Zangari, Maurizio

Introduction Despite the remarkable success of combination treatments, multiple myeloma (MM) still poses significant challenges, with a high percentage of patients experiencing relapse over time. Additional metabolic alterations are found in relapsed and refractory MM; however, their impacts have not been fully elucidated. Expression of thyroid hormone receptor interactor 13 (TRIP13) has been identified by Gene Expression Profile (GEP)-70 to correlate with poor prognosis in MM. TRIP13 is an AAA ATPase that regulates the spindle assembly checkpoint in mitosis. Based on GEP data from 34 paired baseline and relapse samples, TRIP13 continuously increased from baseline to first and to second relapse. Overexpression of TRIP13 induces bortezomib resistance. In this study, we aimed to investigate how TRIP13 might regulate MM cell metabolism and to use this information to develop a novel combination strategy to overcome MM resistance. Methods Knockout (KO) of TRIP13 in OPM2 and MM1.S cells was generated by dual gRNAs CRISPR-Cas9 editing deleting a fragment between exon 4 and exon 5. Cell growth curves and response to bortezomib were recorded in the control and TRIP13-KO cells. Metabolomics was performed to reveal cellular metabolic alterations. Seahorse analysis was used to test the mitochondrial stress and glycolysis stress. To investigate whether TRIP13 KO promotes ferroptosis, an intracellular iron-dependent cell death characterized by the accumulation of lipid peroxides, we applied C11-BODIY staining after treatment with Erastin, a ferroptosis-inducer. Expression levels of cyclooxygenase 2 (encoded by PTGS2, a hallmark of ferroptosis), cystine transporter (xCT), and intracellular glutathione peroxidase 4 (GPX4) were analyzed. In addition, the protein levels of iron metabolic molecules, including iron importer transferrin receptor protein 1 (TFRC), iron exporter ferroportin 1 (FPN1), ferritin heavy chain 1 (FTH1), and nuclear receptor coactivator 4 (NCOA4) were assessed. To study whether TRIP13 KO delays MM progression in an immune-competent MM mouse model, Vκ12653 murine MM cells were genetically modified by CRISPR-mediated TRIP13-KO and then injected into C57BL/6 mice. Mouse survival was monitored. Results The cell-doubling rate of TRIP13-KO cells was decreased by 50% compared to control cells. After 24 hours of treatment with 5 nM bortezomib, cell viability of TRIP13-KO cells decreased by 31% compared with control cells. The metabolomic studies revealed that TRIP13-KO OPM2 and MM1.S cells both had significantly decreased intracellular L-serine (FC = 0.57) but increased pyruvate (FC = 1.79), citric acid (FC = 1.76), and isocitric acid (FC = 1.64), indicating TRIP13 may regulate serine metabolism, mitochondrial function, and cellular redox balance. Seahorse analysis indicated that TRIP13-KO MM cells showed decreased glycolysis (decreased by 30%, p < 0.001) and glycolytic capacity (decreased by 33%, p < 0.001) while oxidative phosphorylation showed little change. Moreover, lipid peroxidation and PTGS2 were increased in TRIP13-KO cells after Erastin treatment, indicating that these cells were more vulnerable to ferroptosis. Mechanistically, inhibition of TRIP13 decreased GPX4, FPN1, FTH1, and increased NCOA4, while overexpression of TRIP13 showed the opposite changes, suggesting that targeting TRIP13 sensitizes MM cells to ferroptosis by impairing cellular redox balance and increasing the labile-iron pool. Lastly, TRIP13-KO significantly prolonged mouse survival in the Vκ12653 mouse model (median survival: TRIP13-KO 93 days vs. Control 60.5 days, p < 0.001). Conclusion Our study revealed that TRIP13 plays a critical role in regulating MM cell metabolism. By suppressing TRIP13, we observed increased sensitivity of MM cells to bortezomib, leading to enhanced ferroptosis-mediated cell death and prolonged mouse survival. These findings strongly suggest that targeting TRIP13 could be a promising approach in MM therapy. Moving forward, our future investigations will concentrate on unraveling the precise molecular mechanisms underlying TRIP13-mediated alterations in cell metabolism. Additionally, we plan to explore the potential synergistic effects of TRIP13 inhibition in combination with metabolic targeted treatments, such as venetoclax, to further enhance therapeutic outcomes in MM.

