EZH2 Mutations Can Be Detected in 23% of t(10;11)(p13;q14)/PICALM-MLLT10 Positive Acute Leukemias,

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 3440-3440
Author(s):  
Vera Grossmann ◽  
Ulrike Bacher ◽  
Valentina Artusi ◽  
Hans-Ulrich Klein ◽  
Wolfgang Kern ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Leukemia ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Signature ◽  
Mutation Load ◽  
Employment Equity ◽  
Acute Leukemias ◽  
Equity Ownership ◽  
Mixed Phenotype

Abstract Abstract 3440 The t(10;11)(p13;q14)/PICALM-MLLT10 (CALM-AF10) rearrangement is most frequently associated with T-lineage acute lymphoblastic leukemia/lymphoma (T-ALL), and is rarely observed in AML. The EZH2 gene, located on 7q36.1, is a highly conserved histone H3 lysine 27 methyltransferase that influences stem cell renewal. EZH2 mutations were observed in 10% of patients with myelofibrosis, myelodysplastic/myeloproliferative neoplasms, or chronic myelomonocytic leukemia. In a previous study, we had investigated AML patients for EZH2 deletions using FISH. 6/20 (30%) of cases had been detected to carry a deletion. Additionally, we had screened these 6 cases for molecular mutations in EZH2 (transcript-ID: ENST00000320356) using an amplicon-based deep-sequencing assay, and one of the 6 patients was harboring both an EZH2 deletion and an EZH2 mutation. More interestingly, this double-mutated case was carrying a PICALM-MLLT10 rearrangement. Therefore, in this study, we were interested to investigate an expanded cohort of 13 cases (T-lineage ALL and AML) harboring a PICALM-MLLT10 rearrangement. Our cohort comprised 12 adults and one pediatric patient (7 males, 6 females) and was characterized by a predominant T-cell origin: 11 patients had T-ALL, 1 patient had mixed phenotype T/myeloid acute leukemia, and 1 patient had AML. EZH2 alterations were detected in 3/13 (including the index case). In more detail, the EZH2 mutation carriers were characterized as follows: Patient #1 (male, 26 years, AML) had a splice site mutation in exon 14 with a mutation load of 13% in a cysteine-rich region. Patient #2 (male, 19 years, T-ALL) harbored a missense mutation (Phe136Leu) with a mutation load of 93%. Patient #3 (female, 53 years, T-ALL) showed three concomitant EZH2 missense mutations in exon 5: His120Gln, Tyr124His, and Gly150Arg. The mutation load detected was 17% for each alteration. A fourth patient had a 1459G>A base substitution (corresponding to Ala487Thr) which to our knowledge had not been described before. However, this alteration had to be interpreted as germline as it was still detectable in the remission state. In contrast, in an independent cohort of 12 patients with PICALM-MLLT10 negative T-ALL (7 females, 5 males) analyzed for comparison no EZH2 mutation was detected. Interestingly, in patients #2 and #3, the mutations were located in exon 5 in the region which interacts with the DNMT1, DNMT3A, and DNMT3B DNA methyltransferase genes (D1). Moreover, DNMT3A mutations were recently identified in patients with AML and MDS in association with poor outcomes. Therefore, we additionally performed investigation for DNMT3A mutations in all 13 patients with PICALM-MLLT10 positive leukemias but detected no mutation. To investigate further molecular associations, we analyzed these cases also for RUNX1 mutations and FLT3-ITD, but we did not detect any mutation in these molecular genes. Further, we compared the gene expression profiles of 8 patients with PICALM-MLLT10 positive T-ALL to the profiles of 21 PICALM-MLLT10 negative T-ALL patients. Hierarchical clustering revealed a distinct gene expression signature of the PICALM-MLLT10 positive cases. Significant upregulation was found for HOXA5 and HOXA9 genes. Other differentially overexpressed HOX were HOXA3, A4, A6, A7, A10. Genes with a function for cell differentiation and regulation of apoptosis (ZAK) as well as for signal transduction (AKT3) were significantly underexpressed. Subsequently, we compared the gene expression profiles of 2 EZH2 mutated patients to 6 EZH2 wild-type patients in the PICALM-MLLT10 positive cohort. By hierarchical clustering, both EZH2 mutated cases showed a distinct gene expression signature. Increased expression was observed for genes with a role for the regulation of transcription (ZNF207, KDM5B, or CASZ1) or for intracellular transport (SARB1). In summary, we detected EZH2 mutations in 3/13 cases in this series of PICALM-MLLT10 positive malignancies, comprising mostly T-ALL, but also AML or mixed phenotype acute leukemia. This further emphasizes a cooperative effect of EZH2 mutations with the PICALM-MLLT10 fusion in acute leukemias of different lineages. Disclosures: Grossmann: MLL Munich Leukemia Laboratory: Employment. Artusi:MLL Munich Leukemia Laboratory: Employment. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Schnittger:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Kohlmann:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.

