Fonseca, Rafaël

Abstract Abstract 2869 Background: Chromosome abnormalities are universal in multiple myeloma (MM) and will ultimately categorize patients into hyperdiploid and non-hyperdiploid MM. Among non-hyperdiploid patients those that exhibit hypodiploidy have the most aggressive clinical phenotype. What genetic features are unique to hypodiploidy are not fully described. Therefore, we performed a comprehensive high-resolution analysis to differentiate and characterize hypodiploid MM. Materials and methods: MM patients were analyzed using a combination of array-based comparative genomic hybridization (aCGH) (n=275) and gene expression profiling (GEP) (n=239). Agilent 244K and Affymetrix U133A Plus 2.0 arrays were used in the aCGH and GEP experiments, respectively. Hypodiploid MM was differentiated using pseudokaryotyping based on aCGH findings. Samples estimated to have less than or equal to 44 chromosomes were designated hypodiploid, 45–47 chromosomes were nonhyperdiploid and greater than or equal to 48 and less than 74 chromosomes were considered hyperdiploid. Using GEP the main gene indices and signatures associated with outcome were determined including the translocation and cyclin D (TC) classification, UAMS 70-gene index, proliferation index, centrosome signature and NF-kB index. Differentially expressed genes were also investigated. Results: A total of 53 (19%) MM patients were classified into the hypodiploid group, mainly characterized by monosomies of chromosomes 13 (83%), 14 (42%), 22 (23%) and × (50%) (females) with p and/or q-arm aberrations including gains of 1q (51%) and 8q (25%) and losses of 1p (49%), 4p (21%), 4q (23%), 6q (38%), 8p (34%), 12p (25%), 12q (26%), 14q (32%), 16p (25%), 16q (51%) and 17p (25%). Patients with loss of 1p were associated with 4p- (p <0.029), 4q- (p<0.0001), 12p- (p<0.007), 12q- (p<0.0002), any 14 (p<0.022), 16p- (p<0.005), 16q- (p<0.001), and monosomy 22 (p<0.024). Patients with loss of 17p were associated with 12p- (p<0.025), 12q- (p<0.039) and 16q- (p<0.031). The main gene indices and signatures in MM showed that nearly one half of the hypodiploid patients having high-risk disease, ranging from 45% with a high 70-gene index, 47% with high centrosome signature and 51% with a high proliferation index. In addition, hypodiploid patients also displayed a translocation type signature in the TC classification defined by 11q13 (24%), 4p16 (24%) and maf (12%). Overall, 253 genes have >2 fold expression change comparing hypodiploid vs. hyperdiploid including a five fold decrease in the heparin-degrading endosulfatase gene SULF2, a decrease of genes in the TGF-b signaling pathway (MYC, ID3, SMAD1, LTBP1) and those involved in Wnt signaling (DKK1, FRZB). Up regulated genes included those from the p53 signaling pathway and cell cycle (CCND2, CDKN1C, RPRM), cell adhesion molecules (ITGB8, CD28) and tight junction pathway (RRAS2, RRAS, CSDA). Conclusion: This represents the most comprehensive genomic characterization of hypodiploid MM to date. These cases exhibit a high propensity for high-risk gene expression profiles and have a high prevalence of −13, −14, 1q gain and 1p loss as predicted. Given our findings it is likely that hypodiploid is not a separate category but rather the genetic “phenotype” of a more advanced clone. Today, using these two platforms together in a routine setting would provide the most comprehensive genetic analysis, important for individualized therapeutics. Disclosures: Fonseca: Consulting :Genzyme, Medtronic, BMS, Amgen, Otsuka, Celgene, Intellikine, Lilly Research Support: Cylene, Onyz, Celgene: Consultancy, Research Funding.

