Literature DB >> 28183851

Monitoring multiple myeloma by next-generation sequencing of V(D)J rearrangements from circulating myeloma cells and cell-free myeloma DNA.

Anna Oberle1, Anna Brandt1, Minna Voigtlaender1, Benjamin Thiele1, Janina Radloff1, Anita Schulenkorf1, Malik Alawi2, Nuray Akyüz1, Manuela März1, Christopher T Ford1, Artus Krohn-Grimberghe3,4, Mascha Binder5.   

Abstract

Recent studies suggest that circulating tumor cells and cell-free DNA may represent powerful non-invasive tools for monitoring disease in patients with solid and hematologic malignancies. Here, we conducted a pilot study in 27 myeloma patients to explore the clonotypic V(D)J rearrangement for monitoring circulating myeloma cells and cell-free myeloma DNA. Next-generation sequencing was used to define the myeloma V(D)J rearrangement and for subsequent peripheral blood tracking after treatment initiation. Positivity for circulating myeloma cells/cell-free myeloma was associated with conventional remission status (P<0.001) and 91% of non-responders/progressors versus 41% of responders had evidence of persistent circulating myeloma cells/cell-free myeloma DNA (P<0.001). About half of the partial responders showed complete clearance of circulating myeloma cells/cell-free myeloma DNA despite persistent M-protein, suggesting that these markers are less inert than the M-protein, rely more on cell turnover and, therefore, decline more rapidly after initiation of effective treatment. Positivity for circulating myeloma cells and for cell-free myeloma DNA were associated with each other (P=0.042), but discordant in 30% of cases. This indicates that cell-free myeloma DNA may not be generated entirely by circulating myeloma cells and may reflect overall tumor burden. Prospective studies need to define the predictive potential of high-sensitivity determination of circulating myeloma cells and DNA in the monitoring of multiple myeloma. Copyright© Ferrata Storti Foundation.

Entities:  

Mesh:

Substances:

Year:  2017        PMID: 28183851      PMCID: PMC5451343          DOI: 10.3324/haematol.2016.161414

Source DB:  PubMed          Journal:  Haematologica        ISSN: 0390-6078            Impact factor:   9.941


Introduction

The introduction of novel proteasome inhibitors,[1] immunomodulatory drugs[2] and monoclonal antibodies[3,4] has led to deeper and longer-lasting responses in patients with multiple myeloma.[5-7] The detection of minimal residual disease has, therefore, become increasingly important for the management of this disease.[8] Bone marrow minimal residual disease studies using multicolor flow cytometry[9] or next-generation sequencing (NGS) of the clonotypic V(D)J immunoglobulin (Ig) rearrangement[10,11] suggest that minimal residual disease predicts progression-free and overall survival. The latter technology is currently the most sensitive, with a detection rate of one per 10−6 bone marrow cells. It does, however, requires repetitive bone marrow sampling, a procedure that is painful for the majority of patients. In solid tumors but also some hematologic cancers, circulating nucleic acids (cell-free DNA) and circulating tumor cells are becoming a promising minimally-invasive tool for monitoring tumor burden and response to treatment.[12-14] To date, diffuse-large B-cell lymphoma is a prototype hematologic disease in which the detection of the disease-specific V(D)J rearrangement in peripheral blood cell-free DNA by NGS has been shown to predict relapses before established radiological staging methods demonstrate evidence of disease recurrence.[15,16] The general applicability of this monitoring concept to multiple myeloma does, however, remain to be determined, since disease distribution, vascularity, spreading and cell turnover differ from those in lymphoma, potentially affecting the circulating malignant cell and cell-free DNA compartments. Here, we investigated the clinical utility of circulating myeloma cells and DNA in the monitoring of multiple myeloma using the clonotypic V(D)J rearrangement as a highly patient-specific detection marker.

