Citations

Abstract Background Although 20 pancreatic cancer susceptibility loci have been identified through genome-wide association studies in individuals of European ancestry, much of its heritability remains unexplained and the genes responsible largely unknown. Methods To discover novel pancreatic cancer risk loci and possible causal genes, we performed a pancreatic cancer transcriptome-wide association study in Europeans using three approaches: FUSION, MetaXcan, and Summary-MulTiXcan. We integrated genome-wide association studies summary statistics from 9040 pancreatic cancer cases and 12 496 controls, with gene expression prediction models built using transcriptome data from histologically normal pancreatic tissue samples (NCI Laboratory of Translational Genomics [n = 95] and Genotype-Tissue Expression v7 [n = 174] datasets) and data from 48 different tissues (Genotype-Tissue Expression v7, n = 74–421 samples). Results We identified 25 genes whose genetically predicted expression was statistically significantly associated with pancreatic cancer risk (false discovery rate < .05), including 14 candidate genes at 11 novel loci (1p36.12: CELA3B; 9q31.1: SMC2, SMC2-AS1; 10q23.31: RP11-80H5.9; 12q13.13: SMUG1; 14q32.33: BTBD6; 15q23: HEXA; 15q26.1: RCCD1; 17q12: PNMT, CDK12, PGAP3; 17q22: SUPT4H1; 18q11.22: RP11-888D10.3; and 19p13.11: PGPEP1) and 11 at six known risk loci (5p15.33: TERT, CLPTM1L, ZDHHC11B; 7p14.1: INHBA; 9q34.2: ABO; 13q12.2: PDX1; 13q22.1: KLF5; and 16q23.1: WDR59, CFDP1, BCAR1, TMEM170A). The association for 12 of these genes (CELA3B, SMC2, and PNMT at novel risk loci and TERT, CLPTM1L, INHBA, ABO, PDX1, KLF5, WDR59, CFDP1, and BCAR1 at known loci) remained statistically significant after Bonferroni correction. Conclusions By integrating gene expression and genotype data, we identified novel pancreatic cancer risk loci and candidate functional genes that warrant further investigation.

Monoclonal B cell lymphocytosis (MBL) has been shown to be the precursor condition that precedes overt diagnosis of chronic lymphocytic leukemia (CLL). Whereas CLL is classified with greater than or equal to 5 × 109/L B lymphocytes in the peripheral blood, MBL is a clonal expansion of B-cells below this threshold. MBL can be further divided into high- or low-count based on whether the B-cell count is above or below 0.5 × 109/L. Approximately 10% of the population over 40 is estimated to have MBL, increasing to >50% over the age of 90. While low-count MBL is unlikely to progress, 1-2% of high-count (HC) MBL individuals progress to CLL requiring therapy per year. It is debatable if all patients with detectable MBL should be classified as an entity requiring monitoring by a hematologist, especially for low-count MBL. In addition, the diagnosis of leukemia is distressing to patients; therefore, it is important to identify HC MBL patients that are more likely to progress to disease requiring treatment and thus should be monitored more closely. As the majority of MBL cases phenotypically resemble CLL, established prognostic markers including recurrent chromosomal aberrations, beta-2 microglobulin levels, and the mutational status of the Immunoglobulin heavy-chain variable region locus (IGHV) have been shown to predict time to treatment (TTT) and overall survival (OS) in a large retrospective study of MBL1. CLL patients can also be divided into three distinct epigenetic subtypes that reflect progressive DNA methylation changes that occurs during B cell development. These 'epitypes' termed low-programmed (LP), intermediate-programmed (IP), and high-programmed (HP) independently predict clinical outcomes irrespective of disease stage and treatment2. LP-CLL patients follow a generally unfavorable clinical course compared to the more indolent HP-CLL patients, while IP CLL patients display an intermediate outcome. Here we sought to determine if epitype forecasts progression to CLL and eventual clinical outcome for individuals with MBL. We analyzed 66 individuals diagnosed