Abstract To further understand the molecular biology of mucosa-associated lymphoid tissue (MALT) lymphoma, we conducted a comprehensive gene expression study on the Affymetrix platform using total RNA extracted from biopsy specimen of 35 well-characterized pulmonary MALT lymphoma samples and compared them to gene expression profiles of a range of B and T cell lymphoma, such as Burkitt’s lymphoma, diffuse large B-cell lymphoma, Waldenstrom macroglobulinemia, chronic lymphocytic leukemia, multiple myeloma, and peripheral T-cell lymphoma, and normal cellular counterparts, such as different B-cell and T-cell populations, normal plasma cells and lung tussue. Analytical strategies were adopted to negate tissue specific effect such that our results can be generalized to MALT lymphoma from other anatomical sites. We further analyzed the data using functional tools such as gene-set enrichment analysis and gene ontology. Network/pathway analysis was performed using Metacore. Unsupervised clustering of whole dataset (including other lymphoid malignancies and normal cellular counterpart) using genes with variable expression, showed that compared to other B-cell lymphoid malignancies, MALT lymphoma have a prominent T-cell signature. Using established gene expression of different B-cell compartment, we confirmed that MALT has a marginal zone B-cell and memory B-cell cell-of-origin signature. Using ANOVA with post-hoc pair-wise comparison, we identified 4 novel transcripts over-expressed in MALT but not other B-cell tumors, and validated one of these using immunohistochemistry. MALT lymphoma with t(11;18) or t(14;18) over-expressed genes enriched for pathways other than the NFKB pathway such as JAK-STAT and SRC signaling pathways, which may represent novel therapeutic targets. We identified a number of genes with ‘outlier’ expression including RARA and RGS13. However, translocation involving RARA and the immunoglobulin heavy chain locus was not detected by FISH, suggesting other mechanisms of deregulating gene expression were involved. Using unsupervised clustering of only the MALT samples, we found that the molecular heterogeneity within MALT is composed of a group characterized by MALT1 translocations and another group with plasmacytic differentiation. Interestingly, further refinement of this grouping can be achieved by clustering the samples based on ‘outlier’ gene expression. Samples with MALT1 translocations have high expression of RARA, samples with plasmacytic differentiation have high FKBP11 expression and samples with high RGS13 expression tend to have trisomy 3 and reactive follicles. Our study therefore identified novel molecular markers, deregulated pathways and genetic defects in MALT. Furthermore, subgroups with distinct pathological features and anchored by unique pattern of ‘outlier’ gene expression were identified.

Abstract Abstract 2942 Background: Pomalidomide (pom) is a distinct immunomodulatory (IMid) drug with high response rates in relapsed MM, including patients previously treated with thalidomide, bortezomib and lenalidomide. However, its duration of response and long term outcome is unknown. We evaluated the long term outcomes of the first Mayo Clinic cohort of pts treated with Pom. Methods: Pts with relapsed/refractory myeloma with 1–3 prior therapies were enrolled. Pom 2 mg/d was given orally continuously on 28 day cycle with weekly dexamethasone 40 mg. All pts received DVT prophylaxis with aspirin, heparin or warfarin. Results: 60 pts with relapsed MM were enrolled from Nov 2007-Aug 2008. Median age was 65.5; 36 were male. Median time from diagnosis was 45.6 mo (9.