ALBERT

Description: Two tools have recently been developed to predict allogeneic HCT tolerance: the HCT-comorbidity index (HCT-CI) (Sorror, Blood 2005) and “risk group” (Artz BBMT, 2006). The HCT-CI focuses on a comprehensive list of comorbidities whereas the “risk group” combines a simple comorbidity tool and performance status to identify patients at high risk for transplant-related mortality (TRM) and inferior survival. However, clinical parameters have limitations of reproducibility and accuracy. Biomarkers represent another promising method of risk stratification. Thus, we analyzed whether biomarkers independently predicted HCT tolerance. Among 112 consecutive transplants on a single protocol, 81 patients with pre-HCT cryopreserved sera underwent analysis of C-reactive protein (CRP) and 79 of interleukin-6 (IL-6) levels. AML and MDS represented the most common diagnosis (57%). All patients underwent HCT using fludarabine (125 mg/m2 IV total), melphalan (140 mg/m2 IV total) and alemtuzumab (100 mg IV total). The median age was 52 yrs and 46% had active disease at HCT. The median follow-up was 42 months. To determine tolerance, we evaluated initial hospital duration, aGVHD, and TRM. Median duration of initial hospitalization for HCT was 23 days and Grade II–IV aGVHD developed in 30 pts. Day 100 and day 180 TRM were 17% and 23% respectively. The median pre-HCT CRP level was 18.5 mg/L (mean, 40.5 mg/L; range, 0.17 to 180). The median IL-6 was 78.3 mg/L (range, 10 to 2258). CRP and IL-6 above the median were tested as adverse risk factors. High CRP was strongly associated with prolonged hospitalization (P=0.007) whereas increased IL-6 was not (P=0.30). HCT-comorbidity index (HCT-CI) ≥ 3(P=0.126) and “risk-group” (P=0.091) did not confer an increased risk of prolonged hospitalization. In univariate analysis, CRP above the median also predicted for grade II–IV aGVHD (P= 0.003). TRM was predicted by CRP (P=0.013) but not IL-6 (P=0.22). Multivariate analysis showed CRP retained independent predictive value for TRM when considering adverse risk factors of age ≥50, HCT-CI ≥ 3, active disease, or “risk group”. The impact of these biomarkers was limited to HCT tolerance as CRP and IL-6 did not predict for increased relapse (P=0.42 for both). Finally, inferior overall survival was associated with pre-HCT CRP (P= 0.029) but not IL-6 (P= 0.48). CRP holds promise as a reproducible biomarker to predict HCT related morbidity and mortality independent of standard measures. IL-6 may be less useful. These findings require validation, but because of the ready availability and reproducibility of CRP, this biomarker could be rapidly integrated into other HCT risk-assessment tools. P value for Transplant-Related Mortality Predictive Factor Univariate Multivariate HCT- CI-hematopoietic cell transplant comorbidity index Age 0.27 0.79 Disease 0.16 0.26 HCT-CI 0.79 0.98 Risk Group 0.11 0.061 C-reactive protein 0.013 0.047 Interleukin-6 0.22 -

Description: Early trials evaluating nonmyeloablative allogeneic hematopoietic transplant (NST) for metastatic renal cell carcinoma (RCC) have reported widely discordant results. We performed a meta-analysis of the published literature to identify prognostic factors for NST. However, known RCC prognostic factors are not often detailed in these studies, hindering the identification of patient differences among studies. We explored the order of patient entry as a surrogate marker for patient selection, hypothesizing that patient selection, rather than treatment differences, accounted for early promising results. Patient specific data and adequate follow-up were available for 76 patients from six studies. The rank of patient entry on each individual trial was recorded as well as ≥ grade 2 acute graft-versus-host disease (aGVHD), recipient age, recipient sex, response (partial plus complete), and survival. Multivariable analyses for response and survival were modeled using logistic and cox proportional hazards regression, respectively. The mean overall response rate was 22/76 (29%), with responses across individual studies significantly varying from 0% to 57% P =.009 by Chi-square). Median Kaplan-Meier survival was 263 days. Neither age nor sex was significantly ( associated with