Citations

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 Glucocorticoid (GC) is an effective anti-myeloma agent by directly inducing apoptosis and growth inhibition in myeloma cells. The anti-myeloma action of GC is mediated by its intracellular receptor, the glucocorticoid receptor (GR). The response to GC is variable among myeloma patients and resistance to glucocorticoid treatment develops almost invariably. The mechanism regulating glucocorticoid-sensitivity in not well understood, although it has been hypothesized that the regulation might occur at both the receptor (GR) and post-receptor level. In MM.1 myeloma cell lines, we observed significant down-regulation of GR expression as the cells develop GC-resistance, suggesting that GR expression level is an important determinant of GC-response. Here we present evidence that GR expression level directly correlates with the clinical outcome in myeloma patients who were treated with GC-containing regimens. First, using a quantitative real-time PCR analysis, we performed a retrospective analysis of GR gene expression on bone marrow plasma cell samples from 35 patients with newly diagnosed myeloma who were then treated on the ECOG study E1A00. This is a phase 3 trial which randomized patients to dexamethasone (dex) alone vs. dex in combination with thalidomide. Pre-treatment expression levels of GR-alpha, GR-beta and GR-P splicing isoforms were measured for 35 patients, of whom, 10 had post-treatment samples available. Interestingly, a large variation of the GR expression levels was observed across the patient samples, with the GR-alpha being the predominant isoform. However, a clear correlation between GR expression and treatment response was not seen, likely due to the small sample size. To access a larger patient pool, we retrospectively analyzed the GR expression levels using the cDNA microarray data from the Arkansas group on 351 newly diagnosed myeloma patients treated with Total Therapy 2, of which dexamethasone was an essential component throughout the treatment course. Of the 351 patients, both event-free (EFS) and overall survivals (OS) were compared between 49 patients (14%) with low levels of GR expression and the rest (86%) with high levels of GR expression. Strikingly, the cases with low levels of GR expression were associated with statistically significantly worse EFS and OS compared to those with high levels of GR expression, with the 5-year EFS of 20% vs. 52% and OS of 34% vs. 68%, P < 0.01. In summary, we have shown for the first time that the baseline level of glucocorticoid receptor expression in newly diagnosed myeloma patients predicts their clinical response to GC-containing regimen and the overall clinical outcome.

Abstract Background: Abemaciclib plus nonsteroidal aromatase inhibitors (AI) demonstrated efficacy with a tolerable safety profile as initial treatment for postmenopausal women with HR+, HER2− advanced breast cancer in the MONARCH 3 trial. In the intent-to-treat (ITT) population, the updated progression-free survival (PFS) with 12 months of additional follow-up was 28.2 versus 14.8 months (HR [95% CI]: 0.525 [0.415, 0.665]; p<.0001) in the abemaciclib plus AI versus placebo plus AI arms, respectively. Here we report data from the additional 12 months of follow-up on tumor response for the ITT population and exploratory subgroups previously identified as significantly prognostic (Di Leo et al. 2018). Methods: In MONARCH 3, a randomized, double-blind, phase III trial, 493 postmenopausal women with HR+, HER2− advanced breast cancer and no prior systemic therapy in the advanced setting, received an AI (anastrozole or letrozole) plus abemaciclib or placebo. From the October 31, 2018 cutoff, secondary endpoints including objective response rate ([ORR], complete response [CR] + partial response [PR]), time to response (TTR), and duration of response ([DoR], time from a confirmed CR or PR until disease progression or death) were assessed in the ITT population and exploratory subgroups previously reported as significantly prognostic. Waterfall plots will be used to illustrate the magnitude of change in tumor size for each subgroup (to be presented). Results: In patients with measurable disease, the ORR was 62.5% (95% CI: 56.7, 68.2) in the abemaciclib plus AI arm and 44.7% (95% CI: 36.2, 53.2) in the placebo plus AI arm (p≤.001). Additional follow-up confirmed that all prognostic subgroups received benefit