Methods

Patients’ characteristics and ethics statement

A cohort of 27 patients with multiple myeloma treated at the University Medical Center Hamburg-Eppendorf was investigated as approved by the ethics committee (Ethikkommission der Landesärztekammer Hamburg) and after informed consent from the patients (Table 1). All patients underwent bone marrow sampling, performed at a time point with active disease, to determine the myeloma clonotype sequence (IGH, IGL or IGK) as well as peripheral blood sampling for analysis of V(D)J rearrangements before and during the course of treatment. Baseline clinical characteristics and remission status, according to the International Myeloma Working Group (IMWG), were assessed for all patients.
Table 1.

Patients’ characteristics.

Patients’ characteristics.

DNA preparation from peripheral blood and bone marrow leukocytes and plasma

Whole blood or bone marrow aspirate was collected into tubes containing heparin and processed within 2 h. Plasma was separated by centrifugation and stored at −80°C. Leukocytes were isolated with erythrocyte lysis using a standard lysis buffer (ammonium chloride 8.29 g/L, EDTA 0.372 g/L, potassium hydrogen carbonate 1 g/L) and frozen in freezing medium (90% fetal bovine serum, Biochrom, VWR, Darmstadt, Germany; 10% dimethyl sulfoxide, Sigma-Aldrich, Taufkirchen, Germany). Genomic DNA was extracted from frozen peripheral blood or bone marrow leukocytes using a Gen Elute Mammalian Genomic DNA Miniprep Kit (Sigma-Aldrich, Taufkirchen, Germany). Cell-free DNA was extracted from plasma using a QIAamp Circulating Nucleic Acid Kit (QIAGEN, Hilden, Germany). DNA was quantified by absorbance (Nano Drop ND-1000, Peqlab) for genomic DNA or fluorometric methods (Qubit 3.0, Thermo Fischer Scientific) for cell-free DNA.

Next-generation sequencing of V(D)J repertoires

Ig V(D)J segments were amplified with BIOMED2-FR1/-FR3 (IGH), -Ig kappa (IGK) or -Ig lambda (IGL) primer pools[17] containing Illumina-compatible adapters and barcodes as described previously with an input of 500 ng (75000 genomes) genomic DNA or 250 ng cell-free DNA.[18] Figure 1 shows a scheme of the amplification strategy. The polymerase chain reaction (PCR) product was cleaned-up using SPRIselect reagent (Beckmann Coulter, Brea, CA, USA) and 2 μL eluted DNA were used for a second PCR, during which Illumina adapter sequences were extended and a sample-specific barcode was added. The final PCR product was size-separated with 1.5% agarose gel electrophoresis and amplicons were purified using the NucleoSpin® Gel and PCR Clean-up Kit (Macherey-Nagel, Düren, Germany). The concentration of the final PCR products was determined on Qubit 3.0 (Thermo Fischer Scientific) and amplicon purity was controlled on an Agilent 2100 Bioanalyzer (Agilent Technologies, Böblingen, Germany). NGS was performed on an Illumina MiSeq sequencer with 500 or 600 cycle single-indexed, paired-end runs.
Figure 1.

Scheme of V(D)J DNA amplification and next-generation sequencing from peripheral blood cellular and cell-free DNA. Illumina adapters are shown in green and blue, barcode sequences are shown in red. cfDNA: cell-free DNA; IGH: immunoglobulin heavy chain; IGK/L: immunoglobulin kappa/lambda chain; FW: forward; RV: reverse; R1/R2: read 1/2; NGS: next-generation sequencing.

Scheme of V(D)J DNA amplification and next-generation sequencing from peripheral blood cellular and cell-free DNA. Illumina adapters are shown in green and blue, barcode sequences are shown in red. cfDNA: cell-free DNA; IGH: immunoglobulin heavy chain; IGK/L: immunoglobulin kappa/lambda chain; FW: forward; RV: reverse; R1/R2: read 1/2; NGS: next-generation sequencing.