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 Background: AT’s have become the standard of care in the management of patients with MM, effecting 10-year survival rates of more than 30%. Long-term sequelae are uncommon, so that even 3rd and 4th AT have been applied. MDS is a well recognized complication of cancer therapy and has been reported in the context of both standard and AT-supported high-dose therapies for lymphoma and MM. It remains unclear whether t-MDS development is caused by prolonged “low” dose chemotherapy or by pre-transplant regimens. The purpose of this study was to survey a cytogenetic (CG) data base (DB) of > 30.000 samples submitted with each bone marrow examination involving 2152 patients receiving at least 1 AT. Patients and Methods: 1886 patients were identified with all relevant information as well as pre- and post-AT CG data available receiving at least 1 AT prior to January 2004. Common criteria for MDS-CA were employed. Routine CG follow-up studies were performed at 3–6 month intervals. MDS-CA development was examined in relationship to age, duration and type of therapy prior to transplant preparative treatment, blood counts, PBSC collection, AT regimen and number, post-transplant platelet and granulocyte recovery, as well as in the context of MM parameters. Interphase FISH analysis for MDS was also performed on PBSC in 42 patients (to detect early MDS cytogenetic changes caused by standard-doses therapies preceding AT) and correlated with MDS-metaphase CA post-AT in 7 patients. Results: Eighty-five patients developed MDS-CA at a median of 33 months after AT (range, 5 to 126 mo); the 10-year incidence estimate was 7%. MDS-metaphase CA development was independently associated with older age (> 55yr), lower CD 34 yield with first collection (< 18 million/kg) and lower platelet recovery 3 mo post-AT. The 10-year probability of MDS-metaphase CA was 1% among 315 patients with 0 risk factor, 6% among 1367 patients with 1 or 2 and 13% among the 199 patients with 3 risk factors (p<.0001). Univariately, >12 mo of pre-AT standard therapy increased the cumulative incidence of MDS-metaphase CA (p=0.04). The median time to MDS-metaphase CA development was shorter (24 mo) in the 55 patients with 2 or 3 risk factors compared to the 28 patients with 0 or 1 risk factors (41 mo; p=0.01). Conclusion: This report is the largest ever on the detection of MDS by metaphase CG, obtained in the context of AT for MM. Results confirm earlier findings that MDS-metaphase CA is infrequent when PBSC are procured early after the start of chemotherapies, in those with robust stem cell collection and prompt platelet recovery post-transplantation (evidence of non-damaged hematopoiesis) and among the younger age group. Thus, t-MDS after AT-supported high-dose therapy can be traced to stem cell damage likely inflicted by and not completely repaired after prolonged pre-AT therapies and accentuated by the post-AT replicative stress, especially in older patients. MDS- FISH data of PBSC will be presented at the meeting.

Abstract 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.