response or survival. Acute GVHD occurred in 42% of individuals and correlated with response (OR=9.9, P =0.012) but not survival (HR=1.53, P=0.28). When adjusting by study, later patient entry rank reduced the probability of response (OR=0.30, P =0.007) and survival (HR=1.89, P =0.006). Alternatively, being among the first five patients enrolled in a given study relative to subsequent patients, increased the probability of response (OR=6.69, 95% CI 1.95–39.1, P=0.005), even when adjusting for aGVHD, and afforded a survival benefit (HR= 0.45, 95% CI 0.022 −0.92, P=0.028). The prognostic strength of patient entry rank strongly suggests “entry bias” in patient selection. This bias potentially accounts for the large variation in outcome among studies and for the promising results in early studies. Entry bias analysis offers a novel method to assess early phase trials for selection bias, when detailed individual prognostic information is lacking. Further study is warranted to determine to what extent, if any, entry bias occurs in other clinical trial settings. Figure Figure

Description: Chromosome translocations are among the most common genetic abnormalities in human leukemia. Each translocation may affect a different pair of genes. The abnormally expressed genes that result from the different translocations provide a rich source for identifying specific markers for clinical diagnosis of each translocation. Microarrays have identified genes differentially expressed in different translocations but the results between laboratories are not always compatible. We used SAGE to quantitate gene expression in bone marrow (BM) samples from 22 patients with four types of AML, namely de novo AML M2 with t(8;21), AML M3 or M3V with t(15;17), AML M4Eo with inv(16), AML M5 with t(9;11) or secondary t(9;11).We generated SAGE libraries from CD15+ leukemic myeloid progenitor cells, collecting over 106 SAGE tags, of which 209,486 were unique tags; 136,010 were known genes and ESTs, and 73,476 were novel transcripts. SAGE tags for further analysis were selected based on a 5-fold difference between patients’ samples and normal CD15+ BM; they were also statistically significantly different at the 5 % level. Using these strict criteria, we identified 1,571 unique tags, of which 1,405 were known genes and ESTs, and 166 were novel transcripts that were either specific for each translocation or were common for all four translocations. Changes in expression of these known genes which fall into different gene ontogeny functional categories varied by translocation. For example, those associated with macromolecular biosynthesis, transport and transcription were most altered in the t(8;21); those related to defense response and apoptosis were altered in the t(15;17); cell proliferation genes were most affected by the t(9;11). Cell surface receptor signaling, intracellular signaling and RNA processing were altered in treatment related but not in de novo t(9;11). From this analysis, we identified the functional molecular signature of each translocation. We designed a custom microarray to validate our SAGE data analysis. Our initial pilot microarray experiment with 96 genes that were specific for each translocation or common for all translocations used mononuclear cells from normal and patient BM and translocation cell lines, ME-1, THP-1, Mono Mac-6, Kasumi 1, NB-4; the array data from BM matched the SAGE data for 48-75 % of genes and the majority of cell lines, except ME-1, matched at least 70 % with the SAGE results for the appropriate translocation. We have now designed a full-scale microarray that contains over 400 probes including 250 known genes, 61 ESTs, 45 novel sequences and 48 genes reported by others. We will test at least 100 patients’ samples with the four translocations to validate which genes provide a robust, reproducible “fingerprint” for each translocation and for all translocations. We will correlate our microarray data with age, sex, race, response to treatment, survival and other mutations (FLT3, MLL ITD, etc) to identify any transcripts that might reliably define these categories. Our results will provide new insights into genes that collaborate with each translocation to lead to a fully leukemic phenotype.