Data analysis and statistics

Demultiplexing and Fastq formatted data output was generated by the MiSeq reporter. Raw sequences were processed to Ig V(D)J clonotypes based on the MiXCR analysis tool,[19] and the different sequencing samples were compared using the tcR R package for Ig analysis.[20] Data were plotted using R statistical software tools as well as GraphPad Prism 5. A sample was considered positive when the clonotypic rearrangement was detected at least twice. Differences between two groups were analyzed using the two-sided Student t test and categorical data were compared by the Fisher exact test. Linear regression analyses were performed to evaluate an association between response to treatment and positivity for circulating myeloma cell V(D)J [cmc-V(D)J] and/or cell-free myeloma V(D)J [cfm-V(D)J]. Analyses were carried out using IBM SPSS version 22. A P value of <0.05 was considered statistically significant.

Results and Discussion

Study design

A total of 27 myeloma patients requiring myeloma-directed treatment were included in this investigation. Clonotypic V(D)J rearrangements of the malignant plasma cell were determined from the bone marrow and subsequently used for clonal tracking in peripheral blood leukocyte DNA [cmc-V(D)J] and cell-free DNA [cfm-V(D)J] before and after treatment initiation at routine clinical remission assessments. Blood sampling was performed after two to four courses of the indicated treatment or within 6 months after high-dose melphalan/allogeneic stem cell transplantation, unless specified otherwise. The patients’ characteristics, treatments and sampling time points are summarized in Table 1 and Online Supplementary Table S1. A diagram of the study workflow is shown in Figure 2.
Figure 2.

Schematic illustration of study workflow. BMMC: bone marrow mononuclear cells; IGH: immunoglobulin heavy chain; IGK/L: immunoglobulin kappa/lambda chain; gDNA: genomic DNA; cfDNA: cell-free DNA.

Schematic illustration of study workflow. BMMC: bone marrow mononuclear cells; IGH: immunoglobulin heavy chain; IGK/L: immunoglobulin kappa/lambda chain; gDNA: genomic DNA; cfDNA: cell-free DNA.

Determination of clonotypic myeloma V(D)J rearrangements

In 19 of 27 patients, an unambiguous IGH VDJ rearrangement could be identified, two cases were biclonal and another two patients only showed an IGL VJ rearrangement (Table 1). The four cases without discernable rearrangements were excluded from further analysis.

Next-generation screening for circulating myeloma cell-V(D)J and cell-free myeloma-V(D)J

Based on previous studies,[21-23] we established optimal PCR and NGS conditions for comprehensive immune repertoire analysis and high-sensitivity detection of V(D)J rearrangements from leukocyte DNA. A sequencing depth of 80,000 reads per sample was sufficient to comprehensively analyze the B-lineage repertoire from 500 ng genomic or 250 ng cell-free DNA, which can typically be extracted from 1–5 mL of blood (Online Supplementary Figure S1A). To measure the sensitivity of our approach, we spiked monoclonal B-cell DNA, derived from the Burkitt lymphoma cell line DG75, into polyclonal leukocyte DNA and determined detection rates of the clonotypic DG75 V(D)J rearrangement by NGS. These experiments showed high fidelity detection even if only low amounts of clonotypic genomes were spiked into the polyclonal background (Online Supplementary Table S2). Using these high-sensitivity detection conditions, the 23 cases with a definable myeloma V(D)J rearrangement underwent further screening of blood samples before and after initiation of treatment when routine remission evaluation was performed (Table 1). Figure 3 shows representative baseline bone marrow and peripheral blood V(D)J plots of patient MM123 with evidence of cmc-V(D)J and cfm-V(D)J. Overall, cmc-V(D)J was detectable in 71% and cfm-V(D)J in 100% of cases at the baseline screening (Figure 4A). At the follow-up time points after treatment initiation, cmc-V(D)J was detectable in 40% and cfm-V(D)J in 34% of samples (Figure 4A). For further analyses, V(D)J sampling was considered positive if cmc-V(D)J or cfm-V(D)J or both resulted positive, which was the case in 47% of follow-up samples (Figure 4B). Clear associations were observed between poor remission status (assessed by M-protein-based IMWG criteria) and positive cmc-V(D)J sampling (regression coefficient 1.60; 95% CI: 0.68–2.50; P=0.002) (Figure 4A), evidence of cfm-V(D)J (regression coefficient 1.49; 95% CI: 0.70–2.27; P=0.001) (Figure 4A) as well as detection of V(D)J in at least one compartment (regression coefficient 1.67; 95% CI: 0.82–2.53; P<0.001) (Figure 4B), and 91% of non-responders (patients with stable or progressive disease) remained positive for cmc-/cfm-V(D)J, compared to 41% of responders (patients with partial remission or better) (P<0.001) (Figure 4B). The percentage of clonotypic to polyclonal cellular and cell-free V(D)J DNA did not differ significantly between responders and non-responders/progressors (P=0.170) (Figure 4C).
Figure 3.

