Molecular underpinnings of clinical disparity patterns in african american vs. Caucasian american multiple myeloma patients

ABSTRACT Caucasian Americans (CA) compared with African Americans (AA) have a twofold increased incidence of multiple myeloma (MM) and have an earlier age of diagnosis. However, there is

sparse information regarding underlying biological differences across racial/ethnic groups. We characterized genetic alterations using a targeted next-generation sequencing assay called

myTYPE, developed at MSKCC, allowing capture of somatic mutations, IgH translocations, gains/losses, and hyperdiploidy. Samples were obtained from the NIH Plasma Cell Dyscrasia Racial

Disparity Cohort. In total, 68 patient samples were successfully sequenced and manually curated based on well-established databases. Of the 68 patient samples (47 CA, 21 AA), 84% had at

least one type of genomic alteration. Importantly, the IgH translocation, t(11;14), was observed more frequently in the AA group (0 vs. 29%, _p_ = 0.001). Known oncogenic somatic

non-synonymous mutations were found in 18 genes and indels in 2 genes. _KRAS_ mutations were the most common mutation found in 16% of patients followed by _NRAS_ and _BRAF_ mutations. _TP53_

somatic mutations appeared to be more common in CA but lacked significance. This proof-of-principle study indicates the presence of varying underlying tumor biology between racial groups

and supports the need of future prospective trials to capture these molecular characteristics. SIMILAR CONTENT BEING VIEWED BY OTHERS COMPREHENSIVE GENETIC ANALYSIS BY TARGETED SEQUENCING

IDENTIFIES RISK FACTORS AND PREDICTS PATIENT OUTCOME IN MANTLE CELL LYMPHOMA: RESULTS FROM THE EU-MCL NETWORK TRIALS Article Open access 16 September 2024 MULTIOMIC PROFILING IDENTIFIES

PREDICTORS OF SURVIVAL IN AFRICAN AMERICAN PATIENTS WITH ACUTE MYELOID LEUKEMIA Article Open access 04 October 2024 GENOMIC ANALYSIS OF PRIMARY PLASMA CELL LEUKEMIA REVEALS COMPLEX

STRUCTURAL ALTERATIONS AND HIGH-RISK MUTATIONAL PATTERNS Article Open access 19 June 2020 INTRODUCTION Despite advancements in the understanding and treatment of multiple myeloma (MM), a

racial disparity in clinical presentation and outcomes remain. Compared with Caucasian Americans (CA), African Americans (AA) matched for socioeconomics, age, and gender have a twofold

increased incidence of MM, have an earlier average age at diagnosis by 5–10 years, and have gained less benefit from the advent of novel agents in the last decade1,2. These differences have

not been shown to be attributable to disparities in access to medical care. In addition, over the past decade, improvements in survival with the introduction of proteasome inhibitors and

immunomodulatory agents is predominantly observed in CA. Costa et al.3 observed improvements in 10-year relative survival rates (RSRs) in all racial groups < 65 years of age and no

improvements for either racial group over 75 years of age. In patients between the ages of 65 and 74 years, CA had an improvement in 10-year RSRs but AA did not. Moreover, although it has

been noted that AA have an increased myeloma-related mortality rate, this is in fact a reflection of the increased incidence of MM in AA rather than worse prognosis. In a pivotal study of

30,000 patients, the authors concluded that AA appear to have a better prognosis compared with CA4. The variation in clinical course suggests an underlying molecular heterogeneity between

races. Despite the increased frequency of MM among AA, most of the known molecular data and association with clinical outcomes, including traditional fluorescence in situ hybridization

