ALBERT

Description: Progression of malignancy to overt disease requires multiple genetic hits. Activation-induced deaminase (AID) can drive lymphomagenesis by generating off-target DNA breaks at loci that harbor highly active enhancers and display convergent transcription. The first active transcriptional profiles from acute lymphoblastic leukemia (ALL) patients acquired here reveal striking similarity at structural variation (SV) sites. Specific transcriptional features, namely convergent transcription and Pol2 stalling, were detected at breakpoints. The overlap was most prominent at SV with recognition motifs for the recombination activating genes (RAG). We present signal feature analysis to detect vulnerable regions and quantified from human cells how convergent transcription contributes to R-loop generation and RNA polymerase stalling. Wide stalling regions were characterized by high DNAse hypersensitivity and unusually broad H3K4me3 signal. Based on 1382 pre-B-ALL patients, the ETV6-RUNX1 fusion positive patients had over ten-fold elevation in RAG1 while high expression of AID marked pre-B-ALL lacking common cytogenetic changes.

Description: Introduction T cell acute lymphoblastic leukaemia (T-ALL) is an aggressive haematological malignancy affecting 10-15% of pediatric ALL patients. Current cure rates of pediatric patients is 80% but increasing understanding of the molecular mechanisms of T-ALL provides possibilities for more effective and targeted therapies. During the last decade targeted therapies with tyrosine kinase inhibitors (TKI) have proven to be effective in BCR-ABL1 fusion positive leukemias. Several studies have suggested TKI dasatinib to be effective also in the treatment of NUP214-ABL1 positive T-ALLs, which comprise approximately 4-10% of the T-ALL cases. Materials and Methods In silico drug screening was performed by comparing gene expression profiles of 4769 leukemic samples to a library of 13384 compounds and their known targets from the Drug signature database (DsigDB). These compounds included FDA approved therapeutic molecules and molecules under studies. Findings were validated in an ex vivo drug screen, consisting of 20 T-ALL bone marrow samples and 9 healthy bone marrow controls. Samples were treated for 72 hours with five different concentrations of dasatinib in 10-fold dilutions (0.1-1000nM), cell viability was measured and the data normalized to negative (DMSO) and positive (benzethonium chloride) controls. The effect of dasatinib was further explored in vitro by treating one NUP214-ABL1 fusion positive and six fusion negative T-ALL cell lines with dasatinib (1-1000nM). Gene expression levels of the known dasatinib targets in these cell lines were measured by Global Run On sequencing (GRO-seq) assay and qRT-PCR. Results In order to find novel targeted therapies for T-ALL, we performed an in silico drug target screen. A dasatinib-targetable gene LCK was strongly expressed in a number of T-ALL cases whereas normal T-lymphoid cells had lower expression. Chemical screen data of the target specificity of dasatinib showed high inhibition of LCK with percent of control (POC) value of 1, meaning that 0.1µM concentration of dasatinib decreases the kinase activity of LCK to 1% in comparison to control. In vitro dasatinib decreased cell viability in fusion negative Jurkat and MOLT-16 cells, and also in fusion positive cell line Peer. GRO-seq and qRT-PCR confirmed the expression of LCK and several other known dasatinib targets, including other SRC family kinases, in Jurkat and MOLT-16 cell lines. However, some LCK-expressing T-ALL cell lines were less sensitive to dasatinib. In further validation, ex vivo drug testing of patient samples revealed a marked response in 6/20 patient samples with IC50 values ranging between 1.3 - 8.2nM, while in healthy bone marrow controls IC50 values were 〉1000nM. Conclusion Our in silico drug screen identified dasatinib as a potential targeted therapy for a subgroup of T-ALL cases, and this finding was further supported by both ex vivo and in vitro studies. The exact mechanism remains to be elucidated but a number of SRC family kinases, which could potentially be targeted by dasatinib, showed expression in T-ALL samples. Disclosures Heckman: Celgene: Research Funding; Pfizer: Research Funding.

