for tumor assessment and metastasis study. CTCs are detectable in premalignant and active MM states, and their enumeration has strong prognostic value, to the extent that it is challenging current stratification systems. In addition, CTCs reflect with high precision both intra- and extra-medullary disease at the phenotypic, genomic, and transcriptomic levels. Despite this high resemblance between tumor clones in distinct locations, some subtle (not random) differences might shed some light on the metastatic process. Thus, it has been suggested that a hypoxic and pro-inflammatory microenvironment could induce an arrest in proliferation forcing tumor cells to recirculate. Herein, we summarize data on the characterization of MM CTCs as well as their clinical and research potential.

1. Introduction

Multiple Myeloma (MM) is the second most common hematological malignancy. It is characterized by an abnormal proliferation of monoclonal plasma cells in the bone marrow (BM), which finally induces organ dysfunction known as CRAB symptoms (i.e., hypercalcemia, renal insufficiency, anemia, and bone destruction) [1]. Despite the important progress in its understanding and treatment, with remarkable advances in patient outcomes, all clinical evaluations and staging are still based on invasive BM aspirates [2]. Together with sampling and longitudinal limitations of this approach, spatial heterogeneity of tumor cells is also a significant factor that hampers a complete characterization of the disease based on single BM aspirates [3,4,5].
The presence of numerous lytic lesions throughout the axial skeleton conferred this neoplasm the surname of “multiple”, suggesting a constant dynamic spreading of tumor cells from the primary tumor to diverse BM niches through the peripheral blood (PB). Moreover, MM is preceded by well-established premalignant states, and therefore, it is an attractive model to study the metastatic process [6,7].
Liquid biopsies have emerged as a promising and minimally invasive alternative for tumor assessment. In addition to showing prognostic value [8,9,10,11,12,13,14], circulating tumor cells (CTCs) would expand the genomic view of circulating cell-free tumor DNA (ctDNA) [15,16,17,18,19] and include potential information about transcriptomic, proteomic, and metabolomic features, as well as the possibility of generating cell cultures or xenografts [20,21]. With the increasing expansion of sorting, microfluidics, and “omics” technologies [22], CTCs might revolutionize not only our comprehension of disease pathogenesis and dissemination but also patient monitorization.
Herein, we summarize different studies characterizing CTCs from a phenotypic, genetic, and transcriptomic point of view in MM, as well as their clinical applicability. Many excellent reviews expanding this topic in other cancer types are available in this journal [23,24] and elsewhere [25,26,27].

2. Immunophenotypic Characterization of CTCs

The presence of CTCs in MM is well known since the 90′s [28,29,30] and has been studied with different approaches (covered in this other review [23]). However, it was not until recently that a systematic comparison with its (primary) BM counterpart was performed using flow cytometry (FC) [8,31,32,33].
Since CTCs egress directly from the BM, theoretically, their phenotype should highly overlap with that of the primary BM tumor cell [8,31,32]. Indeed, the expression of classical B-cell maturation markers (e.g., CD19, CD20, CD27, CD45, or CD79) or adhesion molecules (e.g., CD44, CD54, or ITGB7) remained stable when comparing both tumor cell compartments [8,31,33].
However, the ability of CTCs to egress may also hide singular features favoring disease spreading through systemic circulation. Our group has shown through next-generation FC that CTCs commonly tend to cluster in unique areas of the whole space occupied by their paired BM tumor cells, suggesting commonalities and singularities in the phenotypic profiles of both plasma cells (Figure 1) [31]. Thus, the under-expression of multiple integrins, cytokine receptors, or adhesion molecules, such as CD11a, CD33, CD38, CD49e/d, CD56, CD81, CD117, or CD138, is particularly interesting to explain the presence of CTCs in the PB [8,31,33]. At the same time, each of these markers presents its own cell-intrinsic effects. For example, the reduced expression of CD81 and CD138 supports the idea that CTCs could be a more quiescent and immature population [34,35,36]. Negative expression of CD49 and CD56 has been previously associated with more invasive disease and postulated as a possible hallmark of plasma cell leukemia [37,38]. Although not directly focused on CTCs, diverse studies have suggested that chemokine receptors, such as CXCR4, CCR1, or CCR5, could be key in extra-medullary dissemination and potential targets to prevent disease progression. Interestingly, these molecules also highlight the importance of epithelial-mesenchymal transition (EMT) and hypoxia processes for tumor cell egression [39,40].
The interaction of CTCs with different immune cell types proposes new ideas for understanding tumor dissemination [41,42]. In a bright study, Szczerba et al. have shown in breast cancer that the association between neutrophils and CTCs (vs. CTCs alone) increased the expression of genes related with cell cycle progression and facilitated a faster metastasis development [43]. Although still in the beginning, platelets also displayed to have a crucial role in enhancing the endothelial adhesion and extravasation of CTCs. This platelet-based cloaking, moreover, would create a protective “shield” against external mechanical forces preserving their integrity [44]. Equally important, homotypic interactions among CTCs compose circulating clusters or microemboli already associated with prognostic effects in multiple cancers, such as lung, prostate, or breast cancer [26,45,46].
These flow-based investigational advantages present, however, some important bias: (1) Antibody panels are based on (limited) predefined knowledge, and most importantly, (2) these are manually analyzed. The advent of new methodological approaches, such as single-cell and mass and spectral cytometry, has notably expanded the number of features that can be simultaneously studied. These, in addition, are changing the paradigm of classical manual gating through more holistic analyses combining the expertise’s view with the computational power of pattern recognition [47]. Furthermore, technologies such as cell surface capture protocols could expand our restricted and predefined knowledge to a more “agnostic” and discovery-driven view of the total surfaceome (i.e., plasma membrane proteins with exposed domains towards the extracellular space), facilitating the identification of new drug targets and potential cell interactions [48].