with HC MBL at the Mayo Clinic with a median follow-up of 6.3 years. Developmental epitype was determined using our novel Methylation-iPLEX technique that interrogates 34 CpGs and assigns epitype using a random forest model2. Seventy-seven percent of the MBL cases were assigned to one of the three epitypes: 42.4% HP, 19.7% IP, and 15.2% LP. The residual 23% remained unclassified due to ambiguous (low confidence) epigenetic patterns or insufficient purity (Figure 1A). The overall proportion of HP and IP epitypes in MBL were significantly greater than proportions observed in CLL cohorts (P<0.01). Epitypes remained stable over time as 20/21 cases for which we obtained a high-confidence epitype classification at multiple time points remained unchanged. Epitype significantly predicted MBL individuals progressing to treatment (P=0.001) with LP individuals progressing in a median of 5.6 years (P=0.049 and P=0.0002 versus IP and HP, respectively); median was not reached in HP or IP (Figure 1B). Epitype also predicted overall survival (OS) in MBL (P=0.04), with LP individuals having a significantly shorter OS (median of 11 years versus not reached in IP and HP; P=0.056 and P=0.048 versus IP and HP, respectively). In this study we evaluated a cohort of 66 HC MBL cases and determined that classification using developmental DNA methylation epitypes can be employed in HC MBL to aid in risk stratification. HC MBL patients classified as LP are more likely to progress to requiring treatment and have a significantly reduced OS. The epigenetic classification may help clinicians decide how closely and frequently a HC MBL individual needs to be monitored. Figure 1: (A) Breakdown of the epigenetic subtype assigned to 66 HC MBL samples. (B) Kaplan-Meier analysis of time to treatment and (C) overall survival of MBL patients separated by epitype. 1. Parikh, S. A. et al. Outcomes of a large cohort of individuals with clinically ascertained high-count monoclonal B-cell lymphocytosis. Haematologica103, e237-e240 (2018). 2. Giacopelli, B. et al. Developmental subtypes assessed by DNA methylation-iPLEX forecast the natural history of chronic lymphocytic leukemia. Blood blood.2019000490 (2019). doi:10.1182/blood.2019000490 Disclosures Byrd: Ohio State University: Patents & Royalties: OSU-2S; Janssen: Consultancy, Other: Travel Expenses, Research Funding, Speakers Bureau; BeiGene: Research Funding; TG Therapeutics: Other: Travel Expenses, Research Funding, Speakers Bureau; Novartis: Other: Travel Expenses, Speakers Bureau; TG Therapeutics: Other: Travel Expenses, Research Funding, Speakers Bureau; Genentech: Research Funding; Gilead: Other: Travel Expenses, Research Funding, Speakers Bureau; Novartis: Other: Travel Expenses, Speakers Bureau; Ohio State University: Patents & Royalties: OSU-2S; Gilead: Other: Travel Expenses, Research Funding, Speakers Bureau; Acerta: Research Funding; Genentech: Research Funding; Pharmacyclics LLC, an AbbVie Company: Other: Travel Expenses, Research Funding, Speakers Bureau; Pharmacyclics LLC, an AbbVie Company: Other: Travel Expenses, Research Funding, Speakers Bureau; Acerta: Research Funding. Shanafelt:Patent: Patents & Royalties: US14/292,075 on green tea extract epigallocatechin gallate in combination with chemotherapy for chronic lymphocytic leukemia; Merck: Research Funding; Polyphenon E International: Research Funding; Pharmacyclics: Research Funding; Genentech: Research Funding; Hospira: Research Funding; Glaxo-SmithKline: Research Funding; Abbvie: Research Funding; Cephalon: Research Funding; Celgene: Research Funding. Parikh:Genentech: Honoraria; Janssen: Research Funding; AstraZeneca: Honoraria, Research Funding; Pharmacyclics: Honoraria, Research Funding; MorphoSys: Research Funding; AbbVie: Honoraria, Research Funding; Acerta Pharma: Research Funding; Ascentage Pharma: Research Funding. Kay:Agios: Other: DSMB; MorphoSys: Other: Data Safety Monitoring Board; Infinity Pharmaceuticals: Other: DSMB; Celgene: Other: Data Safety Monitoring Board.

