Morgan, Gareth J.

Abstract Introduction: Multiple Myeloma (MM) is an incurable plasma cell malignancy commonly preceded by the asymptomatic stage smoldering multiple myeloma (SMM). MM is characterized with significant genomic heterogeneity of chromosomal gains and losses (CNVs), translocations, and point mutations (SNVs); alterations that are also observed in SMM patients. However, current SMM risk models rely solely on clinical markers and do not accurately capture progression risk. While incorporating some genomic biomarkers improves prediction, using all MM genomic features to comprehensively stratify patients may increase risk stratification precision in SMM. Methods: We obtained a total of 214 patient samples at SMM diagnosis. We performed whole-exome sequencing on 166 tumors; of these, RNA sequencing was performed on 100. Targeted capture was done on 48 additional tumors. Upon binarization of DNA features, we performed consensus non-negative matrix factorization to identify distinct molecular clusters. We then trained a random forest classifier on translocations, SNVs, and CNVs. The predicted clinical outcomes for the molecular subtypes were further validated in an independent SMM cohort of 74 patients. Results: We identified six genomic subtypes, four with hyperdiploidy (>48 chromosomes, HMC, HKR, HNT, HNF) and two with IgH translocations (FMD, CND) (Table 1). In multivariate analysis accounting for IMWG (20-2-20) clinical risk stages, high-risk (HMC, FMD, HKR) and intermediate-risk (HNT, HNF) genetic subtypes were independent predictors of progression (Hazards ratio [HR]: 3.8 and 5.5, P = 0.016 and 0.001, respectively). The low-risk, CND subtype harboring translocation (11;14) was enriched for the previously defined CD-2 MM signature defined by the B cell markers CD20 and CD79A (FDR = 0.003 ), showed upregulation of CCND1, E2F1, and E2F7 (FDR = 0.01, 0.0004, 0.08), and was enriched for G2M checkpoint, heme metabolism, and monocyte cell signature (FDR = 0.003, 0.003, 0.003, respectively). The FMD subtype with IgH translocations (4;14) and (14;16) was enriched for P53, mTORC1, unfolded protein signaling pathways and plasmacytoid dendritic cell signatures (FDR = 0.01, 0.005, 0.008, respectively). The HKR tumors were enriched for inflammatory cytokine signaling, MYC target genes, T regulatory cell signature, and the MM proliferative (PR) signatures (FDR = 0.02, 0.03, 0.007, 0.02, respectively). The APOBEC mutational signature was enriched in HMC and FMD tumors (P = 0.005), while there was no statistical difference across subtypes in the AID signature. The median follow-up for the primary cohort is 7.1 years. Median TTP for patients in HMC, FMD, and HKR was 3.8, 2.6, and 2.2 years, respectively; TTP for HNT and HNF was 4.3 and 5.2, respectively, while it was 11 years in CND patients (P = 0.007). Moreover, by analyzing the changes in MM clinical biomarkers over time, we found that patients from high-risk subgroups had higher odds of developing evolving hemoglobin and monoclonal protein levels over time (P = 0.01 and 0.002, respectively); Moreover, the absolute increase in M-protein was significantly higher in patients from the high-risk genetic subtypes at one, two, and five years from diagnosis (P = 0.001, 0.03, and 0,01, respectively). Applying the classifier to the external cohort replicated our findings where intermediate and high-risk genetic subgroups conferred increased risk of progression to MM in multivariate analysis after accounting for IMWG staging (HR: 5.5 and 9.8, P = 0.04 and 0.005, respectively). Interestingly, within the intermediate-risk clinical group in the primary cohort, patients in the high-risk genetic subgroups had increased risk of progression (HR: 5.2, 95% CI 1.5 - 17.3, P = 0.007). In the validation cohort, these patients also had an increased risk of progression to MM (HR: 6.7, 95% CI 1.2 - 38.3, P = 0.03), indicating that molecular classification improves the clinical risk-stratification models. Conclusion: We identified and validated in an independent dataset six SMM molecular subgroups with distinct DNA alterations, transcriptional profiles, dysregulated pathways, and risks of progression to active MM. Our