The Spectrum of Exomic Mutation in Elderly Myeloma Differs Substantially from Patients at Younger Ages Consistent with a Different Evolutionary Trajectory to Full Blown Disease Based on Age of Onset
Authors: Williams Louis , Boyle Eileen M , Davies Faith E. , Walker Brian A , Ashby Cody , Bauer Michael A , Wardell Christopher P. , Flynt Erin , Thakurta Anjan , Morgan Gareth 
Published: November 2019

Background: An ever improving understanding of the heterogeneity in clinical behavior of multiple myeloma (MM) in older populations supports frailty-adapted therapy as a potential treatment approach. A deeper understanding of the evolutionary trajectory leading to full blown disease in elderly compared to younger patients may give insights into the interplay between mutations and frailty, allowing us to optimize therapy for this group.

Methods: We analyzed next generation sequencing data from the Myeloma Genome Project (MGP) (n = 1273, mean age = 65) determining single nucleotide variants (SNV), copy number alterations (CNAs), and mutational signatures to determine age associated patterns. An initial analysis compared a population diagnosed older than age 75 (elderly patients, n = 232, mean age = 80 yrs) in comparison to a group diagnosed at age ≤ 74 (n = 1041, mean age = 62 yrs) and a younger subgroup of patients diagnosed at age ≤ 65 (n = 632, mean age = 57 yrs). Using RNASeq from the same dataset, we will analyze expression of TERT and other shelterin genes in an attempt to correlate changes to age, ATRX mutations, genomic instability, NHEJ, and telomere length as estimated using Telomere Hunter and TelSeq. The aim of this latter analysis being to highlight the importance of telomere biology in determining mutation patterns in the elderly population.

Results: We identified age associated patterns in the distribution of mutations with patients age > 74 yrs, when compared to all younger patients showing a significantly greater proportion of SNVs or indels in DIS3 (14.2% vs 8.7%, p = 0.005), HIST1H1E (5.6% vs 3.3%, p = 0.044), and IRF4 (5.6% vs 2.4%, p = 0.005). There were fewer SNVs and indels in CDKN1B (0% vs 1.3%, p = 0.038), FAM46C (6.5% vs 9.7%, p = 0.043), HUWE1 (1.7% vs 6.1%, p = 0.004) and SP140 (0.4% vs 2.9%, p = 0.014). In addition the elderly patient population was found to have proportionally more copy number gains at 1q21: CKS1B (47.4% vs 40.7%, p = 0.031), 5q23: TNFAIP8 (58.2% vs 50.0%, p = 0.012), 5p15: ADCY2 (58.2% vs 50.4%, p =0.016), 6p21: TNXB (39.2% vs 31.4%, p= 0.011), and 17q22: AKAP1 (30.2% vs 23.9%, p = 0.035) along with copy number losses at 16q: CYLD (38.8% vs 32.6%, p = 0.035), 6q25: PARK2 (40.5% vs 33.9% p = 0.028) and 2p23: DNMT3A (28.4% vs 23.0% p = 0.038).

A greater proportion of elderly patients exhibited MYC tandem duplications (9.0% vs 5.6%, p = 0.032) while fewer elderly patients harbored MYC translocations (26.2% vs 19.3%, p = 0.015). These differences were further enhanced in comparisons to patients presenting under the age of 65, with elderly patients exhibiting fewer t(4;14) translocations (9.1% vs 14.4%, p = 0.019).

Recent data has shown a significant time delay between MM initiation and disease presentation, and mutational signatures have been defined at varying evolutionary trajectories. We have examined if these signatures are different in elderly compared to younger cases. We could not identify a difference in the APOBEC mutational signature between any of the age-based series. We will present further analysis of other signatures and the role of telomere length at the meeting.

Conclusions: Our results show significant differences in the genetic alterations between older and younger myeloma patients. These difference may lead to important differences in clinical behavior. The findings suggest disease behavior in the elderly may be driven relatively more frequently by acquired copy number alterations occurring over a period of long disease latency. Ongoing analysis is determining the prognostic impact of mutations in different age strata, which mutational signatures are driving these differences and how these impact clonal structure in the older populations. These results suggest that it should be possible to integrate genetic data and frailty-adaptive risk models to aid in the treatment of multiple myeloma that presents late in life.

Disclosures

Boyle: Amgen, Janssen, Takeda, Celgene Corporation: Other: Travel expenses; Amgen, Abbvie, Janssen, Takeda, Celgene Corporation: Honoraria. Davies:Amgen, Celgene, Janssen, Oncopeptides, Roche, Takeda: Membership on an entity's Board of Directors or advisory committees, Other: Consultant/Advisor; Janssen, Celgene: Other: Research Grant, Research Funding. Walker:Celgene: Research Funding. Flynt:Celgene Corporation: Employment, Equity Ownership. Thakurta:Celgene: Employment, Equity Ownership. Morgan:Celgene: Other: research grant, Research Funding; Amgen, Roche, Abbvie, Takeda, Celgene, Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees.