Bergsagel, Leif

Abstract Background: trALL is an important secondary primary malignancy (SPM) that has recently been appreciated. While patients (pts) with MM have an 8%-17% risk of developing an SPM while on lenalidomide (R) maintenance following autologous stem cell transplant (ASCT), trALL has been rarely observed. Little is known about the characteristics of trALL in pts with MM compared to pts who had other malignancies prior to the development of trALL. We define tALL as any prior exposure to cytotoxic chemotherapy and/or radiation for another malignancy, and herein, we report a comparative analysis of characteristics and outcomes of pts with trALL with and without MM from the Mayo Clinic Cancer Center (MCCC). Methods: We performed a systematic search in the 3-site MCCC registry and included all pts diagnosed and received at least 1 cycle of ALL-directed therapy and/or underwent allogeneic transplantation (AlloHCT) for ALL between 2007-2020. All cases of trALL in this series are B-ALL. Comparisons of characteristics between trALL patients with and without MM were made using a Wilcoxon rank sum test (continuous variables) or Fisher's exact test (categorical variables). Time-to-event variables were compared using unadjusted Cox proportional hazards regression models. Results: 70 trALL pts (14 MM [20%], 56 non-MM [80%]) were identified during the study period. The median age at trALL diagnosis was 63.9 (range 47-71) for MM pts and 63.1 (range 18.2-84) for non-MM pts. Median follow-up from trALL diagnosis was 1.42 years (range 0.02-10.6 years). The most common prior non-MM malignancies among tALL pts were breast (28.6%), lymphoma (17.9%), myeloid (17.9%), and GU/GYN (14.3%). Amongst the 14 MM pts, 100% (n=10) had ISS-I disease, 8 (57%) had IgG K MM, 1 (n=10) had high risk cytogenetics, 8 (57.1%) received R-based induction , 4 (28.6%) received CyBorD induction, 4(28.6%) received VRD induction, 3 (21.4%) received RD induction. 10 (71.4%) pts were actively on R when they developed trALL, 8 (57.1%) pts received prior ASCT. The median time to develop trALL following ASCT for MM was 46.3 months (m), (95% CI 20.4-67.6), 12 (85.7%) pts received R maintenance with a median duration of 53m (95% CI 16.9-121). The median time to develop trALL after initiation of R maintenance was 61.2m (95% CI 16.9-123.4). The most common trALL induction regimen received was HyperCVAD which was used in 71.4% of MM pts and 51.8% of non-MM pts. In comparison to non-MM pts, MM patients had a significantly lower WBC at diagnosis (Median: 2.0 vs. 5.8, P=0.005),no t(9;22) BCR/ABL1 cytogenetics (0.0% vs. 35.7%, P=0.008), a higher frequency of hypodiploid/near-triploid (Ho-Tri) cytogenetics (42.9% vs. 12.5%, P=0.009), and a lower frequency of prior radiation therapy (7.1% vs. 44.6%, P=0.012). MM pts had a higher frequency of AlloHCT for trALL (71.4% vs. 42.9%, P=0.075) and were more commonly in complete response (CR) at transplant (100.0% vs. 70.8%, P=0.078). No trALL in MM pts were Ph-like ALL. There was a statistically significant difference in time to development of trALL between previous malignancy locations (P=0.018), with GU/GYN (Median: 9 years) having the longest time and myeloid (Median: 3 years) having the shortest time; MM was 6 years. In the overall pt cohort, 56 (80%) pts achieved a complete response after induction and 60% (n=25) were minimal residual disease (MRD)+ after induction. Pts with MM had a significantly lower likelihood of being MRD+ after induction (OR=0.09, P=0.015). Overall, 24.3% of pts relapsed after induction therapy for trALL; 39 (55.5%) died after trALL diagnosis including 43% (n=35) who died after AlloHCT. Although pts with MM tended to have better outcomes compared to non-MM trALL pts in terms of overall survival after trALL diagnosis (Figure 1A), survival after AlloHCT (Figure 1B), relapse rate after trALL diagnosis and non-relapse mortality after AlloHCT, none of these findings were statistically significant in this small cohort (Figure 1C). Conclusion: trALL is a unique SPM that can occur in pts with MM. It is associated with Ho-Tri cytogenetics and is BCR/ABL1 negative. The majority of MM pts that developed trALL had undergone prior ASCT and were on R maintenance when they developed trALL. Despite being more likely to have adverse cytogenetics features, MM-associated trALL has similar survival compared to non-MM-associated trALL which speaks to the poor survival outcomes of this SPM. Further research on trALL in MM pts is merited. Figure 1 Figure 1. Disclosures Litzow: Amgen: Research Funding; Pluristem: Research Funding; AbbVie: Research Funding; Actinium: Research Funding; Astellas: Research Funding; Omeros: Other: Advisory Board; Jazz: Other: Advisory Board; Biosight: Other: Data monitoring committee. Bergsagel: Janssen: Consultancy. Fonseca: Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; Bayer: Consultancy; Takeda: Consultancy; Celgene: Consultancy; Amgen: Consultancy; BMS: Consultancy; Mayo Clinic in Arizona: Current Employment; Juno: Consultancy; Kite: Consultancy; Aduro: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy; Novartis: Consultancy; Merck: Consultancy; Sanofi: Consultancy; Pharmacyclics: Consultancy; Janssen: Consultancy. Dispenzieri: Takeda: Research Funding; Alnylam: Research Funding; Pfizer: Research Funding; Oncopeptides: Consultancy; Sorrento Therapeutics: Consultancy; Janssen: Consultancy, Research Funding. Murthy: CRISPR Therapeutics: Research Funding. Ailawadhi: GSK: Consultancy, Research Funding; Sanofi: Consultancy; Pharmacyclics: Consultancy, Research Funding; Takeda: Consultancy; Medimmune: Research Funding; Xencor: Research Funding; Genentech: Consultancy; Janssen: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Cellectar: Research Funding; Beigene: Consultancy; BMS: Consultancy, Research Funding; AbbVie: Consultancy; Karyopharm: Consultancy; Ascentage: Research Funding. Foran: actinium: Research Funding; taiho: Honoraria; bms: Honoraria; servier: Honoraria; pfizer: Honoraria; novartis: Honoraria; OncLive: Honoraria; abbvie: Research Funding; boehringer ingelheim: Research Funding; takeda: Research Funding; trillium: Research Funding; aptose: Research Funding; revolution medicine: Honoraria; syros: Honoraria; sanofi aventis: Honoraria; certara: Honoraria; gamida: Honoraria; kura: Research Funding; h3bioscience: Research Funding; aprea: Research Funding; sellas: Research Funding; stemline: Research Funding.

Abstract Purpose: Rearrangements involving the MYC protooncogene are common in newly diagnosed multiple myeloma, but their prognostic significance is still unclear. The purpose of this study was to assess the impact of MYC rearrangement on clinical characteristics, treatment response, and survival in patients with newly diagnosed multiple myeloma. Experimental Design: This is a retrospective study including 1,342 patients seen in Mayo Clinic in Rochester, MN, from January 2006 to January 2018, who had cytogenetic testing by FISH at diagnosis, including MYC testing using the break apart FISH probe (8q24.1). Results: A rearrangement involving MYC was found in 8% of patients and was associated with elevated β2-microglobulin, ≥50% bone marrow plasma cells, IgA multiple myeloma, and the cooccurrence of trisomies. There were no differences in overall response rates between patients with and without MYC rearrangement when induction chemotherapy was proteasome inhibitor (PI)-based, immunomodulatory drug (IMiD)-based or PI + IMiD-based. Overall survival was shorter in patients with MYC rearrangement compared with patients without MYC rearrangement (5.3 vs. 8.0 years, P < 0.001). MYC rearrangement was associated with increased risk of death on multivariate analysis when high-risk cytogenetic abnormalities, ISS stage III, and ≥70 years of age were included (risk ratio: 1.5; P = 0.007). Conclusions: MYC rearrangement is associated with high disease burden and is an independent adverse prognostic factor in patients with newly diagnosed multiple myeloma.