Representative V(D)J bone marrow and peripheral blood repertoires of patient MM123 at diagnosis. Every dot represents a clonotypic V(D)J rearrangement within the immunoglobulin repertoire. The size of each dot represents the size of the clone. The malignant plasma cell clone is highlighted in the bone marrow as well as in the cellular and cell-free peripheral blood compartments. The plot was generated using R statistical software tools. BM: bone marrow; PB: peripheral blood.

Figure 4.

Monitoring of circulating myeloma cells [(cmc-V(D)J)] and cell-free myeloma DNA [(cfm-V(D)J)] after myeloma treatment by next-generation sequencing. (A) Positivity of patients’ samples for cmc-V(D)J and cfm-V(D)J at diagnosis/relapse and after treatment, respectively. Remission status is indicated according to the IMWG criteria. (B) Positivity of patients’ samples for V(D)J at diagnosis/relapse and after treatment. Time points were considered V(D)J-positive if the malignant clone was detectable in at least one compartment (cellular or cell-free). (C) Quantification of cmc-/cfm-V(D)J per global number of V(D)J rearrangements per compartment. Patients with PD and SD were summarized as non-responders/progressors and patients with PR, vgPR and CR were summarized as responders. PD: progressive disease; SD: stable disease; PR: partial response; vgPR: very good partial response; CR: complete response.

Representative V(D)J bone marrow and peripheral blood repertoires of patient MM123 at diagnosis. Every dot represents a clonotypic V(D)J rearrangement within the immunoglobulin repertoire. The size of each dot represents the size of the clone. The malignant plasma cell clone is highlighted in the bone marrow as well as in the cellular and cell-free peripheral blood compartments. The plot was generated using R statistical software tools. BM: bone marrow; PB: peripheral blood. Monitoring of circulating myeloma cells [(cmc-V(D)J)] and cell-free myeloma DNA [(cfm-V(D)J)] after myeloma treatment by next-generation sequencing. (A) Positivity of patients’ samples for cmc-V(D)J and cfm-V(D)J at diagnosis/relapse and after treatment, respectively. Remission status is indicated according to the IMWG criteria. (B) Positivity of patients’ samples for V(D)J at diagnosis/relapse and after treatment. Time points were considered V(D)J-positive if the malignant clone was detectable in at least one compartment (cellular or cell-free). (C) Quantification of cmc-/cfm-V(D)J per global number of V(D)J rearrangements per compartment. Patients with PD and SD were summarized as non-responders/progressors and patients with PR, vgPR and CR were summarized as responders. PD: progressive disease; SD: stable disease; PR: partial response; vgPR: very good partial response; CR: complete response. Detection of cmc-V(D)J was significantly associated with cfm-V(D)J-positivity (P=0.042). Nevertheless, polyclonal V(D)J repertoires were virtually non-overlapping and a discordance of about 30% was noted between cmc-V(D)J and cfm-V(D)J positivity (example repertoire overlaps are shown in Figure 5). This suggests that circulating cellular and cell-free compartments contain complementary information and that cfm-V(D)J may not be generated entirely within the vascular space (from circulating myeloma cells), but that it reflects myeloma burden in extravascular sites such as the bone marrow or extramedullary manifestations. Concurrent investigation of both compartments does, therefore, appear reasonable.
Figure 5.