–-192.5). Nineteen (32%) were high-risk according to mSMART (msmart.org), and 78% were ISS Stage 2/3. Prior therapies included transplant (65%), bortezomib (33%), thalidomide (47%), lenalidomide (35%) [previous IMiD 60%] and radiation (38%). Toxicities at least possibly attributed to Pom included G3/4 anemia (5/0%), leukopenia (17/3%) and thrombocytopenia (3/0%). G3/4/5 non-hematological toxicities occurred in 30 (50%) and included fatigue (18/0/0%), pneumonia (8/2/2%), hyperglycemia (5/0/0%) and constipation (5/0/0%). Only one pt had grade 3 neuropathy. Treatment adjustment for occurred in 30 pts (50%) for pom (mostly for neutropenia) and 35 pts (58%) for dex. A median of 11.5 cycles were administered. With median follow up of 30.1 months (mo), overall response was seen in 39 (65%) [4 sCR, 5 CR, 16 VGPR, 14 PR]. Median time to first reponse (≥PR) was 1.7 mo (0.8–14.8). Median duration of response (in responders of ≥PR) was 21.3 mo. Median PFS was 13 mo and the 2 year OS rate was 76%. Standard risk pts had median PFS of 18.4 mo and 2 year OS rate was 85%; high risk pts had median PFS of 9.2 mo and 2 year OS rate was 57%. (Figure 1) Conclusions: Pom/dex is highly effective and well tolerated in relapsed MM, with a response rate of 65% even in pts with multiple prior therapies, including other IMiDs, bortezomib and transplant. The response is also durable, with median duration in excess of 21 months. Pom/dex provides long term benefit with median PFS 13 months and 2 year OS rate of 76%. Disclosures: Kumar: Merck: Consultancy, Membership on an entity's Board of Directors or advisory committees. Stewart:Celgene Corporation: Consultancy, Research Funding; Millenium: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Onyx: Consultancy, Research Funding. Fonseca:Consulting :Genzyme, Medtronic, BMS, Amgen, Otsuka, Celgene, Intellikine, Lilly Research Support: Cylene, Onyz, Celgene: Consultancy, Research Funding. Lacy:Celgene: Research Funding.

Abstract Introduction: FISH is able to recognize chromosomal deletions and translocations with a greater sensitivity than conventional cytogenetics. Specific abnormalities have been associated with prognosis. Initial observations suggest a poor outcome for patients with -17p13.1, chromosome 13 abnormalities (Δ13) and t(4;14)(p16.3;q32). In contrast a good outcome has been shown in some series for patients with t(11;14)(q13;q32). We analyzed the value of FISH in patients receiving high dose therapy . Patients and Methods: We studied by cIg-FISH 226 patients undergoing high dose therapy at Mayo Clinic between 1/1990 and 9/2001. All patients had a pretransplant cIg-FISH done on cytospin slides from marrow aspirates for t(11;14)(q13;q32), t(4;14)(p16.3;q32), and -17p13.1(p53). Information was available regarding Δ13 for all patients (+ in 52%). Results: The prevalence of the abnormalities were: t(11;14)(q13;q32) 17% (n=197), t(4;14)(p16.3;q32) 13% (n=153), and -17p13.1 11% (n=168). The overall survival (OS)was significantly shortened in patients with t(4;14)(p16.3;q32) (18.2 vs. 43.3 mo, p=0.001) (figure) and patients with -17p13.1 (14.7 vs. 38.6 mo, p=.01). OS was not different for patients with the t(11;14)(q13;q32) (36.2 vs. 34.8 mo, p=ns). Likewise time to progression (TTP) was shortened in patients with t(4;14)(p16.3;q32) (8.5 vs 17.7 mo, p=.001) and -17p13.1 (8.3 vs. 16.2 mo, p=0.005). TTP was also not affected significantly by the t(11;14)(q13;q32) (20.7 vs. 14.9 mo, p=NS). To dissect the specific contribution of t(4;14)(p16.3;q32) we did a subset analysis of patients who also had Δ13, since 85% of patients with t(4;14)(p16.3;q32) are expected to have Δ13. Of 84 studied for both abnormalities 22 had both Δ13 and t(4;14)(p16.3;q32). The OS was significantly shorter in patients with both abnormalities versus those with Δ13 alone (26.6 vs. 18.2 months, p=0.001). When a multivariable analysis of the impact of Δ13 and t(4;14)(p16.3;q32) were placed into a Cox model the hazard function for t(4;14)(p16.3;q32) was greater