Genomics and Systems Biology.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. sci-51-sci-51
Author(s):  
Todd R. Golub
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Biological Networks ◽  
High Throughput Screening ◽  
Expression Profiles ◽  
Low Cost ◽  
Gene Expression Profiles ◽  
Gene Expression Signature ◽  
Small Molecule Libraries ◽  
Approved Drugs

Genomics holds particular potential for the elucidation of biological networks that underlie disease. For example, gene expression profiles have been used to classify human cancers, and have more recently been used to predict graft rejection following organ transplantation. Such signatures thus hold promise both as diagnostic approaches and as tools with which to dissect biological mechanism. Such systems-based approaches are also beginning to impact the drug discovery process. For example, it is now feasible to measure gene expression signatures at low cost and high throughput, thereby allowing for the screening libraries of small molecule libraries in order to identify compounds capable of perturbing a signature of interest (even if the critical drivers of that signature are not yet known). This approach, known as Gene Expression-Based High Throughput Screening (GE-HTS), has been shown to identify candidate therapeutic approaches in AML, Ewing sarcoma, and neuroblastoma, and has identified tool compounds capable of inhibiting PDGF receptor signaling. A related approach, known as the Connectivity Map (www.broad.mit.edu/cmap) attempts to use gene expression profiles as a universal language with which to connect cellular states, gene product function, and drug action. In this manner, a gene expression signature of interest is used to computationally query a database of gene expression profiles of cells systematically treated with a large number of compounds (e.g., all off-patent FDA-approved drugs), thereby identifying potential new applications for existing drugs. Such systems level approaches thus seek chemical modulators of cellular states, even when the molecular basis of such altered states is unknown.

TGR-1202 Suppresses AML and ALL Cells Via Selective Inhibition of PI3Kδ Kinase.

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 2610-2610 ◽  
Author(s):  
Swaroop Vakkalanka ◽  
Srikant Viswanadha ◽  
Robert Niecestro ◽  
Peter Sportelli ◽  
Michael Savona
Keyword(s):  
Acute Leukemia ◽  
Cell Lines ◽  
Whole Blood ◽  
Leukemia Cell ◽  
Pharmacokinetic Studies ◽  
Employment Equity ◽  
Acute Leukemias ◽  
Leukemia Cell Lines ◽  
Pharmacokinetic Properties ◽  
Equity Ownership

Abstract Abstract 2610 Background: Acute leukemia, characterized by the presence clonal hematopoietic cells in peripheral blood and bone marrow, comprises approximately 40% of newly diagnosed leukemias. First line treatment for acute leukemias with multi-agent cytotoxic chemotherapy is usually associated with significant toxicity. Advances in therapy have been slow, and nearly all effective therapies lead to prolonged marrow suppression and toxicities associated with subsequent cytopenias. Herein, we describe the biological and pharmacokinetic properties of TGR-1202, a novel small molecule PI3Kδ inhibitor with scope to be developed as a safe and effective therapy for acute myeloid (AML) and lymphoblastic (ALL) leukemia. Material & Methods: Activity of TGR-1202 against individual isoforms of the PI3K enzyme was determined via enzyme, cellular, and whole blood based assays. Potency of the compound was confirmed via leukemic cell viability and Annexin V/PI staining besides testing for inhibition of pAkt, a downstream kinase regulating cell survival and growth. These assays were conducted with cell lines (CCRF-CEM, HL-60, and MOLT-4) and patient derived cells. Anti-tumor efficacy of the compound was studied in vivo with the subcutaneous MOLT-4 xenograft model. Lastly, ADME and pharmacokinetic properties of the molecule were determined. Results: TGR-1202 demonstrated significant potency against PI3Kδ (22.2 nM) with several fold selectivity over the α (>10000), β (>50), and γ (>48) isoforms. Additionally, the compound inhibited B-cell proliferation (24.3 nM) and FcεR1 induced CD63 expression in human whole blood basophils (68.2 nM) indicating specificity towards the delta isoform. Viability testing demonstrated that the compound caused a dose-dependent inhibition in growth of immortalized as well as patient-derived AML and ALL cells. Reduction in viability was accompanied by a reduction in pAKT (>50% @ 0.3–1 μM) along with a significant induction in apoptosis in both cell lines (CCRF-CEM, HL-60, and MOLT-4) and patient samples. In tumor xenografts, oral administration of 150 mg/kg RP5264 salt over a 25-day period resulted in significant inhibition (>50%) of MOLT-4 tumor growth in mice. Pharmacokinetic studies across species indicated good oral absorption (>40% bioavailability for mice, rat, and dog) with favorable plasma concentrations (3–10 μM @ 20 mg/kg for mice, rat, and dog) relevant for efficacy. In addition, early toxicological evaluation of the molecule indicated a MTD > 500 mg/kg over a 14-day treatment period in Balb/c mice. Conclusions: TGR-1202, primarily, through its activity at the δ isoform of PI3K, has activity in both myeloid and lymphoid acute leukemia cell lines and primary patient tumors. Further evaluation of this molecule in the treatment of AML and ALL is justified, and current testing of TGR-1202 in various leukemia cell lines and within a variety of primary leukemias is ongoing. Disclosures: Vakkalanka: Rhizen Pharmaceuticals S A: Employment, Equity Ownership. Viswanadha:Incozen Therapeutics: Employment. Niecestro:TG Therapeutics, Inc.: Consultancy, Equity Ownership. Sportelli:TG Therapeutics, Inc.: Employment, Equity Ownership.