Abstract Background Myelofibrosis (MF) is a myeloproliferative neoplasm (MPN) characterised by the frequent presence of driver mutations in genes causing activation of JAK2 signalling pathways (JAK2, CALR and MPL). Additional mutations affecting epigenetic regulators and splicing machinery are common. Anaemia with RBC-transfusion-dependence is common in patients with advanced myelofibrosis and represents a major unmet need. The RESUME study assessed the rates of RBC-transfusion independence (TI) after therapy with Pomalidomide (POM) vs placebo in persons with MPN-associated myelofibrosis and RBC-transfusion dependence. 16% of patients in both the POM and placebo arms became TI. Aims The genetic landscape of strictly confirmed transfusion dependent MF is not fully characterised. Our aim was to analyse the genetics of transfusion-dependent myelofibrosis patients in the RESUME trial and correlate with clinical characteristics, outcome and therapy response. Methods DNA samples were available from 207 of 252 patients and analysed by targeted re-sequencing using a Fluidigm Access Array gene panel followed by next generation sequencing. The panel included JAK2, CALR, MPL, TET2, ASXL1, EZH2, DNMT3A, IDH1/2, CBL, IKZF1, U2AF1, CHEK2, TP53, SF3B1, SRSF2, SH2B3, BARD1, DAP3, HRAS, IRF4, KRAS, KIT, Mir662, NFE2, POLG, SCRIB, SETBP1, TCF12 and VPS45. Results 97% (95-99%) of subjects had a mutation in ≥1 targeted gene. 2 mutations were detected in 41% (34-48%) and ≥3 in 27% (21-33%). 7 had no detectable mutation. Mutations in JAK2V617F, CALR and MPL were identified in 66% (59-72%), 14% (8-19%) and 7% (4-11%) of subjects (Figure 1), with no driver mutation in 27 patients (13%; 9-18%) (triple-negative). 68% (61-74%) had additional non-driver mutations. 42% (35-48%) (N=86) had spliceosome mutations (U2AF1 [21%]; SF3B1 [11%]; SRSF2 [8%]; ZRSR2; [6%]). More spliceosome mutations were detected in men than women (47% [39-55%] vs 27% [15-40%]; p=0.009). Spliceosome mutations were mutually exclusive in 83 subjects and were less common in subjects with prior polycythaemia vera (17% [5-37%]) compared with prior essential thrombocythaemia (39% [22-58%]) and primary MF (46% [38-54%]; p=0.024). Mutations in epigenetic regulators (ASXL1, 28%; TET2, 8%; DNMT3A 5%; EZH2 4%) were detected at similar rates to those previously reported. High molecular risk (HMR) mutations (ASXL1, EZH2, IDH1/2, SRSF2) were detected in 36% [29-43%] of subjects. Only 10 of 105 subjects with an epigenetic regulator gene mutation had ≥1 related mutation. Subjects with JAK2V617F were significantly more likely than subjects with a CALR mutation to have: (1) ≥1 additional mutation (72% [64-79%] vs. 35% [18-54%], p=0.0001); (2) a spliceosome mutation (44% [36-53%] vs. 17% [6-36%], p=0.07), in particular a U2AF1 mutation (24% [17-32%] vs. 0%; p=0.004) and (3) a HMR mutation (38% [30-47%] vs. 21% [8-40%]; p=0.07). Survival at 1.5 years was 62% (55-67%) and was not significantly associated with the presence or number of mutations in this uniformly high-risk cohort. Survival in subjects without an SF3B1 mutation was better than those SF3B1-mutated (80% [56-91%]) vs. 59% [52-65%]; p=0.07). Driver mutation status did not influence the probability of achieving red blood cell (RBC) TI, regardless of therapy. Additional non-driver mutations were more often detected in those failing to achieve RBC-TI than those achieving RBC-TI (70% [63-77%] vs 56% [40-71%], p=0.07). Furthermore, those with additional non-driver mutations were less likely to achieve ≥50% reduction in RBC transfusions (24% [17-32%] vs. 39% [27-51%]; p=0.03). A significant correlation persisted in subjects receiving POM but not in those receiving placebo. There was also a significant correlation between U2AF1 mutations and RBC-TI in POM treated subjects compared with controls; U2AF1-mutated subjects were less likely to achieve RBC-TI (3% [1, 17%]) than U2AF1-unmutated subjects (25% [17, 34%], p=0.008). No other mutations were significantly correlated with response. Conclusion We found a high incidence of spliceosome mutations in persons with MPN-associated MF and RBC-transfusion-dependence. Mutation of U2AF1 correlated with response in subjects receiving POM but not in those receiving placebo. Incorporation of mutation profiling into clinical trial design may help to inform subgroups of patients that may benefit from the intervention. Disclosures Devos: Novartis: Consultancy; Celgene: Consultancy; Takeda: Consultancy. Gisslinger:Shire: Consultancy, Honoraria; Novartis: Honoraria, Research Funding; AOP Orphan Pharmaceuticals AG: Consultancy, Honoraria, Research Funding; Janssen Cilag: Consultancy, Honoraria. Kiladjian:Celgene: Membership on an entity's Board of Directors or advisory committees; AOP Orphan: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding. Mesa:Celgene: Research Funding; UT Health San Antonio - Mays Cancer Center: Employment; Pfizer: Research Funding; Gilead: Research Funding; NS Pharma: Research Funding; CTI Biopharma: Research Funding; Incyte Corporation: Research Funding; Novartis: Consultancy; Promedior: Research Funding; Genentech: Research Funding. Ribrag:argenX: Research Funding; BMS: Consultancy, Honoraria, Other: travel; NanoString Technologies: Consultancy, Honoraria; pharmamar: Other: travel; Infinity: Consultancy, Honoraria; MSD: Honoraria; Amgen: Research Funding; Gilead: Consultancy, Honoraria; Servier: Consultancy, Honoraria; Roche: Honoraria, Other: travel; epizyme: Consultancy, Honoraria; Incyte Corporation: Consultancy. Schiller:Celator/Jazz Pharmaceuticals: Research Funding; Pharmacyclics: Research Funding. Vannucchi:Celgene: Membership on an entity's Board of Directors or advisory committees; ITALFARMACO: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Reiser:Celgene: Employment. Zhong:Celgene: Employment. Mead:Novartis: Consultancy, Honoraria, Research Funding, Speakers Bureau; ARIAD: Consultancy; Bristol-Myers Squibb: Consultancy; Cell Therapeutics: Consultancy; Celgene: Research Funding; Elstar: Research Funding; Evotek: Research Funding.

