The clinical value of circulating cell free DNA in multiple myeloma

doi:10.21203/rs.3.rs-1604204/v1

Purpose: To evaluate the feasibility of the detection of circulating cell free DNA (cfDNA) and the prognostic value of cfDNA in patients with multiple myeloma (MM).

Experimental design: All samples were screened hematopoietic malignancy associated genetic mutations from cfDNA and bone marrow (BM), using the Next-generation sequencing (NGS). The mutated genetic, checked in both cfDNA and BM, were monitered at the different time points during the treatment by droplet-based digital PCR (ddPCR).

Results: Three patients were included in our prospective study. Two patients detected DNMT3A mutations and one patient detected GNAQ mutation. The abundance of mutations decreased as the disease remission, and increased as the disease progresses. The detection of cfDNA has shown to predict relapses before paraprotein.

Conclusions: cfDNA is a sensitive tool for monitoring compared to paraprotein levels. The utility of cfDNA for monitoring disease is Promising.

cell free DNA

mutation

monitoring disease

multiple myeloma

Multiple myeloma (MM) is the second common hematologic malignancy, which is characterised by recurrent cytogenetics and molecular abnormalities. Improvements in the treatment of MM in the past couple of decades, beginning with the use of autologous stem cell transplantation[1–2] and novel treatments such as immunomodulatory drugs (ImIDs)[3–5], proteasome inhibitors (PIs)[6–8],and monoclonal antibodies[9–11],has transformed the natural history of the disease leading to longer survival times[12]. There have reported up to 60–80% of patients reaching a complete response [13].However, some patients still face the problem of relapse. Relapse due to undetected is the leading cause of death. So investigators have explored strategies for detect minimal residual disease (MRD) to monitor relapse. In clinical practice, multiparameter flow cytometry (MFC) is the frequently method to detect MRD [14–17]. However, surface antigen patterns of myeloma cell may change from those at the time of initial diagnosis, raising the possibility of false negative results [18]. Therefore, there is an urgent need to identify a new method for monitoring disease progression.

In 1948, Mandel and Metais detected circulating cell free DNA (cfDNA) in the blood stream of cancer patients [19]. In 1977, Leon et al. demonstrated that serum levels of cfDNA in cancer patients were higher than in healthy individuals [20]. Following this, Stroun et al. described a portion of plasma cfDNA was derived from tumor (ctDNA) and was carrying its molecular characteristics [21–22]. In recent years, there have large developments on the use of cfDNA, for follow-up and treatment of cancer patients [23–25]. And newly developed technologies such as droplet-based digital PCR (ddPCR) and Next-generation sequencing (NGS) increased largely the detection sensitivity, specificity and precision of the analysis of sequences, allowing their detection in cfDNA [26]. In recent years, cfDNA analysis for prognosis and monitoring disease in MM has been reported, but the results were unclear [27–28].

In our study, we detected mutations from cfDNA and paired BM samples using NGS to characterize the mutational profile in individual MM patients and monitored specific mutants in sequential cfDNA samples by ddPCR. The aim of the study was to evaluate the feasibility of the detection of cfDNA and the prognostic value of cfDNA in patients with multiple myeloma, used the method of NGS combined with ddPCR.

1.1 Sample extraction and data

From January 2019 to March 2022, bone marrow (BM) and peripheral bloods (PB) of all MM patients, never receiving treatment, were collected in our hospital. All patients were selected based on IMWG 2014 criteria [29], estimated survival time greater than 5 years. All patients signed an informed consent form which approved by the ethics committee. Blood samples and BM were collected before the first cycle of chemotherapy treatments. All samples were screened hematopoietic malignancy associated genetic mutations from cfDNA and BM, using the Next-generation sequencing. As the high price of the NGS, the mutated genetic, checked in both cfDNA and BM, were monitored at the different time points during the treatment by ddPCR. Paraprotein were checked at the same time to compare the fractional abundance (FA) of mutations. The following data were collected in a prospective database: clinical characteristics (gender, age, ECOG-PS), biological data (type of Myeloma, ISS and DS at diagnosis, Cytogenetic abnormalities) and follow-up data (paraprotein、fractional abundance (FA) of mutation、data of relapse、data of death or last follow-up).

1.2 gene target sequencing

1.2.1 Genomic DNA extraction

Peripheral bloods and BM of all subjects were collected in cell-free DNA storage tubes and EDTA tubes respectively. Genomic DNAs were extracted using QiaAmp Blood DNA Mini kits (Qiagen, CA, USA). The concentration and quality of genomic DNAs were examined with a NanoDrop® ND-1000 (Thermo Fisher Scientific, MA, USA), the Qubit® 3.0 Fluorometer (Thermo Fisher Scientific, MA, USA) and 1% agarose gel electrophoresis.