Description: Gene expression profiles enable global analysis that can interrogate the activity patterns of various cellular pathways across biological conditions. Indeed this approach has generated data across numerous patient populations over the past decades allowing molecular stratification of disease, including hematological and lymphoid malignancies. An emerging theme from cancer genomics studies is the remarkable similarity between specific cancers of different lineages. For example, particular subtypes of bladder cancer resemble breast and lung tumors despite their different tissue of origin. Whether the same may hold true across hematological and lymphoid cancers is currently unknown. In normal development, cells commit to their lineage by activation of densely interconnected transcription factors (TFs), or TF modules, in a series of decision points at which a choice is made between alternative lineage fates. Their mutual exclusivity can be used for discovery of key genes of both normal and malignant hematopoiesis. Importantly, TF translocations represent frequent genetic events in hematological cancer. We harnessed computational methods to organize and characterize samples from 9544 distinct hematological and lymphoid cancer patients, healthy donors and pre-malignant stages generating a pan-cancer resource for interrogating their molecular states. A central part of the resource is a curated transcriptome dataset that we provide across 37 different disease subtypes as an interactive online resource (http://compbio.uta.fi/hemap/). The dimensionality reduction method known as t-Distributed Stochastic Neighbor Embedding (t-SNE) achieved optimal placement of highly similar samples at close proximity in two dimensions, enabling a biologically meaningful visualization of the data set as well as comparative analysis based on gene signatures, drug target expression and regulatory network state. For patient stratification, unsupervised clustering in t-SNE space yielded comparable performance to robust and reproducible classifiers. We further demonstrate with multilevel data from The Cancer Genome Atlas that new samples can be included in context of the existing patient profiles. Data integration highlights the molecular architecture that relates to the clinical and genetic features of the samples studied, revealing new insight on molecular phenotypes that distinguish AML samples that lack a subtype based on current clinical stratification. Finally, we used the resource to provide a roadmap for candidate drug therapies and quantify the regulatory network alterations across hematological malignancies. The divergence of cancer regulatory networks from the reference healthy cell states and mutually exclusive patterns of TF expression that are specific to the different malignancies pave the way towards therapies targeting the cancer epigenome and characterization of downstream targets of TF-fusions or aberrant enhancer usage, as exemplified with independent validation data at the IRX1 and ERG loci. Disclosures No relevant conflicts of interest to declare.

Description: Acute lymphoblastic leukemia (ALL) is the most common childhood cancer and one of the leading causes of death in children with tumors. Genetic changes in T-cell acute lymphoblastic leukemia (T-ALL) are relatively well known, but the underlying molecular processes driving the disease remain insufficiently understood. Better knowledge of molecular events in T-ALL would improve our understanding of the development and maintenance of the disease and could also lead to the development of targeted and more effective treatments. We have compiled a large gene expression data-set from microarray studies of various hematological and lymphoid malignancies and healthy tissues that use a uniform technical platform (Affymetrix HG U133 Plus 2.0) (see abstract by Liuksiala et al). Data-set includes 1302 healthy samples and 4418 leukemia samples: 1713 acute myeloid leukemias, 1648 precursor B-ALLs, 801 chronic lymphocytic leukemias, 385 T-ALLs, and 215 chronic myeloid leukemias. From this data-set, we identified a number of transcription factors (TFs) that were differentially expressed in T-ALL, including high expression of NOTCH1 and BCL11B as previously reported (Weng et al Science 2004; Gutierrez et al Blood 2011). Several novel candidate TFs with specific expression in T-ALLs were also discovered, including strong expression of two poorly characterized TFs, SIX6 and PCBP3. These findings were validated using real-time quantitative PCR (RT-qPCR) in a cell line panel consisting of T-ALL and pre-B-ALL cell lines as well as healthy controls. We next sought to identify novel drug targets in T-ALL by comparing our leukemia expression data-set with the therapeutic target database (TTD). TTD is a database providing information about the known and explored therapeutic protein and nucleic acid targets, and the corresponding drugs aimed at these targets. We identified high expression of a nicotinic acetylcholine receptor (nAChR) subunit CHRNA3 (cholinergic receptor, nicotinic, alpha 3), which is a target of nAChR inhibitor bupropion. RT-qPCR confirmed the high expression of CHRNA3 in T-ALL cell lines but not in pre-B-ALL cells or healthy controls. The effect of bupropion was tested in Jurkat cells which represent T-ALL cell line with high expression of CHRNA3. Increasing concentrations of bupropion (1-100µM) resulted in dose-dependent decrease in proliferation of Jurkat cells as measured by cell viability assay AlamarBlue. As a control, cell lines with low level of CHRNA3 expression (CCRF-CEM and REH) were treated as well but these cells did not show any changes in the rate of proliferation. In summary, we have identified several candidate transcription factors which could have a leukemic role in T-ALL. Furthermore, we identified high expression of CHRNA3 in T-ALL, suggesting a role for the cholinergic system in T-cell leukemia, and thus a novel avenue in search of putative therapeutic options. Disclosures No relevant conflicts of interest to declare.