3. Genetic Characterization of CTCs

Genetic characterization is a key factor for patient stratification in MM [49]. Up to now, BM aspirates have been the gold standard both for fluorescence in situ hybridization (FISH) or targeted sequencing. However, its invasiveness supposes an important caveat that restricts application only to baseline and very specific time points. Although CTCs could offer a more sustained and minimally invasive patient follow-up, their low prevalence keeps challenging their study.
To overcome this limitation, Lohr et al. used an immunodensity microfluidic device (RosetteSep™) coupled with a specific cocktail of antibodies to positively select a total of 155 single CTCs (Table 1) [50]. Other enrichment approaches were based on immunomagnetic CD138 positive selection [18]. However, next-generation FC has resulted in being the technology showing higher yields, with the ability to sort a median of 13,000 CTCs ready for downstream analyses [51,52]. Most importantly, because of the high cellular input and the multiple marker combinations, this information would be superior both in terms of quantity and, especially, quality (≥97% purity [53]).
Through targeted single-cell sequencing, Lohr et al. confirmed that CTC mutations at a single-cell level matched “bulk” BM tumor cells. Nevertheless, when comparing single CTCs with single BM tumor cells, only 7/35 (20%) mutations were shared between both tumor compartments and solely in 4/7 (57%) of patients [50]. In addition, this information would be restricted to a few preselected genes (i.e., targeted panel), which reflects an important limitation if considering the high inter- and intra-patient genetic heterogeneity in MM [5]. On the contrary, upon moving to a broader approach (i.e., whole-exome sequencing [WES]), CTCs were able to mirror up to 93% of mutations detected in BM tumor cells, including >80% of recurrently MM mutated or pan-cancer genes (e.g., KRAS, BRAF, TP53, etc.) [18,51,52]. The same pattern was also observed for copy number alterations, sharing classical prognostic aberrations such as amplification 1q or deletion 17p [52]. Interestingly, we replicated this concordance at the extra-medullary level finding that 84% of mutations were detectable in CTCs whenever present in extra-medullary plasmacytomas (i.e., discrete masses of tumor cells in soft tissues) [52].
According to this resemblance with BM tumor cells, CTCs could represent an ideal minimally invasive tool for risk stratification. Indeed, there are some attractive attempts that could boost their implementation into routine genetic characterization. For example, Foulk et al. coupled CTCs enumeration and isolation through the CellSearch® platform with FISH analysis of main cytogenetic aberrations (i.e., del17p, t(4;14), t(14;16)) and showed 90% concordance between CTCs and BM tumor cells, both in smoldering and active MM [56]. Another promising alternative is the so-called “immuno-flowFISH”: This approach, based on imaging FC, simultaneously combines immunophenotyping with cytogenetic analysis on cells in suspension. Its application has been demonstrated by evaluating chromosome 12 deletion on chronic lymphocytic leukemia with 100% agreement with standard FISH [57].
Notwithstanding, a restricted group of mutations still persists in each cellular location. The presence of clonal mutations (i.e., cancer cell fraction [CCF] ≥ 90%) in CTCs that are subclonal (i.e., CCF < 90%) in BM tumor cells, as well as specific subclonal mutations only detectable in CTCs, reopens the question of whether this circulating population could have a distinct (or earlier) origin than primary BM tumor [51,52]. This information underpins the hypothesis that, beyond representing the main disease clone, CTCs might also consist of multiple medullar subclones from diverse BM niches [3,5], and accordingly, could reflect the disease heterogeneity with more rigor than a single BM aspirate [51,52].

4. Transcriptomic Characterization of CTCs

Transcriptomic profiling has been classically used to define MM subtypes with different biological features and survival rates [58]. Shedding light on mechanisms favoring cell egression might help to elucidate whether CTCs are the clone responsible for MM spreading and, eventually, disease progression [6].
Overall, the reduced number of differentially expressed genes and the highly overlapping transcriptomic profiles indicate that CTCs resemble their BM counterpart. Consequently, samples tended to cluster in a patient-specific way rather than by tumor cell source [7,50,54,55]. These findings would be additionally supported by the significant match between CTCs and BM tumor cells upon reconstructing the B-cell receptor (BCR), which points to the same clonal origin [54]. Remarkably, Lohr et al. also inferred key MM translocations, such as t(11;14) or t(6;14), from transcriptomic data and suggested a similar behavior between both tumor compartments [50].
However, it is plausible that BM egression does not constitute a random process but it is influenced by some specific differences conferring extra advantages. As we demonstrated, despite the few differentially expressed genes, these were able to segregate with notable precision CTCs from BM tumor cells and were prognostically relevant (e.g., EMP3, LGALS1, or IQGAP1) [7]. Later functional analyses indicated that CTCs were enriched in molecular hallmarks such as inflammation (e.g., interferon response, TNFα, and complement signaling), EMT, hypoxia or apoptosis (e.g., P53 pathway), and under-represented in functions related to cell cycle (e.g., DNA repair or G2M checkpoint). Under this idea, a model was proposed: The presence of hypoxic BM niches together with a pro-inflammatory microenvironment could induce an arrest in proliferation, forcing tumor cells to circulate and seek other locations to continue growing [7].

5. Clinical Utility of CTCs

CTCs can be identified as soon as a premalignant clonal plasma cell process is initiated. According to the EuroFlow consortium, 59% of patients with monoclonal gammopathy of undetermined significance (MGUS) have detectable CTCs as compared to 100% of cases with smoldering and overt MM [8,59]. The major difference between these entities is, however, the percentage of CTCs, with increasing (median) frequencies from 0.0002% (0.008 CTCs/µL) to 0.004% (0.16 CTCs/µL) and 0.04% (1.9 CTCs/µL) for MGUS, smoldering and active MM, respectively [8,60]. Remarkably, CTC enumeration was selected as an independent prognostic factor when comparing with classical BM assessments, both by morphology or FC [14,61].
Probably, in the clinical ground, one of the most promising applications of CTCs is their capacity for minimally invasive prognostication. In the very early disease onset (i.e., MGUS), Sanoja-Flores et al. demonstrated that the presence of ≥0.058 CTCs/µL defined a group of patients with a high risk of progression [8]. Regarding the smoldering stage, based on conventional FC (sensitivity of 10−3–10−4), the Mayo Clinic group determined that the mere detection of CTCs was an adverse prognostic factor with a median time to progression of only 10 months [13]. Upon increasing sensitivity to 10−6 through next-generation FC, our group has recently reported that those smoldering MM patients with ≥0.02% CTCs showed ultra-high-risk of transformation (median progression-free survival [PFS] of 11 months) [59]. In particular, early intervention of these patients with high CTC levels has been shown to substantially reduce rates of malignant transformation (PFS not reached) [59,62].
Gonsalves et al., considering active MM by using standard FC, reported that having ≥5 CTCs/µL was able to better sub-stratify the big and heterogeneous group of patients with standard-risk cytogenetics or intermediate risk according to the Revised International Staging System (R-ISS) [14,63]. The Spanish group studied with next-generation FC the prognostic impact of CTCs in uniformly treated transplant-eligible newly diagnosed MM patients (GEM2012MENOS65 clinical trial) and identified it as one the most powerful independent prognostic factor at diagnosis. They were able to define, furthermore, three risk categories: Patients with ≥0.24% CTCs (median PFS of 44 months), <0.24% CTCs (78 months), and undetectable CTCs (PFS and overall survival [OS] rates of 94% and 100%, respectively) [61]. The latter subset of patients resulted especially interesting, and here sensitivity is crucial, since it might constitute a singular group with an exceptional outcome regardless of the depth of response and other risk factors at diagnosis [8,61,64]. Despite these promising results, the standardization of these procedures is still urgently needed to implement them into routine clinical practice.
CTCs quantification could also help to redefine the concept of plasma cell leukemia, the most aggressive variant of monoclonal gammopathies and traditionally diagnosed on the presence of 20% CTCs in PB smears [65]. Thus, several groups have shown that patients with 2–5% CTCs by FC would have a similar outcome to those with plasma cell leukemias, suggesting a different strategy for managing this high-risk entity [66,67,68,69].
Finally, one of the ambitions of CTCs is to replace the BM in the evaluation of measurable residual disease (MRD) and, consequently, facilitate frequent disease monitoring in a minimally invasive way. Hence, every case with MRD positive in PB had also detectable MRD in BM, proposing that the detection of CTCs could be a surrogate of persistent MRD in BM [70]. Conversely, a remaining high rate of false negatives in PB still points out to the requirement of refining MRD assessments based on CTCs [70,71]. Interestingly, longitudinal evaluation of this circulating population highlighted that its sustained presence before and after treatment was associated with a dismal survival (median PFS around 1.5 years). Indeed, when comparing with other risk elements or depth of treatment response, the persistence of CTCs was an independent prognostic factor [70,72].