results underscore the importance of molecular classification in addition to clinical evaluation in better identifying high-risk SMM patients. Moreover, these subgroups may be used to identify tumor vulnerabilities and target them with precision medicine efforts. Figure 1 Figure 1. Disclosures Bustoros: Janssen, Bristol Myers Squibb: Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria. Casneuf: Janssen: Current Employment. Kastritis: Amgen: Consultancy, Honoraria, Research Funding; Takeda: Honoraria; Pfizer: Consultancy, Honoraria, Research Funding; Genesis Pharma: Honoraria; Janssen: Consultancy, Honoraria, Research Funding. Walker: Bristol Myers Squibb: Research Funding; Sanofi: Speakers Bureau. Davies: Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Dimopoulos: Amgen: Honoraria; BMS: Honoraria; Takeda: Honoraria; Beigene: Honoraria; Janssen: Honoraria. Bergsagel: Genetech: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Patents & Royalties: human CRBN mouse; GSK: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Yong: BMS: Research Funding; Autolus: Research Funding; Takeda: Honoraria; Janssen: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; GSK: Honoraria; Amgen: Honoraria. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees. Getz: IBM, Pharmacyclics: Research Funding; Scorpion Therapeutics: Consultancy, Current holder of individual stocks in a privately-held company, Membership on an entity's Board of Directors or advisory committees. Ghobrial: AbbVie, Adaptive, Aptitude Health, BMS, Cellectar, Curio Science, Genetch, Janssen, Janssen Central American and Caribbean, Karyopharm, Medscape, Oncopeptides, Sanofi, Takeda, The Binding Site, GNS, GSK: Consultancy.

Abstract Introduction: A number of cytogenetic abnormalities (CAs) are associated with poorer prognosis in MM, including del(17p), t(4;14), t(14;16), and amp1q21. There is a general consensus that treatment with proteasome inhibitors (PIs) benefits pts carrying these CAs (Sonneveld Blood 2016). This meta-analysis of four phase 3 studies assesses PFS benefit in pts receiving the oral PI ixa vs pbo regarding the specific adverse CAs. Methods: Pts in TOURMALINE-MM1 (N=722; relapsed/refractory MM; Moreau N Engl J Med 2016) and MM2 (N=705; newly diagnosed MM; Facon Blood 2021) received ixa plus lenalidomide-dexamethasone (Rd) vs pbo-Rd (1:1). Pts in TOURMALINE-MM3 (N=656; Dimopoulos Lancet 2019) and TOURMALINE-MM4 (N=706; Dimopoulos J Clin Oncol 2020) received ixa vs pbo (3:2) as maintenance following autologous stem cell transplant or as post-induction maintenance in transplant-ineligible pts, respectively. In TOURMALINE-MM1/MM2, CAs were centrally assessed on CD138 positive sorted cells from bone marrow samples collected at study entry using fluorescence in situ hybridization (FISH). Cutoff values for defining the presence of del(17p), t(4;14), and t(14;16) were 5%, 3%, and 3% positive cells, respectively, based on the false-positive rates (technical cutoffs) of the FISH probes used, and cutoff values of 3% (MM1) and 20% (MM2) were used for amp1q21. In TOURMALINE-MM3/MM4, cytogenetic assessment was performed locally using FISH or conventional karyotyping with locally defined thresholds for positivity. Results: 270/1227 (22%) vs 227/1019 (22%) evaluable pts receiving ixa-based vs pbo-based therapy had high-risk CAs [del(17p), t(4;14), t(14;16)]: 75 vs 62 in MM1, 60 vs 63 in MM2, 61 vs 54 in MM3, and 74 vs 48 in MM4. 957/1227 (78%) vs 792/1019 (78%) had complementary standard-risk CAs: 200 vs 216 in MM1, 231 vs 234 in MM2, 252 vs 152 in MM3, and 275 vs 190 in MM4. 555/1142 (49%) vs 479/955 (50%) evaluable pts receiving ixa-based vs pbo-based therapy had expanded high-risk CAs (high-risk CAs ± amp1q21): 155 vs 154 in MM1, 134 vs 146 in MM2, 116 vs 88 in MM3, and 150 vs 91 in MM4. 