Abstract

Introduction Smouldering multiple myeloma (SMM) is a heterogenous condition, with some patients progressing rapidly (high-risk) while others have MGUS-like disease (low-risk). The International Myeloma Working Group's (IMWG) 2/20/20 risk score predicts 2-year progression, but despite these improvements in risk-stratification, identification of patients requiring early intervention remains elusive. For newly diagnosed multiple myeloma (NDMM), survival varies, and current risk-stratification falls short for poor responders to standard first-line therapy (high-risk). MYC is a potent oncogene implicated in the initiation, maintenance, and progression of various malignancies. MYC overexpression on immunohistochemistry predicts poor clinical outcome in MM 1, implicating MYC overexpression in aggressive disease kinetics in MM. We hypothesised that MYC copy number could improve identification of high-risk SMM and ND MM. In this study, we developed a minimally invasive liquid biopsy approach to characterise the expression of MYC copy number in circulating cell-free DNA (cfDNA) on a unique cohort of peripheral blood samples obtained from the national liquid biopsy biobank, the M1000 that is linked to our clinical registry, the Myeloma and Related Diseases Registry (MRDR). Methods We obtained plasma samples from 64 SMM and 149 ND MM patients from the M1000. cfDNA was extracted from 1mL of plasma collected in Streck™ tubes using QIAamp Circulating Nucleic Acid Kit (QIAGEN) according to manufacturer instructions. Equal amount of cfDNA (5 uL), run in duplicates, from each patient was utilised for copy number assessment developed using the Biorad QX200 Digital Droplet TM PCR. The copy number of MYC was calculated by comparing the number of molecules (using a ratio of positive to negative droplets) to the number of molecules arising from the reference (RPP30) genomic locus using the QuantaSoft TM Analysis Pro Software (Biorad). MYC copy number were correlated with clinical data from MRDR on progression in SMM and overall survival (OS) in NDMM. We use median copy number as cut off to categorise high and low MYC. Results We were able to extract cfDNA from 1 ml of plasma samples from both SMM and NDMM patients, with the median amount being 35.11 ng (11.9 ng - 107.78ng) and 50.26 ng (5.85 ng - 243.45 ng), respectively. The median MYC copy number levels were 1.883 in SMM and 1.860 for NDMM. Within 36 months follow up in the SMM cohort, 18 patients progressed to MM with median time to progression of 14 months (2- 36 months, Figure 1). High MYC copy number (>1.9) at diagnosis was predictive of disease progression with hazard ratio of 5.27 (95% CI 1.99 -14; P = 0.0015). When combined with the 20/20/20 risk stratification model 3, MYC copy number improved the reliability of this score. In the NDMM cohort, median OS for the cohort was not reached at 36 months follow up. Patients with high MYC copy number (>1.9) at diagnosis had shorter OS at 36 months compared to those with low MYC copy number at 36 months (66.05% vs 82.02%. P=0.0012, Figure 2). Conclusion Our study demonstrates a significant association between MYC copy number, assessed via plasma cfDNA, and the risk of progression in patients with SMM. Additionally, in NDMM, MYC copy number at diagnosis directly correlates with OS. These findings require further investigation and validation in a larger cohort of M1000 samples, as they have the potential to introduce a minimally-invasive risk stratification methodology to identify high-risk patients and the introduction of MYC targeted therapies in patients with high MYC copy number.

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