6. Conclusions

Legend says when Pandora opened her box, all evils escaped and spread all over the world. While ctDNA would be each individual vanished evil, CTCs represent Pandora’s box as a whole and expand possibilities for studying the medullar clone in a minimally invasive manner and understanding MM spreading.
CTCs are detectable since the very early stages of monoclonal gammopathies, and the high resemblance with their BM counterpart paves the way for explaining MM pathogenesis [6]. The ability to track specific aberrations in a minimally invasive way overcomes the limitation of serial monitoring in BM and could facilitate seeking the resistant clone and adjusting therapies over time [50,73]. At the same time, CTCs offer a potential model to interrogate metastasis in which subtle and specific features could prompt the identification of targetable mechanisms to overcome disease dissemination [6,7,74].
The box was not left totally empty when Pandora hastened to close it: At the bottom remained Hope. CTCs display a promising prognostication potential, which encourages the scientific community to revise current stratifying systems. Their enumeration, moreover, may be particularly useful in the design of new clinical trials as an alternative endpoint (e.g., absence vs. presence of CTCs, specific cutoffs, PB-based MRD) or as a new biomarker for early detection and early intervention of premalignant stages [59,75].

Author Contributions

Conceptualization and draft preparation, J.-J.G.; writing and revision, J.-J.G., J.S.-M. and B.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Van de Donk, N.W.C.J.; Pawlyn, C.; Yong, K.L. Multiple myeloma. Lancet 2021, 397, 410–427. [Google Scholar] [CrossRef]
  2. Dimopoulos, M.A.; Moreau, P.; Terpos, E.; Mateos, M.V.; Zweegman, S.; Cook, G.; Delforge, M.; Hájek, R.; Schjesvold, F.; Cavo, M.; et al. Multiple myeloma: EHA-ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 2021, 32, 309–322. [Google Scholar] [CrossRef] [PubMed]
  3. Rasche, L.; Chavan, S.S.; Stephens, O.W.; Patel, P.H.; Tytarenko, R.; Ashby, C.; Bauer, M.; Stein, C.; Deshpande, S.; Wardell, C.; et al. Spatial genomic heterogeneity in multiple myeloma revealed by multi-region sequencing. Nat. Commun. 2017, 8, 268. [Google Scholar] [CrossRef] [PubMed]
  4. Schürch, C.M.; Rasche, L.; Frauenfeld, L.; Weinhold, N.; Fend, F. A review on tumor heterogeneity and evolution in multiple myeloma: Pathological, radiological, molecular genetics, and clinical integration. Virchows Arch. 2019, 476, 337–351. [Google Scholar] [CrossRef]
  5. Bolli, N.; Avet-Loiseau, H.; Wedge, D.C.; Van Loo, P.; Alexandrov, L.B.; Martincorena, I.; Dawson, K.J.; Iorio, F.; Nik-Zainal, S.; Bignell, G.R.; et al. Heterogeneity of genomic evolution and mutational profiles in multiple myeloma. Nat. Commun. 2014, 5, 2997. [Google Scholar] [CrossRef] [Green Version]
  6. Ghobrial, I.M. Myeloma as a model for the process of metastasis: Implications for therapy. Blood 2012, 120, 20–30. [Google Scholar] [CrossRef] [Green Version]
  7. Garcés, J.-J.; Simicek, M.; Vicari, M.; Brozova, L.; Burgos, L.; Bezdekova, R.; Alignani, D.; Calasanz, M.-J.; Growkova, K.; Goicoechea, I.; et al. Transcriptional profiling of circulating tumor cells in multiple myeloma: A new model to understand disease dissemination. Leukemia 2020, 34, 589–603. [Google Scholar] [CrossRef]
  8. Sanoja-Flores, L.; Flores-Montero, J.; Garcés, J.J.; Paiva, B.; Puig, N.; García-Mateo, A.; García-Sánchez, O.; Corral-Mateos, A.; Burgos, L.; Blanco, E.; et al. Next generation flow for minimally-invasive blood characterization of MGUS and multiple myeloma at diagnosis based on circulating tumor plasma cells (CTPC). Blood Cancer J. 2018, 8, 117. [Google Scholar] [CrossRef] [Green Version]
  9. Kumar, S.; Rajkumar, S.V.; Kyle, R.A.; Lacy, M.Q.; Dispenzieri, A.; Fonseca, R.; Lust, J.A.; Gertz, M.A.; Greipp, P.R.; Witzig, T.E. Prognostic Value of Circulating Plasma Cells in Monoclonal Gammopathy of Undetermined Significance. J. Clin. Oncol. 2005, 23, 5668–5674. [Google Scholar] [CrossRef]
  10. Bianchi, G.; Kyle, R.A.; Larson, D.R.; Witzig, T.E.; Kumar, S.; Dispenzieri, A.; Morice, W.G.; Rajkumar, S.V. High levels of peripheral blood circulating plasma cells as a specific risk factor for progression of smoldering multiple myeloma. Leukemia 2013, 27, 680–685. [Google Scholar] [CrossRef]
  11. Vagnoni, D.; Travaglini, F.; Pezzoni, V.; Ruggieri, M.; Bigazzi, C.; Dalsass, A.; Mestichelli, F.; Troiani, E.; Falcioni, S.; Mazzotta, S.; et al. Circulating plasma cells in newly diagnosed symptomatic multiple myeloma as a possible prognostic marker for patients with standard-risk cytogenetics. Br. J. Haematol. 2015, 170, 523–531. [Google Scholar] [CrossRef] [PubMed]