(FISH)/cytogenetics and newer NGS methods have been derived from CA cohorts5,6,7,8. At this time there is no single unifying genetic or genomic alteration known to cause MM but there are

multiple alterations frequently identified. Approximately half of MM genomes are hyperdiploid (gain of an additional odd numbered chromosomes)9,10. Most of the non-hyperdiploid MM cases

harbor a translocation involving the immunoglobulin heavy-chain (IgH) gene located on chromosome 149,10. These genetic lesions are thought to be primary events, as they are also found in the

precursor state, monoclonal gammopathy of undetermined significance (MGUS)11. In ~10% of cases, both aberrations co-occur12,13. In general, hyperdiploid MM is associated with an improved

prognosis compared with MM cases with an IgH translocation, except for the cyclin D translocations (t(6;14) and t(11;14)), which are considered neutral14,15. The five most frequent

translocations in descending order are t(11;14), t(4;14), t(14;16), t(14;20), and t(6;14)15,16. Based on karyotyping and interphase FISH, t(4;14), t(14;16), and t(14;20) have been identified

as high-risk primary genetic events, along with the secondary/tertiary events of deletion 17p, deletion 1p32, and 1q gains9,14. The genetic heterogeneity of myeloma is reflected in the

variety of genetic hits including secondary translocations, copy number variants (CNVs), and somatic oncogenic mutations17. To improve our understanding of the underlying biological

mechanisms of the racial disparity in patients with MM, this study used a targeted NGS assay termed myTYPE developed at Memorial Sloan Kettering Cancer Center. myTYPE was specifically

developed to target genomic aberrations known to occur in patients with MM18,19. The myTYPE assay is designed to capture known IgH translocations, hyperdiploidy, CNVs, and somatic mutations

in 120 frequently mutated genes in MM. Using this specific assay we investigated the differences in somatic mutations, translocations, and chromosomal gains/losses between CA and AA MM

patients. METHODS PATIENTS AND TECHINICAL ASSAYS Bone marrow clot sections were obtained from the National Institutes of Health Plasma Cell Dyscrasia Racial Disparity Cohort. A total of 91

pretreatment baseline samples from patients with newly diagnosed MM (NDMM) underwent DNA extraction, 81 samples met DNA quality control (QC) and purity criteria, and underwent NGS library

preparation. Of these, 68 (47 CA, 21 AA) patient samples passed all QC measures for sequencing. In the myTYPE assay, baits were designed to capture the entire IgH locus (where the majority

of the canonical chromosome 14 breakpoints occur) and the partner chromosome, genome-wide single-nucleotide polymorphisms for hyperdiploidy, and other CNVs as well as exons of 120 frequently

mutated genes in MM. With this design, myTYPE detects the IGH translocations and the partner chromosome regardless of which the partner is. In addition, myTYPE detects hyperdiploidy,

arm-level chromosomal gains and losses, as well as the most common and relevant somatic mutations. The target regions from bone marrow clot section samples were amplified and then sequenced

using 126 base-paired end reads using Illumina HiSeq with a mean target depth of 413.5×. Data were analyzed using validated bioinformatic algorithms (Supplementary Methods). Race was

determined from patient self-reporting. Fisher’s exact test was used to calculate two-tailed _p_-values for differences between CA and AA groups. The Bonferroni method was used to correct

for multiple testing and control the family-wise error rate at <0.05. This resulted in a significance threshold of _p_ < 0.0015 for each comparison. RESULTS Of the 68 patient baseline

samples sequenced (47 CA, 21 AA), 57 patients (87%) had at least one genomic alteration (i.e., hyperdiploidy, translocations, chromosomal gains/losses, indels, or somatic non-synonymous

mutations). Of these, 20 (95%) were from AA and 37 (79%) from CA patient samples (Figs. 1 and 2). Putative oncogenic mutations and indels (insertions and deletions) were observed in 19

oncogenic genes (Table 1). _KRAS_ mutations were most common, identified in 16% of patients (13% CA, 24% AA_). NRAS_ (4% CA, 10% AA) and _BRAF_ (2% CA, 14% AA) mutations were the second most

common. _BRAF_ mutations were all V600E non-synonymous mutations and statistically trended to be more common in AA (_p_ = 0.08). _TP53_ somatic mutations also appeared to be more common in