Preclinical Results Supporting Therapeutic Development of Mrg-106, an Oligonucleotide Inhibitor of Mir-155, in CTCL

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 2758-2758 ◽  
Author(s):  
Anita G Seto ◽  
Xuan T Beatty ◽  
Linda A Pestano ◽  
Brent A Dickinson ◽  
Marshelle S Warren ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Therapeutic Potential ◽  
Unmet Need ◽  
Subcutaneous Administration ◽  
Gene Expression Signature ◽  
Primary Objective ◽  
Employment Equity ◽  
Cell Death Pathway ◽  
Equity Ownership

Abstract Treatment-resistant hematological malignancies remain an area of high unmet need and novel therapeutic approaches will be required. microRNAs are small (~ 22 nt) non-coding RNAs that act as negative regulators of gene expression. These small RNAs impact expression of a substantial fraction of the genome, and have powerful effects on cellular phenotypes and physiological processes. miR-155-5p is a well-described oncomiR associated with poor prognosis in multiple malignancies, particularly lymphoma and leukemia. Cutaneous T-cell lymphoma (CTCL) is a rare hematological malignancy with limited treatment options and a strong mechanistic link to increased miR-155-5p. Because of the accessibility of cutaneous lesions, CTCL provides a unique opportunity to determine if inhibition of miR-155-5p has therapeutic potential in lymphomas associated with elevated miR-155-5p. We optimized a LNA-modified oligonucleotide inhibitor of miR-155-5p, MRG-106, based on the ability to de-repress canonical miR-155-5p targets in multiple cell types in vitro. In mycosis fungoides (MF) cell lines, MRG-106 does not require additional formulation to achieve maximum pharmacodynamic efficacy. Inhibition of miR-155-5p resulted in transcriptome changes consistent with miR-155-5p target gene modulation, reduction in cell proliferation, and activation of the programmed cell death pathway. The gene expression and phenotypic effects were inhibitor dose-dependent and sequence-specific. Based on an informatics approach for the expression profiling of MF cell lines treated with MRG-106, a set of 600 genes was identified to represent the translational pharmacodynamic biomarker signature, both direct and downstream of miR-155-5p. GLP preclinical safety studies have been completed in rats and non-human primates, demonstrating an acceptable safety profile for MRG-106. We plan to initiate a 4-week first-in-human clinical trial in CTCL (MF) patients. The trial design is two-part, with Part A testing the effect of direct intra-tumoral injection of MRG-106 into plaque and nodular skin lesions, and Part B testing the effect of systemic (subcutaneous) administration of higher doses of MRG-106. The primary objective of Part A is to profile the pharmacodynamic effect of MRG-106 on the miR-155-5p gene expression signature, establishing a PK/PD model to guide future development. The primary objective of Part B is to establish the safety, tolerability, PK and skin deposition of MRG-106 after systemic delivery. Exploratory objectives include measures for clinical response, immune system effects, and biomarker validation. Disclosures Seto: miRagen Therapeutics: Employment, Equity Ownership. Beatty:miRagen Therapeutics: Employment, Equity Ownership. Pestano:miRagen Therapeutics: Employment, Equity Ownership. Dickinson:miRagen Therapeutics: Employment, Equity Ownership. Warren:miRagen Therapeutics: Consultancy. Rodman:miRagen Therapeutics: Employment, Equity Ownership. Jackson:miRagen Therapeutics: Employment, Equity Ownership.