587/1142 (51%) vs 476/955 (50%) had complementary standard-risk CAs: 122 vs 126 in MM1, 164 vs 153 in MM2, 154 vs 89 in MM3, and 148 vs 108 in MM4. After a median follow-up in the pooled analysis of 25.6 months (mos; 12.7, 54.6, 29.7, and 21.3 mos in MM1, MM2, MM3, and MM4, respectively), the hazard ratio (HR) for PFS with ixa-based vs pbo-based therapy in pts with high-risk CAs was 0.74 (95% confidence interval [CI] 0.59-0.93; median 17.8 vs 13.2 mos) and 0.70, (95% CI 0.62-0.80; median 26.3 vs 17.6 mos) in pts with standard-risk CAs. In the subgroup analyses of expanded high-risk CAs, the HR for PFS with ixa-based vs pbo-based therapy in pts in the expanded high-risk group was 0.75 (95% CI 0.64-0.87; median 18.1 vs 14.1 mos; Figure 1) and 0.71 (95% CI 0.59-0.85; median 36.1 vs 21.4 mos) in the complementary standard-risk group. Analyses of PFS according to the presence of individual CAs (Figure 2) indicated differing magnitudes of PFS benefit. Notably, in pts with t(4;14) (n=124 vs n=102), the HR for PFS with ixa-based vs pbo-based therapy was 0.68 (95% CI 0.48-0.96; median 22.4 vs 13.2 mos), while for pts with amp1q21 (n=380 vs n=312), the HR was 0.77 (95% CI 0.63-0.93; median 18.8 vs 14.5 mos) and for pts with del(17p) (n=141 vs n=107) the HR was 0.80 (95% CI 0.59-1.09; median 15.7 vs 13.2 mos). Conclusions: This pooled analysis demonstrated a PFS benefit with ixa-based therapy vs pbo-based therapy regardless of the presence of specific adverse CAs, with a similar magnitude of benefit in pts with (expanded) high-risk CAs and the respective complementary standard-risk groups. However, due to the differences in study eligibility criteria and pt populations, ixa combined with Rd or as single-agent maintenance therapy may not abrogate the negative impact of high-risk CAs. Analyses of PFS in subgroups with specific CAs indicated that the greatest magnitudes of benefit (lowest HRs) with ixa-based vs pbo-based therapy were in pts with t(4;14) (HR 0.68) and pts with amp1q21 (HR 0.77), suggesting that the improved outcome with ixa-based vs pbo-based therapy in the expanded high-risk subgroup was primarily driven by PFS differences in pts with these more common CAs. Further study is needed, and additional sensitivity analyses will be presented in subsequent publications. Figure 1 Figure 1. Disclosures Chng: BMS/Celgene: Honoraria, Research Funding; Amgen: Honoraria; Takeda: Honoraria; Abbvie: Honoraria; Sanofi: Honoraria; Pfizer: Honoraria; Johnson and Johnson: Honoraria, Research Funding. Lonial: BMS/Celgene: Consultancy, Honoraria, Research Funding; AMGEN: Consultancy, Honoraria; GlaxoSmithKline: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Merck: Honoraria. Morgan: Takeda: Honoraria. Iida: Amgen: Research Funding; Daiichi Sankyo: Research Funding; Glaxo SmithKlein: Research Funding; Ono: Honoraria, Research Funding; Takeda: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Chugai: Research Funding; Abbvie: Research Funding; Janssen: Honoraria, Research Funding; Bristol-Myers Squibb: Research Funding. Moreau: Sanofi: Honoraria; Celgene BMS: Honoraria; Abbvie: Honoraria; Amgen: Honoraria; Janssen: Honoraria; Oncopeptides: Honoraria. Kumar: Bluebird Bio: Consultancy; Carsgen: Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Antengene: Consultancy, Honoraria; Novartis: Research Funding; Oncopeptides: Consultancy; Tenebio: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; Beigene: Consultancy; Merck: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Twumasi-Ankrah: Takeda: Current Employment. Kumar: Takeda: Current Employment, Current holder of stock options in a privately-held company. Dash: Takeda: Current Employment, Current equity holder in publicly-traded company. Vorog: Takeda: Current Employment. Zhang: Takeda: Current Employment. Suryanarayan: Takeda: Current Employment. Labotka: Takeda: Current Employment. Dimopoulos: Janssen: Honoraria; Takeda: Honoraria; Beigene: Honoraria; BMS: Honoraria; Amgen: Honoraria. OffLabel Disclosure: Use of the oral proteasome inhibitor ixazomib for the initial treatment of multiple myeloma and as maintenance treatment following stem cell transplantation or induction therapy in newly diagnosed patients