CA (6%) than AA (0%); however, this difference did not reach statistical significance. Oncogenic mutations were also observed in _FAM46C_, _EGR1_, _PTPN11_, _ATM_, _CCND1_, _DIS3_, _FAT1_,

_FGFR3_, _HIST1H1E_, _KDM6A_, _LTB_, _MAX_, _MYC_, and _SP140_. Indels (frameshift and in-frame variants) were identified in _FAM46C_ and _CYLD_. In 11 of the patient samples, no genomic

alterations were observed. Four different translocations involving IgH were identified in patients (Table 2). The t(11;14) occurred significantly more frequently in the AA group (29% vs. 0,

_p_ = 0.0005). This difference remained significant when adjusting for multiple testing. The incidence of t(4;14), t(8;14), and t(14;16) were not significantly different between the CA and

AA groups. Chromosomal gains and losses were identified in many patients and, as expected, approximately half the patients had hyperdiploid myeloma (Table 2). Moreover, del(1p) was observed

more commonly in the AA population (0 vs. 14%, _p_ = 0.037) as well as amp(1q) (13% vs. 33%, _p_ = 0.091); however, neither remained significant when adjusting for multiple testing. There

were no other significant differences between racial groups including patients with hyperdiploid genomes, del(17p), or del(13q). Given the striking observation that t(11;14) was more common

in AA patient samples, IgH translocation data were downloaded from the Multiple Myeloma Research Foundation (MMRF) CoMMpass (Relating Clinical Outcomes in MM to Personal Assessment of

Genetic Profile) Study (research.themmrf.org) to analyze and determine whether racial differences in t(11;14) were observed in a larger cohort of patients20. To the best of our knowledge,

racial differences in translocations in this cohort had not been previously reported. Based on validated racial classification previously reported, 658 samples were evaluable for chromosome

14 translocations (112 AA; 546 CA). No statistically significant differences were observed between races including t(11;14) (Table 3)8. DISCUSSION These results suggest the presence of

genetic heterogeneity between MM racial groups. Our results are consistent with the existing MM molecular literature including the observation that _KRAS_, _NRAS_, _DIS3_, and _TP53_ are

commonly mutated genes in MM8. As described in many malignancies, in MM, _TP53_ mutations indicate a poor prognosis and shorter survival; however, the effects of other mutations are not well

characterized21,22. We observed _TP53_ mutations more frequently in CA, which is consistent with the findings from a recent study that also observed significantly higher _TP53_ mutation

rates among CA MM cases8. Located on chromosome 17p13.1, _TP53_ encodes for p53 tumor suppressor protein mediating multiple cell cycle pathways including apoptosis, cell cycle arrest, and

inhibition of angiogenesis23. In MM, _TP53_ mutations are a rare occurrence at diagnosis; however, the incidence increases as patients are treated. It is often associated with poor prognosis

and accounts for a significantly lower survival rate24. This finding suggests potentially one etiology for the worse prognosis observed in CA4. Furthermore, this may have important clinical

implications in terms of targeted drug development. For example, compounds in various stages of development including MDM2 inhibitors, focus on restoring wild-type p53 activity25. A few of

these agents have proceeded to first-in-human phase 1 interventional MM clinical trials26. Importantly, in our cohort, t(11;14) was found to be significantly more frequent in the AA group

(_p_ = 0.0005)_._ t(11;14) is a frequent translocation in MM found in about 15–20% of patients10,27,28. Recently, the Mayo Clinic group using traditional FISH methods observed a similar

association with t(11;14) in their AA cohort29. They evaluated 881 patients with monoclonal gammopathies and found that the probability of having t(11;14) (or t(14;16)/t(14;20) was

significantly higher in the 120 patients with highest AA ancestry (≥80%) compared with individuals with lowest levels of AA ancestry. This finding helps to confirm that our results are not

random, and that NGS methods can be used to confirm traditional FISH findings. The t(11;14)(q13;q32) results in upregulation of cyclin D1, thus promoting cell cycle progression10. Most data

support that the presence of t(11;14) is associated with neutral or standard prognostic risk, and that it may confer improved survival and response to treatment compared with the other

commonly observed IgH translocations10,28,30,31. However, a few smaller studies suggest that patients with MM harboring t(11;14) may not have the same prognosis as patients with other

standard risk features32. Interestingly, recent work suggests that AA patients with t(11;14) showed a trend toward shorter median progression-free survival (PFS) compared with AA without the

presence of t(11;14); however, t(11;14) did not impact PFS in non-AA patients33. More importantly, this genetic alteration also has significant implications for drug development. More

recently, it has been observed that patients with this translocation are much more likely to respond to BLC-2 inhibitors, and that this genotype is associated with increased expression of

the anti-apoptotic protein BCL-2 compared with pro-apoptotic family members. For example, the BCL-2 inhibitor, venetoclax, as monotherapy is associated with a response rate of 40% in

patients with t(11;14) compared with 21% in all comers34. In short, we were unable to confirm our finding in the CoMMpass cohort; however, the confirmatory finding in the Mayo AA cohort

reinforces this finding, which has important implications in the AA population and precision drug development. Deletion of chromosome 1p, (del(1p)), which is associated with a poor

prognosis, in our cohort, appeared to also be more frequent (_p_ = 0.037) along with amp(1q) in AA; however, these were not significant after adjusting for multiple testing and no

differences were observed in the CoMMpass cohort8,35. Although t(11;14) is thought to be a primary event as it is observed in the early precursor state of MGUS, del(1p) is thought to be a

secondary event further driving MM clonal evolution36. Therefore, this finding may be biased by the timing of when patients were diagnosed. In addition to risk prognostication, the

differences in somatic mutations among races may have significant implications in the development of targeted therapies. For example, the _BRAF__V600E_ mutation is a frequent and

well-described driver mutation in melanoma and hairy cell leukemia with approximate response rates of 50–60% and 96–100%, respectively, to _BRAF_ inhibition with tyrosine kinase

inhibitors37,38,39. Interestingly, _BRAF_ mutations occur in ~5% of MM cases with tumors that respond to tyrosine kinase inhibition40,41. Albeit a rare driver mutation in MM, it is an

important druggable target. Interestingly, we observed a higher rate of _BRAF__V600E_ mutations in AA (14%) compared with CA (2%); however, this finding was not statistically significant and

was not observed in the independent CoMMpass cohort. Our findings along with other works suggest that the incidence of the various prognostic primary genetic events is not significantly

different between races (e.g., t(4;14) and hyperdiploidy). Rather, the differences between races are predominantly events known to occur later in disease evolution. The molecular pathology

of MM changes overtime and multiple clonal competitions occur in the cancer cell population through branching evolution36. Clonal and sub-clonal evolution occurs in the context of pressures

present in the tumor micro-environment including treatment effect creating a branching nonlinear pathway of multiclonal MM development5. Based on this, one might speculate that the primary

pathogenic events are similar across races, whereas the ensuing disease evolution follows slightly different trajectories, shaped by inter-racial differences in tumor–host interactions42.

This study is limited by the small number of patients. However, it expands upon the limited molecular data from AA with MM. We plan to further study and expand on these findings by examining

the genetic alterations present and associated clinical outcome differences between races in patients with smoldering MM and comparison with NDMM. Our current actively enrolling study

Carfilzomib, Lenalidomide, and Dexamethasone in High Risk Smoldering Multiple Myeloma will be the vehicle to aid in answering these important questions and thus far has shown impressive

results at interim analysis (https://clinicaltrials.gov/ct2/show/NCT01572480)43,44. This information will add to our knowledge of the clonal evolution of MM, prognostic value of genetic

data, and elucidate potential differences in smoldering myeloma compared with NDMM in terms of race. CONCLUSIONS The findings of this work significantly contribute to the understanding of

molecular differences between races in MM, in a relative knowledge desert. These findings argue for more enrichment of AA patients in prospective MM treatment trials and characterization of

molecular profiles. REFERENCES * Waxman, A. J. et al. Racial disparities in incidence and outcome in multiplemyeloma. _a population-based study. Blood_ 116, 5501–5506

https://doi.org/10.1182/blood-2010-07-298760 (2010). Article  CAS  PubMed  Google Scholar  * Greenberg, A. J. & Rajkumar, S. V. Elucidating disparities across racial and ethnic groups in

multiple myeloma patients. _Int. J. Hematol._ 95, 453–454, https://doi.org/10.1007/s12185-012-1040-y (2012). Article  PubMed  Google Scholar  * Costa, L. J. et al. Recent trends in multiple

myeloma incidence and survival by age, race, and ethnicity in the United States. _Blood Adv._ 1, 282–287, https://doi.org/10.1182/bloodadvances.2016002493 (2017). Article  PubMed  PubMed

Central  Google Scholar  * Landgren, O. et al. African-American multiple myeloma patients have a better survival than Caucasian patients: a population-based study including 28,636 patients.

_Blood_ 114, 1832 (2009). Google Scholar  * Bolli, N. et al. Heterogeneity of genomic evolution and mutational profiles in multiple myeloma. _Nat. Commun._ 5, 2997,

https://doi.org/10.1038/ncomms3997 (2014). Article  CAS  PubMed  Google Scholar  * Chapman, M. A. et al.Initial genome sequencing and analysis of multiple myeloma. _Nature_ 471, 467–472,

https://doi.org/10.1038/nature09837 (2011). Article  CAS  PubMed  PubMed Central  Google Scholar  * Lohr, J. G. et al.Widespread genetic heterogeneity in multiple myeloma: implications for

targeted therapy. _Cancer Cell._ 25, 91–101, https://doi.org/10.1016/j.ccr.2013.12.015 (2014). Article  CAS  PubMed  PubMed Central  Google Scholar  * Manojlovic, Z. et al.Comprehensive

molecular profiling of 718 multiple myelomas reveals significant differences in mutation frequencies between African and European descent cases. _PLoS Genet._ 13, e1007087,

https://doi.org/10.1371/journal.pgen.1007087 (2017). Article  CAS  PubMed  PubMed Central  Google Scholar  * Pawlyn, C. & Morgan, G. J. Evolutionary biology of high-risk multiple

myeloma. _Nat. Rev. Cancer_ 17, 543–556, https://doi.org/10.1038/nrc.2017.63 (2017). Article  CAS  PubMed  Google Scholar  * Fonseca, R. et al. Myeloma and the t(11;14)(q13; q32); evidence

for a biologically defined unique subset of patients. _Blood_ 99, 3735–3741 (2002). Article  CAS  PubMed  Google Scholar  * Bergsagel, P. L. & Kuehl, W. M. Molecular pathogenesis and a

consequentclassification of multiple myeloma. _J. Clin. Oncol_ 23, 6333–6338, https://doi.org/10.1200/jco.2005.05.021 (2005). Article  CAS  PubMed  Google Scholar  * Smadja, N. Vr, Bastard,

C., Brigaudeau, C., Leroux, D. & Fruchart, C. Hypodiploidy is a major prognostic factor in multiple myeloma. _Blood_ 98, 2229–2238 (2001). Article  CAS  PubMed  Google Scholar  *

Kazandjian, D., Mailankody, S., Korde, N. & Landgren, O. Smoldering multiple myeloma: pathophysiologic insights, novel diagnostics, clinical risk models, and treatment strategies. _Clin.

Adv. Hematol. Oncol._ 12, 578–587 (2014). PubMed  Google Scholar  * Robiou du Pont, S. et al. Genomics of multiple myeloma. _J. Clin. Oncol._ 35, 963–967,

https://doi.org/10.1200/JCO.2016.70.6705 (2017). Article  PubMed  Google Scholar  * Morgan, G. J., Walker, B. A. & Davies, F. E. The genetic architecture of multiple myeloma. _Nat. Rev.

Cancer_ 12, 335–348 (2012). Article  CAS  PubMed  Google Scholar  * Walker, B. A. et al. Identification of novel mutational drivers reveals oncogene dependencies in multiple myeloma. _Blood_

132, 587–597 https://doi.org/10.1182/blood-2018-03-840132 (2018). * Kazandjian, D. Multiple myeloma epidemiology and survival: a unique malignancy. _Semin. Oncol._ 43, 676–681,

https://doi.org/10.1053/j.seminoncol.2016.11.004 (2016). * Hill, E. M. et al. Molecular underpinnings of clinical disparity patterns in AfricanAmerican (AA) versus Caucasian American (CA)

multiple myeloma (MM)patients. _J. Clin. Oncol_ 36, 8036, https://doi.org/10.1200/JCO.2018.36.15_suppl.8036 (2018). Article  Google Scholar  * Malin Hultcrantz, V. Y., et al. myTYPE targeted

next generation sequencing assay for detection of IgH translocations and copy number alterations in multiple myeloma

https://learningcenter.ehaweb.org/eha/2018/stockholm/214976/malin.hultcrantz.mytype.targeted.next.generation.sequencing.assay.for.html (Abstract PF525 presented at European Hematology

Association Annual Meeting, Stockholm. EHA Learning Center. 2018; 214976). * Lonial, S. et al.Interim analysis of the Mmrf Commpass Trial: identification of novel rearrangements potentially

associated with disease initiation and progression. _Blood_ 124, 722–722 (2014). Google Scholar  * Alexandrov, L. B. et al.Signatures of mutational processes in human cancer. _Nature_ 500,

415–421, https://doi.org/10.1038/nature12477 (2013). Article  CAS  PubMed  PubMed Central  Google Scholar  * Walker, B. A. et al. Mutational spectrum, copy number changes, and outcome:

results of a sequencing study of patients with newly diagnosed myeloma. _J. Clin. Oncol._ 33, 3911–3920, https://doi.org/10.1200/jco.2014.59.1503 (2015). Article  CAS  PubMed  Google Scholar

  * Vousden, K. H. & Lu, X. Live or let die: the cell’s response to p53. _Nat. Rev. Cancer_ 2, 594–604, https://doi.org/10.1038/nrc864 (2002). Article  CAS  PubMed  Google Scholar  *

Chng, W. J. et al.Clinical significance of TP53 mutation in myeloma. _Leukemia_ 21, 582–584, https://doi.org/10.1038/sj.leu.2404524 (2007). Article  CAS  PubMed  Google Scholar  * Zhao, D.,

Tahaney, W. M., Mazumdar, A., Savage, M. I. & Brown, P. H. Molecularly targeted therapies for p53-mutant cancers. _Cell. Mol. Life Sci._ 74, 4171–4187,

https://doi.org/10.1007/s00018-017-2575-0 (2017). Article  CAS  PubMed  PubMed Central  Google Scholar  * Tisato, V., Voltan, R., Gonelli, A., Secchiero, P. & Zauli, G. MDM2/X inhibitors

under clinical evaluation: perspectives for the management of hematological malignancies and pediatric cancer. _J. Hematol. Oncol._ 10, 133, https://doi.org/10.1186/s13045-017-0500-5

(2017). Article  CAS  PubMed  PubMed Central  Google Scholar  * Sawyer, J. R.The prognostic significance of cytogenetics and molecular profiling in multiple myeloma. _Cancer Genet._ 204,

3–12, https://doi.org/10.1016/j.cancergencyto.2010.11.002 (2011). Article  PubMed  Google Scholar  * Avet-Loiseau, H. et al.Genetic abnormalities and survival in multiple myeloma: the

experience of the Intergroupe Francophone du Myelome. _Blood_ 109, 3489–3495, https://doi.org/10.1182/blood-2006-08-040410 (2007). Article  CAS  PubMed  Google Scholar  * Baughn, L. B. et

al.Differences in genomic abnormalities among African individuals with monoclonal gammopathies using calculated ancestry. _Blood Cancer J._ 8, 96, https://doi.org/10.1038/s41408-018-0132-1

