The Genomic Complexity of Multiple Myeloma Precursor Disease Can be Predicted Using Copy Number Signatures on Targeted Sequencing and SNP Array Data
Authors: Maclachlan Kylee H , Zheng-Lin Binbin , Yellapantula Venkata , Rustad Even H , Diamond Benjamin , Hultcrantz Malin , Dogan Ahmet , Zhang Yanming , Morgan Gareth , Landgren Ola , Maura Francesco 
Published: November 2020

Introduction
Current clinical models for predicting the progression from myeloma precursor disease (smoldering multiple myeloma (SMM) and monoclonal gammopathy of undetermined significance (MGUS)) to multiple myeloma (MM) are based on tumor burden, and not designed to capture heterogeneity in tumor biology. With the advent of whole genome sequencing (WGS), complex genomic change including the catastrophic event of chromothripsis has been detected in a significant fraction of MM patients. Chromothripsis is associated with other features of aggressive biology (i.e. biallelic TP53 deletion and increased APOBEC activity), and in newly diagnosed MM (NDMM), patients harboring chromothripsis have a shorter progression free survival (PFS) (Rustad BioRxiv 2019). Chromothripsis has also been demonstrated in SMM which later progressed to MM (Maura Nat Comm 2019) and our preliminary results indicate that the absence of chromothripsis is associated with stable precursor disease (Oben ASH 2020).
We have demonstrated that chromothripsis can be accurately predicted in NDMM using copy-number variation (CNV) signatures on both WGS and whole exome sequencing (Maclachlan ASH 2020). As with WGS, CNV signature analysis in less comprehensive assays (e.g. targeted sequencing panels and single nucleotide polymorphism (SNP) arrays) demonstrated that chromothripsis-associated CNV signatures are associated with shorter PFS. The aim of this study was to define the landscape of CNV signatures in myeloma precursor disease, and to compare the results with CNV signatures in NDMM.
Methods
CNV signature analysis uses 6 fundamental features: i) breakpoint count per 10 Mb, ii) absolute CN of segments, iii) difference in CN between adjacent segments, iv) breakpoint count per chromosome arm, v) lengths of oscillating CN segments, and vi) the size of segments (Macintyre Nat Gen 2018). The number of subcategories for each feature (which may differ between cancer and assay types) was established using a mixed effect model (mclust R package). For both targeted sequencing (myTYPE panel; (n=19, 4 MGUS, 15 SMM) and SNP array (n=78, 16 MGUS, 62 SMM), de novo CNV signature extraction was performed by hierarchical dirichlet process, running the analysis together with NDMM samples for reliable signature detection.
Results
Our analysis identified 4 and 6 CNV signatures from myTYPE and SNP array data respectively, with the extracted signatures being analogous to those from WGS, which are highly predictive of chromothripsis (Maclachlan ASH 2020).
Compared with NDMM (myTYPE; n=113; SNP array; n=217), precursor samples contained significantly fewer breakpoints / chromosome arm (myTYPE; p= 0.0003, SNP; p &lt;0.0001), fewer breakpoints / 10 Mb (both; p &lt;0.0001), shorter lengths of oscillating CN (myTYPE; p= 0.013, SNP; p= 0.018), fewer jumps between CN states (myTYPE; p= 0.0043, SNP; p &lt; 0.0001), lower absolute CN (myTYPE; p= 0.0059, SNP; p &lt; 0.0001) and fewer small segments of CN change (myTYPE; p= 0.0007, SNP; p= 0.0008). Chromothripsis-associated CNV signatures were significantly enriched in NDMM compared to precursor disease (p&lt;0.0001), with only 8.2% of precursors having a significant contribution from these signatures (NDMM; 38.7%). Overall, every CNV feature consistent with chromothripsis was measured at a significantly lower level in precursors than NDMM.
As &lt;5% of the precursors have progressed to MM, and given that we see heterogeneity in the pattern of CNV abnormalities both between MM and precursor disease, and within patients with precursor disease, we are currently investigating the role of CNV abnormalities in relation to clinical progression. As an interim measure; restricting analysis to patients with clinical stability &gt;5 years (n=11), we observed chromothripsis-associated signatures to be absent in all samples.
Conclusion
All individual CN features comprising chromothripsis-associated CNV signatures are significantly lower in stable myeloma precursor disease compared with NDMM when assessed by targeted sequencing and SNP array, along with a lower contribution from chromothripsis-associated signatures. Given the adverse impact of chromothripsis in MM, these data show great promise towards the future refinement of risk prediction estimation in myeloma precursor disease. Our ongoing work involves extending CNV analysis into larger datasets, including precursor patients who subsequently progressed to MM.

Disclosures
Hultcrantz: Intellisphere LLC: Consultancy; Amgen: Research Funding; Daiichi Sankyo: Research Funding; GSK: Research Funding. Dogan:Roche: Consultancy, Research Funding; Physicians Education Resource: Consultancy; Corvus Pharmaceuticals: Consultancy; Seattle Genetics: Consultancy; Takeda: Consultancy; EUSA Pharma: Consultancy; AbbVie: Consultancy; National Cancer Institute: Research Funding. Morgan:Bristol-Myers Squibb: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; GSK: Consultancy, Honoraria. Landgren:Amgen: Consultancy, Honoraria, Research Funding; Karyopharma: Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Seattle Genetics: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Cellectis: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Binding Site: Consultancy, Honoraria; Adaptive: Consultancy, Honoraria; Merck: Other; Pfizer: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Binding Site: Consultancy, Honoraria; Karyopharma: Research Funding; Merck: Other; Pfizer: Consultancy, Honoraria; Seattle Genetics: Research Funding; Juno: Consultancy, Honoraria; Glenmark: Consultancy, Honoraria, Research Funding.