Objective

To identify the association between aneuploidy and clinical outcome in patients with relapsed and/or refractory multiple myeloma (RRMM) who participated in MMRF (Multiple Myeloma Research Foundation) sequencing study at University of Michigan.

Background:

Aneuploidy, defined by abnormal copy number changes of chromosomes, is one of the hallmarks in cancer, reflecting and also contributing to genome instability (Ye, Regan et al. 2018). Approximately 90% of cancers have gained or lost one or both arms of at least one chromosome (Taylor, Shih et al. 2018). In recent years, large scale sequencing efforts have extended aneuploidy study, identifying DNA somatic copy number alterations (SCNAs). Published data suggest aneuploidy has a stronger impact on prognosis than gene mutations in multiple myeloma (Walker, Boyle et al. 2015; Bolli, Biancon et al. 2018; Jamal-Haniani, Wilson et al. 2017).

Methods:

Fifty one RRMM patients from our institute participated in Clinical-Grade Molecular Profiling of Patients with Multiple Myeloma and Related Plasma Cell Malignancies (MMRF-002) from Multiple Myeloma Research Foundation (MMRF) between March 2016 and November 2018.

Genomic DNA was obtained from CD138+ sorted myeloma cells and a peripheral blood sample from each patient. Capture exome sequencing on a targeted panel of 1500 genes was performed by the Illumina HiSeq 2500 (2x115bp paired-end reads, average 520X coverage). Copy number changes, loss of heterozygosity (LOH) and tumor purity were jointly estimated using an in-house pipeline for matched tumor/normal libraries. We assessed aneuploidy using chromosomal and arm level SCNAs which are determined by the median of SCNAs and summation of gain and loss of SCNAs for a given chromosomal arm, which are then used for a time-to-event analysis of overall survival (OS) of the patients. The differences between Kaplan-Meier overall survival curves were tested using the log-rank test. Hazard ratios (HR) were estimated from Cox proportional hazard regression. A threshold of significance was taken as p<0.05.

Results

Patient demographic data is summarized in Table 1.

All the patients in this study harbor DNA somatic copy-number abnormalities (SCNAs) on the arm level for at least one chromosome (Figure 1). Gain of a chromosome or a whole arm mostly occurs on 1q, 3, 5, 7, 9, 11, 15, 19, 21 while loss was noted in 1p, 8p, 13, 16q, and 17p.

At a global-level, we find that whole genomic aneuploidies (genomic-level across all chromosomes) have a significant association with inferior OS. Specifically, patients with a high frequency of genomic aneuploidies (defined as number of SCNA >8, the first quartile) had a significantly worse prognosis than those with low number of SCNA events (<=8) (p=0.037, Figure 2). Arm-level aneuploidies with high incidences include 11q (54.9%), 13q (52.9%), 15q (51.0%), 1q (51.0%), 9q (51.0%), a finding consistent with previous studies (Walker, Leone et al. 2010). Aneuploidies in 10p (11.8%) and 10q (7.8%) are uncommon in agreement with previous studies (Tricot G, Sawyer et al. 2017) as myeloma structural or numerical abnormalities rarely occur on chromosome 10.

Using univariate analysis, we identified five arm-level aneuploidies which are significantly associated with worse OS: 10p, 10q, 11p, 18q and 20q. It is noteworthy that aneuploidies in either arm of chromosome 10 (10p and 10q) had significant negative OS prognostic effect: 10p (median survival for diploid 28.9 vs. aneuploid 4.1 mo; p<0.001) and 10q (28.9 vs 3.8 mo; p=0.015). Similar results with a negative impact on OS were found from 11p (not reached vs 13.8 mo; p=0.036); 18q (28.9 vs 8.5 mo; p=0.025); and 20q (28.9 vs 3.1 mo; p=0.009).

Given the small sample size we did not conduct a multivariable analyses and multiplicity adjustment in this preliminary analyses.

Conclusion

Aneuploidy measured by SCNAs is correlated with unfavorable survival in relapsed/refractory myeloma. The higher the occurrence of aneuploidies, the worse the overall survival, illustrating of the impact of cancer genome instability.

Disclosures

Ye:Janssen: Research Funding; Karyopharm: Research Funding; Portola: Research Funding; MingSight: Research Funding; Sanofi: Research Funding. Talpaz:Imago BioSciences: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; CTI BioPharma: Research Funding; Constellation: Research Funding; Incyte: Research Funding; Novartis: Research Funding; Samus Therapeutics: Research Funding. Bergsagel:Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy.

Author notes

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Asterisk with author names denotes non-ASH members.

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