Abstract Background: Two global, randomized, placebo (pbo)-controlled phase 3 studies of single-agent ixazomib (ixa) maintenance therapy are currently ongoing for newly diagnosed MM patients following primary therapy that included autologous stem cell transplantation (ASCT) (MM3, NCT02181413) or excluded ASCT (MM4, NCT02312258). Both trials have demonstrated statistically significant and clinically meaningful improvement in their primary endpoint of progression-free survival (PFS): for MM3, median 26.5 months (mos) ixa vs 21.3 mos pbo (hazard ratio [HR] 0.720, 95% confidence interval [CI] 0.582-0.890, P=0.002); for MM4, median 17.4 mos for ixa vs 9.4 mos for pbo (HR 0.659, 95% CI 0.542-0.801, P<0.001). Data for the key secondary endpoint, OS, have not previously been published. Methods: Full methods have previously been reported (Dimopoulos, Lancet 2019; Dimopoulos, J Clin Oncol 2020). Eligible patients (MM3, N=656; MM4, N=706) were randomized 3:2 to receive maintenance therapy with single agent ixa or pbo for a maximum of approximately 24 mos (26 cycles, to the nearest complete cycle) or until progressive disease or unacceptable toxicity, whichever occurred first. Results: At the most recent data cut-off (MM3, 29 January 2021; MM4, 15 October 2020), with median follow up of 64 mos and 36 mos, respectively, 27% (MM3, 174/656) and 29% (MM4, 203/706) of the intention-to-treat (ITT) population had OS events. In MM3, the 5-year Kaplan-Meier estimate for OS was 74% for ixa and 73% for pbo, though the median OS had not yet been reached in either arm (HR 1.008, 95% CI 0.744-1.367, P=0.958; Figure). In MM4, the 5-year Kaplan-Meier OS estimate was 55% for ixa and 56% for pbo, though the median OS had also not yet been reached in either arm (HR 1.136, 95% CI 0.853-1.514, P=0.382; Figure). No new safety signals were identified, and the incidence of new primary malignancies in both studies was similar between ixa and pbo. Conclusions: These most current OS data for MM3 and MM4 have not demonstrated a statistically significant difference for the ixa or the pbo arm to date. After 64 mos of follow-up in MM3, the risk of OS does not differ between the study arms. After 36 mos of follow-up in MM4, the OS HR shows a trend that favors the pbo arm. Because interim analyses of OS may be overrepresented by deaths in patients who did not benefit from maintenance therapy, it is not known to what extent these results will be reflective of the ITT population at the time of the final analyses. As treatment options, including anti-CD38 mAb and other new mechanisms of action, for salvage therapies following progression continue to expand, OS is increasingly being confounded by subsequent therapies. Hence, demonstrating OS advantage for early line MM therapies is becoming increasingly challenging. OS data continue to be collected in these studies for later analyses. Figure 1 Figure 1. Disclosures Dimopoulos: Takeda: Honoraria; Amgen: Honoraria; Beigene: Honoraria; Janssen: Honoraria; BMS: Honoraria. Lonial: AMGEN: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding; Merck: Honoraria; GlaxoSmithKline: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; BMS/Celgene: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria. Chng: Johnson and Johnson: Honoraria, Research Funding; BMS/Celgene: Honoraria, Research Funding; Takeda: Honoraria; Abbvie: Honoraria; Sanofi: Honoraria; Pfizer: Honoraria. Iida: Takeda: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Ono: Honoraria, Research Funding; Chugai: Research Funding; Celgene: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; Daiichi Sankyo: Research Funding; Glaxo SmithKlein: Research Funding; Amgen: Research Funding; Abbvie: Research Funding; Bristol-Myers Squibb: Research Funding. Mateos: Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; Regeneron: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bluebird bio: Honoraria; Sea-Gen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene - Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Honoraria; Oncopeptides: Honoraria. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees. Kumar: Takeda: Current Employment, Current holder of stock options in a privately-held company. Suryanarayan: Takeda: Current Employment. Vorog: Takeda: Current Employment. Fergus: Takeda: Current Employment. Labotka: Takeda: Current Employment. OffLabel Disclosure: Use of the oral proteasome inhibitor ixazomib as maintenance treatment for multiple myeloma following stem cell transplantation or induction therapy in newly diagnosed patients.