Multiple myeloma (MM) is a neoplasm thought to arise from a damaged germinal center B-cell that progresses to a plasma cell clone arising in bone marrow. MM comprises 20% of all hematologic cancer deaths. Persons of African ancestry (AA) have a 1.5 to 2-fold higher risk compared to individuals of European ancestry (EA). Genetically driven differences in hematopoiesis may lead to variation in the levels white blood cell (WBC) subsets which could, in turn, be associated with MM etiology. There are differences in genetic determinants of WBC traits between EA and AA populations, with possible implications for the racial disparity in risk. We tested the above hypothesis using Mendelian randomization (MR), an approach that leverages genetic determinants of specific traits (i.e. WBC counts) to estimate their effects on the risk of an outcome; and a transcriptome-wide association study (TWAS), which utilizes genetic predictors of gene expression to identify susceptibility genes. These analytic approaches were applied to data from the African American Multiple Myeloma Study (AAMMS) consisting of 1813 cases and 8871 AA cancer-free controls to examine how differences in heritable WBC gene expression profiles influence MM risk. Genetic determinants of variation in WBC subsets in AA were obtained from the literature and supplemented with new genome-wide association findings in AA subjects from the UK Biobank cohort (n=6108). Odds ratios (OR) for MM per 1 standard deviation (SD) increase in each WBC phenotype were estimated using independent (linkage disequilibrium (LD) r2<0.10) variants with P<10-6 as genetic instruments. Analyses based on variants associated with WBC traits in AA populations did not identify any statistically-significant associations between MM risk and WBC overall (p=0.81, using 15 SNPs) or subsets (lymphocytes, monocytes, eosinophils, neutrophils and basophils, p>0.05 for each). However, when we applied genetic determinants of WBC identified in 330,000 cancer-free EA UK Biobank participants (P<10-8, replication P<0.05, LD r2<0.05), a statistically significant inverse relationship emerged between increasing lymphocyte counts and MM risk (OR=0.80, 95% CI: 0.66-0.97, p=0.02, using 385 SNPs), as well as increasing basophil counts (OR=0.63, 95% CI: 0.41-0.96, p=0.03, using 140 SNPs). Next, we examined the association between WBC gene expression profiles and MM risk in AAMMS data. We applied published and validated ancestry-specific models developed using the PrediXcan approach, which leverage germline genetic and transcriptomic data from the Multi-Ethnic Study of Atherosclerosis (MESA) (Mogil et al. PMID: 30096133). The primary TWAS used gene expression models trained in AA subjects (n=233), with sensitivity analyses using models developed in AA and Hispanic subjects (n=585). The TWAS significance threshold was based on the number of genes with significant germline prediction models (p<0.05 and R2 ≥0.05) in AA, corresponding to P < 0.05/2700 = 1.85×10-5. The expression of two genes was significantly associated with MM risk: KANK1at 9p24.3 (P = 1.01×10-5) and DNAJC27at 2p23.3 (P = 1.56×10-5). KANK1is a candidate tumor suppressor gene for renal cell carcinoma and has recently been associated with MM risk in AA [Du, Blood 2017 130:3058]. Here we provide additional evidence for its role in MM etiology via gene expression-mediated mechanisms. DNAJC27 (previously known as RBJ) is a novel MM risk gene linked to constitutive activation of ERK in solid tumors. We also identified two suggestively associated genes: PRR14 (P = 1.34×10-4; combined AA-Hispanic sample: P = 1.56×10-6), which has been linked to MM risk in EA populations, and PARP16 (P = 9.46×10-5). To our knowledge this is the first study to comprehensively examine variation in WBC traits