Overlap of V(D)J repertoires from cellular and cell-free peripheral blood compartments. Shared V(D)J clonotypes from different compartments were calculated using the tcR tool[20] and overlap repertoires were plotted using the R statistical software tool. The V(D)J rearrangement of the malignant plasma cell clone [(cmc- and/or cfm-V(D)J)] is shown in red.

Overlap of V(D)J repertoires from cellular and cell-free peripheral blood compartments. Shared V(D)J clonotypes from different compartments were calculated using the tcR tool[20] and overlap repertoires were plotted using the R statistical software tool. The V(D)J rearrangement of the malignant plasma cell clone [(cmc- and/or cfm-V(D)J)] is shown in red. One unexpected aspect of our findings was the rather low rate of positivity for cmc-V(D)J (45%) or cfm-V(D)J (39%) or both (68%) in patients with no or incomplete M-protein responses (very good partial response or less). Since our technical approach showed high repertoire coverage as well as high sensitivity of detection, we concluded that this reflects true absence of the clonotypic DNA in the blood-derived genomes used for sequencing library preparation and that only higher genomic input could eventually enhance the sensitivity of detection. To study whether higher genomic PCR input resulted in higher detection rates of the clonotypic V(D)J DNA, we selected two cfm-V(D)J negative cases with progressive disease and scaled up the PCR input from 250 ng (37,500 genomes) to 1250 ng (187,500 genomes), typically extractable from >10 mL of blood. As expected, repertoire diversity increased, but the clonotypic myeloma V(D)J rearrangement could still not be detected in these samples (Online Supplementary Figures S1B–D). This confirmed our previous hypothesis that a fraction of patients with no or incomplete M-protein responses may not release any myeloma DNA into the plasma and that this biomarker potentially has different biological implications than those of M-protein. Taken together, our pilot study gives valuable biological insights into the circulating cellular and cell-free compartments that can be explored by “liquid biopsy” in multiple myeloma. It indicates that cmc-V(D)J and cfm-V(D)J may decline more promptly in response to effective treatments than the relatively inert M-protein and may, therefore, be more informative regarding cell turnover and potentially suitable for immediate estimation of treatment efficacy or even early prediction of minimal residual disease negativity, not only in patients with low- or asecretory myeloma. Due to the limitations of this study (small cohort size, heterogeneous treatments), the actual predictive significance of rapid clearance of cmc-V(D)J or cfm-V(D)J, but also its persistence in M-protein responders cannot be reliably assessed. Future prospective studies will need to address whether this noninvasive diagnostic tool is of predictive importance and therefore of additional value to the established protein-based monitoring approach in multiple myeloma.
  22 in total

1.  Next-generation sequencing of peripheral B-lineage cells pinpoints the circulating clonotypic cell pool in multiple myeloma.

Authors:  Benjamin Thiele; Marie Kloster; Malik Alawi; Daniela Indenbirken; Martin Trepel; Adam Grundhoff; Mascha Binder
Journal:  Blood       Date:  2014-04-21       Impact factor: 22.113

2.  MiXCR: software for comprehensive adaptive immunity profiling.

Authors:  Dmitriy A Bolotin; Stanislav Poslavsky; Igor Mitrophanov; Mikhail Shugay; Ilgar Z Mamedov; Ekaterina V Putintseva; Dmitriy M Chudakov
Journal:  Nat Methods       Date:  2015-05       Impact factor: 28.547

3.  Circulating tumour DNA and CT monitoring in patients with untreated diffuse large B-cell lymphoma: a correlative biomarker study.

Authors:  Mark Roschewski; Kieron Dunleavy; Stefania Pittaluga; Martin Moorhead; Francois Pepin; Katherine Kong; Margaret Shovlin; Elaine S Jaffe; Louis M Staudt; Catherine Lai; Seth M Steinberg; Clara C Chen; Jianbiao Zheng; Thomas D Willis; Malek Faham; Wyndham H Wilson
Journal:  Lancet Oncol       Date:  2015-04-01       Impact factor: 41.316

4.  Improved long-term survival in multiple myeloma up to the age of 80 years.

Authors:  S Y Kristinsson; W F Anderson; O Landgren
Journal:  Leukemia       Date:  2014-01-14       Impact factor: 11.528

5.  Clinical response to ibrutinib is accompanied by normalization of the T-cell environment in CLL-related autoimmune cytopenia.

Authors:  S Schliffke; N Akyüz; C T Ford; T Mährle; T Thenhausen; A Krohn-Grimberghe; S Knop; C Bokemeyer; M Binder
Journal:  Leukemia       Date:  2016-05-25       Impact factor: 11.528

Review 6.  Overview of proteasome inhibitor-based anti-cancer therapies: perspective on bortezomib and second generation proteasome inhibitors versus future generation inhibitors of ubiquitin-proteasome system.

Authors:  Q Ping Dou; Jeffrey A Zonder
Journal:  Curr Cancer Drug Targets       Date:  2014       Impact factor: 3.428

Review 7.  Maintenance Therapy With Immunomodulatory Drugs in Multiple Myeloma: A Meta-Analysis and Systematic Review.

Authors:  Yucai Wang; Fang Yang; Yan Shen; Wenwen Zhang; Jacqueline Wang; Victor T Chang; Borje S Andersson; Muzaffar H Qazilbash; Richard E Champlin; James R Berenson; Xiaoxiang Guan; Michael L Wang
Journal:  J Natl Cancer Inst       Date:  2015-11-18       Impact factor: 13.506

8.  Epidermal growth factor receptor mutation mediates cross-resistance to panitumumab and cetuximab in gastrointestinal cancer.

Authors:  Friederike Braig; Manuela März; Aneta Schieferdecker; Alexander Schulte; Mareike Voigt; Alexander Stein; Tobias Grob; Malik Alawi; Daniela Indenbirken; Malte Kriegs; Erik Engel; Udo Vanhoefer; Adam Grundhoff; Sonja Loges; Kristoffer Riecken; Boris Fehse; Carsten Bokemeyer; Mascha Binder
Journal:  Oncotarget       Date:  2015-05-20

Review 9.  Deep Response in Multiple Myeloma: A Critical Review.

Authors:  Mariateresa Fulciniti; Nikhil C Munshi; Joaquin Martinez-Lopez
Journal:  Biomed Res Int       Date:  2015-12-10       Impact factor: 3.411

10.  Minimal residual disease in myeloma by flow cytometry: independent prediction of survival benefit per log reduction.

Authors:  Andy C Rawstron; Walter M Gregory; Ruth M de Tute; Faith E Davies; Sue E Bell; Mark T Drayson; Gordon Cook; Graham H Jackson; Gareth J Morgan; J Anthony Child; Roger G Owen
Journal:  Blood       Date:  2015-02-02       Impact factor: 22.113

View more
  43 in total

1.  Myeloma MRD by deep sequencing from circulating tumor DNA does not correlate with results obtained in the bone marrow.

Authors:  Céline Mazzotti; Laure Buisson; Sabrina Maheo; Aurore Perrot; Marie-Lorraine Chretien; Xavier Leleu; Cyrille Hulin; Salomon Manier; Benjamin Hébraud; Murielle Roussel; Laura Do Souto; Michel Attal; Hervé Avet-Loiseau; Jill Corre
Journal:  Blood Adv       Date:  2018-11-13

Review 2.  Tracking myeloma tumor DNA in peripheral blood.