Abstract Background: Persons of African ancestry (AA) experience a 1.5-2-fold risk of multiple myeloma (MM) compared to persons of European ancestry (EA). We assembled a set of MM patients with self-reported AA in order to evaluate the contribution of genetics to etiology in this high-risk group. Methods: Here we present the results of a meta-analysis of two GWAS in 1,813 cases and 8,871 controls of AA. We also conducted an admixture mapping scan to identify risk alleles associated with local ancestry, 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. Finally, we conducted an eQTL analysis measuring gene expression in those genes harboring a risk variant in malignant plasma cells from 292 of the patients from a single site. Results: In GWAS analysis, we identified two suggestive novel loci located at 9p24.3 and 9p13.1 at P<1 × 10-6, but 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. 20 of the 23 known EA risk variants showed directional consistency and 9 replicated at P<0.05 in AA individuals. In eight regions, we identified markers that better capture MM risk in persons of AA. AA individuals with a PRS in the top 10% had a 1.82-fold (95%CI: 1.56, 2.11) increased MM risk compared to 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). Conclusion: Our study shows that common genetic variation contributes to MM risk individuals of AA. This abstract is also being presented as Poster C040. Citation Format: Zhaohui Du, Niels Weinhold, Gregory Chi Song, Kristen A. Rand, David J. Van Den Berg, Amie E. Hwang, Xin Sheng, Victor Hom, Sikander Ailawadhi, Ajay K. Nooka, Seema Singhal, Karen Pawlish, Edward Peters, Cathryn Bock, Ann Mohrbacher, Alexander Stram, Sonja I. Berndt, William J. Blot, Graham Casey, Victoria L. Stevens, Rick Kittles, Phyllis J. Goodman, W. Ryan Diver, Anselm Hennis, Barbara Nemesure, Eric A. Klein, Benjamin A. Rybicki, Janet L. Stanford, John S. Witte, Lisa Signorello, Esther M. John, Leslie Bernstein, Antoinette Stroup, Owen W. Stephens, Maurizio Zangari, Frits Van Rhee, Andrew Olshan, Wei Zheng, Jennifer J. Hu, Regina Ziegler, Sarah J. Nyante, Sue Ann Ingles, Michael Press, John David Carpten, Stephen Chanock, Jayesh Mehta, Graham A Colditz, Jeffrey Wolf, Thomas G. Martin, Michael Tomasson, Mark A. Fiala, Howard Terebelo, Nalini Janakiraman, Laurence Kolonel, Kenneth C. Anderson, Loic Le Marchand, Daniel Auclair, Brian C.-H. Chiu, Elad Ziv, Daniel Stram, Ravi Vij, Leon Bernal-Mizrachi, Gareth J. Morgan, Jeffrey A. Zonder, Carol Ann Huff, Sagar Lonial, Robert Z. Orlowski, David V. Conti, Christopher A. Haiman, Wendy Cozen. A meta-analysis of genome-wide association study and eQTL analysis of multiple myeloma among African Americans [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr PR05.