Abstract Abstract 824 There is no standard therapy for MM relapsing after autologous hematopoietic cell transplantation (AHCT). A second AHCT can result in additional progression-free survival (PFS). Nonmyeloablative/reduced intensity conditioning (NST/RIC) allogeneic transplantation (AlloHCT) has the advantages of a tumor-free graft and the potential of a graft-versus-myeloma (GVM) effect. Few studies have compared second AHCT vs. NST/RIC AlloHCT. We compared the outcome of second AHCT or NST/RIC AlloHCTafter relapse from prior AHCT in patients with MM reported to the CIBMTR from 1995–2008. Recipients of planned tandem transplants, AlloHCT for graft failure or second malignancies and myeloablative alloHCT were excluded. 137 patients underwent second AHCT and 152 underwent NST/RIC AlloHCT (32 HLA-identical sibling and 120 unrelated donor). The table below illustrates clinical characteristics and patient outcomes. AlloHCT recipients were significantly younger (median 53 years [yrs] of age vs. 56 yrs in the AHCT cohort (p < 0.001). The groups were similar in Karnofsky performance score (KPS) and gender. Conditioning regimens differed between groups. In the AHCT cohort, 85% were melphalan based. In the NST/RIC alloHCT cohort, 38% received melphalan + other drugs and 24% received total body irradiation +/− other drugs but no melphalan (p <0.001). Time from 1st to 2nd transplant was significantly shorter for the AlloHCT cohort (30 vs. 23 months, p = 0.014). Acute graft-versus-host disease (GVH) was 35% at 60 days while chronic GVHD was 44% at 36 months.Patient CharacteristicsAutologousAllogeneicP-valueNumber of patients137152Age at 2nd transplant, median (range), years56 (28–65)53 (32–65)0.001*Gender Male84 (61)90 (59)0.720Karnofsky Score pre-transplant ≥90%68 (50)76 (50)0.884Time from 1st to 2nd transplant, months, median (range)30 (6–122)23 (6–78)0.014* 6–24 months44 (32)78 (51)0.001* >24 months93 (68)74 (49)OutcomesTreatment-related Mortality (TRM)12 months2 (1–5)13 (8–19)<0.001*60 months4 (2–8)15 (10–21)<0.001*Relapse/Progression12 months51 (43–58)72 (64–79)<0.001*36 months82 (76–88)80 (73–86)0.655Progression-free survival12 months47 (40–54)15 (10–21)<0.001*36 months13 (9–19)6 (3–10)0.038*Overall Survival (OS)12 months83 (77–89)51 (42–58)<0.001*36 months46 (37–54)20 (14–27)<0.001*60 months29 (21–38)9 (5–15)<0.001**Significant difference The most common cause of death in both cohorts was progression of MM. On multivariate analysis risk of death was higher for alloHCT (HR 2.38, p < 0.001), KPS < 90 (HR 1.96, p < 0.001), and year of transplant (2004 or earlier, HR 1.77, p = < 0.001). AlloHCT was associated with significantly higher risk of TRM (HR 7.14, p < 0.001). Durie-Salmon Stage III was associated with a higher risk of relapse (HR 2.70, 95% CI: 1.93–3.80, P<0.001) and treatment failure in the alloHCT group (HR 3.05, 95% CI: 2.20–4.22, p < 0.001). We conclude that patients with MM who underwent NST/RIC alloHCT after AHCT failure experienced higher TRM and lower probability of survival compared with those who received second AHCT. Because genetic risk data were not available for these patients, we cannot exclude the possibility that the alloHCT population was a higher risk population. Despite this limitation, our data demonstrate that the value of alloHCT after relapse from prior AHCT is limited. Disclosures: No relevant conflicts of interest to declare.