(2018). Article  PubMed  PubMed Central  Google Scholar  * Moreau, P. et al. Recurrent 14q32 translocations determine the prognosis of multiple myeloma, especially in patients receiving

intensive chemotherapy. _Blood_ 100, 1579–1583, https://doi.org/10.1182/blood-2002-03-0749 (2002). Article  CAS  PubMed  Google Scholar  * Leiba, M. et al. Translocation t(11;14) in newly

diagnosed patients with multiple myeloma: Is it always favorable?. _Genes Chromosomes Cancer_ 55, 710–718, https://doi.org/10.1002/gcc.22372 (2016). Article  CAS  PubMed  Google Scholar  *

Lakshman, A. et al.Natural history of t(11;14) multiple myeloma. _Leukemia_ 32, 131–138, https://doi.org/10.1038/leu.2017.204 (2018). Article  CAS  PubMed  Google Scholar  * Gasparetto, C.

J. et al.Differential effect of t(11;14) abnormality on survival and depth of response in African American (AA) and non-AA (NAA) patients (Pts) with newly diagnosed multiple myeloma (NDMM)

in the Connect® MM Registry. _Blood_ 130, 3101–3101 (2017). Google Scholar  * Kortüm, K. M. & Einsele, H. First targeted therapy in multiple myeloma. _Blood_ 130, 2359–2360,

https://doi.org/10.1182/blood-2017-09-805341 (2017). Article  CAS  PubMed  Google Scholar  * Ouyang, J., Gou, X., Ma, Y., Huang, Q. & Jiang, T.Prognostic value of 1p deletion for

multiple myeloma: a meta-analysis. _Int. J. Lab. Hematol._ 36, 555–565, https://doi.org/10.1111/ijlh.12189 (2014). Article  CAS  PubMed  Google Scholar  * Manier, S. et al.Genomic complexity

of multiple myeloma and its clinical implications. _Nat. Rev. Clin. Oncol._ 14, 100–113, https://doi.org/10.1038/nrclinonc.2016.122 (2017). Article  CAS  PubMed  Google Scholar  * Chapman,

P. B. et al.Improved survival with Vemurafenib in melanoma with BRAF V600E mutation. _New Engl. J. Med._ 364, 2507–2516, https://doi.org/10.1056/NEJMoa1103782 (2011). Article  CAS  PubMed 

Google Scholar  * Hauschild, A. et al.Dabrafenib in BRAF-mutated metastatic melanoma: a multicentre, open-label, phase 3 randomised controlled trial. _Lancet_ 380, 358–365,

https://doi.org/10.1016/s0140-6736(12)60868-x (2012). Article  CAS  PubMed  Google Scholar  * Tiacci, E. et al.Targeting mutant BRAF in relapsed or refractory hairy-cell leukemia. _New Engl.

J. Med._ 373, 1733–1747, https://doi.org/10.1056/NEJMoa1506583 (2015). Article  CAS  PubMed  Google Scholar  * Andrulis, M. et al.Targeting the BRAF V600E mutation in multiple myeloma.

_Cancer Discov._ 3, 862–869, https://doi.org/10.1158/2159-8290.Cd-13-0014 (2013). Article  CAS  PubMed  Google Scholar  * Raje, N. et al.Vemurafenib (VEM) in relapsed refractory multiple

myeloma harboring BRAFV600 mutations (V600m): a cohort of the Histology-Independent VE-Basket Study. _Blood_ 126, 4263–4263 (2015). Google Scholar  * Özdemir, B. C. & Dotto, G.-P. Racial

differences in cancer susceptibility andsurvival: more than the color of the skin?. _Trends Cancer_ 3, 181–197, https://doi.org/10.1016/j.trecan.2017.02.002 (2017). Article  PubMed  PubMed