Description: Introduction Community respiratory viruses (CRV) are important agents of morbidity and mortality after HCT. Although several studies have included subsets of patients 50 years and older, no studies have specifically investigated the incidence of CRV infections in older patients nor the influence of health impairments as measured by Geriatric Assessment (GA). Methods We retrospectively reviewed patients undergoing allogeneic HCT, age 50 years or older at HCT, and who completed GA prior to HCT. The GA instruments and thresholds for prognostic marker analyses followed our prior published results (Muffly L et al, Haematologica 2014). We limited the cohort to transplant years 2009-2013 to coincide with the availability of a multiplex PCR for CRV covering respiratory syncytial virus (RSV), parainfluenza virus (PIV) 1-4, influenza A and B, Human metapneumovirus (HMPV), coronavirus (hCoV), entero/rhinovirus (human EV), and adenovirus (Adv). Testing was performed at the discretion of the treating physician as clinically indicated through either nasal swab and/or bronchoalveolar lavage. We analyzed cumulative incidence of CRV after HCT at day 100 and one year, outcomes, and univariate risk factors for infection. Results The baseline characteristics of the 121 evaluable patients included: median age 58 years (50 - 73); AML (39%); MDS (17%); not in remission/response (39%); haplo-cord (21%); myeloablative conditioning (16%); and T-cell depletion by ATG/alemtuzumab (95%). Thirty-three first-episode CRV infections occurred among 121 evaluable patients for a cumulative incidence of CRV by day 100 of 10.5% (95% CI: 5.5 – 17.2) and 1 year of 24.8% (CI: 16.9-33.4%). Multi-plex PCR identified the following CRV infections: influenza A or B (n=4, 12%), RSV (n=7, 21%), PIV (n=5, 15%), human EV (n=11, 33%), hCoV (n=1, 3%), Adv (n=3, 9%), and HMPV (n=2, 6%). Co-infections occurred in roughly half of the cases and commonly included cytomegalovirus, Pseudomonas aeruginosa, Aspergillus fumigatus and Clostridium difficile. Morbidity and mortality were restricted to those who developed lower respiratory tract infections (LRTI). The outcomes of LRTIs are presented in table 1. Viruses were grouped within the table according to characteristic disease course as previously described (Wolfromm et al, BBMT 2014). Twenty-two (67%) patients with CRV infection required hospitalization with a median length of stay of 11 days. Importantly, CRV directly contributed to death in 7 patients (32% of LRTIs). Of these, 5 patients had a bacterial co-infection and 6 of the 7 were receiving steroids at CRV onset. CRV infection showed no association with GA measures of high HCT-CI (p=.34), high CRP (p=.86), lower performance status (p=.66), impairments in instrumental activities of daily living (p=.96), frail (p=.83), low self-report physical function (p=1.0), and low self-report mental function (p=.51). The one-year incidence of CRV was non-significantly lower for albumin below 3.5 g/dL (11% vs. 28%, p=0.17), slow walk speed (11% vs 31%, p=.06) and age 60+ versus 50-59 (20% vs 28%, p=.36). The limited sample size precluded an analysis of CRV associated morbidity by GA. Conclusions The incidence of CRV infection of 25% among older allogeneic HCT recipients by multi-plex PCR appears similar or slightly lower than previously reported data for HCT in general. Health impairments by GA did not translate into heightened risk of CRV infection. Most LRTI related deaths occurred in patients receiving steroids at the onset of infection. Larger prospective studies will be needed to determine if patient health status influences CRV related morbidity in older adults. Table 1: Type and Outcomes of Lower Respiratory CRV Infections in Older HCT Patients Outcome N (%) LRTI 22 Etiology Flu/RSV/PIV/HMPV 13 (59) AdV 3 (14) hCoV/human EV 6 (27) ICU 9 (41) Ventilation 6 (27) Non CRV co-infection 16 (72) Death 7 (32) Disclosures Larson: Novartis: Consultancy, Research Funding. Stock:Sigma-Tau: Membership on an entity's Board of Directors or advisory committees, Research Funding. Artz:Miltenyi Biotec: Research Funding.

Description: Key Points Dnmt3b acts as a haploinsufficient tumor suppressor in Myc-induced lymphomas.