Abstract Abstract 504 Background: There is limited data on the outcomes of AHCT for MM as a salvage approach following relapse after an initial (upfront) AHCT. We analyzed the outcomes of second salvage AHCT for relapsed MM. Methods: From the CIBMTR database we identified 187 patients (63% male) who had second AHCT (AHCT2) as salvage for MM relapsing after a prior AHCT (AHCT1). Planned tandem AHCT and AHCT2 within 6 months after AHCT1 were excluded. Cumulative incidence of relapse, non-relapse mortality (NRM) and the probabilities of progression-free (PFS) and overall survival (OS) were analyzed and multivariate analysis performed using proportional hazards models. Results: Median age at transplant was 57 (range 28–72) and 59 years (range 28–74) respectively at AHCT1 and AHCT2 respectively. Median follow up after AHCT2 was 47 months (range: 3–97). The proportion of IgG, IgA and free light chain subtypes was 48, 20 and 18% respectively. Durie Salmon stage at diagnosis was stage I (6%), II (21%), III (59%) respectively. Seven and 13% had serum creatinine > 1.5 mg/dl at AHCT1 and AHCT2 respectively. Conditioning regimen was single agent melphalan was in 78% and 84% for AHCT1 and AHCT2 respectively. At the time of AHCT2, majority (60%) were chemotherapy resistant compared with 80% patients with sensitive disease prior to AHCT1. Median time from AHCT1 to relapse was 18 months, with 32% relapsing within the first year, 29% in the second year, 19% in the third year and 19% beyond the third year after AHCT1. There was heterogeneity in the time to AHCT2 after relapse although 67% had AHCT2 within a year after relapse (median 7 months). Median between AHCT1 and AHCT2 was 32 months with the majority (69%) beyond 24 months. After AHCT2, day 28 engraftment rates of neutrophils and platelets were 96% (95% CI: 93–98) and 88% (95% CI: 83–92). Complete remission (CR) rates after AHCT1 were 43% whereas it was only 25% following AHCT2. After AHCT2, cumulative incidence of relapse at 1, 3 and 5 years was 51, 82, and 91% respectively. After AHCT2, one year NRM was 2%. PFS was 47%, 13% and 5% and OS was 83%, 46% and 29% at 1, 3 and 5 years respectively. In multivariate analysis, those relapsing within 36 months of AHCT1 had a higher risk of relapse (HR 1.58, p = 0.036) and higher risk of treatment failure and inferior PFS (HR1.52, p= 0.04). Mortality was higher for those relapsing early after AHCT1 (<36 months, HR 1.91, p = 0.02). OS was superior and mortality lower for AHCT2 performed after 2004-08 (HR 0.61, p=0.02). Conclusion: AHCT2 can be safely performed in relapsed MM with the best outcomes observed in later relapses (>36 months from AHCT1). OS has improved since 2004, but NRM, relapse and PFS after AHCT2 were not impacted by year of AHCT2. Thus the improved OS likely reflects greater availability of novel agents for post AHCT progression. Disclosures: No relevant conflicts of interest to declare.

Abstract In CN-AML, mutations of specific genes have prognostic significance and are used for treatment guidance. However, these mutations are in general not mutually exclusive and CN-AML patients (pts) frequently harbor mutational combinations with unclear prognostic significance because of the concurrent presence of mutations with favorable (CEBPA, NPM1) and unfavorable (ASXL1, DNMT3A, IDH1, IDH2, FLT3-ITD, MLL-PTD, RUNX1, TET2, WT1) impact on outcome. Here, we report the frequency and clinical significance of isolated mutations and their combinations in 364 CN-AML pts. Younger [<60 years (y); n=163] and older (≥60 y; n=201) pts were intensively treated on frontline CALGB/Alliance protocols and, per protocol, did not undergo allogeneic stem cell transplantation in first complete remission (CR). Median follow-up for pts alive was 8.7 y. Pts were analyzed centrally for mutations in the ASXL1, CEBPA, DNMT3A, IDH1, IDH2, NPM1, RUNX1, TET2, and WT1 genes and the presence of FLT3-ITD, FLT3-TKD and MLL-PTD. We found that 99% of pts had ≥1 and 88% ≥2 of the 12 mutations. Specifically, 11% of pts had 1 gene mutated (n1), 40% had 2 (n2), 31% 3 (n3), and 18% harbored ≥4 mutated genes (n4). The distribution of the different n groups in younger and older pts was similar (Fig 1). The expected frequency of each mutated gene in every numerical (n) group was calculated as follows: [no. of pts with the mutated gene] x [total no. of mutations in the n group] / [total no. of mutations in all pts], and compared with the observed frequency. CEBPA, IDH2, NPM1, and RUNX1 mutations were overrepresented in the n1 and n2 groups (P<.001, P<.001, P=.002, P=.02, respectively) and thus likely represent “early” events in leukemogenesis. In contrast, DNMT3A, FLT3-ITD, and TET2 mutations were more frequent in the n3 and n4 groups (P=.008, P<.001, P=.02, respectively) suggesting that they are likely “late” events. The distribution among age groups and clinical impact of the “early” mutations (i.e., CEBPA, NPM1, IDH2) presenting as isolated mutations or in combination with other mutations were then investigated. Isolated