from the addition of abemaciclib to AI, consistent with the ITT population, as evidenced by change in ORR, with the largest effects observed in subgroups of patients with liver metastases, progesterone receptor-negative tumors, high grade tumors, or treatment-free interval of <36 months (Table). Median TTR was similar between the two treatment arms (abemaciclib plus AI: 3.62 months; placebo plus AI: 3.65 months), and this was generally consistent across subgroups. Responses were more durable in the abemaciclib plus AI arm (median DoR: 32.71 months; 95% CI: 25.74, -) versus the placebo plus AI arm (median DoR: 17.49 months; 95% CI: 11.61, 22.19), including in poor prognostic subgroups. Conclusions: Extended follow-up in the MONARCH 3 trial further confirmed that the addition of abemaciclib to AI is associated with durable tumor responses, including those in patients with clinically poor prognostic characteristics. References: Di Leo A et al. NPJ Breast Cancer. 2018;4:41. Clinical trial information: NCT02246621 This study was funded by Eli Lilly and Company Table: Objective Response RateAbemaciclib + AIPlacebo + AIORR ChangeNn (%)Nn (%)%Measurable disease population269168 (62.5)13259 (44.7)17.8Liver metastasesYes4727 (57.45)306 (20.00)37.45No222141 (63.51)10253 (51.96)11.55Progesterone receptor statusNegative6137 (60.66)298 (27.59)33.07Positive206130 (63.11)10351 (49.51)13.60Treatment-free interval<36 months3117 (54.84)225 (22.73)32.11≥36 months7442 (56.76)3216 (50.00)6.76Tumor gradeHigh5739 (68.42)2811 (39.29)29.13Intermediate/ Low13992 (66.19)7535 (46.67)19.52ECOG PS111275 (66.96)5022 (44.00)22.96015793 (59.24)8237 (45.12)14.12Bone-only diseaseNo256162 (63.28)12253 (43.44)19.84YesNA*NA*NA*NA*NA*N= total number of patients in each subgroup; n= number of responder patients; NA=not applicable*Response rate not reported for bone-only disease because majority of lesions were not measurable Citation Format: Joyce O’Shaughnessy, Stephen Johnston, Miguel Martin, Jens Huober, Masakazu Toi, Valerie AnneMarie Andre, Holly Rene Martin, Molly Catherine Hardebeck, Angelo Di Leo, Matthew Philip Goetz. Updated subgroup tumor response of abemaciclib plus aromatase inhibitor for hormone receptor positive (HR+), HER2 negative advanced breast cancer (MONARCH 3) [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-19-09.

TPS1140^ Background: NKTR-102 is a topoisomerase I inhibitor-polymer conjugate that hydrolyzes to provide continuous exposure to SN-38. A phase 2 trial of single-agent NKTR-102 was conducted in pts with 3rd-line MBC; 2 schedules (q14d; q21d) investigated a dose of 145 mg/m2. ORR was 29% (including 3% CR) with the prior ATC subset demonstrating an ORR of 31%. Dosing q21d was better tolerated; in this arm, median PFS and OS equaled 5.3m and 13.1m, respectively. Trial Design: Pts will be randomized 1:1 to receive single-agent NKTR-102 or TPC in an open-label, randomized, multicenter phase 3 study in pts with advanced breast cancer. Key Entry Criteria: Adult females, with ECOG 0 or 1 with adequate liver, kidney and marrow function. All pts must have received prior therapy with ATC (these drugs can be administered in the neo/adjuvant or locally advanced/metastatic setting). Prior A is not mandated if contraindicated. Prior toxicities must have resolved to ≤ Grade 1 (except sensory neuropathy ≤ Grade 2; complete resolution of prior diarrhea). Pts with brain metastases may be eligible, if lesions are stable for prior 3 weeks without steroids. Methods: Primary efficacy endpoint is OS. Secondary endpoints include: ORR by RECIST v1.1, clinical benefit rate (ORR+SD > 6 months), PFS and QoL. NKTR-102 is given IV at 145 mg/m2 over 90-min every 21 days without premedications. Pts randomized to TPC receive 1 of the following: eribulin, ixabepilone, vinorelbine, gemcitabine, paclitaxel, docetaxel or nab-paclitaxel (the agent must be available at the treating institution). Pts are stratified by region, prior eribulin and receptor status (TNBC, Her2+ or Other). Target Accrual: ~840 pts will be required for sufficient events to occur in the planned follow-up time; OS will be compared using a two-sided log-rank test; 1 interim analysis will occur when 50% of the deaths are reported. PK sampling is performed in a subset of pts. CTCs (isolated by Apocell ApoStream technology) are serially assessed for potential predictive markers of response and toxicity. Enrollment is expected to remain open until late 2013.

1087 Background: EP is a unique topoisomerase 1 inhibitor that provides continuous exposure to SN38. EP demonstrated a 29% overall response rate in patients with mBC, leading to a phase III global BEACON study in patients with mBC. CTCs in patient blood samples provide a minimally invasive approach to detect PD markers of drug activity. We developed quantitative multiplex immunofluorescent assays to measure target-specific PD biomarkers for EP in CTCs isolated pre- and post-treatment. Methods: Assays for Top1, Top2, g-H2Ax, Rad51, ABCG2, and Ki-67 were developed using control (0.1% DMSO) and drug-treated (SN38, 10 uM) tumor cell lines (HCT116, MCF7, A549, SKBr3) and PBMCs from healthy donors. The optimal antibody for each biomarker was then multiplexed in a panel with antibodies against cytokeratin, CD45 and DAPI for phenotypic identification of CTCs. For analysis of BEACON pts, serial 7.5 mL whole blood samples were drawn and shipped ambient to Apocell (Houston, TX) for further processing. PBMCs were separated and CTCs were isolated using ApoStream technology. CTCs were stained for PD markers and analyzed using an iCys laser scanning cytometer equipped with image analysis software. Results: Antibodies bound to tumor cells showed staining confined to the nucleus (Top1, Top2, g-H2AX, Ki-67) or membrane (ABCG2), exhibited defined peak separation from their isotype controls, and signal strength correlated with cellular expression of high and low levels of markers. To date, ~ 80% of BEACON pts consent to participate in the CTC sub-study. As of 30 Oct 2012, data from 167 pre-dose blood samples from BEACON pts were available. 99% of blood samples were successfully processed. 93% had detectable CTCs, yielding a median of 216 CTCs (range: 7.5-14816). Staining was positive for Top1, Top2, g-H2Ax, Rad51, ABCG2, and Ki-67 in 82%, 89%, 16%, 53%, 31%, 52% of samples, respectively. Conclusions: EP target-specific pharmacodynamic biomarkers can be reliably measured in CTCs isolated from patients participating in BEACON. Sample collection and analysis of pre- and post-treatment samples is ongoing. Clinical trial information: NCT01492101.

Background: Annual rankings by US News and World Report are a widely utilized metric by both health care leaders and patients. One longstanding measure is time to treatment of femur shaft fractures. Hospitals able to provide at least 80% of pediatric patients with an operating room start time within 18 hours of admission to the emergency department score better as part of the overall pediatric orthopaedic ranking. Therefore, it is important to determine whether the 18-hour treatment time for pediatric femur shaft fractures is a clinically meaningful metric. Methods: A retrospective review of clinical outcomes of 174 pediatric patients (aged below 16 y) with isolated femur shaft fractures (Injury Severity Score=9) was conducted from 1997 to 2017 at a single level I pediatric trauma center. The 2 comparison groups were patients receiving fracture reduction within 18 hours of emergency department admission (N=87) or >18 hours (N=87). Results: Patient, injury, and surgical characteristics were similar between the 2 groups. Both groups had a similar mean age (treatment <18 h=7.5 y; treatment >18 h=8.1 y). Patients who received treatment within 18 hours were more often immobilized postoperatively (70.1% vs. 53.5%; P=0.0362) and had a shorter median hospital length of stay (2 vs. 3 d; P=0.0047). There were no statistically significant differences in any outcomes including surgical site infection, time to weight-bearing (treatment <18 h mean=48.1 d vs. 52.5 d), time to complete radiographic fracture healing (treatment <18 h mean=258.9 d vs. 232.0 d), decreased range of motion, genu varus/valgus, limb length discrepancy, loss of reduction, or persistent pain. Conclusions: Treatment of pediatric femur shaft fractures within 18 hours does not impact clinical outcomes. National quality measures should therefore use evidence-based metrics to help improve the standard of care. Level of Evidence: Therapeutic level III.

Abstract Abstract 3229 Poster Board III-166 Introduction Low platelet count prior to mobilization is a significant predictive factor for mobilization failure in patients with non-Hodgkin's lymphoma (NHL) or Hodgkin's disease (HD) undergoing autologous hematopoietic stem cell (HSC) transplantation (auto-HSCT; Hosing C, et al, Am J Hematol. 2009). The purpose of this study is to assess the efficacy of HSC mobilization with plerixafor plus G-CSF in patients with concomitant thrombocytopenia undergoing auto-HSCT. Methods Patients who had failed successful HSC collection with any mobilization regimen were remobilized with plerixafor plus G-CSF as part of a compassionate use program (CUP). Mobilization failure was defined as the inability to collect 2 ×106 CD34+ cells/kg or inability to achieve a peripheral blood count of ≥10 CD34+ cells/μl without having undergone apheresis. As part of the CUP, G-CSF (10μg/kg) was administered subcutaneously (SC) every morning for 4 days. Plerixafor (0.24 mg/kg SC) was administered in the evening on Day 4, approximately 11 hours prior to the initiation of apheresis the following day. On Day 5, G-CSF was administered and apheresis was initiated. Plerixafor, G-CSF and apheresis were repeated daily until patients collected the minimum of 2 × 106 CD34+ cells/kg for auto-HSCT. Patients in the CUP with available data on pre-mobilization platelet counts were included in this analysis. While patients with a platelet count <85 × 109/L were excluded from the CUP, some patients received waivers and were included in this analysis. Efficacy of remobilization with plerixafor + G-CSF was evaluated in patients with platelet counts ≤ 100 × 109/L or ≤ 150 × 109/L. Results Of the 833 patients in the plerixafor CUP database, pre-mobilization platelet counts were available for 219 patients (NHL=115, MM=66, HD=20 and other=18.). Of these, 92 patients (NHL=49, MM=25, HD=8 and other=10) had pre-mobilization platelet counts ≤ 150 × 109/L; the median platelet count was 115 × 109/L (range, 50-150). The median age was 60 years (range 20-76) and 60.4% of the patients were male. Fifty-nine patients (64.1%) collected ≥2 × 109 CD34+ cells/kg and 13 patients (14.1%) achieved ≥5 × 106 CD34+ cells/kg. The median CD34+ cell yield was 2.56 × 106 CD34+ cells/kg. The proportion of patients proceeding to transplant was 68.5%. The median time to neutrophil and platelet engraftment was 12 days and 22 days, respectively. Similar results were obtained when efficacy of plerixafor + G-CSF was evaluated in 29 patients with platelet counts ≤ 100 × 109/L (NHL=12, MM=10, HD=3 and other=4). The median platelet count in these patients was 83 × 109/L (range, 50-100). The median age was 59 years (range 23-73) and 60.4% of the patients were male. The minimal and optimal cell dose was achieved in 19(65.5%) and 3(10.3%) patients, respectively. The median CD34+ cell yield was 2.92 × 106 CD34+ cells/kg. The proportion of patients proceeding to transplant was 62.1%. The median time to neutrophil and platelet engraftment was 12 days and 23 days, respectively. Conclusions For patients mobilized with G-CSF alone or chemotherapy ±G-CSF, a low platelet count prior to mobilization is a significant predictor of mobilization failure. These data demonstrate that in patients with thrombocytopenia who have failed prior mobilization attempts, remobilization with plerixafor plus G-CSF allows ∼65% of the patients to collect the minimal cell dose to proceed to transplantation. Thus, in patients predicted or proven to be poor mobilizers, addition of plerixafor may increase stem cell yields. Future studies should investigate the efficacy of plerixafor + G-CSF in front line mobilization in patients with low platelet counts prior to mobilization. Disclosures Micallef: Genzyme Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding. Jacobsen:Genzyme Corporation: Research Funding. Shaughnessy:Genzyme Corporation: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Marulkar:Genzyme Corporation: Employment, Equity Ownership. Mody:Genzyme Corporation: Employment, Equity Ownership. van Rhee:Genzyme Corporation: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract We have taken a genetic strategy to define the mechanisms underlying the increased risk of VTE in myeloma patient treated with thalidomide using clinical trial data derived from the MRC, ECOG, Little Rock and HOVON trial groups. Baseline VTE rates in the MRC IX study, (CTD vs CVAD; n=748 no prophylactic anticoagulation), were 14%. Rates in both arms were equal but in the thalidomide arm, thrombotic events were all DVT’s or PE’s, whereas in the CVAD arm they split equally between line related and VTE’s. In the other comparison (CTD vs MP; n=542), the VTE rate was 17%, all in the thalidomide arm. Rates were similar or higher (30%) in the other trials depending on the intensity of treatment used. We carried out a nested case control on DNA from MRC IX on 90 VTE (45 thalidomide, 45 non-thalidomide related), matched to 180 controls. SNPlex analysis for 60 SNPs in the coagulation cascade, DNA repair, thalidomide, metabolism and myeloma specific pathways, identified 7 genes which affected risk (NBS1, MTHFR, Ku80, Cyp3A5, LGAL52 and IL12B). This suggests three major pathways contribute to the risk of thalidomide related VTE, metabolism of thalidomide, tumour responsiveness and some pro-thrombotic factors. These factors differ from non-thalidomide related risk where a strong pro-inflammatory response is most relevant. We tested this hypothesis further using an expanded panel of 3,404 SNPs (BOAC panel), using the Megalle system. Statistical analysis of this data defined the top 20 thalidomide and non-thalidomide associated SNPs, which were consistent with the initial hypothesis, the results of which exclude variation in the coagulation cascade as being a significant risk factor. We explored this hypothesis further using pathway-based analysis focussing on the MTHFR, DNA repair and WNT pathways on data from the ECOG, HOVON and Little Rock data sets. The biological relevance of the SNPs and genes identified was validated by correlating the level of expression using the Affymetrix U133 CHIP with genetic variation at the DNA level. To verify the relevance of DNA repair variability to tumour response further, we correlated gene expression profiling with response and again Ku80 was identified as one of a cluster of genes defining increased risk. The data suggests that the risk of thalidomide related VTE relates to rapid tumour lysis combined with a prothrombotic tendency, whereas in line related thrombosis, risk is dependent upon a strong proinflammatory response. We are building this genetic data into a clinical risk model in order to allow us to develop a rationale for intervention strategies.

Abstract Background: Chronic GVHD contributes to morbidity and mortality after allogeneic transplantation. Several phase 2 and 3 open label studies suggested that ATLG (formerly referred to as ATG-F) reduces cGVHD without impacting relapse or survival. This report describes the first prospective randomized double blind phase 3 trial to assess the effect of ATLG (Neovii) when added to tacrolimus / methotrexate prophylaxis on cGVHD free survival. Patients and Methods: This study was conducted at 27 sites in the United States and Australia. 260 patients were randomized (132 placebo, 128 ATLG). 6 patients who were randomized never received ATLG/placebo (4 placebo and 2 ATLG). Data presented include the 128 placebo and 126 ATLG patients who underwent treatment and transplant. Patients were ages 18-65 years with a diagnosis of acute myeloid (AML, 64%) or lymphoblastic (ALL, 21%) leukemia in first or subsequent complete remission or myelodysplastic syndrome (MDS, 15%) with less than 10% marrow blasts. Median age was 48 years (18-65) with 55% males. All patients received 8/8 HLA (A, B, C, DR) allele matched unrelated products (80% mobilized peripheral blood, 20% bone marrow). Myeloablative conditioning was with cyclophosphamide and total body irradiation (Cy/TBI, 27%), busulfan and cyclophosphamide (Bu/Cy, 33%), or busulfan and fludarabine (Bu/Flu, 40%). All patients received tacrolimus starting on Day -1 and standard mtx on days 1, 3, 6, and 11 for GVHD prophylaxis. Patients received ATLG or placebo on Days -3, -2, -1 at an ATLG dose of 20 mg/kg per day (60 mg/kg total). The primary endpoint of the study was moderate/severe chronic GVHD free survival. cGVHD was scored by the investigator and then confirmed or overturned by an independent adjudication committee. Secondary endpoints included engraftment, acute GVHD , moderate/severe cGVHD, non-relapse mortality, GVHD and relapse free survival (GRFS), progression free (PFS) and overall survival (OS). The impact of ATLG on immune reconstitution was assessed in 91 patients. Results: Treatment arms were balanced with respect to age, diagnosis, remission status, cytogenetics, graft source (PB or BM), CMV serostatus and conditioning regimen. Median follow up of survivors was 745 days (61, 1425). Regarding the primary endpoint, there was no difference in 2 year moderate/severe cGVHD free survival between ATLG and placebo (48 vs 44%, p = 0.57), nor was there a difference in GRFS (Table 1). Neutrophil engraftment at Day 30 was less in the ATLG arm (85% vs 95%, p=0.00001). Grade 2 or higher infusion reactions were higher in the ATLG arm (16% vs 2.3%, p=0.0001). Reactivation of CMV was similar (27% vs 31%, p=0.58). The Day 180 cumulative incidence of grades 2-4 acute GVHD was lower in the ATLG arm (23 vs 40%, p = 0.003) as was the 2-year cumulative incidence of moderate/severe cGVHD (12 vs 33%, p = 0.000006). However, both 2-year OS and PFS were lower in ATLG treated patients (58 v 76% and 47 vs 67%, respectively) due in part to a higher incidence of relapse (32 vs 19%, p=0.068). Multivariable models confirmed that ATLG was associated with inferior OS, HR 1.85 (1.1-2.9, p=.0075) and PFS, HR 1.63 (1.1-2.41, p=0.015). We then conducted an unplanned post-hoc analysis and discovered a striking influence of conditioning regimen on outcomes. There were no differences in OS or PFS between ATLG and placebo arms in patients receiving Bu/Cy or Bu/Flu but a dramatic difference in patients receiving Cy/TBI conditioning (OS, 88% vs 48% p =0.006 and PFS, 75 vs 29%, p=0.007 in placebo and ATLG arms, respectively). 91 patients (44 ATLG, 47 placebo) participated in the immune reconstitution study. ATLG treatment was associated with lower CD3, CD4, and CD8 counts at Days 30, 100, and 360, respectively. In turn lower CD3, CD4, and CD8 counts assessed as time dependent variables were all associated with inferior OS and PFS as well as higher NRM (data not shown). Conclusion: In this first ever prospective, randomized, double blind trial of ATLG added to tacrolimus /methotrexate in recipients of matched unrelated donor grafts after myeloablative conditioning, there was no significant difference in 2 year moderate/severe cGVHD free survival or GRFS. ATLG did significantly reduce grades 2-4 acute and moderate/severe chronic GVHD. However, overall survival was higher in the placebo arm, driven mainly by patients who received Cy/TBI conditioning. Further analyses are needed to understand the proper role for ATLG in HCT. Disclosures Soiffer: Juno: Membership on an entity's Board of Directors or advisory committees; Kiadis: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees. Porter:Genentech: Employment; Novartis: Patents & Royalties, Research Funding. Jagasia:Therakos: Consultancy. Szer:Alnylam: Honoraria, Membership on an entity's Board of Directors or advisory committees; Ra Pharma: Honoraria, Membership on an entity's Board of Directors or advisory committees; Alexion Pharmaceuticals, Inc.: Honoraria, Membership on an entity's Board of Directors or advisory committees. Ritz:Kiadis: Membership on an entity's Board of Directors or advisory committees. Glavin:Neovii Biotech: Employment. Chen:Incyte Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding.

Abstract Background: Bone disease in myeloma occurs as a result of complex interactions between myeloma cells and the bone marrow microenvironment. To date, no studies have evaluated the potential impact of genetic polymorphisms upon and/or within this microenvironment. Patients and Methods: Peripheral blood DNA from 282 patients enrolled in the Total Therapy 2 (TT2) protocol was studied using the previously reported Affymetrix 3k BOAC custom chip to evaluate relevant genetic polymorphisms. DKK1 gene expression and high risk GEP gene signatures were assessed as previously reported (Blood109:4470–4477 and 109:2276–2284 2007). Patients were classified using both full skeletal x-rays and MRI findings. The lower cut-off used was absence of focal abnormalities on x-ray and/or <7 focal lesions on MRI (see JCO25:1121–1128 2007). Results: The top 200 SNPs were first evaluated based upon univariate P values linked to limited or extensive bone disease. The top 50 SNPs with the smallest P values were then selected (eliminating closely linked genes) for recursive partitioning analysis. The recursive partitioning was conducted both with and without the insertion of known biologically relevant SNPs. The pruned tree developed with recursive partitioning proved to be quite stable and incorporated 4 dominant SNPs: rs 3766934 Epoxide hydrolase (EPHX1); sr3783408 MAP kinase; sr 1062637 RNA helicase DDX18; and sr3181366 TNFSF8-TNF-α. No alternate SNP substituted for EPHX1 in the recursive model. The 4 SNP signature of EPHX1 GG, MAP4K5 AG/AA, DDX18 GG/CC plus TNFSF8 CT/TT was highly correlated with the number of MRI lesions: mean 8.66 lesions versus 3.33 for alternate SNPs (P<.001). In the univariate association of standard prognostic factors, 4 SNP signature and 70 and 17 gene models for high risk, the best correlation with bone disease was with the 4 SNPs (P<.0001) followed by the 17 gene GEP model (P=0.05). Using stepwise multivariate regression analysis the best correlation with bone disease was Log2DKK-1 expression (P=.0001) but closely followed by SNPs EPHX1 (P=.0009); MAP4K5 (P=.0071) plus TNFSF8 and serum LDH (P=.02). The various analyses training (2/3) and validation sets were used with assessment of sensitivity and specificity. The microenvironmental 4 SNP signature combined with GEP DKK1 provided the best prediction of myeloma bone disease and overall outcome. Conclusions: This first assessment of genetic polymorphisms linked to myeloma bone disease in myeloma has led to the identification of polymorphisms with both potential biologic relevance and utility in prognostic models of myeloma bone disease including risk stratification for bisphosphonate use. Validation of these findings may allow a search for potential therapeutic targets.