Abstract Although multiple myeloma is clinically defined as the accumulation of clonal, malignant plasma cells in the bone marrow, the disease shows significant heterogeneity with regard to progression and therapeutic response. It is likely that germline genetic polymorphisms contribute significantly to an individual’s disease course and response. Bank On A CureTM (BOAC) is a collaborative project initiated with the International Myeloma Foundation (IMF) to develop a DNA bank of myeloma patient samples, through international cooperation. As the bank continues to expand, it provides a resource for the development of cooperative partnerships to assess specific clinical trials as well as conglomerate data. In this study, we have combined data from 3 phase III trials: ECOG trial E9486 (VBMCP + cyclophosphamide or interferon); SWOG trial 9321 (VAD induction followed by randomization to PBSC-supported high dose therapy versus standard dose therapy of VBMCP); and the MRC7 trial (comparing conventional chemotherapy [ABCM] with intravenous chemotherapy and high dose melphalan [CVAMP + HDM +PBSCT]). Five single nucleotide polymorphisms were examined based on associations demonstrated in previous individual trials: GSTP1, IL-6, LT-a, TNF-a, and ERCC2 (DNA repair). Patient samples included 412 from E9486, 569 from S9321, and 232 from MRC7 (total=1213). As a combined data set, genotype associations with overall survival did not reach statistical significance, suggesting SNP associations with survival may be therapy specific. One single association that did reach significance in the conglomerate data was TNF-a genotype association with age of disease onset (the high producer variant AA genotype showing a 5 year later age of onset than the AG/GG genotypes, or the overall mean of the trial, age 61 vs 56; p=.03). This is interesting in light of reports suggesting high production of TNF-a is found in myeloma patients and may actually promote myeloma. Yet, high production may also delay disease onset due to its role in other immunologic processes. Additional parameters including genotype associations with bone disease, CR rates, toxicities, and stage are being evaluated and will be presented. Preliminary results show that when SNPs from S9321 were included in a linear regression model, genotype risk groups could be identified; showing 73% correct association with ISS stage 1, and 75% correct association with ISS stage 3. This model, however, was not validated in the E9486 trial, with less than 50% genotype predictive ISS stage association. A number of highly significant ethnic genotype associations were noted, and are presented in a separate abstract. Given the likely complex interactions of multiple genetic variations, collective genotype comparisons may be required from larger pooled data sets BOAC is assembling to increase significant associations.

Abstract There is a high background risk of venous thrombotic events (VTE) in patients with Multiple Myeloma (MM) and this is significantly increased following treatment with Thalidomide. The risk may be mediated by the production of a prothrombotic state, via an angiopathic mechanism, or alternatively it may arise from the release of prothrombotic factors from dying myeloma cells targeted by the treatment. No unifying evidence of a prothrombotic state has been demonstrated to date and it remains important to understand the mechanism underlying VTEs in order to prevent them effectively. Inherited genetic variants in the form of SNPs can affect the behaviour of clotting factors. These SNPs are functional and well characterised in the coagulation system and we have used them to analyse the mechanism underlying thalidomide induced VTE. As part of the Bank On A Cure TM project we have carried out an analysis of SNPs in 11 genes: GPIIA, ITGA, MTHFR, MTR, PPARG2, TFP1, VCAM, factor V, HABP2, prothrombin and THDB in a case series of 90 patients treated with thalidomide, with and without DVTs. This study showed a significant association with MTHFR and factor V polymorphisms. In order to validate and extend these initial observations we carried out an analysis of a second nested case control study using 131 Myeloma patients from within the MRC Myeloma IX trial, comprising 47 cases with a VTE and 94 age and sex matched controls exposed to the same treatment. We used a SNPlex panel containing 16 functional venous thrombosis associated SNPs, all of these functional polymorphisms have reported mechanisms and biology. We looked at polymorphisms in the genes: fibrinogen A (Ala331Thr), B (-455G>A, Lys478Arg) and G polypeptides (His140Tyr); Coagulation factors V(Arg534Gln), VIII (Glu1260Asp), IX(Ala194Thr), XI (Phe339Cys) and XII(-4G>T); PROCR(Gly219Ser); prothrombin(19911A>G ,20210G>A); MTHFR(Glu429Ala, Ala222Val); Serpine1(-844G>A) and TFPI(Met292Val). We have typed our selected SNPs using the SNPlex™ Genotyping System, which is based on the oligonucleotide ligation/PCR assay with a universal fluorescently labeled ZipChute™ probe detection. The probes are hybridized to complementary ZipCode™ sequences that are part of genotype-specific amplicons. Probes are eluted and detected on a Applied Biosystems 3130x DNA Analyser. This approach is an attractive alternative to existing genotyping methodologies, as it requires only three unlabeled probes per SNP, and consumes very little gDNA. The data analysis on the polymorphisms frequencies is ongoing to examine associations of thalidomide treatment with VTE events and will be presented. These datasets will allow a better definition of the pathogenesis of thalidomide related VTE, so that we can intervene therapeutically in a rational fashion.