RUNX1 mutations could not be evaluated due to small sample size. Younger pts were more likely to harbor an isolated “early” mutated gene than older pts (21% v 12%, P=.03). Specifically, isolated CEBPA and NPM1 mutations were more frequent in younger pts compared with the older (7% v 2%, P=.007; 7% v 3%, P=.07, respectively). In pts with isolated CEBPA mutations, these were always bi-allelic. Single “early” mutated genes varied in their impact on CR achievement (CEBPA: 100%, NPM1: 94%, IDH2: 67%), and overall survival (OS; 3-y OS: CEBPA: 87%, NPM1: 69%, IDH2: 22%). Acquisition of additional mutations adversely modified the prognostic significance of favorable “early” mutations in the CEBPA (Fig 2A) and NPM1 (Fig 2B) genes, but left unchanged the unfavorable effect of IDH2 mutations (Fig 2C). Two mutational combinations were exceptions to this: younger pts with both NPM1 and DNMT3A mutations (8 pts) had an excellent prognosis with 3-y OS of 100%; and older pts with concurrent NPM1 and IDH2 mutations (9 pts; all IDH2 mutations were in codon R140) had a high 3-y OS rate of 67%. We conclude that CEBPA and NPM1 mutations mainly define pts with favorable prognosis when they present as single markers, whereas mutated IDH2 predominantly associates with adverse outcome. The NPM1+DNMT3A and NPM1+IDH2 mutational patterns may define novel favorable molecular subtypes in younger and older CN-AML pts, respectively, but these results require corroboration in larger pt cohorts. Disclosures: Mrózek: AMERICAN SOCIETY OF HEMATOLOGY: I will review abstracls submitted for presentation at the 55th ASH Annual Meeting in the 611 category Other. Eisfeld:AMERICAN SOCIETY OF HEMATOLOGY: I will review abstracls submitted for presentation at the 55th ASH Annual Meeting in the 608 category Other.

Abstract In chronic lymphocytic leukemia (CLL), it is recognized that germinal center experience by the cell-of-origin has an influence on the malignant phenotype and impacts clinical behavior. Measurement of the mutational status of the IGHV locus is a commonly used approach that subgroups patients according to this natural history. Other biomarkers correlating with these subgroups, such as ZAP70 expression and ZAP70 DNA methylation, are also used. Recently, we and others have found vast differences in global DNA methylation profiles among CLL cases that classifies distinct subgroups related to their natural history. Here we systematically evaluate various markers of natural history of the cell-of-origin for their ability to discriminate clinical outcomes. IGHV mutational status and ZAP70 expression are currently the most widely used prognostic markers of germinal center experience. These markers are generally concordant, with IGHV-unmutated associating with ZAP70 positivity. To compare their relative prognostic impact, we selected CLL cases from a large sample cohort collected as a prospective study maintained by the CLL Research Consortium that were discordant (IGHV-mutated and ZAP70(+) or IGHV-unmutated and ZAP70(-); n=192). We found a significant difference in time-to-first-treatment (TTFT; P=0.002) and overall survival (OS; P=0.032) between discordant groups, with curves separating according to IGHV as opposed to ZAP70 (Fig.1A). DNA methylation-based classification of CLL according to their natural history has not been compared to IGHV status. This classification strategy subgroups the majority of cases into low-programmed CLL (LP-CLL) or high-programmed (HP-CLL) subgroups that generally correlate with IGHV-unmutated/ZAP70(+) and IGHV-mutated/ZAP70(-), respectively, along with a minority of cases in an intermediate (IP-CLL) subgroup. We used our previously-described DNA methylation assay, based on MassARRAY interrogating 7 genomic loci including ZAP70, to group the 192 samples with discordant IGHV/ZAP70 results with 327 samples analyzed previously from the same cohort. DNA methylation (LP-CLL vs. HP-CLL) outperformed IGHV classification (unmutated vs. mutated) in terms of separating TTFT curves (n=432; LP-CLL vs. HP-CLL median difference=4.8 years, unmutated vs. mutated IGHV median difference=2.9 years) and OS curves (n=491; LP-CLL vs. HP-CLL median difference=12.9 years, unmutated vs. mutated IGHV median difference=6.8 years). To evaluate whether DNA methylation subgroups provide additional prognostic information to IGHV, we compared TTFT and OS within IGHV subtypes. We found that the ability of DNA methylation subgrouping to separate OS curves was maintained in the IGHV-unmutated subtype (P<0.001; Fig.1B) and TTFT in both IGHV-unmutated and mutated subtypes (P<0.001 and P=0.016, respectively). Conversely, IGHV subtypes did not significantly separate outcomes within DNA methylation subgroups (P>0.5 for all). Further investigation of the features of DNA methylation subgroups as defined revealed a strong bias in the frequency of several recurrent adverse prognostic somatic mutations. In particular 17p deletions and EGR2, SF3B1 and XPO1 mutations occurred frequently within the LP-CLL subgroup. We next