  12. Nowakowski, G.S.; Witzig, T.E.; Dingli, D.; Tracz, M.J.; Gertz, M.A.; Lacy, M.Q.; Lust, J.A.; Dispenzieri, A.; Greipp, P.R.; Kyle, R.A.; et al. Circulating plasma cells detected by flow cytometry as a predictor of survival in 302 patients with newly diagnosed multiple myeloma. Blood 2005, 106, 2276–2279. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Gonsalves, W.I.; Rajkumar, S.V.; Dispenzieri, A.; Dingli, D.; Timm, M.M.; Morice, W.G.; Lacy, M.Q.; Buadi, F.K.; Go, R.S.; Leung, N.; et al. Quantification of circulating clonal plasma cells via multiparametric flow cytometry identifies patients with smoldering multiple myeloma at high risk of progression. Leukemia 2016, 31, 130–135. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Gonsalves, W.T.; Rajkumar, S.V.; Gupta, V.; Morice, W.G.; Timm, M.M.; Singh, P.P.; Dispenzieri, A.; Buadi, F.K.; Lacy, M.Q.; Kapoor, P.; et al. Quantification of clonal circulating plasma cells in newly diagnosed multiple myeloma: Implications for redefining high-risk myeloma. Leukemia 2014, 28, 2060–2065. [Google Scholar] [CrossRef] [Green Version]
  15. Kis, O.; Kaedbey, R.; Chow, S.; Danesh, A.; Dowar, M.; Li, T.; Li, Z.; Liu, J.; Mansour, M.; Masih-Khan, E.; et al. Circulating tumour DNA sequence analysis as an alternative to multiple myeloma bone marrow aspirates. Nat. Commun. 2017, 8, 15086. [Google Scholar] [CrossRef]
  16. Mithraprabhu, S.; Khong, T.; Ramachandran, M.; Chow, A.; Klarica, D.; Mai, L.; Walsh, S.; Broemeling, D.; Marziali, A.; Wiggin, M.; et al. Circulating tumour DNA analysis demonstrates spatial mutational heterogeneity that coincides with disease relapse in myeloma. Leukemia 2017, 31, 1695–1705. [Google Scholar] [CrossRef]
  17. Gerber, B.; Manzoni, M.; Spina, V.; Bruscaggin, A.; Lionetti, M.; Fabris, S.; Barbieri, M.; Ciceri, G.; Pompa, A.; Forestieri, G.; et al. Circulating tumor DNA as a liquid biopsy in plasma cell dyscrasias. Haematologica 2018, 103, e245–e248. [Google Scholar] [CrossRef] [Green Version]
  18. Manier, S.; Park, J.; Capelletti, M.; Bustoros, M.; Freeman, S.; Ha, G.; Rhoades, J.; Liu, C.J.; Huynh, D.; Reed, S.; et al. Whole-exome sequencing of cell-free DNA and circulating tumor cells in multiple myeloma. Nat. Commun. 2018, 9, 1691. [Google Scholar] [CrossRef]
  19. Guo, G.; Raje, N.S.; Seifer, C.; Kloeber, J.; Isenhart, R.; Ha, G.; Yee, A.J.; O’Donnell, E.K.; Tai, Y.-T.; Richardson, P.G.; et al. Genomic discovery and clonal tracking in multiple myeloma by cell-free DNA sequencing. Leukemia 2018, 32, 1838–1841. [Google Scholar] [CrossRef]
  20. Pantel, K.; Alix-Panabières, C. Liquid biopsy and minimal residual disease—Latest advances and implications for cure. Nat. Rev. Clin. Oncol. 2019, 16, 409–424. [Google Scholar] [CrossRef]
  21. Heitzer, E.; Haque, I.S.; Roberts, C.E.S.; Speicher, M.R. Current and future perspectives of liquid biopsies in genomics-driven oncology. Nat. Rev. Genet. 2019, 20, 71–88. [Google Scholar] [CrossRef] [PubMed]
  22. Rushton, A.; Nteliopoulos, G.; Shaw, J.; Coombes, R. A Review of Circulating Tumour Cell Enrichment Technologies. Cancers 2021, 13, 970. [Google Scholar] [CrossRef] [PubMed]
  23. Sanoja-Flores, L.; Flores-Montero, J.; Pérez-Andrés, M.; Puig, N.; Orfao, A. Detection of Circulating Tumor Plasma Cells in Monoclonal Gammopathies: Methods, Pathogenic Role, and Clinical Implications. Cancers 2020, 12, 1499. [Google Scholar] [CrossRef] [PubMed]
  24. Ramos-Medina, R.; López-Tarruella, S.; del Monte-Millán, M.; Massarrah, T.; Martín, M. Technical Challenges for CTC Implementation in Breast Cancer. Cancers 2021, 13, 4619. [Google Scholar] [CrossRef] [PubMed]
  25. Lianidou, E.S.; Markou, A.; Strati, A. Molecular characterization of circulating tumor cells in breast cancer: Challenges and promises for individualized cancer treatment. Cancer Metastasis Rev. 2012, 31, 663–671. [Google Scholar] [CrossRef] [PubMed]
  26. Hou, J.-M.; Krebs, M.; Lancashire, L.; Sloane, R.S.; Backen, A.; Swain, R.; Priest, L.J.; Greystoke, A.; Zhou, C.; Morris, K.; et al. Clinical Significance and Molecular Characteristics of Circulating Tumor Cells and Circulating Tumor Microemboli in Patients With Small-Cell Lung Cancer. J. Clin. Oncol. 2012, 30, 525–532. [Google Scholar] [CrossRef] [PubMed]
  27. Lin, D.; Shen, L.; Luo, M.; Zhang, K.; Li, J.; Yang, Q.; Zhu, F.; Zhou, D.; Zheng, S.; Chen, Y.; et al. Circulating tumor cells: Biology and clinical significance. Signal Transduct. Target. Ther. 2021, 6, 404. [Google Scholar] [CrossRef]
  28. Billadeau, D.; Quam, L.; Thomas, W.; Kay, N.; Greipp, P.; Kyle, R.; Oken, M.M.; Van Ness, B. Detection and quantitation of malignant cells in the peripheral blood of multiple myeloma patients. Blood 1992, 80, 1818–1824. [Google Scholar] [CrossRef]
  29. Ruiz-Argüelles, G.J.; A Katzmann, J.; Greipp, P.R.; Gonchoroff, N.J.; Garton, J.P.; A Kyle, R. Multiple myeloma: Circulating lymphocytes that express plasma cell antigens. Blood 1984, 64, 352–356. [Google Scholar] [CrossRef] [Green Version]
  30. Rawstron, A.C.; Owen, R.G.; Davies, F.E.; Johnson, R.J.; Jones, R.A.; Richards, S.J.; Evans, P.A.; Child, J.A.; Smith, G.M.; Jack, A.S.; et al. Circulating plasma cells in multiple myeloma: Characterization and correlation with disease stage. Br. J. Haematol. 1997, 97, 46–55. [Google Scholar] [CrossRef] [Green Version]
  31. Paiva, B.; Paino, T.; Sayagues, J.-M.; Garayoa, M.; San-Segundo, L.; Martín, M.; Mota, I.; Sanchez, M.-L.; Bárcena, P.; Aires-Mejia, I.; et al. Detailed characterization of multiple myeloma circulating tumor cells shows unique phenotypic, cytogenetic, functional, and circadian distribution profile. Blood 2013, 122, 3591–3598. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Kraj, M.; Kopeć-Szlęzak, J.; Pogłód, R.; Kruk, B. Flow cytometric immunophenotypic characteristics of 36 cases of plasma cell leukemia. Leuk. Res. 2011, 35, 169–176. [Google Scholar] [CrossRef] [PubMed]