1.2.2 DNA Library Preparation

The paired-end DNA sequencing libraries were prepared through genomic DNA shearing, use a Covaris™ (Woburn, MA, USA) sonicator, followed by peak detection, end repair, poly A-tailing, paired-end adaptor ligation, and amplification. The qualified genomic DNA sample was randomly fragmented by Covaris ™ (Woburn, MA, USA) and the size of the library fragments is mainly distributed between 250 bp and 300 bp. End-Repair and A-tailing were then applied to facilitate ligation of the adapters, containing unique barcodes for each sample, specific to the Illumina technology for amplification and sequencing. KAPA Hyper Prep kit was used for these steps, according to the manufacturer’s instructions.

1.2.3 Targeted sequencing

Target gene capture was performed using SureSelect custom designs (Agilent Technologies, Inc., Santa Clara, CA) targeting whole exons of the 393 most frequent hematologic genes. Each captured library was then loaded on novase6000 platform (Illumina, Inc., San Diego, CA) at the Wuhan Kindstar Global gene Technology (Kindstar, Wuhan, China). Each sample was sequenced at the mean depth of 1500–2500× to achieve high sensitivity and accuracy for mutations detection.

1.2.4 Data analysis

Raw image files were processed by Illumina basecalling Software for base-calling with default parameters and the sequences of each individual were generated as 150 bp pair-end reads. Paired-end reads were aligned to the human reference genome (GRCh37/HG19) using the Burrows-Wheeler Aligner(BWA, v0.7.17). The strict data quality control (QC) was performed in the whole analysis pipeline for the clean data, the mapping data, the variant calling, etc. The Genome Analysis ToolKit ༈GATK, v4.1.1.0༉ was used for insertion/deletion realignment, quality score recalibration, and variant identification with duplicate reads removed by the Picard-tools (v4.1.1.0). ANNOVAR (v201804) was used to annotate mutations.

After sequence alignment and variant calling, synonymous variants, intronic variants far away from the exon/intron boundaries, and variants with a minor allelic frequency (MAF) ≥ 1% in the 1000 Genomes Project, the dbSNP database, and the Exome Aggregation Consortium (ExAC) database were removed from further analysis. NGS reads were visualized using an integrated genomic viewer (IGV).

1.2.5 Variant classification

Accurate classification of somatic genetic alterations detected by next-generation sequencing (NGS) was of paramount importance to ensure the provision of high-quality clinical data. Clinical significance of variants can be assessed and tiered based on guidelines from the Association for Molecular Pathology (AMP), the American Society of Clinical Oncology, and the College of American Pathology for the interpretation of somatic sequence variants identified in cancer. A four-tiered system to categorize somatic sequence variations based on their clinical significances is proposed: tier I, variants with strong clinical significance; tier II, variants with potential clinical significance; tier III, variants of unknown clinical significance; and tier IV, variants deemed benign or likely benign. Tiers I to III must be reported in descending order of clinical importance. It is not recommended to include tier IV or benign/likely benign variants/alterations in the report.

Then, we placed verified germline variants into the following categories according to guidelines from the American College of Medical Genetics and Genomics (ACMG) and Association of Molecular Pathology (AMP) : pathogenic (P), likely pathogenic (LP), variant of uncertain significance (VUS), likely benign (LB) and benign (B).

1.3 Droplet Digital PCR (ddPCR) Technology

1.3.1 Introduction

The unique sample partitioning step of digital PCR, paired with Poisson statistical data analysis, allows higher precision than traditional PCR and qPCR methods. Accordingly, digital PCR is particularly well suited for applications that require the detection of small amounts of input nucleic acid or finer resolution of target amounts among samples, for example, rare sequence detection, copy number variation (CNV) analysis, and gene expression analysis of the rare targets.

Droplet Digital PCR (ddPCR) is a method for performing digital PCR that is based on water-oil emulsion droplet technology. A sample is fractionated into 20,000 droplets, and PCR amplification of the template molecules occurs in each individual droplet. ddPCR technology uses reagents and workflows similar to those used for most standard TaqMan probe-based assays. This technique has a smaller sample requirement than other commercially available digital PCR systems, reducing cost and preserving precious samples.

1.3.2 QX200 Droplet Digital PCR System Workflow

1) Genomic DNA extraction

Genomic DNAs were extracted using QIAamp Circulating Nucleic Acid Kit (Qiagen, CA, USA) according to the manufacturer’s instructions.

2) Prepare PCR-Ready Samples prior to Starting ddPCR

Combine DNA sample and primers and probes with the ddPCR supermix to create prepared sample on ice. Load 20µl of our prepared sample into individual well.