Authors:  Johannes M Waldschmidt; Tushara Vijaykumar; Birgit Knoechel; Jens G Lohr
Journal:  Best Pract Res Clin Haematol       Date:  2020-01-14       Impact factor: 3.020

3.  Blood monitoring of circulating tumor plasma cells by next generation flow in multiple myeloma after therapy.

Authors:  Luzalba Sanoja-Flores; Juan Flores-Montero; Noemi Puig; Teresa Contreras-Sanfeliciano; Roberia Pontes; Alba Corral-Mateos; Omar García-Sánchez; María Díez-Campelo; Roberto José Pessoa de Magalhães; Luis García-Martín; José María Alonso-Alonso; Aranzazú García-Mateo; Carlos Aguilar-Franco; Jorge Labrador; Abelardo Barez-García; Angelo Maiolino; Bruno Paiva; Jesús San Miguel; Elaine Sobral da Costa; Marcos González; María Victoria Mateos; Brian Durie; Jacques J M van Dongen; Alberto Orfao
Journal:  Blood       Date:  2019-12-12       Impact factor: 22.113

4.  Circulating tumor DNA as a liquid biopsy in plasma cell dyscrasias.

Authors:  Bernhard Gerber; Martina Manzoni; Valeria Spina; Alessio Bruscaggin; Marta Lionetti; Sonia Fabris; Marzia Barbieri; Gabriella Ciceri; Alessandra Pompa; Gabriela Forestieri; Erika Lerch; Paolo Servida; Francesco Bertoni; Emanuele Zucca; Michele Ghielmini; Agostino Cortelezzi; Franco Cavalli; Georg Stussi; Luca Baldini; Davide Rossi; Antonino Neri
Journal:  Haematologica       Date:  2018-02-22       Impact factor: 9.941

5.  Detection of the MYD88L265P and CXCR4S338X mutations by cell-free DNA in Waldenström macroglobulinemia.

Authors:  Yan-Yan Wu; Ming-Nan Jia; Hao Cai; Yu Qiu; Dao-Bin Zhou; Jian Li; Xin-Xin Cao
Journal:  Ann Hematol       Date:  2020-06-23       Impact factor: 3.673

6.  What to do with minimal residual disease testing in myeloma.

Authors:  Elisabet E Manasanch
Journal:  Hematology Am Soc Hematol Educ Program       Date:  2019-12-06

Review 7.  Comprehensive characterization of circulating and bone marrow-derived multiple myeloma cells at minimal residual disease.

Authors:  Johannes M Waldschmidt; Praveen Anand; Birgit Knoechel; Jens G Lohr
Journal:  Semin Hematol       Date:  2018-03-01       Impact factor: 3.851

Review 8.  The value of cell-free DNA for molecular pathology.

Authors:  Caitlin M Stewart; Prachi D Kothari; Florent Mouliere; Richard Mair; Saira Somnay; Ryma Benayed; Ahmet Zehir; Britta Weigelt; Sarah-Jane Dawson; Maria E Arcila; Michael F Berger; Dana Wy Tsui
Journal:  J Pathol       Date:  2018-03-12       Impact factor: 7.996

9.  Circulating cell-free DNA in the peripheral blood plasma of patients is an informative biomarker for multiple myeloma relapse.

Authors:  Hiroshi Yasui; Masayuki Kobayashi; Kota Sato; Kanya Kondoh; Tadao Ishida; Yuta Kaito; Hideto Tamura; Hiroshi Handa; Yutaka Tsukune; Makoto Sasaki; Norio Komatsu; Norina Tanaka; Junji Tanaka; Masahiro Kizaki; Toyotaka Kawamata; Junya Makiyama; Kazuaki Yokoyama; Seiya Imoto; Arinobu Tojo; Yoichi Imai
Journal:  Int J Clin Oncol       Date:  2021-07-14       Impact factor: 3.402

Review 10.  Liquid biopsy: an evolving paradigm for the biological characterisation of plasma cell disorders.

Authors:  Sridurga Mithraprabhu; Maoshan Chen; Ioanna Savvidou; Antonia Reale; Andrew Spencer
Journal:  Leukemia       Date:  2021-07-14       Impact factor: 11.528

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.