and gene expression profiles with respect to MM risk in AA. Our TWAS analysis leveraged data from the largest collection of genetic and gene expression data in AA, enabling ancestry-matched inference and identification of two novel risk genes. Although the limited availability of genetic instruments for WBC limited the power of MR analysis, findings using variants identified in European populations may offer some insight into trans-ethnic etiologic pathways and contribute to risk stratification strategies using genetic and blood cell count biomarkers. Future studies, particularly with MGUS-free controls, are needed to validate these results. Disclosures Song: Millennium Pharmaceuticals Inc: Employment. Rand:Ancestry.com: Employment. Ailawadhi:Cellectar: Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy; Pharmacyclics: Research Funding; Amgen: Consultancy, Research Funding; Takeda: Consultancy. Nooka:Takeda: Honoraria, Other: advisory board participation; Janssen: Honoraria, Other: advisory board participation; GSK: Honoraria, Other: advisory board participation; Spectrum pharmaceuticals: Honoraria, Other: advisory board participation; Adaptive technologies: Honoraria, Other: advisory board participation; Amgen: Honoraria, Other: advisory board participation; Celgene: Honoraria, Other: advisory board participation; BMS: Honoraria, Other: advisory board participation. Singhal:Bureau of Millennium/Takeda, Celgene, Janssen, Celgene, Bristol-Myers Squibb and Bluebird: Speakers Bureau. van Rhee:Takeda: Consultancy; Sanofi Genzyme: Consultancy; Castleman Disease Collaborative Network: Consultancy; EUSA: Consultancy; Adicet Bio: Consultancy; Kite Pharma: Consultancy; Karyopharm Therapeutics: Consultancy. Mehta:Millennium/Takeda, Celgene; stock in Celgene, Bristol-Myers Squibb and Bluebird: Speakers Bureau. Wolf:Takeda: Consultancy; Celgene: Consultancy; Novartis: Consultancy; Janssen: Consultancy; Amgen: Consultancy. Martin:Roche and Juno: Consultancy; Amgen, Sanofi, Seattle Genetics: Research Funding. Fiala:Incyte: Research Funding. Terebelo:Jannsen: Speakers Bureau; Celgene: Honoraria; Newland Medical Asociates: Employment. Anderson:Bristol-Myers Squibb: Other: Scientific Founder; Oncopep: Other: Scientific Founder; Amgen: Consultancy, Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Takeda: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau; Sanofi-Aventis: Other: Advisory Board. Vij:Bristol-Myers Squibb: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Genentech: Honoraria; Janssen: Honoraria; Karyopharm: Honoraria; Sanofi: Honoraria; Takeda: Honoraria, Research Funding. Bernal-Mizrachi:TAKEDA: Research Funding; Kodikas Therapeutic Solutions, Inc: Equity Ownership; Winship Cancer Institute: Employment, Patents & Royalties. Morgan:Amgen, Roche, Abbvie, Takeda, Celgene, Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Other: research grant, Research Funding. Zonder:Celgene Corporation: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Intellia: Consultancy, Membership on an entity's Board of Directors or advisory committees; Caelum: Consultancy, Membership on an entity's Board of Directors or advisory committees; Alnylam: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Consultancy, Membership on an entity's Board of Directors or advisory committees. Huff:Member of Safety Monitoring Board for Johnson and Johnson: Membership on an entity's Board of Directors or advisory committees; Karyopharm, Sanofi, MiDiagnostics: Consultancy. Lonial:Karyopharm: Consultancy; Takeda: Consultancy, Research Funding; Amgen: Consultancy; BMS: Consultancy; Janssen: Consultancy, Research Funding; GSK: Consultancy; Celgene Corporation: Consultancy, Research Funding; Genentech: Consultancy. Orlowski:Poseida Therapeutics, Inc.: Research Funding.