Abstract A gain in chromosome 1q (+1q) is among the most common cytogenetic abnormalities in multiple myeloma (MM). It is unclear whether +1q is independently associated with decreased overall survival (OS). The objective of this study was to evaluate the impact of +1q on clinical characteristics, treatment response, and survival outcomes. We included 1376 Mayo Clinic patients diagnosed with MM from 2005 to 2018 who underwent fluorescence in situ hybridization testing at diagnosis with a panel including the +1q probe. A gain in 1q was found in 391 patients (28%) and was associated with anemia, hypercalcemia, high tumor burden, International Staging System (ISS) stage 3, high-risk (HR) translocations, and chromosome 13 abnormalities. There was no difference in overall response or deeper responses to proteasome inhibitor (PI)–, immunomodulatory drug (iMiD)–, or PI plus IMiD–based induction. Time to next treatment was shorter in patients with +1q compared with those without +1q (19.9 vs 27.7 months; P < .001). On univariate analysis, +1q was associated with increased risk of death (risk ratio [RR], 1.9; P < .001), and decreased OS was seen in all treatment groups. +1q was independently associated with decreased OS on multivariate analysis when other HR cytogenetic abnormalities, ISS stage 3, and age ≥70 years were included (RR, 1.5; P < .001). Gain of >1 copy of 1q was not associated with worse OS compared with gain of 1 copy (4.9 vs 4.3 years; P = .21). +1q was associated with high tumor burden, advanced disease stage, and HR translocations. It is independently associated with decreased OS, even in the setting of novel therapy and transplant.

Abstract Background: Thalidomide is an active anti-myeloma agent with single agent activity in all stages of the disease. When combined with dexamethasone disease response is seen in nearly two-thirds of patients with newly diagnosed MM. Though initially evaluated for treatment because of its anti-angiogenic properties and the finding of increased bone marrow angiogenesis in MM, it has become clear that thalidomide exerts its anti-myeloma activity through several different mechanisms. While the mechanism of action of dexamethasone has been studied to a great extent in the setting of myeloma, the molecular basis for the activity of this combination is largely unknown. Methods: Myeloma cells from patients with newly diagnosed MM who were enrolled in a phase III trial (ECOG E1A00) comparing thalidomide and dexamethasone combination (T+D) with dexamethasone alone (D) were studied by gene expression profiling. Samples from diagnosis were compared to those at four months following therapy to study the effect of treatment. Four patients who achieved a partial response with T+D were studied and in addition compared to four patients achieving a partial response with D alone. Plasma cells from bone marrow aspirates were separated by magnetic bead selection of CD138 positive cells and studied using Affymetrix HG-U133A chips using standard methodology. The arrays were analyzed using Genespring 7.2 software following GCRMA normalization and genes with differential expression between the two datasets were examined. Differentially expressed genes were further analyzed using Ingenuity Pathways Analysis program Results: 549 genes were two fold or more differentially expressed between the pretreatment and post treatment samples in the T+D group, of which 275 genes were mapped to different networks using the Ingenuity program. Examination of the canonical pathways demonstrated several pathways that were altered as a result of the therapy. The most significant pathways included those related to immune function including antigen presentation (multiple HLA class II antigens), and PRG2 (NK cell activator). Several mediators of B cell signaling including CD45, PKC (α ,β), SHP2, and Erk1/2 were up regulated following treatment. Increase in the expression of several adhesion molecules including β integrins, fibronectin, VCAM1, L selectin, and LFA3 were noted. Up regulation of IL-6 signaling pathway including gp130, JAK2 and IL8 were noted following therapy. Striking increase in the gene expression for CXCR4 and its ligand SDF-1α was noted as well. 129 genes were differentially regulated by dexamethasone alone, of which 67 were mapped to different networks. The pathways influenced by the dexamethasone alone differed significantly from that of the combination. Conclusion: This exploratory analysis offers an interesting insight into the potential mechanisms of action of the T+D combination in MM. The up regulation of immune response related genes are in accordance with some of the mechanisms described for thalidomide. Also, the differences observed between the patients receiving T+D vs. D allows for assessment of the effects of thalidomide. These findings need confirmation in a larger study.