3) Droplet Generation

Prior to droplet generation, nucleic acid samples are prepared as they are for any real-time assay: using primers, fluorescent probes (TaqMan probes with FAM and HEX or VIC), and a proprietary supermix developed specifically for droplet generation. Samples are then placed into the QX200 Droplet Generator, which utilizes proprietary reagents and microfluidics to partition the samples into 20,000 nanoliter-sized droplets. The droplets created by the QX200 Droplet Generator are uniform in size and volume.

4) PCR Amplification of Droplets

Droplets are transferred to a 96-well plate for PCR amplification with end point(40cycles)using thermal cycler (Table 1).

5) Droplet Reading

Following PCR amplification of the nucleic acid target in the droplets, the samples are placed in the QX200 Droplet Reader, which analyzes each droplet individually using a two-color detection system (set to detect FAM and either HEX or VIC), enabling multiplexed analysis for different targets in the same sample. The droplet reader and its bundled QuantaSoft™ software count the PCR-positive and PCR-negative droplets.

6) Analyze Results

Positive droplets, containing at least one copy of the target, exhibit increased fluorescence over negative droplets. In ddPCR, the QuantaSoft software measures the numbers of droplets that are positive and negative for each fluorophore (for example, FAM and HEX) in a sample. The fraction of positive droplets is then fitted to a Poisson distribution to determine the absolute initial copy number of the target DNA molecule in the input reaction mixture in units of copies/µl.

2.1 Patients characteristics

So far, three patients were included in this prospective study. The patients characteristics were summarized in Table 2.

2.2 Associated genetic mutations

BM and PB of 3 patients were collected before the first cycle of chemotherapy treatments. Then, DNA extracted form the PB and BM. We checked genetic mutations from cfDNA and BM in all these patients by NGS. In patient1, we found FGFR3 mutation (4.8%) and DNMT3A mutation (1.2%) in BM and DNMT3A mutation (0.5%) in cfDNA. In patient2, NRAS mutation (4.3%), DNMT3A mutation (1.2%) and KMT2C mutation (0.4%) were detected in BM and DNMT3A mutation (1.4%) was detected in cfDNA. In patients3, GNAQ mutation (1.8%) and FGFR3 mutation (20.5%) were detected in BM and GNAQ mutation (3.6%) was detected in cfDNA.

2.3 Mutation in cfDNA can monitor patient progress

We selected mutations that were detected in both BM and cfDNA as the target mutations. The utility of mutations quantitation with ddPCR for the monitoring of disease was evaluated in three patients. We found the fractional abundance (FA) of mutation had a decline when disease was in remission. The FA of mutation were also checked when paraprotein were negative. When disease keep in complete remission, the FA of mutations were also not detected. One patient had earlier detectable cfDNA compared to paraprotein. The FA of previously identified mutations rapidly increased coincident with relapse of the disease. So far, there are two patients still keep disease in complete remission (CR), which the FA of mutation still not to be checked. (Table 3)

Circulating cell free DNA (cfDNA) consists of fragments of double stranded DNA that are found in the blood. It is readily accessible through venepuncture that is fairly safe and non-invasive compared to bone marrow biopsy or primary tissue biopsy. Besides that, recent studies suggest that cfDNA may represent power tools for disease monitoring in patients with solid and hematological malignancies. With the development of DNA sequencing technology, especially the development of NGS, the use of cfDNA becomes more feasible because more gene mutations associated with malignant tumors have been found.

Multifocal tumor deposition and intraclonal Heterogeneity in MM highlight the spatial and temporal limitations of single site bone marrow biopsies. Liquid biopsy, such as cfDNA, provides an interesting alternative as tumour cells and genetic materials of all sites tend to leak into the blood stream. Some studies have shown that cfDNA can be used to obtain the molecular profile of myelomas instead of the bone marrow aspirate, and they have supported the concept of cfDNA as a prognostic marker [30–32]. So we selected mutations of cfDNA to monitor the state of disease and the response of treatment. Besides that, mutations of cfDNA was sensitive detected than BM. Li found that the mutation rates of BRAF, KRAS and NRAS were higher in cfDNA samples than in BM samples (53% VS 34%) [33]. Mithraprabhu found that in comparison with BM samples, cfDNA samples showed a higher proportion of TP53 mutations [34–35]. In our study, we found the same results. In our three patients, blood samples of two patients showed a higher proportion of mutations compared with BM samples.