  33. Klimienė, I.; Radzevičius, M.; Matuzevičienė, R.; Sinkevič-Belliot, K.; Kučinskienė, Z.A.; Pečeliūnas, V. Adhesion molecule immunophenotype of bone marrow multiple myeloma plasma cells impacts the presence of malignant circulating plasma cells in peripheral blood. Int. J. Lab. Hematol. 2021, 43, 403–408. [Google Scholar] [CrossRef] [PubMed]
  34. Chaidos, A.; Barnes, C.; Cowan, G.; May, P.; Melo, V.; Hatjiharissi, E.; Papaioannou, M.; Harrington, H.; Doolittle, H.; Terpos, E.; et al. Clinical drug resistance linked to interconvertible phenotypic and functional states of tumor-propagating cells in multiple myeloma. Blood 2013, 121, 318–328. [Google Scholar] [CrossRef] [PubMed]
  35. Kawano, Y.; Fujiwara, S.; Wada, N.; Izaki, M.; Yuki, H.; Okuno, Y.; Iyama, K.; Yamasaki, H.; Sakai, A.; Mitsuya, H.; et al. Multiple myeloma cells expressing low levels of CD138 have an immature phenotype and reduced sensitivity to lenalidomide. Int. J. Oncol. 2012, 41, 876–884. [Google Scholar] [CrossRef] [Green Version]
  36. Paiva, B.; Gutiérrez, N.-C.; Chen, X.; Vídriales, M.-B.; Montalbán, M.Á.; Rosiñol, L.; Oriol, A.; Martínez-López, J.; Mateos, M.-V.; López-Corral, L.; et al. Clinical significance of CD81 expression by clonal plasma cells in high-risk smoldering and symptomatic multiple myeloma patients. Leukemia 2012, 26, 1862–1869. [Google Scholar] [CrossRef] [Green Version]
  37. Bataille, R.; Jégo, G.; Robillard, N.; Barille-Nion, S.; Harousseau, J.-L.; Moreau, P.; Amiot, M.; Pellat-Deceunynck, C. The phenotype of normal, reactive and malignant plasma cells. Identification of “many and multiple myelomas” and of new targets for myeloma therapy. Haematologica 2006, 91, 1234–1240. [Google Scholar]
  38. Deceunynck, C.; Barille-Nion, S.; Jego, G.; Puthier, D.; Robillard, N.; Pineau, D.; Rapp, M.-J.; Harousseau, J.-L.; Amiot, M.; Bataille, R. The absence of CD56 (NCAM) on malignant plasma cells is a hallmark of plasma cell leukemia and of a special subset of multiple myeloma. Leukemia 1998, 12, 1977–1982. [Google Scholar] [CrossRef] [Green Version]
  39. Vandyke, K.; Zeissig, M.N.; Hewett, D.R.; Martin, S.K.; Mrozik, K.M.; Cheong, C.M.; Diamond, P.; To, L.B.; Gronthos, S.; Peet, D.J.; et al. HIF-2α Promotes Dissemination of Plasma Cells in Multiple Myeloma by Regulating CXCL12/CXCR4 and CCR1. Cancer Res. 2017, 77, 5452–5463. [Google Scholar] [CrossRef] [Green Version]
  40. Roccaro, A.M.; Mishima, Y.; Sacco, A.; Moschetta, M.; Tai, Y.-T.; Shi, J.; Zhang, Y.; Reagan, M.R.; Huynh, D.; Kawano, Y.; et al. CXCR4 Regulates Extra-Medullary Myeloma through Epithelial-Mesenchymal-Transition-like Transcriptional Activation. Cell Rep. 2015, 12, 622–635. [Google Scholar] [CrossRef] [Green Version]
  41. García-Ortiz, A.; Rodríguez-García, Y.; Encinas, J.; Maroto-Martín, E.; Castellano, E.; Teixidó, J.; Martínez-López, J. The Role of Tumor Microenvironment in Multiple Myeloma Development and Progression. Cancers 2021, 13, 217. [Google Scholar] [CrossRef] [PubMed]
  42. Sun, Y.-F.; Wu, L.; Liu, S.-P.; Jiang, M.-M.; Hu, B.; Zhou, K.-Q.; Guo, W.; Xu, Y.; Zhong, Y.; Zhou, X.-R.; et al. Dissecting spatial heterogeneity and the immune-evasion mechanism of CTCs by single-cell RNA-seq in hepatocellular carcinoma. Nat. Commun. 2021, 12, 4091. [Google Scholar] [CrossRef] [PubMed]
  43. Szczerba, B.M.; Castro-Giner, F.; Vetter, M.; Krol, I.; Gkountela, S.; Landin, J.; Scheidmann, M.C.; Donato, C.; Scherrer, R.; Singer, J.; et al. Neutrophils escort circulating tumour cells to enable cell cycle progression. Nature 2019, 566, 553–557. [Google Scholar] [CrossRef] [PubMed]
  44. Anvari, S.; Osei, E.; Maftoon, N. Interactions of platelets with circulating tumor cells contribute to cancer metastasis. Sci. Rep. 2021, 11, 15477. [Google Scholar] [CrossRef] [PubMed]
  45. Okegawa, T.; Ninomiya, N.; Masuda, K.; Nakamura, Y.; Tambo, M.; Nutahara, K. AR-V7 in circulating tumor cells cluster as a predictive biomarker of abiraterone acetate and enzalutamide treatment in castration-resistant prostate cancer patients. Prostate 2018, 78, 576–582. [Google Scholar] [CrossRef]
  46. Aceto, N.; Bardia, A.; Miyamoto, D.T.; Donaldson, M.C.; Wittner, B.S.; Spencer, J.A.; Yu, M.; Pely, A.; Engstrom, A.; Zhu, H.; et al. Circulating Tumor Cell Clusters Are Oligoclonal Precursors of Breast Cancer Metastasis. Cell 2014, 158, 1110–1122. [Google Scholar] [CrossRef] [Green Version]
  47. Botta, C.; Maia, C.; Garcés, J.-J.; Termini, R.; Perez, C.; Manrique, I.; Burgos, L.; Zabaleta, A.; Alignani, D.; Sarvide, S.; et al. FlowCT for the analysis of large immunophenotypic datasets and biomarker discovery in cancer immunology. Blood Adv. 2022, 2, 690–703. [Google Scholar] [CrossRef]
  48. Bausch-Fluck, D.; Hofmann, A.; Bock, T.; Frei, A.P.; Cerciello, F.; Jacobs, A.; Moest, H.; Omasits, U.; Gundru, R.L.; Yoon, C.; et al. A Mass Spectrometric-Derived Cell Surface Protein Atlas. PLoS ONE 2015, 10, e0121314. [Google Scholar] [CrossRef] [Green Version]