Abstract Introduction The 2-3 fold excess risk of multiple myeloma (MM) among family members of cases suggests a heritable contribution to risk. Recently, a genome-wide association study (GWAS) identified two genome-wide significant and one promising novel loci associated with multiple myeloma risk. To confirm these associations and identify additional novel risk loci, we performed a four-center, genome-wide association meta-analysis. Methods A fixed effects model was used for the meta-analysis which included a total of 1248 cases and 1485 controls, all of European descent, genotyped and analyzed at four separate centers with samples contributed by 10 studies. After quality control and imputation using the 1000 Genomes Project, the analysis included ∼9.5 million variants (λ=1.024). Associations between (single nucleotide polymorphisms) SNP genotypes and MM risk were evaluated under a log-additive model of inheritance, with each study adjusting for age, sex, and up to 10 principal components to control for population stratification. Promising results were replicated in an independent set of 1587 cases and 1770 controls using TaqMan, for a total of 2835 and 3255 cases and controls, respectively, in a combined meta-analysis. Results The discovery meta-analysis did not reveal any genome-wide significant associations (defined as p<5 x 10-8). We used a novel pruning algorithm to identify the top 35 most promising single nucleotide polymorphisms (SNPs) to advance to replication. We successfully genotyped 22 SNPs in the replication set. In the combined discovery and replication meta-analysis, rs1345359 at 12q23.1 was the most strongly associated SNP (P=9 x 10-8, Table 1). The variant allele C was associated with reduced risk (odds ratio discovery set [OR]= 0.69, OR replication set = 0.78, OR combined = 0.74). A second locus at 20q13.2 (rs150220835), was associated with a two-fold increased risk (P=1.22 x 10-6), a borderline increased risk (P=0.0900) and 45% increased risk (P=2.44 X 10-5) in the discovery, replication, and combined analysis sets respectively (Table 1). We also confirmed the association between MM risk and two previously published SNPs (rs4487645, p=0.0007and rs105251, p=0.0044) (Broderick et al., Nat. Genet., 2011). The third previously suggested SNP (rs6746082) was of nominal significance (p=0.0517) in the meta-analysis. Discussion We confirmed the association between MM risk and two previously published SNPs and identified a possible association with a novel SNP in chromosome 12q23.1 (rs1345359). This SNP is not located in a gene nor associated with biofeatures in ENCODE, thus further examination of correlated SNPs is necessary to identify a functional SNP linked to this locus. We also found suggestive evidence for a second locus at 20q13.2 requiring additional replication. Larger studies would improve risk variant discovery for this rare hematologic malignancy. Disclosures: Wolf: Celgene: Honoraria, Research Funding; Millenium: Honoraria; Onyx: Honoraria. Anderson:Celgene, Millennium, BMS, Onyx: Membership on an entity’s Board of Directors or advisory committees; Acetylon, Oncopep: Scientific Founder , Scientific Founder Other.

Abstract Introduction Over the last 15 years gene expression profiling (GEP) has been used to define myeloma molecular subgroups and to determine clinical prognosis. Two major molecular subgroup classifications have been used: the UAMS which determines 7 subgroups and the TC classification based on the presence of IgH translocations and expression of D group cyclins. For prognosis, although a number of different GEP signatures have been defined, the widely used GEP70 identifies 15% of patients with high risk (HR) disease who have a median PFS and OS of 1.75 and 2.83 years. An ideal classification system would identify clinically relevant subgroups with distinct etiology and biology using standardized techniques. We have examined a large group of patients characterized at multiple genetic levels to optimize the diagnostic approach of newly diagnosed patients going forward. Materials and methods Study subjects included 1349 cases enrolled in Total Therapy trials (median follow up 7.5 years). Gene expression profiling was used to determine GEP70 risk status, molecular subgroup by UAMS and TC classifications, and to devise a new and extended TC classification (TC10). Interphase FISH associated with IgH translocations and 1q+ and 17p- were used to build GEP proxies. Data from mutational analysis generated by the FoundationOne targeted sequence panel was also incorporated. Results were validated on the UK MRC MyelomaIX and Hovon65/GMMG-4 studies. Results An initial agnostic analysis of GEP data using sparse k-means