Faculty Bibliography

Newly diagnosed multiple myeloma (NDMM) patients treated with immunomodulatory drugs (IMiDs) are at high venous thrombosis (VTE) risk, but data are lacking from large prospective cohorts. We present thrombosis outcome data from Myeloma IX (n=1936) and Myeloma XI (n=4358), phase III randomized controlled trials for NDMM, treating transplant-eligible and ineligible patients before and after publication of thrombosis prevention guidelines. In Myeloma IX, compared to CTD (cyclophosphamide, thalidomide and dexamethasone), transplant-eligible patients randomized to CVAD induction (cyclophosphamide, vincristine, doxorubicin and dexamethasone) had higher VTE risk (22.5%(n=121/538) vs 16.1%(n=89/554), aHR:1.46,95%CI:1.11-1.93). For transplant-ineligible patients, compared to MP (melphalan and prednisolone), patients randomized to CTDa (attenuated CTD) induction had higher VTE risk (16.0%(n=68/425) vs 4.1%(n=17/419), aHR:4.25,95%CI:2.50-7.20). In Myeloma XI, there was no difference in VTE or arterial thrombosis risk between transplant-eligible pathways, CRD (cyclophosphamide, lenalidomide and dexamethasone) and CTD (VTE:12.2%(n=124/1014) vs 13.2%(n=133/1008), aHR:0.92,95%CI:0.72-1.18; arterial events:1.2%(n=12/1014) vs 1.5%(n=15/1008), aHR:0.80,95%CI:0.37-1.70). For transplant-ineligible patients, there was no difference in VTEs between CRDa (attenuated CRD) and CTDa (10.4%(n=95/916) vs 10.7%(n=97/910), aHR:0.97, 95%CI:0.73-1.29). However, arterial risk was higher with CRDa than CTDa (3.1%(n=28/916) vs 1.6%(n=15/910), aHR:1.91,95%CI:1.02-3.57). Thrombotic events occurred almost entirely within 6m of treatment initiation. Thrombosis was not associated with inferior progression-free or overall survival (OS), apart from inferior OS for patients with arterial events (aHR:1.53, 95%CI:1.12-2.08) in Myeloma XI. The Myeloma XI trial protocol incorporated IMWG thrombosis prevention recommendations and compared to Myeloma IX, more patients were on thromboprophylaxis (80.5% vs 22.3%) with lower VTE rates for identical regimens (CTD:13.2% vs 16.1%, CTDa:10.7% vs 16.0%). However, thrombosis remained frequent in spite of IMWG-guided thromboprophylaxis, suggesting new approaches are needed.

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.

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.

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.