Abstract Background: We reported a high response rate with the combination of lenalidomide plus dexamethasone (Rev/Dex) as initial therapy for newly diagnosed multiple myeloma (Blood2005;106:4050–3). We now present new data on time to progression (TTP), progression free survival (PFS), and overall survival (OS) from this phase II trial, and also include updated response data. Patients and Methods: 34 patients (23 male and 11 female) were enrolled. Lenalidomide was given orally 25 mg daily on days 1–21 of a 28-day cycle. Dexamethasone was given orally at a dose of 40 mg daily on days 1–4, 9–12, 17–20 of each cycle. Patients were allowed to go off treatment after 4 cycles of therapy to pursue autologous stem cell transplantation (SCT), but treatment beyond four cycles was permitted at the physician’s discretion. For patients continuing therapy beyond 4 months, the dose of dexamethasone was reduced to 40 mg on days 1–4 of each cycle. Response was assessed by modified EBMT/International Myeloma Working Group Uniform Response criteria. All patients received aspirin (81 mg or 325 mg daily) as prophylaxis against DVT. Results: The median age was 64 years (range, 32–78). All patients were evaluable for response and toxicity. Median follow up is 21 months. Thirteen patients proceeded to SCT following initial therapy with Rev/Dex and were censored at that time point for purposes of calculation of response, TTP and PFS. Patients who discontinued therapy to proceed to SCT received a median of 4 cycles of therapy (range, 4–13), while those staying on Rev/Dex (n=21) received a median of 19 cycles of therapy (range, 2–30). Thirty-one of 34 patients (91%) achieved an objective response to therapy; including 6 patients (18%) achieving a complete response (CR) and 13 patients (38%) achieving very good partial response for a CR+VGPR rate of 56%. The CR+VGPR rate among the 21 patients staying on Rev/Dex as primary therapy without SCT was 67% (CR 24%, VGPR 43%). Median TTP, PFS, and OS have not been reached (Figure). By Kaplan-Meier method, the estimated 2 year progression rate was 18%. The 2-year PFS rate and OS rate were 74% and 91%, respectively. Fifty-five percent of patients experienced grade 3 or higher non-hematologic toxicity at any point during therapy, most commonly fatigue (21%), neutropenia (21%), anxiety (6%), pneumonitis (6%), muscle weakness (6%), and rash (6%). Two patients died on study: one attributed to infection unrelated to therapy, the patient had stopped all therapy for over a month before the fatal infection occurred; the other death was due to infection felt possibly related to therapy. One patient developed a pulmonary embolism (grade 4 toxicity), but recovered with therapy; no other patient developed deep vein thrombosis or pulmonary embolism. Conclusion: Rev/Dex is highly active and well tolerated in the treatment of newly diagnosed multiple myeloma with a high CR+VGPR rate of 56% for the trial, and 67% among the subset of patients receiving this regimen as primary therapy. Responses are durable with a low progression rate at 2 years.

Abstract Chromosomal translocations involving the immunoglobulin heavy-chain (IgH) locus at 14q32 are genetic hallmarks of human multiple myeloma (MM) and are likely primary oncogenic events in myeloma transformation. Common recurrent translocation partner genes of IgH account for 40–50% of cases and these rearrangements result in the transcriptional dysregulation of various proto-oncogenes such as CCND1, CCND3, FGFR3/MMSET, c-MAF, MAFB, MUM 1(IRF-4), c-MYC and it is becoming clear that these dysregulated genes play important roles in the pathogenesis and the treatment outcome of MM. Although the frequency of other partner genes of IgH translocations may be rare (<1%), it is possible that many genes not currently identified as translocation partners are functionally converged into several signal pathways and play important roles as well. Through global gene expression profiling (GEP) studies of a large population of newly diagnosed MM, we have recently reported that dysregulation of any one of the three D-type cyclins is seen in almost all newly diagnosed MM and most patients with high levels of CCND2 expression was highly correlated with expression of c-MAF, MAFB or FGFR3/MMSET. In order to identify novel translocation partner genes in MM we combined GEP and fluorescence in situ hybridization (FISH) analysis in a large population of newly diagnsoed myeloma. We investigated whether IGH-CCND2 and IGH-MAFA translocations are seen in patients exhibiting high levels of CCND2 expression but not expressing c-MAF, MAFB, or FGFR3/MMSET. We also looked for a breakpoint and targeted gene at a non-random breakpoint at the 11q23 locus in 20 MM patients with 11q23 abnormalities detected by conventional G-banding. Here we report evidence of an IGH-CCND2 fusion in a newly diagnosed case of MM and an IGH-MAFA fusion in a newly diagnosed MM and an MGUS and also mapped the 11q23 breakpoint to within 100 kb in a newly diagnosed MM and a human myeloma cell line, NCU-MM-1 and identified an IGL Pou2AF1 fusion in the NCU-MM-1 line. Importantly these translcoatios do not involve the MLL or ATM genes. These three genes were all highly expressed as detected by GEP or quantitive RT-PCR in the samples in which these genes were translocated to IG loci. Although the frequency of these 3 translocations are very rare, they further the concept of a critical role of CCND2 expression in myelomegenesis and identifed the transcriptional coactivator Pou2AF1, which specifically associates with either OCT1 or OCT2, in the B-cell response to antigens and required for the formation of germinal centers, as a potential new translocation target in myeloma. Finally, our strategy of combining GEP signatures and cytogenetic analyses appears to be a practical means of identifying new IG- mediated translocations in myeloma.

Abstract Introduction: We investigated tumor gene expression profiling (PGx) to identify predictive classifiers of response and elucidate the mechanism of efficacy of proteasome inhibition in multiple myeloma (MM) by bortezomib, a first-in-class proteasome inhibitor. Analysis of 44 samples from the SUMMIT phase 2 trial identified predictive classifiers of response in patients (pts) with refractory MM (ASH 2002, Abstr 1519). We now report on the PGx analysis of the SUMMIT-derived classifier in an independent sample set from the APEX phase 3 trial, a multicenter randomized study of 669 pts that demonstrated the superiority of bortezomib to high-dose dex for relapsed MM following 1 to 3 prior therapies (Richardson NEJM 2005). As well, we report on a new classifier of response built by combining samples from SUMMIT and MLN34101-040 (040) (a study of bortezomib in pts who failed dex in APEX or were not eligible for APEX) and tested in the APEX samples. Methods: 93 participating clinical centers collected pretreatment bone marrow aspirates and enriched tumor cells on site before centralized RNA extraction and microarray analysis. Approximately 85% of pts consented to whole-genome analysis. Quality control for RNA integrity and quantity, microarray data quality, and tumor cell purity resulted in 129 patient samples from APEX evaluable for PGx; 67 were from the bortezomib arm and 62 from the dex arm. 48 samples were evaluable from 040. Patient characteristics of this subset were similar to those of the overall study population. Predictive classifiers were identified and tested via separate training and test datasets. Results: Consistent with the SUMMIT analysis, the APEX and 040 datasets exhibit biologic features expected of MM, including the overexpression of translocation target genes such as cyclin D1, MAF, and MMSET/FGFR3. Classifiers built using only the SUMMIT samples predict the outcome of pts in the APEX bortezomib arm with an overall accuracy of 51% (sensitivity [Sn] = 49%, specificity [Sp] = 55%; P = .81) and in the dex arm with an overall accuracy of 56% (Sn = 64%, Sp = 50%; P = .31). If the classifier was built using data from both the SUMMIT and 040 trials (87 total samples), the prediction of response in the APEX bortezomib arm was statistically significant (66% accuracy, Sn = 89%, Sp = 42%; P = .006). The same classifier only predicts the response of dex-treated pts with an overall accuracy of 46% (Sn = 72%, Sp = 28%; P = .88), suggesting specificity for bortezomib. Many of the overexpressed genes associated with bortezomib insensitivity relate to protein synthesis (EIF4B, RPL5) and RNA binding (SFRS10, CPSF6). Future Directions: This work highlights practical and scientific issues that inform future efforts to implement PGx in clinical trials. Predictive accuracy may be influenced by the clinical end point, limited sample size, and/or technical considerations associated with distinct trials and genomic data. The influence of study differences (relating to pts’ time from diagnosis, number of prior therapies, molecular subtypes, or hybridization characteristics) on the ability to build classifiers and identify biologic pathways related to response are being examined; these and potential insights on drug mechanisms will be presented.

Abstract Introduction: The Multiple Myeloma Research Consortium (MMRC) has established a tissue bank for the deposition of bone marrow (BM) samples from patients with multiple myeloma (MM) to be mailed and processed under good laboratory practices (GLP). To date over 300 samples have been collected. The ability for multiple sites to utilize the same GLP protocols for isolation and storage of BM for batch testing can be cost prohibitive and can introduce derived variability. Currently, limited information is available on shipped BM aspirates in regards to cell viability, cell yield, cell purity, and subsequent RNA yield and quality. Materials and methods: To test these determinants we performed a pilot study on behalf of the MMRC where samples were drawn at Mayo Clinic Rochester (MCR) pooled and split into 2 equal aliquots. One-half of each sample was processed following the provided GLP compliant standard operating procedures (SOP’s), immediately after sample procurement, at MCR. The CD138+ cells were stored in TRIZOLTM and the RNA was isolated and analyzed in a single batch. The other half of the aspirate was sent overnight to Mayo Clinic Scottsdale (MCS) where they were processed using an identical protocol. At both locations samples were tested for the following quality determinants; cell yield, RNA yield and integrity, and viability using a 3 color flow cytometric method (CD45, CD38 and 7ADD). Cell counts were performed on the CD138+ fraction to determine plasma cell recovery and a slide based immunofluorescent assay used to determine purity. RNA recovery and integrity were assessed using the Agilent BioanalyzerTM. Lastly, gene expression profiles was compared to determine the “signature” emanating from the shipment of samples. Results: In aggregate, all quality determinants showed similar values when the two sets of samples were compared. Cell viability was similar in both sets of samples as was our ability to collect a highly enriched plasma cell population. The cell yield was very similar (r2=0.52) but slightly lower in the shipped samples (median 71% of locally processed, range 40–140%), probably due to some shipment-associated apoptosis with subsequent loss of cell surface CD138 antigen. The purity of the shipped samples was very similar to that of locally processed (median 94%). Subjective qualitative analysis of the RNA was similar between both groups (shipped yield being 80% of local) with no evidence of degradation in the shipped samples. Details regarding the shipment signatures using gene expression profiling will be presented at the meeting. Conclusion: Here we show that the shipment of samples is feasible with no appreciable loss in cell yield or quality of derived products. % APOPTOTIC+ DEAD PC YIELD PURITY (%) RNA Recovered MCR MCS MCR MCS MCR MCS MCR MCS 8.9 ND 15.2 6.8 93 90 11.4 12.8 6.2 ND 2 2.8 95 99 8 17.2 17.4 8 1.4 1.4 75 88 2.2 0 2.1 18.7 2.8 1.6 96 68 7.2 2 18.1 9.5 1.2 .8 100 94 3.4 .4 5 5.0 8.5 7.2 98 91 12.6 17.8 3.7 5.1 .4 .4 88 87 ND ND 19.5 10.3 1.6 1.2 88 79 .4 3.6 29.1 ND 9.2 1.2 100 56 30.2 1.4 5.6 ND 9.2 7.6 92 88 5.2 16.8 20.3 62.8 2.4 .8 84 61 4.6 0.2 4.6 3.7 10.4 2.4 100 94 29.4 8.4 26.5 13.5 5.8 2.8 ND 97 16.8 5.8 4.5 6.2 2.2 2 100 99 10 4.4 17.5 ND 19.2 12 ND ND 16.4 14

Abstract Background: Multiple myeloma (MM) is a plasma cell malignancy that is incurable with current approaches. The median survival for patients with MM is around four years and a significant proportion of patients experience a course characterized by multiple relapses treated with different therapies. The median survival for patients relapsing after the initial therapy is nearly 18 months and successive treatment strategies result in decreasing response durations, likely reflecting acquired drug resistance. In order to better understand the biological changes associated with advanced, relapsed, refractory MM, we compared gene expression profiles (GEP) of malignant plasma cells isolated from patients with relapsed refractory MM and compared them to plasma cells from patients with newly diagnosed MM. Methods: In order to obtain two relatively homogenous group of patients, we compared samples from 44 patient with newly diagnosed MM enrolled in the ECOG E1A00 clinical trial (comparing thalidomide and dexamethasone to dexamethasone alone) to 44 patients with relapsed refractory MM enrolled in a phase II trial of Velcade (SUMMIT), where most patients had four or more previous relapses. Plasma cells from bone marrow aspirates were separated by magnetic bead selection of CD138 positive cells and studied using Affymetrix HG-U133A chips using standard methodology. The arrays were analyzed using Genespring 7.2 software following GCRMA normalization and genes with differential expression between the two datasets were examined. Differentially expressed genes were further analyzed using Ingenuity Pathways Analysis program. Results: A total of 864 genes were identified which were at least two fold and significantly different between the newly diagnosed and relapsed patients. Using Ingenuity software, 437 of these genes were mapped to different biological networks. Examination of the canonical pathways demonstrated several important cellular pathways differentially regulated between the two groups. Several important mediators of the cytokines, receptors and respective signaling pathways appear to be down regulated in the relapsed group and included IGF-1, HGF, SDF-1 alpha, gp130 and importantly the MEK/ERK pathway. Additionally expression of adhesion molecules such as VCAM1 and PECAM was decreased in the relapsed group compared to newly diagnosed pts. There appear to increased tissue hypoxia in the relapsed marrow as indicated by up regulation of HIF-1 alpha as well increased levels of Placental growth factor. Myeloma cells from relapsed disease were characterized by decreased expression of mcl1, FLIP1, and bcl-xL and increased caspase 8 relative to newly diagnosed group. Also seen was decreased expression of the glucocorticoid and interferon receptors in the relapsed setting. Conclusion: Comparison of the GEP between MM cells from newly diagnosed and relapsed pts demonstrates important differences that have potential biological relevance. The plasma cell in the relapsed setting appears to be more independent of the tumor microenvironment. Additionally, differential expression of some of the genes provides clues to mechanisms of drug resistance commonly observed in the relapsed pts. We are in the process of validating some of the key findings from these analyses.

Abstract Background: Thalidomide and dexamethasone combination has been shown to be an effective therapy for multiple myeloma (MM). In newly diagnosed patients with MM, the combination results in overall response rates of over 70% of patients. However, a considerable number of patients do not respond and will need to proceed to other therapies. The ability to identify patients who are unlikely to respond to particular therapies will allow tailoring of therapeutic approaches and in turn will reduce unnecessary toxicity and morbidity from lack of rapid disease control. With the availability of new technology such as gene expression profiling (GEP), this has become a possibility. We employed this approach to identify genes that can reliably predict lack of response to the thalidomide dexamethasone combination. Methods: Patient samples from the Eastern Co-operative Oncology Group clinical trial (E1A00), that compared thalidomide-dexamethasone combination with dexamethasone, and patient samples from the phase II Mayo Clinic study of thalidomide and dexamethasone, both for newly diagnosed MM, were used for this study. Thirty newly diagnosed patients who were evaluable for response were included in the analysis. GEP was performed using the Affymetrix U133A microarray platform as per manufacturer’s recommendation. The Affymetrix output (CEL files) was imported into Genespring 7.2 (Agilent Technologies) microarray analysis software, normalized across chips using GCRMA followed by per gene normalization to median. For the purposes of this study, responses were defined as reduction in the serum paraprotein (or the urinary M protein in the absence of a serum M protein) of >=50% (PR), 25 to 49% (MR), increase of >=50% (PD) and stable disease for the remaining. Results: Five of the 30 patients had no response to the thalidomide Dexamethasone therapy. Using the class prediction tool available in Genespring (Support Vector Machines), we identified 25 genes that reliable predicted non responders (PD, NC) from the responders (MR, PR, and CR). See table for a list of predictive genes with identified genes. Conclusion: Using a combination of two datasets we have identified a set of genes that can be used to reliably predict lack of response to thalidomide and dexamethasone in patients with newly diagnosed MM. We think that this represents a step towards creation of custom microarrays spotted with genes that are capable of predicting lack of response (or response) for the purpose of tailoring therapy to the patient. Gene Predictive strength hypothetical protein FLJ20719 1.655 Tubulin alpha 6 1.653 SEC3-like 1 (S. cerevisiae) 1.649 Ribosomal protein L18 1.546 Ribosomal protein L4 1.432 Tubulin alpha 6 1.409 cleavage stimulation factor 1.393 Tubulin, alpha, ubiquitous 1.393 CD99 antigen 1.38 hmel2 1.346 Leptin receptor 1.291 Integrin-linked kinaseSMC4 structural maintenance of chromosomes 4-like 1 1.269 Cornichon homolog 1.255 Williams-Beuren syndrome chromosome region 5 1.217 Ubiquitin specific protease 1 1.2 Sarcoma antigen NY-SAR-91 1.2