  49. Palumbo, A.; Avet-Loiseau, H.; Oliva, S.; Lokhorst, H.M.; Goldschmidt, H.; Rosinol, L.; Richardson, P.; Caltagirone, S.; Lahuerta, J.J.; Facon, T.; et al. Revised International Staging System for Multiple Myeloma: A Report From International Myeloma Working Group. J. Clin. Oncol. 2015, 33, 2863–2869. [Google Scholar] [CrossRef]
  50. Lohr, J.G.; Kim, S.; Gould, J.; Knoechel, B.; Drier, Y.; Cotton, M.J.; Gray, D.; Birrer, N.; Wong, B.; Ha, G.; et al. Genetic interrogation of circulating multiple myeloma cells at single-cell resolution. Sci. Transl. Med. 2016, 8, 363ra147. [Google Scholar] [CrossRef] [Green Version]
  51. Mishima, Y.; Paiva, B.D.L.; Shi, J.; Park, J.; Manier, S.; Takagi, S.; Massoud, M.; Perilla-Glen, A.; Aljawai, Y.; Huynh, D.; et al. The Mutational Landscape of Circulating Tumor Cells in Multiple Myeloma. Cell Rep. 2017, 19, 218–224. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Garcés, J.-J.; Bretones, G.; Burgos, L.; Valdes-Mas, R.; Puig, N.; Cedena, M.-T.; Alignani, D.; Rodriguez, I.; Puente, D.Á.; Álvarez, M.G.; et al. Circulating tumor cells for comprehensive and multiregional non-invasive genetic characterization of multiple myeloma. Leukemia 2020, 34, 3007–3018. [Google Scholar] [CrossRef] [PubMed]
  53. Paíno, T.; Paiva, B.; Sayagués, J.M.; Mota, I.; Carvalheiro, T.; Corchete, L.A.; Aires-Mejía, I.; Pérez, J.J.; Sanchez, M.L.; Barcena, P.; et al. Phenotypic identification of subclones in multiple myeloma with different chemoresistant, cytogenetic and clonogenic potential. Leukemia 2015, 29, 1186–1194. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Ledergor, G.; Weiner, A.; Zada, M.; Wang, S.-Y.; Cohen, Y.C.; Gatt, M.E.; Snir, N.; Magen, H.; Koren-Michowitz, M.; Herzog-Tzarfati, K.; et al. Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma. Nat. Med. 2018, 24, 1867–1876. [Google Scholar] [CrossRef]
  55. Fokkema, C.; de Jong, M.M.E.; Tahri, S.; Kellermayer, Z.; den Hollander, C.; Vermeulen, M.; Papzian, N.; van Duin, M.; Wevers, M.J.W.; Sanders, M.A.; et al. Abstract #1566: High Levels of Circulating Tumor Cells Are Associated with Increased Bone Marrow Proliferation in Newly Diagnosed Multiple Myeloma Patients. In Proceedings of the 63rd ASH Annual Meeting & Exposition, Atlanta, GA, USA, 11–14 December 2021. [Google Scholar]
  56. Foulk, B.; Schaffer, M.; Gross, S.; Rao, C.; Smirnov, D.; Connelly, M.C.; Chaturvedi, S.; Reddy, M.; Brittingham, G.; Mata, M.; et al. Enumeration and characterization of circulating multiple myeloma cells in patients with plasma cell disorders. Br. J. Haematol. 2018, 180, 71–81. [Google Scholar] [CrossRef] [Green Version]
  57. Hui, H.; Fuller, K.A.; Chuah, H.; Liang, J.; Sidiqi, H.; Radeski, D.; Erber, W.N. Imaging flow cytometry to assess chromosomal abnormalities in chronic lymphocytic leukaemia. Methods 2018, 134–135, 32–40. [Google Scholar] [CrossRef]
  58. Zhan, F.; Huang, Y.; Colla, S.; Stewart, J.P.; Hanamura, I.; Gupta, S.; Epstein, J.; Yaccoby, S.; Sawyer, J.; Burington, B.; et al. The molecular classification of multiple myeloma. Blood 2006, 108, 2020–2028. [Google Scholar] [CrossRef] [Green Version]
  59. Garcés, J.-J.; Puig, N.; Termini, R.; Cedena, M.-T.; Moreno, C.; Pérez, J.J.; Alignani, D.; Sarvide, S.; Oriol, A.; González-García, E.; et al. Abstract #76: Circulating Tumor Cells (CTCs) in Smoldering and Active Multiple Myeloma (MM): Mechanism of Egression, Clinical Significance and Therapeutic Endpoints. In Proceedings of the 63rd ASH Annual Meeting & Exposition, Atlanta, GA, USA, 11–14 December 2021. [Google Scholar]
  60. Vasco-Mogorrón, M.A.; Campillo, J.A.; Periago, A.; Cabañas, V.; Berenguer, M.; García-Garay, M.C.; Gimeno, L.; Soto-Ramírez, M.F.; Martínez-Hernández, M.D.; Muro, M.; et al. Blood-based risk stratification for pre-malignant and symptomatic plasma cell neoplasms to improve patient management. Am. J. Cancer Res. 2021, 11, 2736–2753. [Google Scholar]
  61. Garcés, J.-J.; Cedena, M.-T.; Puig, N.; Burgos, L.; Pérez, J.J.; Cordon, L.; Flores-Montero, J.; Sanoja-Flores, L.; Oriol, A.; Blanchard, M.-J.; et al. Abstract S185: Circulating tumor cells (CTCs) are the most relevant diagnostic biomarker in transplant-eligible multiple myeloma (MM). In Proceedings of the The European Hematology Association Congress, Virtual, Den Haag, The Netherlands, 12 June 2021. [Google Scholar]
  62. Mateos, M.-V.; Lopez, J.M.; Rodríguez-Otero, P.; Gonzalez-Calle, V.; Gonzalez, M.S.; Oriol, A.; Gutierrez, N.C.; Rios, R.; Rosinol, L.; Alvarez, M.A.; et al. Abstract #1829: Curative Strategy (GEM-CESAR) for High-Risk Smoldering Myeloma (SMM): Carfilzomib, Lenalidomide and Dexamethasone (KRd) As Induction Followed By HDT-ASCT, Consolidation with Krd and Maintenance with Rd. In Proceedings of the 63rd ASH Annual Meeting & Exposition, Atlanta, GA, USA, 11–14 December 2021. [Google Scholar]
  63. Gonsalves, W.I.; Jevremovic, D.; Nandakumar, B.; Dispenzieri, A.; Buadi, F.K.; Dingli, D.; Lacy, M.Q.; Hayman, S.R.; Kapoor, P.; Leung, N.; et al. Enhancing the R-ISS classification of newly diagnosed multiple myeloma by quantifying circulating clonal plasma cells. Am. J. Hematol. 2020, 95, 310–315. [Google Scholar] [CrossRef]