explored whether the presence of each mutation correlated with clinical outcome within its corresponding DNA methylation subgroup, and found heterogenous results. EGR2 mutations separated TTFT in the LP-CLL subgroup (P=0.0009), but 17p, XPO1 and SF3B1 did not (P>0.3). This suggests that the cell-of-origin should be taken into account to accurately assess the clinical impact of somatic mutations. In summary, we demonstrate that among discordant cases for IGHV/ZAP70, IGHV appears to be a stronger prognostic factor for both TTFT and OS. When then evaluating the relative importance of DNA methylation subgrouping and IGHV status on TTFT and OS, we find that DNA methylation subgrouping is the more powerful prognostic factor. Among the markers of the cell-of-origin, DNA methylation is a strong tool for risk-stratifying patients and should be considered in development of risk prediction models. Figure 1. A) Kaplan-Meier analysis of TTFT of IGHV-mutated and ZAP70(+) versus IGHV-unmutated and ZAP70(-) (P=0.002, median difference=4.4 years). B) Kaplan-Meier analysis of OS in patients with unmutated IGHV separated by DNA methylation subgroup (P<0.001). Figure 1. A) Kaplan-Meier analysis of TTFT of IGHV-mutated and ZAP70(+) versus IGHV-unmutated and ZAP70(-) (P=0.002, median difference=4.4 years). B) Kaplan-Meier analysis of OS in patients with unmutated IGHV separated by DNA methylation subgroup (P<0.001). Disclosures Wierda: Acerta: Research Funding; Novartis: Research Funding; Gilead: Research Funding; Genentech: Research Funding; Abbvie: Research Funding. Brown:Infinity: Consultancy; Janssen: Consultancy; Gilead Sciences: Consultancy; Roche/Genentech: Consultancy; Celgene: Consultancy; Sun BioPharma: Consultancy; Pfizer: Consultancy; Abbvie: Consultancy. Kipps:Roche: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Gilead: Consultancy, Honoraria, Speakers Bureau; Pharmacyclics, LLC, an AbbVie Company: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria, Research Funding.

Abstract Abstract 4217 Background: AHCT improves survival (OS) in newly diagnosed MM patients (pts) in large randomized trials. These trials have limited eligibility to younger, healthier pts. Selection of older pts and those with co-morbid illness for AHCT is problematic. HCT-CI, originally developed as predictor of post-allogeneic transplant outcomes, maybe valuable in stratifying risk of transplant related mortality (TRM) risk and OS in the AHCT setting. We investigated the relative impact of HCT CI along with other patient and MM related variables on outcomes after AHCT in a large cohort of transplant recipients. Methods: Outcomes of 1156 MM pts receiving AHCT after high dose Melphalan (MEL) between 2007 and 2010 reported to the CIBMTR (Center for International Blood and Marrow Transplant Research) were analyzed. HCTCI scores and individual comorbidities were prospectively reported at time of AHCT. Median follow up of survivors was 26 month. The impact of HCTCI and other potential prognostic factors including Karnofsky performance status (KPS) on OS were studied in multivariate Cox regression models. Results: HCTCI score was 0, 1, 2, 3, >3 in 42%, 18%, 13%, 13% and 14% respectively. Most common co-morbidities included pulmonary, diabetes, obesity, psychiatric, cardiac, renal and prior solid tumor. Using consolidated HCTCI scores, patients were stratified initially into 3 risk groups – HCTCI 0 (42%) vs. HCTCI 1–2 (32%) vs. HCTCI >2 (26%). Males and Caucasians were more likely to have greater HCTCI score. Higher HCTCI was associated with lower KPS <90 (33% in HCTCI 0 cohort vs. 50% in HCTCI >2). HCTCI score >2 was associated with MEL dose reduction to 140 mg/m2 (22% vs. 10% in score 0 cohort). Cytogenetic risk and MM related factors were not correlated with HCTCI. TRM at 12 month was 2%, 2%, and 3% for 3 risk groups. With extremely few TRM events, multivariate analysis did not suggest an impact of HCTCI. OS was 95%, 92%, 92% at 1 year and 87%, 81%, 80% at 2 year, respectively. OS was inferior for HCTCI >2 cohort (RR of death 1.48, p=0.02) and HCTCI cohort 1–2 (RR 1.37, p=0.04) compared with HCTCI 0 cohort. There was no significant difference in OS between HCTCI >2 vs. HCTCI 1–2 (p=0.64). Therefore the latter 2 groups were combined as the HCTCI >0 cohort [N=667] and compared with HCTCI=0 [N=489] in multivariate models. HCTCI >0 predicted inferior OS (RR of death= 1.41, p=0.01). Other significant predictors of inferior survival were KPS <90 (RR of death 1.61, p<0.01), IgA subtype (RR 1.64, p<0.01), >1 pretransplant regimen (RR 1.47, p<0.01), resistant MM at AHCT (RR 1.78, p<0.01). Major cause of death in both groups was progressive MM. Conclusion: In clinical practice, higher HCTCI score was associated with MEL dose reduction. Mortality after AHCT is predominantly related to MM progression/relapse with low incidence of TRM. Higher HCTCI scores were independently associated with inferior OS. KPS remains an important tool for risk stratification. Disclosures: No relevant conflicts of interest to declare.

