Abstract
Background: The main genetic features of myeloma identified so far have been the presence of balanced translocations at the immunoglobulin heavy chain (IGH) region and copy number abnormalities. Novel methodologies such as massively parallel sequencing have begun to describe the pattern of tumour acquired mutations detected at presentation but their biological and clinical relevance has not yet been fully established.
Methods: Whole exome sequencing was performed on 463 presentation patients enrolled into the large UK, phase III, open label, Myeloma XI trial. DNA was extracted from germline DNA and CD138+ plasma cells and whole exome sequencing was performed using SureSelect (Agilent). In addition to capturing the exome, extra baits were added covering the IGH, IGK, IGL and MYC loci in order to determine the breakpoints associated with translocations in these genes. Tumour and germline DNA were sequenced to a median of 60x and data processed to generate copy number, acquired variants and translocation breakpoints in the tumour. Progression-free and overall survival was measured from initial randomization and median follow up for this analysis was 25 months. These combined data allow us to examine the effect of translocations on the mutational spectra in myeloma and determine any associations with progression-free or overall survival.
Results: We identified 15 significantly mutated genes comprising IRF4, KRAS, NRAS, MAX, HIST1H1E, RB1, EGR1, TP53, TRAF3, FAM46C, DIS3, BRAF, LTB, CYLD and FGFR3. By analysing the correlation between mutations and cytogenetic events using a probabilistic approach, we describe the co-segregation of t(11;14) with CCND1 mutations (Corr 0.28,BF=1.5x106 (Bayes Factor)) and t(4;14) with FGFR3 (Corr=0.40, BF=1.12x1014) and PRKD2 mutations (Corr=0.23, BF=3507).
The mutational spectrum is dominated by mutations in the RAS (43%) and NF-κB (17%) pathway, however they are prognostically neutral. We describe for the first time in myeloma mutations in genes such as CCND1 and DNA repair pathway alterations (TP53, ATM, ATR and ZFHX4 mutations) that are associated with a negative impact on survival in contrast to those in IRF4 and EGR1 that are associated with a favourable overall-survival.
By combining these novel risk factors with the previously described adverse cytogenetic features and ISS we were able to demonstrate in a multivariate analysis the independent prognostic relevance of copy number and structural abnormalities (CNSA) such as del(17p), t(4;14), amp(1q), hyperdiploidy and MYC translocations and mutations in genes such as ATM/ATR, ZFHX4, TP53 and CCND1. We demonstrate that the more adverse features a patient had the worse his outcome was for both PFS (one lesion: HR=1.6, p=0.0012, 2 lesions HR=3.3, p<0.001, 3 lesions HR=15.2, p< 0.001) and for OS (one lesion: HR=2.01, p=0.0032, 2 lesions HR=4.79, p<0.001, 3 lesions HR=9.62, p< 0.001). When combined with ISS, we identified 3 prognostic groups (Group 1: ISS I/II with no CNSA or mutation, Group 2: ISS III with no CNSA or mutation or ISS I/II/III with one CNSA or mutation, Group 3: Two CNSA or mutation regardless of their ISS) thus identifying three distinct prognostic groups with a high risk population representing 13% of patients that both relapsed [median PFS 10.6 months (95% CI 8.7-17.9) versus 27.7 months (95% CI 25.99-31.1), p<0.001] and died prematurely [median overall survival 23.2 months (95% CI 18.2-35.3.) versus not reached, p<0.001] regardless of their ISS score.
Finally, we have also identified a list of potentially actionable mutations for which targeted therapy already exists opening the way into personalized medicine in myeloma.
Conclusion: We have refined our understanding of genetic events in myeloma and identified clinically relevant mutations that may be used to better stratify patients at presentation. Identifying high risk populations or patients that may benefit from targeted therapy may open the way into personalized medicine for myeloma.
Disclosures
Walker: Onyx Pharmaceuticals: Consultancy, Honoraria.
Advancing Personalized Medicine Through the Application of Whole Exome Sequencing and Big Data Analytics