than Δ13 (2.6 versus 1.6). Δ13 had only borderline significance in this model (p=0.06). Conclusion: We have been unable to corroborate the improved outcome after transplant for patients with t(11;14)(q13;q32). As has been reported in patients with conventional and high dose therapy -17p13(p53) and t(4;14)(p16.3;q32) have clinical importance for estimation of OS and TTP. In this patient group the t(4;14)(p16.3;q32) carried a greater adverse impact than did Δ13, and identifies a subset of patients whose time to progression is 8.5 months. These patients likely do not benefit from autologous transplant and are candidates for novel therapeutic approaches. Outcome Successful determination Patients with translocation or deletion Survival (months) with/without abnormality Time to Progression (months) with/without abnormality * p<0.01;**p<0.001 t(11;14)(q13;q32) 197 34(17%) 36.2/34.8 20.7/14.9 p53 168 18(11%) 14.7/38.6 * 8.3/16.2 * t(4;14)(p16.3;q32) 153 26(17%) 18.2/43.3 **figure 8.5/17.7 ** t(4;14)(p16.3;q32)/Δ13+ patients 84 22(26%) 18.2/26.6 8.2/12.8 * Figure Figure

Abstract Abstract 4184 Introduction: FACT-MM was developed with the aim to create a disease-specific, patient-reported outcomes (PRO) measure as part of the FACT measurement system to assess multiple myeloma (MM)-related symptoms. Literature review identified 52 MM specific symptoms and concerns. 13 MM expert clinicians rated these 52 items on relevance to health-related quality of life (HRQL) for MM patients and added 11 items for comprehensive PRO assessment in MM. These 63 candidate items were rated by 13 MM patients recruited through the International Myeloma Foundation website. Disease-related symptoms and concerns were described and provided by patients through free-text comments. Information from both the MM expert clinician and MM patient surveys including free-text items was analyzed and 14 highest ranked items were selected for the FACT-MM. The FACT-MM subscale (score 0–56), the FACT-G physical and functional well-being subscales (score 0–28), and the FACT-Neurotoxicity (FACT-Ntx) subscale (score 0–44) was administered to 48 E1A05 participants to assess disease and treatment-related symptoms of patients with newly diagnosed MM receiving 8 cycles of VRd versus Vd. Methods: In this study, the first three of seven sequential assessments were evaluated: baseline, cycle 5 and end of treatment (after cycle 8 or early diconstinuation). Instruments were scored as per the FACT scoring guidelines. Descriptive statistics were provided for each timepoint and changes in scores from baseline. The Wilcoxon rank sum test was used to assess differences in scores by ISS stage and ECOG PS. Pearson correlation coefficients were obtained between the scores. Cronbach's alpha was used to evaluate internal consistency of the FACT-MM. Results: At baseline and end of treatment, 46 and 41 patients, respectively, completed assessments. The FACT-MM demonstrated good to very good internal consistency (Cronbach alpha 0.79 – 0.89). The FACT-MM subscale was significantly correlated with the FACT physical and functional well-being subscales at baseline (r = 0.72), cycle 5 (r = 0.64; r = 0.49) and end of treatment (r = 0.72; r = 0.66). All scores declined modestly by cycle 5 and end of treatment from baseline. While sample size was limited, there appeared association with ECOG PS with higher scores for patients with PS 0 status. Conclusions: The 14-item FACT-MM scale is feasible for use to measure myeloma-related symptoms and has demonstrated acceptable psychometric properties based on findings from E1A05, an ECOG myeloma trial. Disclosures: Cella: Novartis: Research Funding. Fonseca:Consulting:Genzyme, Medtronic, BMS, Amgen, Otsuka, Celgene, Intellikine, Lilly Research Support: Cylene, Onyz, Celgene: Consultancy, Research Funding.