Abstract Abstract 597 Back-ground: Planned upfront AHCT after initial induction improves both overall and progression free survival (OS and PFS) for patients with MM in randomized trials. However, it is unclear whether patients with less than partial response after a finite period of initial therapy should receive AHCT immediately or undergo salvage therapy to improve the level of response pre-AHCT. Methods: We addressed this question by querying the CIBMTR (Center for International Blood and Marrow Transplant Research) database. Between 1995 and 2010, 575 patients received upfront AHCT (within 12 mo of diagnosis) for MM after failing to achieve partial or complete response (PR/CR) to initial induction therapy. The 2326 patients who achieved CR/PR pre- AHCT had superior survival and were excluded (Fig 1). The study groups included: the 324 who received additional salvage chemotherapy after non-response to first line therapy (SALVAGE) and then proceeded to AHCT; and 251 who had no additional salvage chemotherapy but proceeded to AHCT immediately (NOSALVAGE). Outcomes of non-relapse mortality (NRM), relapse, PFS, and OS were compared measuring overall survival from diagnosis. Multivariate analysis using Cox proportional hazard models incorporated the following variables: age, gender, Karnofsky performance score, immunoglobulin isotype, stage, serum creatinine at diagnosis, disease status pre-AHCT, conditioning regimen, time from diagnosis to transplant, type of transplant (single/tandem), novel agents (thalidomide/bortezomib/lenalidomide), time-period of transplant and number of lines of pre-transplant therapy (= or &gt; 1 line) on outcomes. Results: Study cohorts were well balanced with respect to age, gender, Karnofsky performance status, immunoglobulin isotype, disease stage. More pts in NOSALVAGE received VAD (60% vs. 47%, p=0.002) for first line therapy and had a base-line creatinine &gt; 1.5mg/dl (27% vs. 17%, p=0.008). In the SALVAGE cohort, 245 (75%) received one, 65 (20%) two and 14 (4%) three salvage regimens. 197/245(61%), 47/65(59%), 2/14 (14%) were sensitive to 1st, 2nd and 3rd line salvage therapies prior to transplant resulting in 55% in SALVAGE achieving CR/PR pre-AHCT. In NOSALVAGE, 93% were in a minimal response or stable disease state and 7% with progressive disease. In SALVAGE, 55% received AHCT after 2004 compared with 38% in NOSALVAGE likely reflecting additional agents available for salvage. The majority of patients in either cohort (&gt;90%) received Melphalan based AHCT and a minority (&lt;20%) planned tandem AHCT. In the SALVAGE cohort, 44% had AHCT &lt;8 months after diagnosis as compared to 74% in NOSALVAGE (P&lt;0.001). Median follow-up for survivors was 61 mo and 68 mo, respectively. Six year NRM (p=1.0), relapse (p=0.2), PFS (p=0.2) or OS (p=0.5) were no different between the two cohorts). On multivariate analysis, a higher creatinine at diagnosis (&gt; 1.5 vs &lt; 1.5) predicted for higher NRM (p=0.008) and inferior OS (p=0.003), and the use of novel agents predicted for lower risk of NRM (HR = 0.37, P=0.04). Since more time elapsed between diagnosis and transplant among patients who received additional salvage chemotherapy, a left-truncated multivariate analysis was performed to reduce this potential waiting time bias. This multivariate analysis produced similar outcomes for TRM, relapse, treatment failure and OS irrespective of whether or not salvage regimens were used (Fig 2). Conclusions: In the setting of upfront AHCT, no additional benefit could be demonstrated for salvage regimens to upgrade response prior to AHCT. Disclosures: No relevant conflicts of interest to declare.