Identification of synthetic lethal interactions with the BRAF oncogene in colorectal cancer.

2013 ◽  
Vol 31 (4_suppl) ◽  
pp. 403-403
Author(s):  
Loredana Vecchione ◽  
Valentina Gambino ◽  
Giovanni d'Ario ◽  
Sun Tian ◽  
Iris Simon ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Signature ◽  
Gene Signature ◽  
Braf V600e ◽  
P Value ◽  
Synthetic Lethal ◽  
Braf Mutant

403 Background: Approximately 8-15% of colorectal (CRC) patients carry an activating mutation in BRAF. This CRC subtype is associated with poor outcome and with resistance, both to chemotherapeutic treatments and to tailored drugs. We recently showed that BRAF (V600E) colon cancers (CCs) have a characteristic gene expression signature (1, 2) which is found also in subsets of KRAS mutant and KRAS-BRAF wild type (WT2) tumors. Tumors having this gene signature, referred as “BRAF-like”, have a similar poor prognosis irrespective of the presence of the BRAF (V600E) mutation. By using a shRNA-based genetic screen in BRAF mutant CC cell lines we aimed to identify genes and pathways necessary for survival and growth of BRAFmutant CC. Such studies may reveal additional targets for therapy and potentially provide new biomarkers for patient stratification Methods: We identified 363 genes that are selectively overexpressed in BRAF mutant tumors as compared to WT2 type tumors, based on gene expression profiles of the PETACC3 (1) and Agendia (2) datasets. The TRC human genome-wide shRNA collection (TRC-Hs1.0) was used to generate a 1815 hairpins sub-library targeting those identified genes (BRAF library). BRAF(V600E) CC cell lines were infected with the BRAF library and screened for shRNAs that cause lethality. LIM1215 CC cell line (WT2) was used as a control. Cells stably expressing the shRNA library were cultured for 13 days, after which shRNAs were recovered by PCR. Deep sequencing was applied to determine the specific depletion of shRNA in BRAF(V600E) cells as compared to LIM1215 cells Results: Candidate genes were identified by using following filtering criteria: depletion in BRAF(V600E) cells by at least 50% and depletion in BRAF(V600E) cells 1, 5-fold higher than in control cells with the corresponding p-value to be ≤ 0.1. A total of 34 genes met our criteria of which 6 genes were presented with more than one hairpin and were concordant across the cell lines selected for validation. Conclusions: We identified candidate synthetic lethal genes in BRAF mutant CC cell lines. Functional analysis is ongoing. Data will be presented. References 1. J Clin Oncol 2012 Apr 20;30(12):1288-9 2. Gut (2012). doi:10.1136/gutjnl-2012-302423

scholarly journals Baseline and on-Treatment Bone Marrow Microenvironments Predict Myeloma Patient Outcomes and Inform Potential Intervention Strategies

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1882-1882 ◽  
Author(s):  
Samuel A Danziger ◽  
Mark McConnell ◽  
Jake Gockley ◽  
Mary Young ◽  
Adam Rosenthal ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
Board Of Directors ◽  
Research Funding ◽  
Expression Profiles ◽  
Cell Types ◽  
Employment Equity ◽  
Advisory Committees ◽  
Equity Ownership