Description: Abstract 225 Cancer cells are characterized by abnormal DNA methylation, including overall genomic hypomethylation with concurrent region-specific hypo- and hyper-methylation, causing aberrant activation of some genes and the silencing of others. Three DNA methyltransferase (DNMT) enzymes catalyze DNA methylation in eukaryotic cells, DNMT1, DNMT3A, and DNMT3B. We discovered previously that cancer cells exhibit aberrant splicing of the DNMT3B gene, which produces transcripts containing premature stop codons that encode truncated proteins lacking the catalytic domain. When we bred transgenic mice expressing DNMT3B7, one of the aberrantly spliced DNMT3B isoforms found most commonly in cancer cells, with the Eμ-Myc mice, a mouse model for B cell lymphomas, we observed an acceleration of mediastinal lymphomagenesis along with changes in the expression of several genes involved in oncogenesis. The acceleration in tumorigenesis was associated with global DNA hypermethylation, and further analyses showed that these changes in DNA methylation were heterogeneous in tumors derived from Eμ-Myc/DNMT3B7 mice, a phenomenon reminiscent of human tumors. We hypothesized that DNMT3B7 altered DNA methylation by functioning as a dominant negative isoform of full-length endogenous mouse Dnmt3b, and therefore tested a second mouse model that has defects in DNA methylation. The introduction of Dnmt3b heterozygosity (Dnmt3b+/−) into the Eμ-Myc background accelerated mediastinal lymphomagenesis to an even greater extent, with more than 90% of the Eμ-Myc/Dnmt3b+/− mice developing mediastinal lymphomas within the first 120 days. This was also associated with an increase in global DNA methylation as measured by liquid chromatography-mass spectrometry, to a larger extent than in the Eμ-Myc/DNMT3B7 mice. Interestingly, the tumors from Eμ-Myc mice themselves showed global hypermethylation when compared to non-transformed cells from Eμ-Myc mice, suggesting that the transformation of cells that express Myc is a key aspect in the induction of global DNA hypermethylation. These observations led us to the hypothesis that Myc-mediated tumorigenesis is particularly sensitive to changes in DNA methylation. Brenner et al. demonstrated that Myc binds to Dnmt3a/b and recruits the methyltransferases to promoter regions of Myc targets, leading to DNA hypermethylation in these regions. We have also found previously that DNNMT3B7 binds with full-length DNMT3B, by co-immunoprecipitation studies. We hypothesize that either in the presence of DNMT3B7 or with Dnmt3b heterozygosity, Myc-Dnmt3a/b binding at promoters is enhanced, which leads to hypermethylation and repression of gene expression. Using Mycbp, a gene that was repressed in Eμ-Myc/DNMT3B7 tumors, we demonstrated that its promoter region was hypermethylated in both Eμ-Myc/DNMT3B7 and Eμ-Myc/Dnmt3b+/− tumors. The E-box, a conserved sequence located ∼100bp upstream of the transcription start site that Myc binds specifically, was hypomethylated in the Eμ-Myc/DNMT3B7 tumors, suggesting that there was an enrichment of Myc binding at this region. Chromatin immunoprecipitation analyses confirmed increased binding of Myc at the E-box of Mycbp in the Eμ-Myc/DNMT3B7 tumors. Furthermore, we also demonstrated that Myc expression induced all the three DNA methyltransferases, suggesting that Myc-mediated lymphomagenesis may occur using a feedback loop which enhances expression of the DNA methyltransferases to regulate particular genes involved in tumorigenesis. This study offers an insight into the mechanism behind Myc-mediated tumorigenesis and provides evidence for the central role played by changes in DNA methylation patterns in this process. Disclosures: No relevant conflicts of interest to declare.