Abstract Introduction Multiple Myeloma is not a single disease. There is increasing support for risk classification in combination with treatment decision making because of its impact on clinical outcomes. Here we demonstrate additional evidence of the prognostic value of SKY92, an established genetic marker of high risk Multiple Myeloma in a multicenter collection of samples with undisclosed treatments. Materials Methods A public GEP dataset (MMRC, MMGI portal) contained 114 cases of untreated Multiple Myeloma and was used for SKY92 high risk OS prediction (Kuiper et al. Leukemia 2012). In collaboration with MMRC, OS (with a minimum of at least 2 year follow-up) was collected for 91 of 114 cases for the purpose of this analysis. Briefly, CD138-positive plasma cells had been purified prior to total RNA extraction and subsequent gene expression profiling on Affymetrix U133Plus2.0 GeneChips. The 91 cases represented 9 different clinical sites and their CEL files were normalized as a single batch against a reference cohort of 329 cases after which the SKY92 risk scores were determined as either standard risk or high risk. Results SKY92 resulted in 19 high risk (20.9%) versus 72 standard risk (79.1%) cases in the unselected 91 case-cohort. Comparisons with other high risk GEP signatures will be performed. The OS analysis (Figure 1) shows that the HR cases have significantly shorter survival (Hazard Ratio 11, p = 7 x 10-5). Table 1 shows that high risk patients had more elevated B2M (26.3% vs 13.9%), more low albumin (26.3% vs 16.7%) and more high creatinine (26.3% vs 11.0%). There was no difference between high and standard groups in diagnosis dates (not shown). Cause of the 16 (84.2%) deaths among the high risk cases, and 21 (29.1%) deaths among the standard risk cases indicates that high risk contains less disease progression deaths (57.1% vs 31.3%), and more unknown deaths (56.3% vs 23.8%). Conclusions The SKY92 classifier identified 19 of 91 cases (21%) as high risk, recapitulating the percentage of high risk in previously studied cohorts (Kuiper et al. 2012). Moreover the hazard ratio of 11 when events up to 24 months or 8.18 when all events are considered, emphasizes the unmet medical need of high risk cases identified with SKY92 as 69% of all deaths within 2 years (9/13 death events) were in this category. Acknowledgments This research was performed within the framework of CTMM, the Center for Translational Molecular Medicine, project BioCHIP grant 03O-102. Rafael Fonseca is a Clinical Investigator of the Damon Runyon Cancer Research Fund. This work is supported by grants R01 CA83724, ECOG CA 21115T, Predolin Foundation, Mayo Clinic Cancer Center and the Mayo Foundation. Disclosures: van Beers: Skyline Diagnostics: Employment. Van Vliet:Skyline Diagnostics: Employment. Anderson:celgene: Consultancy; onyx: Consultancy; gilead: Consultancy; sanofi aventis: Consultancy; oncopep: Equity Ownership; acetylon: Equity Ownership. Jagannath:Celgene: Honoraria; Millennium: Honoraria. Jakubowiak:BMS: Consultancy, Membership on an entity’s Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees, Speakers Bureau; Millennium: 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. Kumar:Celgene: Clinical Trial Support Other, Membership on an entity’s Board of Directors or advisory committees; Cephalon: Clinical Trial Support, Clinical Trial Support Other; Millennium: Clinical Trial Support, Clinical Trial Support Other, Membership on an entity’s Board of Directors or advisory committees; Novartis: Clinical Trial Support, Clinical Trial Support Other; Onyx: Clinical Trial Support Other, Membership on an entity’s Board of Directors or advisory committees. Lebovic:Celgene: Speakers Bureau; Onyx: Speakers Bureau. Lonial:Millennium: Consultancy; Celgene: Consultancy; Novartis: Consultancy; BMS: Consultancy; Sanofi: Consultancy; Onyx: Consultancy. Reece:Onyx: Honoraria; Novartis: Honoraria; Millennium: Research Funding; Merck: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; BMS: Research Funding. Siegel:Celgene: Membership on an entity’s Board of Directors or advisory committees, Speakers Bureau; Millennium: Membership on an entity’s Board of Directors or advisory committees, Speakers Bureau; Onyx: Membership on an entity’s Board of Directors or advisory committees, Speakers Bureau. Vij:Celgene: Honoraria, Research Funding, Speakers Bureau; Millennium: Honoraria, Speakers Bureau; Onyx: Honoraria, Research Funding, Speakers Bureau. Zimmerman:Celgene: Honoraria; Millennium: Honoraria; Onyx: Honoraria. Fonseca:Medtronic: Consultancy; Otsuka: Consultancy; Celgene: Consultancy; Genzyme: Consultancy; BMS: Consultancy; Lilly: Consultancy; Onyx: Consultancy, Research Funding; Binding Site: Consultancy; Millennium: Consultancy; AMGEN: Consultancy; Cylene: Research Funding; Prognostication of MM based on genetic categorization of the disease: Prognostication of MM based on genetic categorization of the disease, Prognostication of MM based on genetic categorization of the disease Patents & Royalties.