Abstract MM has a median age at presentation of 65–68 years, rarely occurring in pts before age 40. Here, we present clinical, laboratory and survival data in the largest ever-analyzed cohort of young pts compared with pts aged ≥ 40 years. 259 pts aged < 40 and 8,296 aged ≥ 40 years were enrolled. These 8.555 pts were part of the cohort analyzed for the ISS staging project. Previously untreated pts entered into randomized trials with standard (SDT) or high dose treatment (HDT). Clinical data were available for all pts, 526 pts had karyotypic analysis and 454 pts testing for Del 13 testing by FISH. The Chi-Square test was used to identify significant prognostic factors. A maximum likelihood model was used to estimate relative excess risk (RER) for younger versus older pts treated with SDT or HDT. Observed survival was adjusted by life expectancy depending on age, gender, and nationality. Young pts had significantly lower ISS stage I than older pts (stage I; 39% vs. 28%, stage II: 28 vs. 39%, stage III 33% vs. 33%, p<. 001, p<001, p=0.863, respectively) due primarily to a lower prevalence of high ß2m levels and low albumin levels in the younger age group. Paradoxically, the younger pts more frequently presented with higher M-component levels and Durie Salmon stage III (65% vs. 54%, p<0.001). Prevalence of other important prognostic parameters like Hb, platelet count, creatinine, calcium, CRP, LDH and bone marrow PC infiltration was similar in both groups. Younger pts had more frequently good ECOG performance status (PS) (0–2), (87% vs. 81%, p<0.01). Karyotypic abnormalities and Del 13 were similarly distributed between both cohorts. Median OS was significantly longer in young pts (median: 4.8 years vs. 3.7 years; hazard ratio (HR): 1.46; 95%, CI (1.27, 1.68); p=<0.001). Better outcome in the younger cohort was seen in all three ISS stages, independent of gender. Median OS survival was significantly longer after SDT in the young cohort (median: 49 months (mos) vs. 39 mos; HR 1.49; CI (1.27, 1.75); p<0.001). Median survival: 86 mos in young and 68 mos in older pts on HDT; this did not translate into a statistical difference (HR 1.8; CI (0.81,1.44); p=0.6). Risk estimates in a maximum likelihood analysis revealed that, apart from ISS Stage, young age followed by female sex was statistical significant for a good outcome for pts treated with SDT (Age RER: 1.43, CI (1.20, 1.70), p<0.001; Sex RER: 1.07, CI (1.02, 1.14); p < 0.013). For pts treated with HDT, only ISS Stage was found to be a risk factor, but not age or gender. Conclusions: Patients < 40 have better prognosis and survival than older patients. The most important features of young patients are low SßM, normal S. Alb, and good performance status.

Abstract While there are certain common clinical features in myeloma, the disease shows significant heterogeneity with regard to disease progression, and responses to therapy, affecting both survival and toxicities. Heritable variations in a wide variety of genes and pathways affecting cellular functions and drug responses likely impact patient outcomes. In the Bank On A Cure (BOAC) program we have developed a custom chip that assesses 3,404 SNPs representing variations in cellular functions and pathways that may be involved in myeloma progression and response. The chip has gone through rigorous quality controls checks for high call rates, accuracy, and reproducibility that will be presented. Using the BOAC chip, we have conducted studies to look for SNPs that may identify biologic variations that are associated with good or poor response across a variety of treatments. In this study we looked for SNPs that may distinguish short term and long term survivors in two phase III clinical trials: ECOG E9486 and intergroup trial S9321. E9487 patients were treated with VBMCP followed by randomization to no further treatment, IFN-alpha, or cylcophosphamide; and, although there was variation in survival, no significant differences in survival were noted among the 3 arms of the trial. Patients included in this SNP study from S9321 received VAD induction followed by randomization to VBMCP or high dose melphalan + TBI. SNP profiles were obtained for patients with less than 1 year EFS (n=20 in E9487; n=50 in S9321) and patients showing greater than 3 years EFS (n=32 in E9486; n=41 in S9321). Statistical approaches were performed to identify single and groups of SNPs that best discriminated the survival groups. Previous studies have suggested genetic variations in drug metabolism genes, p-glycoprotein transport, and DNA repair genes may influence survival outcomes. Our results show significant survival associations of genetic variations in genes within these functional categories (eg. GST, XRCC, ABCB, and CYP genes). Although genetic variations were found that were uniquely associated with each clinical trial, several of these genetic variations show survival associations that increase in significance when the two trials were examined as a conglomerate data set. Grouping genetic variations through common pathway approaches using gene set enrichment analysis, as well as clustering or partitioning algorithms, further improve the value of the SNPs as potential prognostic markers of survival outcomes. These results and statistical approaches will be presented, and represent steps toward identifying patient variations in biologic mechanisms important in predicting therapeutic outcomes.