  64. Paiva, B.; GEM (Grupo Español de MM)/PETHEMA (Programa para el Estudio de la Terapéutica en Hemopatías Malignas) Cooperative Study Group; Vídriales, M.-B.; Rosiñol, L.; Martínez-López, J.; Mateos, M.-V.; Ocio, E.M.; Montalbán, M.-Á.; Cordón, L.; Gutiérrez, N.C.; et al. A multiparameter flow cytometry immunophenotypic algorithm for the identification of newly diagnosed symptomatic myeloma with an MGUS-like signature and long-term disease control. Leukemia 2013, 27, 2056–2061. [Google Scholar] [CrossRef] [Green Version]
  65. De Larrea, C.F.; Kyle, R.A.; Durie, B.G.M.; Ludwig, H.; Usmani, S.; Vesole, D.H.; Hajek, R.; San-Miguel, J.F.; Sezer, O.; Sonneveld, P.; et al. Plasma cell leukemia: Consensus statement on diagnostic requirements, response criteria and treatment recommendations by the International Myeloma Working Group. Leukemia 2013, 27, 780–791. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. De Larrea, C.F.; Kyle, R.; Rosiñol, L.; Paiva, B.; Engelhardt, M.; Usmani, S.; Caers, J.; Gonsalves, W.; Schjesvold, F.; Merlini, G.; et al. Primary plasma cell leukemia: Consensus definition by the International Myeloma Working Group according to peripheral blood plasma cell percentage. Blood Cancer J. 2021, 11, 192. [Google Scholar] [CrossRef] [PubMed]
  67. Jelinek, T.; Bezděková, R.; Zihala, D.; Sevcikova, T.; Capkova, L.; Polackova, P.; Stork, M.; Knechtova, Z.; Venglar, O.; Jurczyszyn, A.; et al. Abstract #546: Circulating Plasma Cells Are the Most Powerful Prognostic Marker in Transplant Ineligible Multiple Myeloma with 2% as a New Cut-Off for Primary Plasma Cell Leukemia. In Proceedings of the 63rd ASH Annual Meeting & Exposition, Atlanta, GA, USA, 11–14 December 2021. [Google Scholar]
  68. An, G.; Qin, X.; Acharya, C.; Xu, Y.; Deng, S.; Shi, L.; Zang, M.; Sui, W.; Yi, S.; Li, Z.; et al. Multiple myeloma patients with low proportion of circulating plasma cells had similar survival with primary plasma cell leukemia patients. Ann. Hematol. 2015, 94, 257–264. [Google Scholar] [CrossRef] [PubMed]
  69. Evans, L.A.; Jevremovic, D.; Nandakumar, B.; Dispenzieri, A.; Buadi, F.K.; Dingli, D.; Lacy, M.Q.; Hayman, S.R.; Kapoor, P.; Leung, N.; et al. Utilizing multiparametric flow cytometry in the diagnosis of patients with primary plasma cell leukemia. Am. J. Hematol. 2020, 95, 637–642. [Google Scholar] [CrossRef] [PubMed]
  70. Sanoja-Flores, L.; Flores-Montero, J.; Puig, N.; Contreras-Sanfeliciano, T.; Pontes, R.; Corral-Mateos, A.; García-Sánchez, O.; Díez-Campelo, M.; De Magalhães, R.J.P.; García-Martín, L.; et al. Blood monitoring of circulating tumor plasma cells by next generation flow in multiple myeloma after therapy. Blood 2019, 134, 2218–2222. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  71. Kim, S.-M.; Lee, Y.; Jeong, D.; Yun, J.; Yoon, S.-S.; Hwang, S.M.; Lee, N.; Lee, D.S. Significance of analyzing circulating plasma cells in multiple myeloma: Differences from measuring minimal residual diseases in bone marrow. Leuk. Lymphoma 2021, 63, 487–490. [Google Scholar] [CrossRef]
  72. Chakraborty, R.; Muchtar, E.; Kumar, S.K.; Jevremovic, D.; Buadi, F.K.; Dingli, D.; Dispenzieri, A.; Hayman, S.R.; Hogan, W.J.; Kapoor, P.; et al. Serial measurements of circulating plasma cells before and after induction therapy have an independent prognostic impact in patients with multiple myeloma undergoing upfront autologous transplantation. Haematologica 2017, 102, 1439–1445. [Google Scholar] [CrossRef] [Green Version]
  73. Gao, Y.; Ni, X.; Guo, H.; Su, Z.; Ba, Y.; Tong, Z.; Guo, Z.; Yao, X.; Chen, X.; Yin, J.; et al. Single-cell sequencing deciphers a convergent evolution of copy number alterations from primary to circulating tumor cells. Genome Res. 2017, 27, 1312–1322. [Google Scholar] [CrossRef]
  74. Chang, B.Y.; Francesco, M.; De Rooij, M.F.M.; Magadala, P.; Steggerda, S.M.; Huang, M.M.; Kuil, A.; Herman, S.E.M.; Chang, S.; Pals, S.T.; et al. Egress of CD19+CD5+ cells into peripheral blood following treatment with the Bruton tyrosine kinase inhibitor ibrutinib in mantle cell lymphoma patients. Blood 2013, 122, 2412–2424. [Google Scholar] [CrossRef]
  75. Termini, R.; Zihala, D.; Botta, C.; Maia, C.; Garcés, J.-J.; Terpos, E.; Pérez Montaña, A.; Jelinek, T.; Bargay, J.; Ocio, E.; et al. Abstract OAB-035: Minimally invasive profiling of tumor and immune cells to stratify risk in smoldering multiple myeloma (SMM): The iMMunocell study. In Proceedings of the The European Hematology Association Congress, Vienna, Austria, 11–21 June 2020. [Google Scholar]
Cancers 14 01430 g001 550
Figure 1. Example of the immunophenotypic features of paired bone marrow (BM) tumor cells, in green, and circulating tumor cells (CTCs), in red, from a representative patient. Results are represented by principal component analysis (PCA) based on the expression levels of eight antigens (CD19, CD27, CD38, CD45, CD56, CD81, CD117, CD138) together with forward (FSC) and side scatter (SSC). The significance (sig.) of each parameter in discriminating cells with different phenotypes is represented in the table. Of note, BM tumor cells are composed of two clones with negative and positive expression of CD138 and different morphology (FSC and SSC), whereas only CTCs without CD138 have egressed into circulation. Furthermore, CTCs show downregulation of CD38, CD56, CD81 and CD117 when compared to BM tumor cells.