Abstract Background: Folate receptor (FR) is a recently recognized tumor marker that is being investigated for targeted delivery of drugs, imaging of cancer cells, and tumor immunotherapy. The small size of the molecule, simple conjugation chemistry and lack of immunogenicity make folate an ideal candidate for targeted delivery to tumors bearing the FR (Lu Y, Low PS. Folate-mediated delivery of macromolecular anticancer therapeutic agents. Adv Drug Deliv Rev. 2002 Sep 13;54(5):675–93). FR is overexpressed in 90% of ovarian cancers and in other human tumors including endometrial, colorectal, breast, and neuroendocrine carcinomas, it is negatively or only weakly expressed in most normal tissues including some myeloid cells. In view of the above we wished to learn if overexpression of FR by marrow plasma cells (PC) might differentiate MM from MGUS and SMM. Methods: We obtained marrow biopsy sections from 20 patients each with MM, MGUS, or SMM, and 11 normals and stained them by immunohistochemistry using a FR-beta antibody. Expression was graded 0 (negative), 1 (equivocal), 2 (positive) or 3 (strong positive) based on immunohistochemical staining of marrow PC. We recorded the percent PC, percent positive PC, and the staining of other cell types including myeloid, erythroid, lymphocytes, macrophages, megakaryocytes and control tissues. Results: PC of patients with MM positively expressed FR compared to MGUS, SMM and normal PC (p<0.0001) (Figure 1 and 2). Of interest, 6 of 20 SMM patients showed positive expression. Maturing myeloid cells were also positive, acting as internal positive controls. Macrophages were often positive; some endothelial cells were positive; early erythroid precursors were rarely positive; megakaryocytes, maturing erythroid precursors and lymphocytes did not express FR. The chromosomal location of the FOLR gene cluster is 11q13. Gene expression arrays of purified PC failed to show evidence of overexpression of FOLR1, FOLR2, or FOLR3. We are currently performing Western blots to examine levels of protein product of purified PC. Conclusion: Immunohistochemistal studies show differential expression of FR in MM PC compared to absent or equivocal expression in MGUS and normal PC and variable expression in SMM. The data suggest that FR may be used as a diagnostic tumor marker and has implications for development of folate-based tumor imaging, drug delivery, and immunotherapy. Figure Figure Figure Figure

Abstract Background: Multiple myeloma (MM) is a heterogeneous disease with very divergent outcomes that are dictated in a large part by specific cytogenetic abnormalities, as well as other prognostic factors such as the proliferative rate of marrow plasma cells. Prognostic systems incorporating these factors have shown clinical utility in identifying high-risk patients, and are increasingly being utilized for treatment decision-making. However, the prognostic relevance of these factors may change with the application of novel therapies. The objective of this study was to determine the impact of risk-stratification (incorporating plasma cell metaphase cytogenetics, interphase fluorescent in-situ hybridization (FISH) and the slide-based plasma cell labeling index (PCLI)) in a cohort of patients with newly diagnosed MM treated initially with lenalidomide + dexamethasone (Rev-Dex). Methods: From March 2004 to November 2007, 100 consecutive patients treated with Rev (25mg/day) on days 1 through 21 of a 4-week cycle in combination with dexamethasone as initial therapy for newly diagnosed myeloma, were identified. High-risk MM was defined as presence of any one or more of the following: hypodiploidy, monoallelic loss of chromosome 13 or its long arm (by metaphase cytogenetics only), deletion of p53 (locus 17p13) or PCLI ≥ 3% or immunoglobulin heavy chain (IgH) translocations, t(4;14) (p16.3;q32) or t(14;16)(q32;q23) on FISH. PFS and OS survival estimates were created using the Kaplan Meier method, and compared by log-rank tests. Results: The median estimated follow-up of the entire cohort (N=100) was 36 months. The median PFS was 31 months; the median OS has not been reached. The 2- and 3-year OS estimates were 93% and 83%, respectively. 