Description: The t(4;11)(q21;q23) translocation is a hallmark of infant acute lymphoblastic leukemia (ALL), which results in the fusion of the MLL gene on chromosome 11 and the AF4 gene on chromosome 4. MLL-AF4 fusion is the most common consequence of chromosomal translocations in infant leukemia and is associated with a poor prognosis. To identify leukemia-related genes, we used the SAGE technique to compare gene expression profiles between two MLL-AF4 patient samples and one normal sample (CD19+ progenitor B cells; 216,464 tags in total). We identified 61 candidate genes that appear to be abnormally expressed in the leukemia samples (29 up- and 32 down-regulated). Remarkably, we found that many candidate genes appear to play important role in the development of B cells. In addition, many candidate genes can bind with and/or regulate other candidates in the candidate gene list. For example, SYK, BTK and BLNK can bind directly and regulate each other. SYK can also bind directly with TNFRSF1B. In addition, EBF may positively regulate BLK, while BLK can bind directly with BTK. All six of these genes are significantly down-regulated in MLL-AF4 leukemia samples. BTK, SYK and BLK are tyrosine kinases. BTK (B-cell progenitor tyrosine kinase) is a key regulator in B-lymphocyte differentiation and activation. BLK (B-lymphocyte-specific tyrosine kinase) is expressed only in B lymphocytes, and controls pre-B cell development. SYK (spleen tyrosine kinase) is widely expressed in hematopoietic cells, which can phosphorylate BLNK (B-cell linker protein). BLNK represents a central linker protein that bridges the B-cell receptor-associated kinases and may regulate B-cell function and development. EBF (early B-cell factor) is a tissue-specific and differentiation stage-specific DNA-binding protein, and mice lacking Ebf are unable develop B lymphoid cells. TNFRSF1B is strongly expressed on stimulated T and B lymphocytes. Moreover, previous studies indicate that BTK, BLK, SYK, BLNK and TNFRSF1B can positively regulate apoptosis, while BTK can also positively regulate differentiation. Thus, their down-regulation may inhibit apoptosis and differentiation, and thereby contribute to leukemogenesis. In contrast, GNA12, a transforming oncogene which can enhance proliferation and transformation and can bind directly with BTK, is significantly up-regulated in MLL-AF4 leukemia cells. Its up-regulation may also be important to leukemogenesis. Taken together, the deregulation of the important candidate genes may contribute to leukemogenesis through inhibiting apoptosis and differentiation while promoting proliferation of hematopoietic cells. We have validated the expression patterns of the candidate genes with real-time quantitative RT-PCR and are studying the functions and pathways of the validated candidate genes using RNAi and retrovirus transduction over-expression methods. In addition, we will also establish knock-in or knock-out mouse models for the most promising functional candidate genes to see the effect on the development of leukemia. Our studies will provide important insights into the complex functional pathways related to MLL rearrangements in the development of acute lymphoblastic leukemia, which may lead to more effective therapy for these leukemias.

Description: Although more than 50 different loci translocate to the MLL gene at chromosome band 11q23, resulting in either acute myeloid leukemia (AML) or acute lymphoblastic leukemia (ALL), no unifying property is shared by all partner genes. The translocations result in a functional fusion of the N-terminal part of MLL gene and the C-terminal part of each partner gene, presumably leading to changes in the expression of the normal target genes, most of which have not been identified. Although genetically engineered mouse leukemia models have been widely used, few systematic studies have evaluated whether such models are valid equivalents of human leukemia. We used serial analysis of gene expression (SAGE) to obtain genome-wide gene expression profiles in normal myeloid progenitor cells from human CD15+ and mouse Gr-1+ cells. We also analyzed four patient samples (two with each fusion) and two retrovirally-induced mouse leukemias containing either MLL-ELL [t(11;19)(q23;p13.1)] or MLL-ENL [t(11;19)(q23;p13.3)] fusions, and a cell line from a leukemia mouse transduced with an MLL-ELL fusion. MLL-ELL and MLL-ENL fusions are frequently involved in human AML, while MLL-ENL is also seen in human ALL. 484,303 SAGE tags were identified from the nine samples (40,000 to 100,000 tags per sample), yielding 