8000 Background: The diagnostic criteria for MM were revised in 2014, recategorizing ultra-high risk (i.e., 80% at two years) as active myeloma requiring therapy. The removal of patients at the highest risk of progression from the smoldering group requires reassessment of current risk stratification models. Methods: We designed a multicenter, retrospective study of SMM patients to develop a risk stratification model. Patients diagnosed with SMM on/after January 1, 2004 were included if they had no disease progression within 6 months, had baseline data from diagnosis (+/- 3 months), had a follow up of ≥1 year, and did not participate in a therapeutic trial of SMM. Various clinical and laboratory factors were explored to identify factors that predicted progression at 2 years. Univariate Cox regressions were run for each factor. For factors with p-values ≤ 0.25, optimal cut points were identified using Youden’s index. Binary factors were used in stepwise regression to fit multivariable Cox model and significant risk factors were determined (F-test). Results: Overall, 2004 patients were included (ages 26-93, 51% female). Factors independently associated with progression (optimal cutoffs) included: Serum M protein (2 g/dL), involved to uninvolved serum-free light chain ratio (20), and marrow plasma cell % (20%). Patients were stratified using the risk factors: Low- (0 factors), Intermediate- (1), and High-Risk (≥2). Previously used high risk markers such as Bence Jones proteinuria (≥500 mg/24 hours) and severe immunoparesis (50% decrease in uninvolved immunoglobulin levels) were both significant in univariate analysis, but were eliminated on step wise selection. Compared to the low risk group, intermediate- and high-risk groups had significantly higher rate of progression (Table). Within the high-risk group, having all 3 risk factors (n=61) versus 2 did not add to the model, with insufficient separation between 2 and 3 factors. Conclusions: We have developed a risk stratification model for SMM that incorporates revised cutoffs for previously used parameters (20/2/20) that can be universally applied. Additonal analysis are being conducted to develop models that utilize common cytogenetic abnormalities, as well as those without FLC given lack of availability of all tests across the world. [Table: see text]

8095 Background: Multiple myeloma (MM) remains incurable and invariably relapses after initial therapy. There is no uniform approach to management of relapsed MM and is dictated by type of initial therapy and the response seen, and availability of new drugs. The outcomes associated with current approaches have not been systematically examined. Methods: We enrolled 383 patients (pts) with myeloma who had a documented relapse between Jan 2007 and June 2010, including 220 from Europe, 106 from Asia, and 31 from South America and 26 from North America. Time Zero (T0) was defined as date of treatment after first relapse. Results: Across the study, 61% were male and 49% were over 65 years at T0. ISS stage distribution at diagnosis included 26, 40 and 33% of patients in stages 1, 2 and 3, respectively. Among regimens used at first relapse; bortezomib (Btz) containing regimens were most common (54%), followed by lenalidomide (Len, 25%) and cyclophosphamide (21%). The overall response rate (>=PR) to first therapy after relapse was 58% including 14% with a CR. There was a progressive decrease in the response rates with successive regimen; 45%, 30% and 15% for regimens 2, 3 and 4 respectively. Len use was considerably lower in the Asian cohort, while Btz use was comparable across the regions. The median PFS from T0 was 13 mos and OS was 35 mos, for the entire cohort. The PFS to first salvage regimen was similar across the regions, while OS was shorter for the Asian and South American cohorts. In a univariate analysis, ISS stage 3, presence of cytogenetic abnormalities, history of plasma cell leukemia or extramedullary disease (EMD), bone marrow PC% > 33% and presence of renal insufficiency were all associated with a shorter PFS as well as shorter OS (P<=0.01). In a multivariate model, ISS stage 3 and presence of EMD were most associated with short OS. Conclusions: Median PFS for current second line regimens, typically containing Btz or Len appear to be 1 yr with an OS from first relapse of about 3 yrs. The OS from first relapse is nearly double that seen in a cohort of patients in first relapse prior to introduction of new drugs. Clear-cut regional differences can be seen in terms of patterns of drug use and likely reflect drug availability and healthcare costs.