16% patients were deemed high-risk by at least one of the 3 tests (cytogenetics, FISH or PCLI). Response rates (PR or better) were 81% versus 89% in the high-risk and standard risk groups, respectively, P=NS; corresponding values for CR plus VGPR rates were 38% and 45% respectively. The median PFS was 18.5 months in high-risk patients compared to 37 months in the standard-risk patients (n=84), P<0.001(Figure). Corresponding values for TTP were 18.5 months and 36.5 months, respectively, P=<0.001. OS was not statistically significant between the two groups; 92% 2-year OS was noted in both the groups. Overall, 95 patients had at least one of the 3 tests to determine risk, while 55 patients could be adequately stratified based on the availability of all the 3 tests, or at least one test result that led to their inclusion in the high-risk category. The significant difference in PFS persisted even when the analysis was restricted to the 55 patients classified using this stringent criterion; 18.5 months vs. 36.5 months in the high-risk and standard- risk groups respectively; P<0.001. In a separate analysis, patients who underwent SCT before the disease progression were censored on the date of SCT to negate its effect, and PFS was still inferior in the high-risk group (p=0.002). Conclusion: The TTP and PFS of high-risk MM patients are inferior to that of the standard-risk patients treated with Rev-Dex, indicating that the current genetic and proliferation-based risk-stratification model remains prognostic with novel therapy. However, the TTP, PFS, and OS obtained in high-risk patients treated with Rev-Dex in this study is comparable to overall results in all myeloma patients reported in recent phase III trials. In addition, no significant impact of high-risk features on OS is apparent so far. Longer follow-up is needed to determine the impact of risk stratification on the OS of patients treated with Rev-Dex. Figure Figure

Abstract Background: Though STI-571 (Gleevec™) was designed to specifically inhibit the bcr-abl gene product, it inhibits other receptor tyrosine kinases at physiologically attainable concentrations, including c-kit (steel factor receptor, stem cell factor receptor, SCF-R or CD 117). Studies have shown that about one-third of patients with multiple myeloma or monoclonal gammopathy of undetermined significance have plasma cells that display reactivity for c-kit. Others have shown that several myeloma cell lines and fresh myeloma bone marrow cells proliferate in response to stem cell factor. mRNA transcripts for c-kit ligand and, more commonly, its receptor have been detected in myeloma cell lines RPMI 8226, JJN3, U266 B1, NCI-H929, ARH77 and HS-Sultan by RT-PCR. Methods: Patients were eligible for study if they presented with relapsed or refractory myeloma, an ECOG performance score < 3, ability to sign informed consent, serum creatinine <3.5 mg/dL, direct bilirubin <2 mg/dL, alkaline phosphatase <750 U/L, absolute neutrophil count > .5 x 10(9)/L and platelet count > 50 x 10(9)/L. Patients were treated with Gleevec™ 400 mg po qd. The purpose of the study was to assess the response rate of the drug Gleevec™ in patients with relapsed multiple myeloma. Secondary objectives included assessment of the tolerability of the regimen in this cohort of patients and to correlation between disease response and c-kit positivity (CD117) by flow cytometry. Results: Overall, 23 patients were enrolled between 12/01 and 11/02, of which 16 were male and 12 had a prior PBSCT. Median age was 63 years (range: 49 – 82), and the baseline performance scores were 0 (8 patients), 1 (10), and 2 (4). Median time from MM diagnosis to enrollment was 57 months (range: 11 – 133). Patients were high risk with a median PCLI of 1.3 (range: 0 – 23%) and a median beta-2 microglobulin of 4.6 (range: 1.4 – 10.1). Eleven of 21 tested patients had positive CD117 (≥20%) staining of their bone marrow plasma cells by flow cytometry. None were PDGF positive. The treatment was well tolerated. After treatment with Gleevec™, there were only 3 non-hematologic grade 3 or 4 toxicities, and they occurred in 1 patient: he developed a capillary leak type syndrome, ascites, and dyspnea as part of his terminal phase of myeloma which included evolution into plasma cell leukemia. Grade 3 myelosuppression was observed in a minority of patients: thrombocytopenia (3), neutropenia (1), and anemia (3). There were no responses, and no patients are currently receiving treatment. Patients ended treatment due to progressive disease (18 patients), death on study (3), adverse reactions (1), and other reasons (1). The median duration of treatment was 48 days (range: 12 to 349). Of the 7 patients who remained on study for more than 60 days, CD117 status was as follows: positive (4); negative (3); not ascertained (1). As of August 2004, 12 patients have died. Conclusions: Though forty-eight percent of the enrolled myeloma patients were CD117 positive, providing a putative target for the Gleevec™, no responses were seen in this high risk population of relapsed patients. Further study will be necessary to determine the significance of the observed stabilization of disease in 36% of the CD117 positive patients.

Abstract Background: Increasingly thalidomide (Thal) plus dexamethasone (Dex) is being used as initial therapy for multiple myeloma (MM), but there is a need to minimize non-hematologic toxicity with this regimen. CC-5013 (lenalidomide; Revlimid™) is a more potent analog of thalidomide with significantly fewer non-hematologic toxicities that has shown promising results in relapsed refractory myeloma. We report the initial results of the first phase II trial using the combination of CC-5013 plus Dex (Rev/Dex) as initial therapy for newly diagnosed MM. Methods: The trial is designed to accrue 31 eligible patients; 13 patients (pts) (11 male and 2 female) were analyzed in this interim report. Patients were enrolled between February 2004 and July 2004. CC-5013 was given orally at a dose of 25 mg daily on days 1–21 of a 28-day cycle. Dex was given orally at a dose of 40 mg daily on days 1–4, 9–12, 17–20 of each cycle. Patients also received an aspirin once daily as thrombosis prophylaxis. Response was defined as a decrease in serum monoclonal (M) protein by 50% or higher and a decrease in urine M protein by at least 90% or to a level less than 200 mg/24 hours. Responses were assessed on an intent to treat basis. Results: The median age was 61 years (range, 32–78). 8 patients (62%) had Stage III myeloma. 11 of the 13 patients achieved an objective response yielding a response rate of 85% within 1–2 months of therapy. So far 6 patients have experienced grade 3 adverse events. These include one episode each of CD4-count < 200/mm3, anemia, neutropenia, increased liver enzymes, muscle weakness, agitation, hyperglycemia, cardiac arrhythmia, pneumonitis, and colonic perforation (underlying diverticulitis and dexamethasone suspected). No deep vein thrombosis or grade 4 or higher adverse events have been observed so far. Conclusions: Rev/Dex appears active and well tolerated in the treatment of newly diagnosed MM and is a potential alternative to Thal/Dex. However, these results are preliminary and responses are still being evaluated and need to be confirmed in the final analysis of this trial. A large randomized trial using Rev/Dex as initial therapy for MM is expected to be activated by the Eastern Cooperative Oncology Group later this year.