Central  Google Scholar  * Kazandjian, D. et al. Sustained minimal residual disease negativity in newly diagnosed multiple myeloma (NDMM) patients treated with Carfilzomib (CFZ),

Lenalidomide (LEN), and Dexamethasone (DEX) followed by 2 years of lenalidomide maintenance (CRd-R): updated results of a phase 2 study. _Blood_ 128, 4527–4527 (2016). Google Scholar  *

Mailankody, S. et al. Baseline mutational patterns and sustained MRD negativityin patients with high-risk smoldering myeloma. _Blood Adv_ 1, 1911–1918,

https://doi.org/10.1182/bloodadvances.2017005934 (2017). Article  CAS  PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS We thank Dr. Martin Mendoza and the Office

of Minority Affairs, U.S. Food and Drug Administration and the Memorial Sloan Kettering Core Grant (P30 CA008748) for grant support in conducting this work. The views expressed in this

manuscript are those of the author and do not reflect the official policy of the Department of Army/Navy/Air Force, Department of Defense, or U.S. Government. AUTHOR INFORMATION AUTHORS AND

AFFILIATIONS * Myeloma Program, Lymphoid Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA Dickran Kazandjian &

 Elizabeth Hill * Myeloma Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York City, NY, USA Malin Hultcrantz, Evan H. Rustad, Theresia Akhlaghi, Neha Korde, 

Sham Mailankody & Ola Landgren * Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA Venkata Yellapantula & Elli Papaemmanuil * Department

of Hematology-Oncology, Walter Reed National Military Medical Center, Bethesda, MD, USA Alex Dew & Mary Kwok * Hematology Section, Department of Laboratory Medicine Clinical Center,

National Institutes of Health, Bethesda, MD, USA Irina Maric Authors * Dickran Kazandjian View author publications You can also search for this author inPubMed Google Scholar * Elizabeth

Hill View author publications You can also search for this author inPubMed Google Scholar * Malin Hultcrantz View author publications You can also search for this author inPubMed Google

Scholar * Evan H. Rustad View author publications You can also search for this author inPubMed Google Scholar * Venkata Yellapantula View author publications You can also search for this

author inPubMed Google Scholar * Theresia Akhlaghi View author publications You can also search for this author inPubMed Google Scholar * Neha Korde View author publications You can also

search for this author inPubMed Google Scholar * Sham Mailankody View author publications You can also search for this author inPubMed Google Scholar * Alex Dew View author publications You

can also search for this author inPubMed Google Scholar * Elli Papaemmanuil View author publications You can also search for this author inPubMed Google Scholar * Irina Maric View author

publications You can also search for this author inPubMed Google Scholar * Mary Kwok View author publications You can also search for this author inPubMed Google Scholar * Ola Landgren View

author publications You can also search for this author inPubMed Google Scholar CORRESPONDING AUTHORS Correspondence to Dickran Kazandjian or Ola Landgren. ETHICS DECLARATIONS CONFLICT OF

INTEREST The authors declare that they have no conflict of interest. ADDITIONAL INFORMATION PUBLISHER’S NOTE: Springer Nature remains neutral with regard to jurisdictional claims in

published maps and institutional affiliations. SUPPLEMENTARY INFORMATION SUPPLEMENTAL_METHODS-REVISED-CLEAN RIGHTS AND PERMISSIONS OPEN ACCESS This article is licensed under a Creative

Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the

original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in

the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended

use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit

http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Kazandjian, D., Hill, E., Hultcrantz, M. _et al._ Molecular underpinnings of

clinical disparity patterns in African American vs. Caucasian American multiple myeloma patients. _Blood Cancer Journal_ 9, 15 (2019). https://doi.org/10.1038/s41408-019-0177-9 Download

citation * Received: 29 September 2018 * Revised: 18 December 2018 * Accepted: 14 January 2019 * Published: 04 February 2019 * DOI: https://doi.org/10.1038/s41408-019-0177-9 SHARE THIS

ARTICLE Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy to clipboard

Provided by the Springer Nature SharedIt content-sharing initiative