Abstract Introduction The multiple myeloma (MM) tumor microenvironment (TME) strongly influences patient outcomes as evidenced by the success of immunomodulatory therapies. To develop precision immunotherapeutic approaches, it is essential to identify and enumerate TME cell types and understand their dynamics. Methods We estimated the population of immune and other non-tumor cell types during the course of MM treatment at a single institution using gene expression of paired CD138-selected bone marrow aspirates and whole bone marrow (WBM) core biopsies from 867 samples of 436 newly diagnosed MM patients collected at 5 time points: pre-treatment (N=354), post-induction (N=245), post-transplant (N=83), post-consolidation (N=51), and post-maintenance (N=134). Expression profiles from the aspirates were used to infer the transcriptome contribution of immune and stromal cells in the WBM array data. Unsupervised clustering of these non-tumor gene expression profiles across all time points was performed using the R package ConsensusClusterPlus with Bayesian Information Criterion (BIC) to select the number of clusters. Individual cell types in these TMEs were estimated using the DCQ algorithm and a gene expression signature matrix based on the published LM22 leukocyte matrix (Newman et al., 2015) augmented with 5 bone marrow- and myeloma-specific cell types. Results Our deconvolution approach accurately estimated percent tumor cells in the paired samples compared to estimates from microscopy and flow cytometry (PCC = 0.63, RMSE = 9.99%). TME clusters built on gene expression data from all 867 samples resulted in 5 unsupervised clusters covering 91% of samples. While the fraction of patients in each cluster changed during treatment, no new TME clusters emerged as treatment progressed. These clusters were associated with progression free survival (PFS) (p-Val = 0.020) and overall survival (OS) (p-Val = 0.067) when measured in pre-transplant samples. The most striking outcomes were represented by Cluster 5 (N = 106) characterized by a low innate to adaptive cell ratio and shortened patient survival (Figure 1, 2). This cluster had worse outcomes than others (estimated mean PFS = 58 months compared to 71+ months for other clusters, p-Val = 0.002; estimate mean OS = 105 months compared with 113+ months for other clusters, p-Val = 0.040). Compared to other immune clusters, the adaptive-skewed TME of Cluster 5 is characterized by low granulocyte populations and high antigen-presenting, CD8 T, and B cell populations. As might be expected, this cluster was also significantly enriched for ISS3 and GEP70 high risk patients, as well as Del1p, Del1q, t12;14, and t14:16. Importantly, this TME persisted even when the induction therapy significantly reduced the tumor load (Table 1). At post-induction, outcomes for the 69 / 245 patients in Cluster 5 remain significantly worse (estimate mean PFS = 56 months compared to 71+ months for other clusters, p-Val = 0.004; estimate mean OS = 100 months compared to 121+ months for other clusters, p-Val = 0.002). The analysis of on-treatment samples showed that the number of patients in Cluster 5 decreases from 30% before treatment to 12% after transplant, and of the 63 patients for whom we have both pre-treatment and post-transplant samples, 18/20 of the Cluster 5 patients moved into other immune clusters; 13 into Cluster 4. The non-5 clusters (with better PFS and OS overall) had higher amounts of granulocytes and lower amounts of CD8 T cells. Some clusters (1 and 4) had increased natural killer (NK) cells and decreased dendritic cells, while other clusters (2 and 3) had increased adipocytes and increases in M2 macrophages (Cluster 2) or NK cells (Cluster 3). Taken together, the gain of granulocytes and adipocytes was associated with improved outcome, while increases in the adaptive immune compartment was associated with poorer outcome. Conclusions We identified distinct clusters of patient TMEs from bulk transcriptome profiles by computationally estimating the CD138- fraction of TMEs. Our findings identified differential immune and stromal compositions in patient clusters with opposing clinical outcomes and tracked membership in those clusters during treatment. Adding this layer of TME to the analysis of myeloma patient baseline and on-treatment samples enables us to formulate biological hypotheses and may eventually guide therapeutic interventions to improve outcomes for patients. Disclosures Danziger: Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment. Gockley:Celgene Corporation: Employment. Young:Celgene Corporation: Employment, Equity Ownership. Schmitz:Celgene Corporation: Employment, Equity Ownership. Reiss:Celgene Corporation: Employment, Equity Ownership. Davies:MMRF: Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; TRM Oncology: Honoraria; Abbvie: Consultancy; ASH: Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria. Copeland:Celgene Corporation: Employment, Equity Ownership. Fox:Celgene Corporation: Employment, Equity Ownership. Fitch:Celgene Corporation: Employment, Equity Ownership. Newhall:Celgene Corporation: Employment, Equity Ownership. Barlogie:Celgene: Consultancy, Research Funding; Dana Farber Cancer Institute: Other: travel stipend; Multiple Myeloma Research Foundation: Other: travel stipend; International Workshop on Waldenström's Macroglobulinemia: Other: travel stipend; Millenium: Consultancy, Research Funding; European School of Haematology- International Conference on Multiple Myeloma: Other: travel stipend; ComtecMed- World Congress on Controversies in Hematology: Other: travel stipend; Myeloma Health, LLC: Patents & Royalties: : Co-inventor of patents and patent applications related to use of GEP in cancer medicine licensed to Myeloma Health, LLC. Trotter:Celgene Research SL (Spain), part of Celgene Corporation: Employment, Equity Ownership. Hershberg:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Dervan:Celgene Corporation: Employment, Equity Ownership. Ratushny:Celgene Corporation: Employment, Equity Ownership. Morgan:Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Research Funding.

scholarly journals FOS Expression Distinguishes Two Groups of Atypical CML (aCML) Allowing Targeted Therapy