clustering verified the existence of TC based groups. Six groups were identified that corresponded overwhelmingly with known TC subgroups; CCND1-t(11;14), D1-HRD, D2-HRD, MMSET, MAF/CCND2, and CCND3. Further comparisons between the molecular subgroup and TC classifier revealed that the UAMS 7 subgroups clustered strongly within one predominant TC group: CD-1 and CD-2 to t(11;14), HY to D1, LB and PR to D2, MF to t(14;16) or t(14;20), and MS to t(4;14). As the UAMS molecular subgroups are largely contained within the TC framework, we aimed to extend the TC by developing the TC10. To extend the known TC subgroups, unsupervised clustering was applied to the 3 largest subgroups [t(11;14), D1, and D2] to determine the strongest single divisor within each respective subgroup. The dominant feature within the t(11;14) cases was CD20 expression, while the D1 and D2 subgroups both split according to RRAS2. CD20 is associated with PAX5 and VPREB3 expression, and RRAS2 is associated with decreased PTP4A3 and increased TNFAIP3 and BIRC3 expression. RRAS2 activation within D1 subgroup and CD20 activation within t(11;14) cases corresponds to an increased time to response to induction therapy suggesting they constitute important biological subgroups. The TC10 combines the known etiologic subgroups of the TC with functionally relevant subdivisions to create 10 novel subgroups: t(11;14) CD20+/-, D1: RRAS2+/-, D2: RRAS2+/-, t(4;14), t(14;16), t(14;20), and t(6;14). Analysis of mutational data revealed that RRAS2 and CD20 activation within the D1, D2, and t(11;14) subgroups reduced the number of mutations in the MAPK pathway. Further mutational analysis revealed that median mutational load was highest in t(14;16) and lowest in D2: RRAS2+ subgroups. The GEP70 score identifies 15% of patients with HR disease and is specific for this purpose. In an analysis of risk assessment methods, we compared GEP detected adverse lesions [t(4;14), t(14;16), t(14;20), 17p- and 1q+] with the GEP70 and revealed that GEP70 HR identified samples have lower OS rates than cases with more than one adverse lesion (validated in external sets). GEP70 HR segregates non-uniformly across molecular subgroups as over 40% of all HR cases are found in the TC10 t(4;14), t(14;16), and t(14;20) subgroups. GEP70 HR cases also have a higher mutational load than low risk cases. Furthermore, GEP70 HR is uniquely associated with 1q+ and 17p- as cases with at least one of these adverse lesions are 4.9 times as likely to be GEP70 HR as cases with neither. Conclusion GEP profiling has a central role in simplifying and standardizing the molecular subgroup designation and risk stratifying of MM patients. The GEP70 risk score reliably identifies HR cases and outperforms FISH in risk assessment, even in validation data sets. The TC10 provides a classification system that improves upon previous methods by defining both etiological and functionally meaningful subgroups. Disclosures Stein: University of Arkansas for Medical Sciences: Employment. Davies:University of Arkansas for Medical Sciences: Employment; Celgene: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Onyx: Consultancy. Heuck:University of Arkansas for Medical Sciences: Employment; Celgene: Consultancy; Janssen: Other: Advisory Board; Millenium: Other: Advisory Board; Foundation Medicine: Honoraria. Weinhold:University of Arkansas for Medical Sciences: Employment; Janssen Cilag: Other: Advisory Board. Chavan:University of Arkansas for Medical Sciences: Employment. Thanendrarajan:University of Arkansas for Medical Sciences: Employment. Epstein:University of Arkansas for Medical Sciences: Employment. Yaccoby:University of Arkansas for Medical Sciences: Employment. Zangari:University of Arkansas for Medical Sciences: Employment; Novartis: Research Funding; Onyx: Research Funding; Millennium: Research Funding. van Rhee:University of Arkansa for Medical Sciences: Employment. Kaiser:Janssen: Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; BristolMyerSquibb: Consultancy; Chugai: Consultancy. Sonneveld:Janssen-Cilag, Celgene, Onyx, Karyopharm: Honoraria, Research Funding; novartis: Honoraria. Goldschmidt:Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Onyx: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Millenium: Honoraria, Research Funding, Speakers Bureau; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Chugai: Honoraria, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Barlogie:University of Arkansas for Medical Sciences: Employment. Morgan:Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; MMRF: Honoraria; CancerNet: Honoraria; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; University of Arkansas for Medical Sciences: Employment; Weismann Institute: Honoraria; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Abstract Abstract 2865 Poster Board II-841 BACKGROUND: The ubiquitin-proteasome pathway, which has been shown to be an essential cellular degradative system in myeloma cells, can also regulate bone formation through its effects on osteoblast differentiation. Retrospective analysis of variation of ALP during treatment with bortezomib indicates a close correlation between myeloma response and serum ALP levels which multiple studies have shown to be of bone origin. To determine if this effect is a class effect of proteasome inhibitors (PIs), this retrospective study analyzed variation of ALP in relationship to myeloma response during treatment with carfilzomib, the first in a new class of selective epoxyketone PIs that has demonstrated encouraging safety and efficacy in two phase 2 studies of relapsed or refractory myeloma patients. METHODS: Retrospective analysis of serum ALP was performed on relapsed or refractory myeloma patients enrolled on two phase 2 studies (PX-171-003 and PX- 171-004) evaluating the safety and efficacy of single agent carfilzomib. We analyzed data from 38 patients in the first cohort of the PX-171-003 study, a relapsed and refractory myeloma trial for patients who have received ≥ 3 prior therapies including bortezomib and an IMiD and 29 patients in PX-171-004, a relapsed or refractory myeloma trial that included bortezomib naïve patients. All patients received 20 mg/m2 of carfilzomib on Days 1, 2, 8, 9, 15, and 16 of a 28-day cycle. RESULTS: Seventy-seven patients were enrolled. The median age was 63 years with a median time since diagnosis of 4.6 years, 52% were male, 84.% had relapsed after autologous transplants, 82.% were previously exposed to bortezomib, and 92% were previously exposed to an IMiD. Sixty seven patients with ALP data were evaluable for response. In PX-171-003 the ORR (≥PR) was 18% and the clinical benefit response (CBR; ≥MR) was 26%, while in PX-171-004 the ORR was 35.5% overall and 57% in bortezomib naive patients. ALP increment from baseline, which was most evident during the second cycle of treatment, was statistically different in patients who achieved ≥VGPR compared to all others on Days 1 (P=0.0049) and 8 (P=0.006) of Cycle 2. In all patients achieving a VGPR or better, ALP increased more than 15 units per liter at Cycle 2 Day 1 over baseline An ALP increase over the same period of time was seen in 26 %, 13%, and 11% of patients achieving PR, MR, and SD, respectively. None of the patients with progressive disease exhibited a similar increase. Our study indicates that response first assessed on Day 15 of Cycle 1 parallels the ALP elevation which returned to baseline levels at the end of Cycle 3. CONCLUSIONS: This retrospective analysis on a subset of patients in these ongoing phase 2 studies of single agent carfilzomib in relapsed or refractory multiple myeloma suggests that elevation in ALP may be associated with best response. Taken with previous publications describing bortezomib treatment, these results suggest that this specific anabolic bone phenomenon could be a class effect of proteasome inhibitors. These phase 2 studies are ongoing with a higher dose of carfilzomib (27 mg/m2) being evaluated. The data from this small subset analysis suggests that further exploration of this relationship is warranted. Disclosures: Zangari: Milllennium: Honoraria, Research Funding; Novartis: Research Funding; Celgene: Honoraria; OrthoBiotech: Honoraria; Optum Health: Honoraria; Educational Concepts Group, LLC: Membership on an entity's Board of Directors or advisory committees. Vij:Proteolix: Consultancy, Research Funding. Jagannath:Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria; Merck: Honoraria. Siegel:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Stewart:Millennium: Consultancy, Research Funding; Proteolix: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Celgene: Honoraria. Wang:Proteolix: Honoraria, Research Funding. Belch:Ortho Biotech: Honoraria, Research Funding. Jakubowiak:Millennium Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Centocor Ortho Biotech: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Exelixis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol-Myers-Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Trudel:Celgene: Honoraria, Speakers Bureau; Ortho Biotech: Honoraria. Bahlis:Celgene: Honoraria, Speakers Bureau; Ortho Biotech: Honoraria, Speakers Bureau. Lonial:Celgene: Consultancy; Millennium: Consultancy, Research Funding; BMS: Consultancy; Novartis: Consultancy; Gloucester: Research Funding. Singhal:Celgene: Speakers Bureau; Millennium: Speakers Bureau.