Figure 1. Example of the immunophenotypic features of paired bone marrow (BM) tumor cells, in green, and circulating tumor cells (CTCs), in red, from a representative patient. Results are represented by principal component analysis (PCA) based on the expression levels of eight antigens (CD19, CD27, CD38, CD45, CD56, CD81, CD117, CD138) together with forward (FSC) and side scatter (SSC). The significance (sig.) of each parameter in discriminating cells with different phenotypes is represented in the table. Of note, BM tumor cells are composed of two clones with negative and positive expression of CD138 and different morphology (FSC and SSC), whereas only CTCs without CD138 have egressed into circulation. Furthermore, CTCs show downregulation of CD38, CD56, CD81 and CD117 when compared to BM tumor cells.
Cancers 14 01430 g001
Table
Table 1. Genomic and transcriptomic studies comparing circulating tumor cells (CTCs) vs. bone marrow (BM) tumor cells in multiple myeloma (MM).
Table 1. Genomic and transcriptomic studies comparing circulating tumor cells (CTCs) vs. bone marrow (BM) tumor cells in multiple myeloma (MM).
# Samples#CTCsEnrichment
Technology		Sequencing
Approach		Key IdeasRef.
7155RosetteSep™ +
manual selection (CD138 + CD45-)		(single-cell)
Targeted panel
(n = 13)		- All mutations clinically detected by bulk BM genotyping are also detected in single CTCs.
- There are mutations with greater frequency in CTCs compared to BM tumor cells.
- CTCs analysis allows the detection of LOH in tumor suppressor-associated genes.		[50]
831,700
(median)		multiparametric FCWES- CTCs are detectable in 61% of MGUS and 100% of smoldering and active MM.
- Around 15–20 mL of PB would suffice to sort 30,000 CTCs in a significant fraction of MM patients.
- Non-recurrent but potential driver mutations and copy-number alterations are detected on CTCs.		[51]
4-CD138 positive
selection		WES- Tumor fraction in enriched CTCs (and ctDNA) correlates with MM progression.
- Combined WES of CTCs (and ctDNA) reflects a high concordance in clonal somatic mutations (99%) and copy number alterations (81%) with BM aspirates.		[18]
8 + 10 + 3511,090
(median)		multiparametric FCWES +
barcoded WES +
CGH arrays		- Most mutations (≥82%) are simultaneously present in medullar or extra-medullar clones and can be readily monitored through the genetic characterization of CTCs (both by WES and CGH arrays).
- Other abnormalities with prognostic value (e.g., amp1q, or TP53) or potential role in progression but not routinely tested (e.g., MYC) are detectable in CTCs whenever present in BM tumor cells.		[52]
221RosetteSep™ + manual selection (CD138 + CD45-)scRNAseq
(Smart-seq2)		- Transcriptomic profiling of CTCs reproduces gene expression of BM tumor cells and can be used to detect targetable antigens (e.g., CD38, SLAMF7, and BCMA).
- CTCs are feasible for inferring single-cell expression for translocation-based classification.		[50]
29 + 35200
(median) +
266		multiparametric FCExpression arrays + scRNAseq
(Precise WTA
Single Cell
Assay, BD)		- There is a significant correlation in gene expression between paired CTCs and BM tumor cells (both at single-cell and bulk levels).
- Subtle differences between CTCs and BM tumor cells display prognostic potential.
- It is suggested that a hypoxic and pro-inflammatory BM microenvironment induces an arrest in proliferation, forcing tumor cells to circulate.		[7]
152299
(median)		FACS (CD138+ CD38+) (in plate)scRNAseq
(MARS-seq)		- CTC signatures highly resemble the BM transcriptional state(s), with few changes likely resulting from the different environments.
- The tumor load in the BM and the PB differs by several orders of magnitude.		 [54]
544,779
(median)		-scRNAseq- The absence of specific clusters and the transcriptional similarity suggest that CTC levels are not driven by a transcriptionally-primed migratory clone.
- BM tumor cell proliferation is a significant differential factor between high and low levels of CTCs.		[55]
Number of samples refers only to patient-matched CTCs and BM tumor cells used for paired comparisons. Number of CTCs is given as absolute values if nothing different is indicated. FC, flow cytometry; FACS, fluorescence-activated cell sorting; WES, whole-exome sequencing; CGH, comparative genomic hybridization (array); scRNAseq, single-cell RNA-sequencing; BD, Becton Dickinson; MGUS, monoclonal gammopathy of undetermined significance; PB, peripheral blood; LOH, loss of heterozygosity; ctDNA, circulating-free tumor DNA.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Garcés, J.-J.; San-Miguel, J.; Paiva, B. Biological Characterization and Clinical Relevance of Circulating Tumor Cells: Opening the Pandora’s Box of Multiple Myeloma. Cancers 2022, 14, 1430. https://doi.org/10.3390/cancers14061430

AMA Style

Garcés J-J, San-Miguel J, Paiva B. Biological Characterization and Clinical Relevance of Circulating Tumor Cells: Opening the Pandora’s Box of Multiple Myeloma. Cancers. 2022; 14(6):1430. https://doi.org/10.3390/cancers14061430

Chicago/Turabian Style

Garcés, Juan-José, Jesús San-Miguel, and Bruno Paiva. 2022. "Biological Characterization and Clinical Relevance of Circulating Tumor Cells: Opening the Pandora’s Box of Multiple Myeloma" Cancers 14, no. 6: 1430. https://doi.org/10.3390/cancers14061430

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop