Sekeres, Mikkael A.

Abstract Background: A majority of younger pts with AML receive initial therapy with a 7+3 scheme. Escalated doses of daunorubicin (dauno) in combination with standard doses of ara-C may lead to improved outcomes (Fernandez; NEJM 2009). A phase II trial of idarubicin and high-dose ara-C (IA) in combination with the histone deacetylase inhibitor vorinostat (IA+V) resulted in historically high response rates compared to IA or 7+3 (Garcia-Manero; JCO 2011). SWOG 1203 tested whether a high-dose ara-C induction with or without vorinostat could result in improved outcomes for younger AML pts compared to 7+3. Methods: The primary endpoints were comparison of event-free survival (EFS) of 7+3 vs IA vs IA+V and evaluating the frequency of allogeneic hematopoietic cell transplantation. Secondary endpoints included toxicity, remission rate, relapse-free survival (RFS), and overall survival (OS) according to treatment arm, and by cytogenetic and molecular subgroups. Because of the historical trend of cured pts in S0106, EFS was modeled with an exponential cure model, and it was assumed IA or IA+vorinostat increased the proportion of pts cured from 35% to 45% and increased median EFS among those not cured from 4.7 months to 7.1 months. Main inclusion criteria included a diagnosis of previously untreated non-APL AML by WHO criteria, age 15 to 60 years, and preserved cardiac function but no severe comorbidities. Pts with known CBF rearranged or FLT3 mutant leukemias were eligible if no other alternative clinical trials existed. Treatment was as follows: Induction: 7+3 arm: dauno 90 mg/m2 IV QD x 3 on days 1-3 with ara-C CI 100 mg/m2 QD x 7 days on days 1 to 7. IA arm: ida 12 mg/m2 QD x 3 on days 1 to 3 with 24 hours CI ara-C 1.5 gm/m2 QD for 4 days on days 1 to 4. IA+vorinostat was as IA but with vorinostat 500 mg orally TID for 3 days on days 1 to 3. Consolidation: 7+3 arm: standard high-dose ara-c at 3 gm/m2 over 3 hrs q12 hours x 6 doses for 1 to 4 cycles depending on transplant availability. IA arm: idarubicin 8 mg/m2 IV QD x 2 days on days 1 to 2 with ara-C 0.75 gm/m2 CI for 3 days on days 1 to 3 for 4 cycles. The number of consolidation cycles depended on transplant indication; a secondary aim of the study was to transplant all cytogenetically-determined high risk pts (presented in a different abstract). Results: Of 754 pts randomized, 738 were eligible (261 to 7+3, 261 to IA, and 216 to IA+V). Baseline characteristics were well balanced among all three groups. Most (75%) pts were between 40 and 60 years, 51% were male, median (range) WBC and platelets were 10.8 (0.3-800) and 49 (3-3900), respectively; marrow blast percentage was 60% (0%-100%); cytogenetics were favorable in 13% of pts, intermediate in 63%, and high-risk in 22%; FLT-3 was mutated in 21% and unknown in 31%; NPM1 was mutated in 20% and was unknown in 37%. Complete remission (CR) rates were 75% for 7+3, 79% for IA, and 77% for IA+V (p=0.58). Significantly more pts received reinduction with 7+3 (24%) versus 11% with IA and 9% with IA+V (p=0.001). 48% of intermediate and unfavorable risk pts received transplant in CR1, with no significant differences among arms (p=0.44). There were no significant differences in EFS, RFS or OS among all three arms (Figure 1, all p>0.5). By cytogenetic or molecular group, there were no differences in outcome for any standard risk subset (FLT-3, NPM1, or CEBPα or cytogenetic subset), although pts with favorable cytogenetics had significantly better EFS, RFS and OS with 7+3 therapy compared to IA or IA+V (Figure 2, all p≤0.015). 30% of pts received 1 consolidation cycle, 18% 2, 16% 3, and 37% 4 cycles. Grade 5 induction toxicity rates were 4% (7+3), 8% (IA), and 9% (IA+V) by arm (p=0.07), and for toxicities related to therapy, 2%, 7%, 8%, respectively (p=0.01). Grade 4 induction toxicity rates were similar across arms (p=0.38). Conclusion: Treatment with IA is not more effective than 7+3 in younger pts with AML. Outcomes with IA or IA plus vorinostat are similar. In pts with favorable cytogenetics, outcomes were inferior with IA or IA+V when compared to 7+3, perhaps related to use of lower doses of ara-C during consolidation. Clinical Trials Registry: NCT #0180233 Support: NIH/NCI grants CA180888, CA180819, CA18020, CA180821, CA180863, CA077202; CCSRI grant #021039 Disclosures Othus: Glycomimetics: Consultancy; Celgene: Consultancy. Radich:Novartis: Consultancy, Research Funding; Ariad: Consultancy; BMS: Consultancy. Strickland:Alexion Pharmaceuticals: Consultancy; Astellas Pharma: Research Funding; Baxalta: Consultancy; Cyclacel: Research Funding; Celator: Research Funding; Abbvie: Research Funding; Ambit: Consultancy; Boehringer Ingelheim: Consultancy, Research Funding; CTI Biopharma: Consultancy; Daiichi Sankyo: Consultancy; Sunesis Pharmaceuticals: Consultancy, Research Funding; GlaxoSmithKline: Research Funding; Karyopharm Therapeutica: Research Funding; Sanofi: Research Funding. Savoie:Novartis: Consultancy, Honoraria, Speakers Bureau; BMS: Consultancy, Honoraria; Pfizer: Consultancy; Celgene: Consultancy; Amgen: Consultancy; Jazz: Consultancy; Lundbeck: Consultancy. Sekeres:Millenium/Takeda: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Stone:Amgen: Consultancy; Karyopharm: Consultancy; Juno Therapeutics: Consultancy; Roche: Consultancy; Pfizer: Consultancy; Agios: Consultancy; Jansen: Consultancy; Xenetic Biosciences: Consultancy; Celator: Consultancy; Merck: Consultancy; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy; ONO: Consultancy; Seattle Genetics: Consultancy; Sunesis Pharmaceuticals: Consultancy. Erba:Sunesis: Consultancy; Seattle Genetics: Consultancy, Research Funding; Celgene: Consultancy, Speakers Bureau; Gylcomimetics: Other: DSMB; Ariad: Consultancy; Astellas: Research Funding; Gylcomimetics: Other: DSMB; Amgen: Consultancy, Research Funding; Pfizer: Consultancy; Gylcomimetics: Other: DSMB; Astellas: Research Funding; Astellas: Research Funding; Amgen: Consultancy, Research Funding; Daiichi Sankyo: Consultancy; Celgene: Consultancy, Speakers Bureau; Juno: Research Funding; Seattle Genetics: Consultancy, Research Funding; Jannsen: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Celgene: Consultancy, Speakers Bureau; Daiichi Sankyo: Consultancy; Sunesis: Consultancy; Millennium Pharmaceuticals, Inc.: Research Funding; Incyte: Consultancy, DSMB, Speakers Bureau; Agios: Research Funding; Celator: Research Funding; Ariad: Consultancy; Agios: Research Funding; Incyte: Consultancy, DSMB, Speakers Bureau; Jannsen: Consultancy, Research Funding; Juno: Research Funding; Agios: Research Funding; Jannsen: Consultancy, Research Funding; Novartis: Consultancy, Speakers Bureau; Novartis: Consultancy, Speakers Bureau; Incyte: Consultancy, DSMB, Speakers Bureau; Juno: Research Funding; Pfizer: Consultancy; Millennium Pharmaceuticals, Inc.: Research Funding; Celator: Research Funding; Daiichi Sankyo: Consultancy; Agios: Research Funding; Celator: Research Funding; Daiichi Sankyo: Consultancy; Millennium Pharmaceuticals, Inc.: Research Funding; Pfizer: Consultancy; Celator: Research Funding; Jannsen: Consultancy, Research Funding; Sunesis: Consultancy; Seattle Genetics: Consultancy, Research Funding; Astellas: Research Funding; Ariad: Consultancy; Ariad: Consultancy; Millennium Pharmaceuticals, Inc.: Research Funding; Seattle Genetics: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Sunesis: Consultancy; Pfizer: Consultancy; Juno: Research Funding; Celgene: Consultancy, Speakers Bureau; Gylcomimetics: Other: DSMB.

Abstract Background: Patients (pts) withHR-MDS have a median OS of 4 to 6 months (mo) after HMA failure (Prebet et al, J Clin Oncol 2011) and no approved salvage therapy. Development of new therapeutics for this population will benefit from the availability of surrogate endpoints and markers that can predict survival. Gore et al established response to azacitidine (Vidaza®) in first-line therapy for HR-MDS as a reasonable surrogate to predict survival (Gore et al, Haematologica 2013). Rigosertib, a novel dual PI3K/PLK pathway inhibitor, has been shown to reduce bone marrow blasts (BMBL) in these pts (Seetharam et al, Leuk Res 2012). Silverman et al described complete or partial bone marrow (BM) response, or stabilization after 4-8 weeks (wks) of treatment with rigosertib as a potential surrogate for predicting survival in pts with HR-MDS after failure of primary HMA therapy (Silverman et al, Hematol Oncol 2014). We tested this hypothesis in the context of a randomized Phase III trial. Methods:Pts with HR-MDS were randomly assigned 2:1 to rigosertib or best supportive care (BSC) after progressing on, failing to respond to, or relapsing after HMA treatment. BM aspirates were assessed pretreatment, at 4 weeks and at 8-week intervals thereafter. Central slide review was undertaken in a representative population of samples. The BMBL response at each time point was assessed using the following definitions: bone marrow complete response (mCR) = BMBL ≤ 5% and decrease of ≥ 50% from baseline; bone marrow partial response (mPR) = BMBL decrease from baseline of ≥ 50%, but BMBL still > 5%; stable disease (SD) = BMBL decrease or increase from baseline of < 50%; progressive disease (PD) = BMBL increase from baseline of ≥ 50% by an absolute minimum of 5%; Not evaluable (NE). Results:Bone marrow assessment was carried out in 156 patients (pts) on the rigosertib arm and 24 pts on the BSC arm at 4 wks after enrollment, and in 86 and 20 pts, respectively, at 12 wks. The invasive BM procedure was optional on the BSC arm, which accounts for the low number of assessments in this group. BM responses at the 2 time points are presented in Table 1. Since no difference in overall survival was noted between pts who had objective BM response and those who did not progress (ie, stable disease), a landmark analysis was conducted that separated pts who were alive at the 4-wk landmark time into two 4-wk response categories: BM response + SD vs. PD. Results of this analysis in rigosertib-treated patients were statistically significant at p = 0.011, with a hazard ratio (HR) of 0.62 and a median OS (from 4 wks onward) of 9.8 months in the mCR + mPR + SD group vs. 4.6 months in the PD group (Figure 1). Another landmark analysis was conducted at 12-wks. Results of this analysis were also significant (p < 0.001) in rigosertib-treated patients, with an HR of 0.39 and a median OS (from 12 wks onward) of 10.4 months in the mCR + mPR + SD group vs.7.5 months in the PD group (Figure 2). A time-dependent Cox regression of OS by 4-wk BMBL response reinforced the validity of the 4-wk and 12-wk BM assessments as surrogate biomarkers for survival (Table 2). Conclusions: These data suggest that BMBL response at 4 or 12 weeks was correlated with OS in this population of pts with HR-MDS treated with rigosertib after HMA failure and are consistent with previous observations in Phase II studies. Table 1 4- and 12-week Bone Marrow Blast Response (Intention-to-Treat Population) Number (%) of Patients 4-wk BMBL Response 12-wk BMBL Response Rigosertib N = 199 BSC N = 100 Rigosertib N = 199 BSC N = 100 Pts with BMBL assessment 156 (78) 24 (24)* 86 (43) 20 (20)* BM complete response (mCR) 22 4 11 5 BM partial response (mPR) 8 2 9 2 Stable disease (SD) 77 9 32 8 Progressive disease (PD) 49 9 34 5 * Bone marrow assessment was not required on the BSC arm. Figure 1 Figure 1. Figure 2 Figure 2. Table 2 Time-dependent Cox Regression of Overall Survival by Bone Marrow Blast Response Analysis Rigosertib BSC Wald P-value Hazard Ratio (95% Confidence Interval) Wald P-value Hazard Ratio (95% Confidence Interval) By 4-wk BMBL response 0.051 0.72 (0.51 - 1.00) 0.56 0.83 (0.45 - 1.54) By 12-wk BMBL response 0.0005 0.55 (0.39 - 0.77) 0.16 0.68 (0.39 - 1.17) *Stratified by pretreatment BMBL: 5%-19% vs. 20%-30% Disclosures Fenaux: Celgene: Research Funding; Janssen: Research Funding; Novartis: Research Funding. Sekeres:Celgene Corp.: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Boehringer Ingelheim: Membership on an entity's Board of Directors or advisory committees. Wilhelm:Onconova Therapeutics, Inc: Employment, Equity Ownership. Azarnia:Onconova Therapeutics, Inc: Employment.

Abstract Introduction: CMML is a heterogeneous malignancy characterized by peripheral monocytosis and a propensity for AML transformation. Given the low incidence of CMML and its broad range complexity, a large disease-specific data set is necessary to discern independent co-variates predictive of disease behavior. Several prognostic models derived from smaller data sets have been developed to stratify CMML patients into distinct groups that are predictive for OS. However, the validity of these models in a large international data set has never been investigated. Here, we present the largest International CMML data base established to date and report key baseline characteristics predictive for disease outcome. We externally validate and perform a detailed statistical comparison between the International Prognostic Scoring System (IPSS), Revised (R)-IPSS, Global MD Anderson Scoring System (MDASC), MD Anderson Prognostic Score (MDAPS), Dusseldorf Score (DS), Mayo, and Spanish Scoring Systems (CPSS). Methods: Data were abstracted by each contributing institution and deposited for central data review at the Moffitt Cancer Center. Manual review of cases was performed to ensure data quality prior to analysis. Only WHO defined CMML was included. The primary objective of this study was to establish an international CMML data set and validate the above models calculated at the time of presentation to each center. All prognostic models were calculated as previously described. The Kaplan–Meier (KM) method was used to estimate median OS and the log rank test was used to compare KM survival estimates with SPSS version 21.0. Random Forest Survival (RSF) and ROC analysis was done with R. Results: Between July 1981 and June 2014, 1832 CMML patients were captured in the International CMML database. Median age at diagnosis was 70 (16-93) years with a male (67%) predominance. By the WHO classification, the majority of patients had CMML-1 (79.9% vs. 20.1%) and most patients were evenly subcategorized as MPN-CMML (49.8%) versus MDS-CMML (50.2%) by FAB criteria. Splenomegaly was demonstrable in 25%. Most patients had favorable cytogenetics by both IPSS (70%) and CPSS (67.6%) classification schemas. Mean BM blast % was 5.6 and mean monocyte count was 4.85x103/dL. Median OS of the entire data set was 31.6 months. All tested prognostic models were valid and able to predict OS (p<0.0001). To compare relative model performance, 1013 complete cases with sufficient data to calculate all risk models were compared using time dependent receiver operator characteristic (ROC) curves and their area under the curves (AUC). ROC curves were calculated for OS at 36 months. The R-IPSS model had the highest AUC (0.694) while the DS model had the lowest (0.635). The difference in AUC between the R-IPSS and DS models was statistically significant (p = 0.003) whereas there was no significant difference from other models tested. To determine which models were most vulnerable to reclassification from low-risk to higher risk, we calculated a vulnerability score defined by the number of models able to up-stage low-risk disease in more than 15% of cases. Using this metric, the Mayo and MDASC scores were least vulnerable to up-staging by other models. Lastly, we used an orthogonal method for determination of variable importance (RSF for OS at 36 months). RSF is an ensemble method of classification and regression tree methodology. Ranking of variables by their weighted importance determined by this procedure identified the top five variables were hemoglobin, circulating blasts, platelet count, number of cytopenias, and karyotype. A new risk model was developed using the results of the RSF with an AUC of 0.720, which was comparable but not significantly different than other modern scoring systems. Conclusions: This represents the largest international CMML-specific data set derived from eight unique centers of excellence. All modern prognostic models were valid and performed comparably. Despite novel strategies for variable importance discovery, an improved model could not be constructed. This data suggest that the power of clinical variables has reached an asymptote and highlights the need for incorporation of molecular and other novel biological features. This data set is currently being populated with molecular data and is being leveraged to test published CMML molecular prognostic models which will be disclosed at the time of presentation. Disclosures Garcia-Manero: Epizyme, Inc: Research Funding. Fenaux:Celgene, Janssenm, Novartis: Research Funding. Kantarjian:ARIAD, Pfizer, Amgen: Research Funding. Sekeres:Celgene: Membership on an entity's Board of Directors or advisory committees; Amgen Corp: Membership on an entity's Board of Directors or advisory committees; Boehringer-Ingelheim Corp: Membership on an entity's Board of Directors or advisory committees.

Abstract Introduction: Patients with RR-AML, particularly older adults, have dismal outcomes and limited therapy options. Given low response rates and high toxicity with salvage intensive chemotherapy, and frequent ineligibility for allogeneic stem cell transplantation (alloSCT), many patients are treated with HMAs. Robust data regarding use of HMAs in AML predominates in the frontline setting, while their use in RR-AML has limited supportive data. Here wesought to analyze theoutcomes and their predictors in patients with RR-AML treated with HMAs. Methods:We collected data, spanning a period from 2006 to 2016, from 7 centers in the United States and 4 centers in Europe regarding patients treated with HMAs for RR-AML. Responses were defined by International Working Group criteria. Kaplan-Meier methods estimated overall survival (OS) from initiation of HMAs to death or end of follow-up. Multivariable logistic regression models estimated odds for response, and multivariable Cox Proportional Hazard (CPH) models estimated hazards ratios (HR) for OS. Covariates considered included HMA received, age at diagnosis (in years), AML classification at diagnosis (AML with myelodysplasia-related changes [AML-MRC], therapy-related [t]-AML), disease status (relapsed vs. refractory), number of therapy lines prior to HMA (1 vs. 2 vs. >=3), duration of first complete remission (CR1), white blood cell count, peripheral blood blast percentage, bone marrow (BM) cellularity (<=20% vs. > 20%), BM blast percentage (<=20% vs. >20%), cytogenetic risk group, and the presence of complex or chromosome 7 abnormalities. Results: Of 514 patients, 217 patients (42.2%) had refractory and 297 (58%) had relapsed AML. By end of study, 415 patients (88.5%) had died. Median follow-up for living patients was 11.6 months.Median age at diagnosis was 64 years (range [R], 16-92). AML-MRC was diagnosed in 29.0% while 8.2% had t-AML. Median number of prior therapies was 2 (R, 1-7), with 48.3% receiving 1 prior line, 30.2% receiving 2 prior lines, and 21.5% receiving >=3 prior lines. Prior alloSCT was performed in 21.2%. Only 1.9% had good risk (core binding factor) karyotype, while 56.2% had intermediate risk karyotype, and 41.9% had poor risk karyotype. Azacitidine was used in 45.8% and decitabine in 54.2%; median number of azacitidine cycles was 4 (Interquartile range [IQR], 2-6) compared to 2 for decitabine (IQR, 1-4, p <0.001). Best response to HMAs was CR in 11.7% (95%CI, 9%-14%), CRi in 6.4% (95%CI, 4.3%-8.8%), hematologic improvement (HI) in 8% (95%CI, 5.7%-10.5%), stable disease (SD) in 9.8% (95%CI, 7.2%-12.5%), while 64.1% (95%CI, 57.7%-66.2%) had progressive disease (PD). Median OS from HMA initiation for all patients was 6.9 months (IQR, 3.0-13.3). There was a significant difference in OS based on best response achieved [Figure 1]. Unadjusted OS showed an insignificant trend for worsening with increasing number of prior lines of therapy [Figure 2A]. In unadjusted analyses, there was no difference in OS based on HMA received in all patients [Figure 2B] or the subset who received only 1 prior line of therapy (median OS: Azacitidine vs. decitabine 8.4 vs 7.3 months, p=0.88). Following HMA therapy, the median number of subsequent therapies was 0 (R, 0-6), and only 12.8% underwent alloSCT. In multivariate CPH models, HMA used was not significantly associated with OS (HR=0.80, 95%CI, 0.42-1.51, p=0.49), while increasing age, and presence of complex cytogenetics and chromosome 7 abnormalities were significantly associated with risk of death [Table 1]. In multivariable logistic regression models, HMA used was not associated with achieving CR+CRi (Odds ratio=0.56, p=0.32). Conclusions: In this largest reported cohort of patients with RR-AML treated with HMAs, we found that HMAs are often used as alast line of therapy, with a minority of patients receiving subsequent treatment. Nonetheless, the minority of patients who achieve CR (11.7%) with HMA therapy had a median OS of 25.6 months. Therefore, use of HMAs for management of RR-AML is a reasonable intervention in the absence of clinical trial options. There appears to be no difference in OS or probability of achieving CR+CRi based on HMA used. Ongoing analyses in this dataset include further evaluations of predictors, including genetic mutations, and the development of prediction tools for clinical outcomes with HMA therapy. Figure 1. Figure 1. Disclosures Podoltsev: Ariad: Consultancy, Honoraria; Incyte: Consultancy, Honoraria. Ritchie:Novartis: Honoraria; Incyte: Speakers Bureau; Arian: Speakers Bureau; Pfizer: Honoraria; Celgene: Speakers Bureau. Sekeres:Millenium/Takeda: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Komrokji:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Speakers Bureau. Al-Kali:Onconova Therapeutics, Inc.: Research Funding; Celgene: Research Funding. Santini:Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Onconova: Consultancy; Amgen: Consultancy; Astex: Consultancy. Roboz:Cellectis: Research Funding; Agios, Amgen, Amphivena, Astex, AstraZeneca, Boehringer Ingelheim, Celator, Celgene, Genoptix, Janssen, Juno, MEI Pharma, MedImmune, Novartis, Onconova, Pfizer, Roche/Genentech, Sunesis, Teva: Consultancy. Fenaux:Celgene, Janssen,Novartis, Astex, Teva: Honoraria, Research Funding. Prebet:celgene: Consultancy, Honoraria; Novartis: Consultancy, Honoraria. Gore:celgene: Consultancy, Honoraria. Zeidan:Ariad: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Incyte: Consultancy, Honoraria; Celgene: Consultancy, Honoraria.

Abstract Introduction: Surveillance, Epidemiology, and End Results (SEER) registry captures data on acute leukemias (AL) that morphologically do not conform to well characterized acute leukemic entities in the established French-American-British (FAB) nomenclature. Not much is known about population level outcomes of these rare AL. We performed a comprehensive survival analysis of these rare AL over a forty-year period using the SEER registry. Methods: SEER 18 dataset was queried to identify adult patients (pts > 18 years) diagnosed with Acute Myeloid Leukemia (AML), Acute Lymphoblastic Leukemia (ALL) and rare AL from 1973-2012. Rare AL cases were identified using ICD-O-3 morphology codes and included Leukemias, Not Otherwise Specified (NOS) (9800-01, 9860) and myeloid sarcoma (9930). Data on demographics,follow up and survival was abstracted. Univariate and multivariate analysis was performed to identify factors influencing survival of rare AL and survival patterns were compared to AML and ALL. Hazard ratios (HR) with 95% confidence intervals (CIs) are reported. Results: We identified 61,550 AML pts, 13,457 ALL pts, 525 myeloid sarcoma pts and 13,446 pts with leukemia NOS. Median age at diagnosis was 68 years (yrs, range 18-100) for AML, 54 yrs (18-100) for ALL, 65 yrs (18-95) for myeloid sarcoma and 76 yrs (18-100) for leukemia NOS. Gender distribution was similar with females comprising 45.9% of AML, 43% of ALL, 42.3% of myeloid sarcoma and 47.2% of leukemia NOS cases. Over 80% pts in all categories were Caucasian. Median survival was as follows: 0.4 (0-40) yrs for AML; 1.4 (0-39.8) yrs for ALL; 0.7 (0-32.9) yrs for Myeloid Sarcoma; and 0.3 (0-38.9) yrs for leukemia NOS. In univariable analysis, younger age predicted for better survival for both myeloid sarcoma and leukemia NOS (p<0.0001). Improvement in survival over time period was observed for pts with leukemia NOS (p < 0.0001). In multivariable analysis, advancing age was an independent prognostic factor for rare AL, both as a continuous variable and when categorized as older vs. younger pts using age cutoff of 60 yrs. Compared to younger pts, HR for pts > 60 yrs with rare AL was as follows: 2.68 (2.13-3.37; p <0.0001) for myeloid sarcoma; and 2.18 (2.07-2.31; p<0.0001) for leukemia NOS. No difference in survival over time was seen for myeloid sarcoma. Pts with leukemia, NOS diagnosed in yrs 2006-2012 (HR=0.87; 0.82-0.92; p < 0.0001) had better survival compared to the referent years 1973-80. Race and gender did not impact survival. In multivariable analysis, pts with leukemia NOS did worse overall compared to AML (HR=1.13; 1.11-1.53; p<0.0001) and ALL (HR=1.86; 1.81-1.91; p<0.0001). Interestingly, younger pts had a poorer outcome than AML (HR=1.10; 1.04-1.16; p=0.0003), but pts > 60 yrs had a better outcome (HR=0.93; 0.91-0.95; p<0.0001). Both younger (HR=1.33; 1.26-1.41; p<0.0001) and older pts (HR=1.45, 1.40-1.50: p<0.0001) compared unfavorably to ALL (Figure 1). Pts with myeloid sarcoma had a better survival compared to AML pts (HR=0.77; 0.7-0.85; p<0.0001) (Figure 2). This was uniform for pts <60 yrs (HR=0.77; 0.64-0.93; p=0.006) and > 60 yrs (HR= 0.77; 0.69-0.87; p<0.0001). Conclusion: Pts with leukemias NOS have poor outcomes compared to AML and ALL but prognosis has improved in recent years. The most probable explanation is identification of prognostic molecular mutations that has allowed accurate risk-stratification of pts using the WHO classification and in turn better selection of treatment strategies. Several reasons have limited our understanding of these rare AL - small numbers, exclusion from clinical trials and wide variability in treatment options used. Efforts at better molecular characterization of these AL holds the most promise for improving outcomes. Disclosures Sekeres: Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; TetraLogic: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees.

Abstract Background: No approved treatment options are available to HR-MDS pts after HMA therapy. Study 04-21 (“ONTIME” trial) was a Phase III, randomized, controlled study of the efficacy and safety of rigosertib, a novel small molecule inhibitor of PI3-kinase and PLK pathways, in a heterogeneous population of MDS pts who had relapsed after, failed to respond to, or progressed during administration of HMAs. The study was conducted at 87 sites in the United States and 5 European countries. Methods:From Dec 2010 to Aug 2013, 299 HR-MDS pts [<30% bone marrow blasts (BMBL)] who had progressed on (37% of total enrollment), failed to respond to (25%), or relapsed after (38%) HMA treatment were stratified on BMBL count and randomized 2:1 to receive rigosertib (199 pts) or BSC (100 pts). Rigosertib was administered at 1800 mg/24 hr for 72-hr as a continuous intravenous (CIV) ambulatory infusion, every 2 weeks for the first 16 weeks, and then every 4 weeks. The primary endpoint was overall survival (OS), analyzed on an intention-to-treat (ITT) basis using the Kaplan-Meier method stratified on BMBL (5% to 19% vs. 20% to 30%). The trial had a 95% power to detect a 13-wk increase in median OS from 17 wks on BSC, with a 2-sided alpha = 0.05. The following results are based on 242 deaths: 161 in the rigosertib arm and 81 in the BSC arm. Results : Overall, the 2 arms were balanced in terms of baseline characteristics, with the majority of pts being male (66%), and White (82%). Age ranged from 50-90 yrs in the rigosertib arm and 55-86 years in the BSC arm (median, 74 yrs). The majority of pts (85%) had an Eastern Cooperative Oncology Group (ECOG) score of 0 or 1. The median duration of the last HMA therapy was 8.8 months (mo) in the rigosertib arm and 10.3 mo in the BSC arm; 127 (64%) of rigosertib pts and 57% of BSC pts were classified as “primary HMA failure” (ie, they failed to respond to or progressed during HMA therapy, as defined by Prebet et al, J Clin Oncol, 2011). A 2.3-mo improvement in median OS was found in the overall (ITT) population (8.2 mo rigosertib vs. 5.9 mo BSC) (Figure 1). The ITT survival for rigosertib was similar to that noted in Phase I/II studies (35 weeks). The stratified log-rank p-value was 0.33. The stratified hazard ratio was 0.87, which was quite different from the ratio of medians (5.9/8.2 = 0.72), due to the fact that the 2 survival curves converged at 15 mo. Notably, among the 184 patients with primary HMA failure, the median OS was 8.6 mo in the rigosertib arm (N = 127) vs. 5.3 mo in the BSC arm (N = 57), HR= 0.69, p= 0.040 (Figure 2). Multivariate Cox regression, adjusting for pretreatment prognostic factors, showed little change in the treatment effect. The following subgroups were correlated with better OS: pts with failure of/progression on HMA treatment, pts with duration of HMA treatment ≤ 9 mo, pts < 75 years of age, and pts with very high risk per IPSS-R (Figure 3). Rigosertib was well tolerated, with a median dose intensity of 92%. There were no significant compliance or operations issues related to ambulatory continuous infusion. Protocol-defined dose reductions were reported in 5% of pts, with 24% experiencing dose delays of >7 days, mostly due to unrelated adverse events (AEs). No obvious differences between rigosertib and BSC were found in the incidence of AEs (rigosertib, 99%; BSC, 85%) or of ≥ Grade 3 AEs (rigosertib, 79%; BSC, 68%). In the rigosertib arm, AEs reported by ≥ 20% of pts, irrespective of severity or causality, were nausea (35%), diarrhea (33%), constipation (31%), fatigue (30%), fever (27%), anemia (22%), and peripheral edema (21%). Rigosertib had low myelotoxicity, consistent with previous clinical experience. Conclusions:Although the primary endpoint in this Phase III study of rigosertib vs BSC in pts with HR-MDS did not reach statistical significance in the ITT population, encouraging rigosertib treatment-related improvement in OS was noted in several subgroups of MDS pts, including those with “primary HMA failure and in patients in the IPSS-R Very High Risk category. CIV therapy with rigosertib had a favorable safety profile in this orphan population of elderly pts with MDS. Figure 1 Figure 1. Figure 2 Figure 2. Figure 3 Figure 3. Disclosures Fenaux: Celgene: Research Funding; Janssen: Research Funding; Novartis: Research Funding. Sekeres:Celgene Corp.: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Boehringer Ingelheim: Membership on an entity's Board of Directors or advisory committees. Roboz:Novartis: Consultancy; Agios: Consultancy; Celgene: Consultancy; Glaxo SmithKline: Consultancy; Astra Zeneca: Consultancy; Sunesis: Consultancy; Teva Oncology: Consultancy; Astex: Consultancy. Wilhelm:Onconova Therapeutics, Inc: Employment, Equity Ownership. Wilhelm:Onconova Therapeutics, Inc: Employment. Azarnia:Onconova Therapeutics, Inc: Employment. Maniar:Onconova Therapeutics, Inc: Employment.