MM is an incurable disease at present with treatment aimed at prolonging event free progression. Monitoring the response of treatment in MM is essential to determining the best way to optimize patient management. In order to monitor disease relapse or MRD assessment, serial measurement of paraprotein and serum free light chain is the current standard of care. But several goals have been used to determine the efficacy and reliability of cfDNA as a monitoring tool to compare protein levels [36]. Rustad et al. found that the mutations level in cfDNA correlated well with serum paraprotein. In their report, two patients had earlier detectable cfDNA compared to paraprotein, one patient had cfDNA detected later, and the remaining three patients were detected simultaneously [37]. We also indicate the prognostic value of circulating cfDNA. In our studies, we found the FA of mutation had a decline when disease was in remission. The FA of mutation were also checked when paraprotein were negative. When disease keep in complete remission, the FA of mutations were also not detected. Besides that, mutations were earlier detected in cfDNA compared to paraprotein in one relapsed patients. These studies showed that cfDNA may better indicate the tumor burden than paraprotein.

The results can also find in DLBCL and acute leukaemia. They observed the detection of disease-specific rearrangement in peripheral blood cfDNA by NGS has shown to predict relapses in diffuse-large B-cell lymphoma (DLBCL) before established radiological staging methods demonstrated evidence of disease recurrence [38–39]. Short et al. analysed paired plasma cfDNA and bone marrow (BM) samples from acute leukaemia patients using NGS. They observed cfDNA mutations (IDH1 and ASXL1) were detected in two patients during remission that were subsequently detected in the BM following morphological relapse 3 and 14 months later [40]. Jilani et al. also looked at using PCR technique to detect FLT3-ITD in cfDNA and BM samples of newly diagnosed AML, MDS and ALL patients. The found FLT3-ITD mutation was easier to detect in cfDNA using PCR as it produced a stronger band [41]. Therefore, cfDNA have a potential role in monitoring disease relapse by providing more data on tumor evolution and early relapse identification.

In our study, we found that cfDNA is a sensitive tool for monitoring compared to paraprotein levels. The utility of cfDNA for monitoring disease is Promising. As the limited sample, we need large prospective clinical studies to determine the potential of cfDNA use in clinical practice.

Funding

Huo Shi Foundation of Fujian Provincial Hospital (2019HSJJ19)

Contributions

Tong Yang identified patients, collect and analyzed the data, wrote the manuscript; Yun Lin designed and performed the experiments, analyzed and interpreted data, wrote the manuscript; Weiming Chen, Zhihong Wang, Tiannan Wei, Jin Shang identified and managed patients, collected clinical data. All authors read and approved the final manuscript.

Ethics declarations

Ethics approval and consent to participate

Our study had been approved by the appropriate ethics committee and have therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. All persons gave their informed consent prior to their inclusion in the study.

Conflict of interest

The author declares no conflict of interest.

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Table 1 The temperature and times of PCR amplification

Temperature	Time	Cycle
95℃	10min
94℃	30sec	40
60℃	1min	40
98℃	10min
4℃	1min

Table 2 Characteristics of patients enrolled in this study

	patient1	patient2	patient3
Gender	F	M	M
Age	68	60	70
ECOG-PS	0	0	0
Type of Myeloma	IgG-λ	IgG-κ	IgG-λ
ISS at diagnosis	I	II	III
DS at diagnosis	IIIA	IIIA	IIIA
Cytogenetic abnormalities
Chromosome	Normal	Normal	Normal
RB1	84%	ND	73%
D13S319	84%	ND	70%
IGH	74%	ND	69%
CCND1/IGH	ND	ND	ND
IGH/MAF	ND	ND	ND
IGH/MAFB	ND	ND	ND
IGH/FGFR3	84%	ND	34%
IGH/CCND3	ND	ND	ND
1q21	75%	ND	ND
P53/CEP17	ND	ND	ND

Table 3 The continuous variation of mutations and paraprotein

patient1	2019.1	2019.03	2019.06	2019.08	2019.09	2020.02	2020.07	2020.12	2021.05	2021.09	2022.01
paraprotein	positive	positive	positive	negative	negative	negative	negative	negative	negative	positive	negative
DNMT3A	0.50%	0.32%	0.28%	0.12%	ND	ND	2.86%	3.22%	3.68%	3.89%	3.22%
patient2	2019.04	2019.1	2020.05	2020.09	2020.12	2021.06
paraprotein	positive	negative	negative	negative	negative	negative
DNMT3A	1.40%	0.48%	ND	ND	ND	ND
patient3	2018.10	2019.03	2019.10	2020.01	2020.06	2020.12	2021.05
paraprotein	positive	positive	negative	negative	negative	negative	negative
GNAQ	3.60%	0.36%	0.02%	ND	ND	ND	ND

No competing interests reported.

The clinical value of circulating cell free DNA in multiple myeloma

Status:

Version 1

Abstract

Introduction

Materials And Methods

Result

Discussion

Conclusion

Declarations

References

Tables

Additional Declarations

Status:

Version 1