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 3893-3893
Author(s):  
Manja Meggendorfer ◽  
Wencke Walter ◽  
Stephan Hutter ◽  
Wolfgang Kern ◽  
Claudia Haferlach ◽  
...  
Keyword(s):  
Gene Expression ◽  
Myeloproliferative Neoplasms ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Next Generation Sequencing Data ◽  
Employment Equity ◽  
Expression Levels ◽  
Equity Ownership ◽  
Cox 2 ◽  
Fos Expression

Abstract BCR-ABL1 negative myeloproliferative neoplasms not only include atypical chronic myeloid leukemia (aCML), but also chronic myelomonocytic leukemia (CMML), chronic neutrophilic leukemia (CNL), and myelodysplastic/myeloproliferative neoplasm, unclassifiable (MDS/MPN, U). Despite the recent advances in characterizing aCML more specifically, based on next generation sequencing data, the differential diagnosis and subsequent treatment decisions remain difficult. Therefore, we analyzed the transcriptome and performed whole genome sequencing (WGS) in a cohort of morphologically defined 231 patients (pts): 49 aCML, 30 CNL, 50 MDS/MPN, U, and 102 CMML all diagnosed according to WHO classification. WGS libraries were prepared with the TruSeq PCR free library prep kit and sequenced on a NovaSeq 6000 or HiSeqX instrument with 100x coverage (Illumina, San Diego, CA). The Illumina tumor/unmatched normal workflow was used for variant calling. To remove potential germline variants, each variant was queried against the gnomAD database, variants with global population frequencies >1% where excluded. For transcriptome analysis total RNA was sequenced on the NovaSeq 6000 with a median of 50 mio. reads per sample. The obtained estimated gene counts were normalized and the resulting log2 counts per million (CPMs) were used as a proxy of gene expression. Unsupervised exploratory analysis techniques, such as principal component analysis (PCA) and hierarchical clustering (HC) were used to identify groups of samples with similar expression profiles. We observed a high similarity between the different entities with CMML being the most distant entity, followed by CNL. MDS/MPN, U and aCML were the most similar entities. Due to high within-group heterogeneity, we found that it was impossible to identify a gene expression signature that separated aCMLs reliably from the other MDS/MPN overlap entities. Surprisingly, PCA as well as HC indicated the existence of two subgroups within the aCMLs. Therefore, we searched for genes with a bimodal-like expression profile. We found that FOS expression levels strongly separated aCMLs into two groups of 16 pts (FOSlow) and 33 pts (FOShi), respectively. Interestingly, FOShi correlated with mutations in SETBP1 (12/33, 36% vs. 3/16, 19%), a known marker typically mutated in aCML (Figure 1a). Addressing the mutational landscape of these two groups (FOShivs.FOSlow) we found that ASXL1 (88% vs. 100%), TET2 (33% vs. 50%), SRSF2 (45% vs. 56%), EZH2 (27% vs. 31%), and NRAS (21% vs. 25%) showed rather similar mutation frequencies. GATA2 (15% vs. 31%) and RUNX1 (18% vs. 38%) mutations were less frequent in FOShi, whereas SETBP1 and CBL (18% vs. 6%) were more frequent in this group. Consistent with known features of SETBP1 mutation this group showed a higher white blood cell count (78 x109/L vs. 52 x109/L) and platelet count (158x109/L vs. 90x109/L), although none of these differences were significant. The two groups were further analyzed for gene expression differences and we found 16 genes with synchronized upregulation within the FOShi group that were differentially expressed (FDR < 0.05, absolute logFC > 1.5) compared to FOSlow. Functional enrichment analysis linked those genes with regulation of cell proliferation (p<0.001), negative regulation of cell death (p<0.001), and the AP-1 complex (p<0.001). Those 16 genes included the transcription factors JUN, FOSB, EGR3, and KLF4, the cancer-related genes DUSP1, RHOB, OSM, TNFRSF10C, and CXCR2, and the FDA approved drug targets JUN, COX-2, and FCGR3B (Figure 1b). JUN/FOS are the main components of the AP-1 complex, a regulator of cell life and death. The upregulation of these genes results in increased proliferation as clinically observed in aCML pts. Furthermore, for these pts a treatment with INFα might result in an anti-proliferative effect by modulation of FOS transcript levels. Further, COX-2 inhibitors might also suppress proliferation and differentiation of leukemia cells. However, for the FOSlow pts these treatments might not be as effective due to the already low expression levels of the respective genes. Since the expression levels of FOShi equal those of MDS/MPN overlap, whereas FOSlow levels are closer to the ones of a healthy control cohort, SETBP1 mutation might be a marker and indicator for pts with high FOS expression and therefore providing further treatment options by targeting specifically the FOS mediated pathways. Disclosures Meggendorfer: MLL Munich Leukemia Laboratory: Employment. Walter:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.

