Faculty Bibliography

PURPOSE/OBJECTIVE:Copy number changes and translocations have been studied extensively in many datasets with long term follow-up. The impact of mutations remains debated given the short time to follow-up of most datasets. METHODS:we performed targeted panel sequencing covering 125 myeloma-specific genes and the loci involved in translocations in 223 newly diagnosed myeloma samples recruited into one of the Total Therapy Trials (TT). RESULTS:As expected, the most commonly mutated genes were NRAS, KRAS, and BRAF making up 44% of patients. Double-Hit, BRAF and DIS3 mutations had an impact on outcome alongside classical risk factors in the context of an intensive treatment approach. We were able to identify both V600E and non-V600E BRAF mutations, 58% of which were predicted to be hypoactive or kinase dead. Interestingly, 44% of the hypoactive/kinase dead BRAF mutated patients showed co-occurring alterations in KRAS, NRAS or activating BRAF mutations suggesting they play a role in the oncogenesis of multiple myeloma (MM) by facilitating MAPK activation and may lead to chemo resistance. CONCLUSION/CONCLUSIONS:Overall, these data highlight the importance of mutational screening to better understand newly diagnosed MM (NDMM) and may lead to patient specific mutation-driven treatment approaches.

Multiple myeloma is the second most common lymphoproliferative disorder, characterized by aberrant expansion of monoclonal plasma cells. In the last years, thanks to novel next generation sequencing technologies, multiple myeloma has emerged as one of the most complex hematological cancers, shaped over time by the activity of multiple mutational processes and by the acquisition of key driver events. In this review, we describe how whole genome sequencing is emerging as a key technology to decipher this complexity at every stage of myeloma development: precursors, diagnosis and relapsed/refractory. Defining the time windows when driver events are acquired improves our understanding of cancer etiology and paves the way for early diagnosis and ultimately prevention.

Disease factors such as tumor burden and molecular risk affect myeloma patient outcomes as well as patient factors that impact the capacity to deliver treatment. How the relative importance of these factors changes with patient age has not previously been investigated comprehensively. We analyzed data from 3894 patients of all ages uniformly treated in a large clinical trial of myeloma patients, Myeloma XI. Even with novel therapeutic approaches progression-free survival (PFS) and overall survival (OS) are affected by age with a stepwise reduction in PFS and OS with each decade increase. Renal function deteriorated with increasing age whilst the frequency of t(4;14) and del(17p) decreased and gain(1q) increased. The relative contribution of performance status, international staging score and molecular risk to progression-free and overall survival varied by age group. Molecular events have a larger effect on outcome in younger patients with their relative contribution diminishing in the elderly. Performance status is important for patient outcome at all ages suggesting that physical frailty may be a more important predictor of outcome than age itself. Significant differences in the factors driving patient outcomes at different ages are important to consider as we design disease segmentation strategies to deliver personalized treatment approaches.

As multiple myeloma (MM) treatments evolve, frequent updates are required to monitor the long-term effect of changes in approach. Traditionally, MM is considered an incurable disease, with most patients eventually relapsing. However, improvements in treatments has raised the possibility that MM might be functionally curable. To examine improvements in long-term survival, we followed 4329 patients with newly diagnosed MM treated with autologous stem cell transplantation (ASCT) at the University of Arkansas for Medical Sciences from 1989 through 2018. Overall survival (OS) and progression-free survival (PFS) were evaluated using Kaplan-Meier analysis, Cox proportional hazards models, relative survival analysis, and cure modeling among different time periods, risk groups, and demographic traits. Steady improvements in OS were found, with patients treated in 2014 or later having superior OS (hazard ratio, 0.35; 95% confidence interval [CI], 0.27-0.45) and reduced excess risk for MM death (relative excess risk, 0.30; 95% CI, 0.22-0.41) compared with patients treated in 1997 or earlier. Patients treated during intervening time periods often had intermediate survival, but trends in OS, PFS, and landmarked analyses were inconsistent. Cure models support the potential for cure, ranging from 6.3% to 31.3%, depending on the year of treatment, with 10.0% to 18.6% of patients achieving their normal life expectancy across multiple periods. There was some evidence of reductions in early mortality within 3 years of diagnosis, longer complete response (CR) duration, and reductions in relapse after achieving CR. However, results differed depending on age, risk group, and cytogenetic characteristics.

Persons of African ancestry (AA) have a twofold higher risk for multiple myeloma (MM) compared with persons of European ancestry (EA). Genome-wide association studies (GWASs) support a genetic contribution to MM etiology in individuals of EA. Little is known about genetic risk factors for MM in individuals of AA. We performed a meta-analysis of 2 GWASs of MM in 1813 cases and 8871 controls and conducted an admixture mapping scan to identify risk alleles. We fine-mapped the 23 known susceptibility loci to find markers that could better capture MM risk in individuals of AA and constructed a polygenic risk score (PRS) to assess the aggregated effect of known MM risk alleles. In GWAS meta-analysis, we identified 2 suggestive novel loci located at 9p24.3 and 9p13.1 at P < 1 × 10-6; however, no genome-wide significant association was noted. In admixture mapping, we observed a genome-wide significant inverse association between local AA at 2p24.1-23.1 and MM risk in AA individuals. Of the 23 known EA risk variants, 20 showed directional consistency, and 9 replicated at P < .05 in AA individuals. In 8 regions, we identified markers that better capture MM risk in persons with AA. AA individuals with a PRS in the top 10% had a 1.82-fold (95% confidence interval, 1.56-2.11) increased MM risk compared with those with average risk (25%-75%). The strongest functional association was between the risk allele for variant rs56219066 at 5q15 and lower ELL2 expression (P = 5.1 × 10-12). Our study shows that common genetic variation contributes to MM risk in individuals with AA.