103,899 unique SAGE tags in the human and 60,993 in the mouse samples. Analysis of the SAGE data identified 43 candidate genes that appear to be abnormally expressed in both human and mouse myeloid leukemia progenitor cells with either MLL-ELL or MLL-ENL fusions (9 up-regulated and 34 down-regulated; Table 1). Increasing evidence suggests that endogenous antisense RNAs may play critical roles in gene regulation and cancer. Natural antisense RNAs include cis-encoded antisense RNAs transcribed from the opposite strand of the same genomic locus as the sense target genes, and trans-encoded antisense RNAs such as microRNAs (miRNAs) transcribed from a genomic locus different from the sense target genes. 26 of the 43 candidate genes have antisense partners (with a total of 7 cis-encoded antisense RNAs and 36 trans-encoded miRNAs) and thereby might be regulated by endogenous antisense RNAs. We are currently validating the expression pattern of the 43 candidate genes in at least 30 different human and mouse leukemia and normal control samples with quantitative RT-PCR, and measuring the level of expression of all known miRNAs via microarray in these samples. Our studies on the abnormally expressed genes and their potential antisense partners will provide important insights into the complex functional pathways related to MLL rearrangements in the development of acute leukemia, which may lead to more effective therapy for these leukemias. Table 1. Genes deregulated in both human and mouse leukemiasa Total number Up-regulated genes Down-regulated genes Genes with antisense partner(s) aThe genes have at least 3 fold difference in expression with a significance P 〈 0.05 between each leukemia sample and the normal control sample. In MLL-ELL fusions 21 1 20 12 In MLL-ENL fusions 33 8 25 21 In both types of fusions 11 0 11 7 Total unique genes 43 9 34 26

Description: Chromosome translocations are among the most common genetic abnormalities in human leukemia. The abnormally expressed genes from each translocation can be used to identify specific markers for clinical diagnosis of each translocation. Microarrays have identified genes differentially expressed in different translocations but the results between laboratories are not always compatible. We used SAGE to quantitate gene expression in bone marrow(BM) samples from 22 patients with four types of AML, [de novo AML M2 with t(8;21), AML M3 or M3V with t(15;17), AML M4Eo with inv(16), AML M5 with t(9;11) or secondary t(9;11)].We made SAGE libraries from CD15+ leukemic myeloid progenitor cells, collecting over 106 SAGE tags, of which 209,486 were unique tags; 136,010 were known genes and ESTs, and 73,476 were novel transcripts. SAGE tags for further analysis were selected based on a 5-fold difference between patient’s samples and normal CD15+ BM; they were also statistically significantly different at the 5% level. Using these strict criteria, we identified 2,381 unique tags, of which 2,053 were known genes and ESTs, and 328 were novel transcripts that were either specific for each translocation or were common(55) SAGE tags for all 4 translocations. The major change in all translocations was a decrease in expression in leukemia cells compared with normal cells; the decrease was least in the t(8;21) cells. Changes in expression of these known genes, which fall into different gene ontology functional categories, varied by translocation. Those associated with macromolecular biosynthesis, transport and transcription were most altered in the t(8;21); those related to defense response and apoptosis were altered in the t(15;17); cell proliferation genes were most affected by the t(9;11). From this analysis, we identified the functional molecular signature of each translocation. We designed a custom microarray to validate our SAGE data analysis. Our initial microarray contained 349 probes including 212 known genes, 61 ESTs, 28 novel sequences based on our data and 48 genes reported by others. We have now included 65 additional probes that appeared to be correlated with survival. Using 63 samples with the four translocations [16 inv(16), 4 t(9;11), 20 t(15;17), 4 t(8;21) and 19 other translocations], we are validating which genes provide a robust, reproducible “fingerprint” for each translocation, for all translocations, and which ones provide reliable information related to prognosis and survival. Our results will provide new insights into genes that collaborate with each translocation to lead to a fully leukemic phenotype as well as which genes appear to provide valid prognostic information.