Gene Expression Profiling in Adult Acute Lymphoblastic Leukemia, Biphenotypic Acute Leukemia, and Acute Myeloid Leukemia M0: Confirmation of Immunophenotypic and Cytogenetic Diagnostic Findings.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 993-993
Author(s):  
Wolfgang Kern ◽  
Alexander Kohlmann ◽  
Claudia Schoch ◽  
Martin Dugas ◽  
Sylvia Merk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diagnostic Criteria ◽  
Prediction Accuracy ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Gene Expression Profiles ◽  
Support Vector ◽  
Specific Gene ◽  
Acute Leukemias ◽  
Acute Myeloid

Abstract Diagnosis and classification of acute lymphoblastic leukemias (ALL) and their distinction from biphenotypic acute leukemias (BAL) and acute myeloid leukemias with minimal differentiation (AML M0) is largely based on immunophenotyping. The EGIL classification, adopted by the WHO classification, defines 4 different subtypes of both B-precursor and T-precursor ALL as well as detailed criteria for BAL. Specific cytogenetic features useful for classificationare found in some cases only. We analyzed gene expression profiles in 173 such patients (Pro-B-ALL n=25, c-ALL/Pre-B-ALL n=65 (with t(9;22) n=35, without t(9;22) n=30), mature B-ALL n=13, Pro-T-ALL n=6, Pre-T-ALL n=13, cortical T-ALL n=20, BAL (myeloid and T-lineage) n=17, AML M0 n=14). All cases were assessed by cytomorphology, immunophenotyping, cytogenetics, and molecular genetics. All cases with Pro-B-ALL had t(4;11)/MLL-AF4, all cases with mature B-ALL had t(8;14). Samples were hybridized to both U133A and U133B microarrays (Affymetrix). Top 300 differentially expressed genes were identified for each group in comparison to all other groups and individual other groups and used for classification by various Support Vector Machines (SVM) with 10-fold cross validation (CV). Prediction accuracy for discriminating T- from B-precursor ALL was 100%. Accordingly, principal component analysis (PCA) yielded a complete separation of both groups. PCA of B-precursor ALL cases showed distinct clusters for Pro-B-ALL, c-ALL/Pre-B-ALL, and mature B-ALL, however, c-ALL/Pre-B-ALL with t(9;22) were not completely discriminated from those without. Accordingly, classifying B-precursor ALL with SVM resulted in a 87.4% accuracy. Pre-T-ALL cases clustered distinct from cortical T-ALL with hte exception of two cases. The other Pre-T-ALLs clustered together with Pro-T-ALL. Analyzing T-precusor ALL with SVM and 10-fold CV resulted in an accuracy of only 56.4%. Including BAL and AML M0 into these analyses revealed significant overlaps between samples from these entities and T-ALL cases in PCA; prediction accuracy using SVM and 10-fold CV was 79.8%. This accuracy was confirmed applying 100 runs of SVM with 2/3 of samples being randomly selected as training set and 1/3 as test set which resulted in a median accuracy of 77.2% (range, 67.5% to 85.1%). A 100% prediction accuracy was achieved in Pro-B-ALL and mature B-ALL. Misclassifications were: c-ALL/Pre-B-ALL with t(9;22) as c-ALL/Pre-B-ALL without t(9;22) (6/35) and vice versa (6/30). Of the 13 Pre-T-ALL cases 4 were classified as BAL and 3 as cortical T-ALL. Of the 6 Pro-T-ALL cases 2 were classified as AML M0, 3 as BAL, and 1 as Pre-T-ALL. Of the 17 BAL cases 2 were classified as AML M0, 1 as c-ALL/Pre-B-ALL, 2 as Pre-T-ALL, and 1 as Pro-T-ALL. These analyses confirm that gene expression profiles allow the identification of Pro-B-ALL with t(4;11) and mature B-ALL with t(8;14) but do not unequivocally identify the presence of t(9;22) in c-ALL/Pre-B-ALL. Cortical T-ALL are characterized by a specific gene expression profile which is, however, shared by few cases currently diagnosed as Pre-T-ALL. Thus, diagnostic criteria (surface expression of CD1a only) should be optimized. The same applies to diagnostic criteria for more immature T-ALL, BAL, and AML M0. Loss of 5q is frequently observed in all of these latter entities and may be a future diagnostic marker superseding flow cytometry.

scholarly journals KIR+ CD8+ T Lymphocytes in Cancer Immunosurveillance and Patient Survival: Gene Expression Profiling

Cancers ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2991
Author(s):  
Lourdes Gimeno ◽  
Emilio M. Serrano-López ◽  
José A. Campillo ◽  
María A. Cánovas-Zapata ◽  
Omar S. Acuña ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Cd8 T Cells ◽  
Expression Profiles ◽  
Killer Cell ◽  
Gene Expression Profiles ◽  
Gene Expression Signature ◽  
Oncologic Patients ◽  
Survival Gene ◽  
Cancer Immunosurveillance

Killer-cell immunoglobulin-like receptors (KIR) are expressed by natural killer (NK) and effector T cells. Although KIR+ T cells accumulate in oncologic patients, their role in cancer immune response remains elusive. This study explored the role of KIR+CD8+ T cells in cancer immunosurveillance by analyzing their frequency at diagnosis in the blood of 249 patients (80 melanomas, 80 bladder cancers, and 89 ovarian cancers), their relationship with overall survival (OS) of patients, and their gene expression profiles. KIR2DL1+ CD8+ T cells expanded in the presence of HLA-C2-ligands in patients who survived, but it did not in patients who died. In contrast, presence of HLA-C1-ligands was associated with dose-dependent expansions of KIR2DL2/S2+ CD8+ T cells and with shorter OS. KIR interactions with their specific ligands profoundly impacted CD8+ T cell expression profiles, involving multiple signaling pathways, effector functions, the secretome, and consequently, the cellular microenvironment, which could impact their cancer immunosurveillance capacities. KIR2DL1/S1+ CD8+ T cells showed a gene expression signature related to efficient tumor immunosurveillance, whereas KIR2DL2/L3/S2+CD8+ T cells showed transcriptomic profiles related to suppressive anti-tumor responses. These results could be the basis for the discovery of new therapeutic targets so that the outcome of patients with cancer can be improved.

scholarly journals Identification of a Gene Expression Signature Common to Distinct Cancer Pathways

2012 ◽  
Vol 11 ◽  
pp. CIN.S9542 ◽  
Author(s):  
Niklaus Fankhauser ◽  
Igor Cima ◽  
Peter Wild ◽  
Wilhelm Krek
Keyword(s):  
Gene Expression ◽  
Web Application ◽  
Cell Transformation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Signature ◽  
Malignant Cell ◽  
Cancer Genes ◽  
Data Set ◽  
Cancer Pathways

Mutations in cancer-causing genes induce changes in gene expression programs critical for malignant cell transformation. Publicly available gene expression profiles produced by modulating the expression of distinct cancer genes may therefore represent a rich resource for the identification of gene signatures common to seemingly unrelated cancer genes. We combined automatic retrieval with manual validation to obtain a data set of high-quality gene microarray profiles. This data set was used to create logical models of the signaling events underlying the observed expression changes produced by various cancer genes and allowed to uncover unknown and verifiable interactions. Data clustering revealed novel sets of gene expression profiles commonly regulated by distinct cancer genes. Our method allows retrieval of significant new information and testable hypotheses from a pool of deposited cancer gene expression experiments that are otherwise not apparent or appear insignificant from single measurements. The complete results are available through a web-application at http://biodata.ethz.ch/cgi-bin/geologic .

A Shared Gene-Expression Signature in Innate-Like Lymphocytes.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 3903-3903
Author(s):  
Tetsuya Yamagata ◽  
Christophe Benoist ◽  
Diane Mathis
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Signature ◽  
Cell Types ◽  
Self Recognition ◽  
Distinct Cell ◽  
Invariant Structures ◽  
Small Set ◽  
High Degree

Abstract Innate and adaptive immunity are the two major arms of the immune system. They rely on very distinct cell-types, primarily distinguished by the source of diversity for non-self recognition, of germline or somatic origin. There exists, however, a subset of lymphocytes whose receptors require rearrangement but result in semi-invariant structures with a high degree of self-specificity. We hypothesized that these innate-like lymphocytes might share a common gene transcription signature. To test this notion, we made pair-wise comparisons of the gene-expression profiles of innate-like lymphocytes and closely paired adaptive system counterparts (NKT vs. CD4T, CD8ααT vs. CD8αβT, B1 vs. B2), and bioinformatically extracted common features and common genes distinguishing innate from adaptive cell-types. A statistically significant “innate signature” was indeed distilled, composed of a small set of genes over- and under-expressed in innate vs. adaptive lymphocytes. Particularly intriguing was the high representation of interferon-inducible GTPases crucial for resistance against intracellular pathogens, and of small G proteins involved in intracellular vacuole maturation and trafficking. Overall, this combined expression pattern can thus be designated as an “innate signature” among lymphocytes.

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