scholarly journals Kinomics platform using GBM tissue identifies BTK as being associated with higher patient survival

2021 ◽  
Vol 4 (12) ◽  
pp. e202101054
Author(s):  
Sofian Al Shboul ◽  
Olimpia E Curran ◽  
Javier A Alfaro ◽  
Fiona Lickiss ◽  
Erisa Nita ◽  
...  

Better understanding of GBM signalling networks in-vivo would help develop more physiologically relevant ex vivo models to support therapeutic discovery. A “functional proteomics” screen was undertaken to measure the specific activity of a set of protein kinases in a two-step cell-free biochemical assay to define dominant kinase activities to identify potentially novel drug targets that may have been overlooked in studies interrogating GBM-derived cell lines. A dominant kinase activity derived from the tumour tissue, but not patient-derived GBM stem-like cell lines, was Bruton tyrosine kinase (BTK). We demonstrate that BTK is expressed in more than one cell type within GBM tissue; SOX2-positive cells, CD163-positive cells, CD68-positive cells, and an unidentified cell population which is SOX2-negative CD163-negative and/or CD68-negative. The data provide a strategy to better mimic GBM tissue ex vivo by reconstituting more physiologically heterogeneous cell co-culture models including BTK-positive/negative cancer and immune cells. These data also have implications for the design and/or interpretation of emerging clinical trials using BTK inhibitors because BTK expression within GBM tissue was linked to longer patient survival.


2014 ◽  
Vol 29 (2) ◽  
pp. 181-198 ◽  
Author(s):  
Katie E. Baker ◽  
Sara J. Bonvini ◽  
Chantal Donovan ◽  
Rachel E. Foong ◽  
Bing Han ◽  
...  


Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 3855-3855
Author(s):  
Sreeja Karathedath ◽  
Sukanya Ganesan ◽  
Wei Zhang ◽  
Ajay Abraham ◽  
Savitha Varatharajan ◽  
...  

Abstract Nuclear Hormone Receptors (NHRs) are a large superfamily of ligand dependent transcription factors regulating a plethora of genes involved in metabolism, growth and differentiation. Recent studies identified the overwhelming role played by NHR in determining hematopoiesis as well as HSC function and fate, suggesting that defective proliferation and maturation of progenitors in hematopoietic malignancies could be attributed to altered expression of these transcriptional regulators. NHRs are attractive but relatively unexplored drug targets and there is limited data on their expression profile in hematological malignancies. Our preliminary screening of AML cell lines and primary cells revealed differential expression of PPARg and RXRα among AML cell lines and decreased expression of these targets in resistant cell lines vs. sensitive ones (Fig.1a). We extended this study aiming to identify the expression pattern of all NHRs in myeloid malignancies, compare with ex-vivo chemoresistance and prognostic markers. Myeloid leukemia cell lines AML(n=12) carrying AML-ETO, Inv-16, PML-RAR fusion transcript, NPM1 or FLT3 mutation and CML(n=8), pooled primary samples harboring only FLT3 ITD mutation and only NPM1 mutation, CD34+ cells and total cellular RNA samples from normal donors were included in the study. Bone marrow samples from AML patients and peripheral blood samples from healthy volunteers were collected after informed consent. PBSC from normal donors (n=7) were enriched for CD34+ cells by magnetic enrichment. RNA extraction followed by purification and cDNA synthesis were done as per manufacturer’s recommendation. The expression of 42 NHRs and 42 co-regulators was profiled by qRT PCR using RT2 PCR Profiler array (SABiosciences, Germany). Expression of each gene was normalized to the average of 5 housekeeping genes (GAPDH, b-actin, b2-microglobulin, HGPRT & RPLP0) and differential expression was calculated by normalizing this with normal total RNA samples to determine fold change. Data normalization and differential gene expression were computed using SABiosciences web-based analysis software. Ex-vivo cytotoxicity to cytarabine and daunorubicin was analyzed using MTT assay and IC50 was calculated using Adapt software. Based on Ara-C and Dnr IC50, AML samples were categorized as sensitive or resistant (IC50<6.25µM and >6.25µM for Ara-C;<0.5µM and >0.5µM for Dnr). FLT3-ITD and NPM1 mutations were screened using PCR followed by GeneScan methods. Upon analysis we observed steroid receptors except PPARG, NR5A1, RXRG, NR1D2, glucocorticoid receptors, androgen receptors and their co-regulatory molecules were expressed across all cell lines. In contrast, expression of certain NHRs such as NR2E3, NR1I2, NR1H4, NR0B1, NR0B2, ESRRG, ESRRB, NR1D1 and the co-regulator HDAC7 were less or at undetectable levels, while estrogen receptors and its co-regulatory molecules except PPARGC1B, steroid receptors HNF4A, NR2F1, NR2F2, RARG and NR3C2 were moderately expressed across the cell lines (Fig.1b). Analysis also revealed significantly reduced expression of ESR2, ESRRG, NR2F2, NR2F6 and THRB and increased PPARG in AML compared to CML cell lines. Comparison between CD34+ progenitor population and well differentiated hematopoietic cells demonstrated >6 fold change in NR4A1 expression. Likewise, expression of several differentiation markers NR1H3, NR1D2, RORA, RORB, RARA and VDR were elevated in cells with increased degree of maturation. The receptor inducing terminal differentiation of erythrocytes THRA, had lesser expression (<60 Fold) in erythroleukemic cell lines K562, HEL and LAMA-84 compared to normal (Fig.1c). In Ara-C resistant cell lines, expression of NR1I3, AHR, and HNF4A were up-regulated while PPARG and RXRA were down-regulated. NPM1 positive cell lines and patient samples had increased expression of PPARG, PPARGC1A, NOTCH2, NR1H3 and NR2F2 than the FLT3 mutated group (Fig.1d) and similar expression of PPARG and RXRA was validated in NPM mutated primary AML blasts (Fig.1e). Our comprehensive analysis of NHRs and its co regulatory molecules provides us with insights on NHR expression on differentiation and drug resistance across myeloid leukemia cell lines with various genetic and molecular characteristics. This could be further explored to identify potential novel drug targets to be used as combination therapy in myeloid leukemias to overcome drug resistance. Disclosures: No relevant conflicts of interest to declare.



Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 3210-3210
Author(s):  
Arnold Bolomsky ◽  
Fred Gruber ◽  
Kathrin Stangelberger ◽  
Leon Furchtgott ◽  
Dominik Arnold ◽  
...  

Abstract Introduction Regardless of significant advances in the therapy of multiple myeloma (MM) there is still a lack of effective treatment options for patients with high-risk disease. In this context, we recently developed a network of high-risk disease based on more than 30 000 genomic and clinical variables from 645 patients of the CoMMpass dataset (Gruber et al., ASH 2016). Validation of these findings has been performed in the IFM/DFCI 2009 trial dataset (Furchtgott et al., ASH 2017). This comprehensive computational approach revealed a network of 17 genes driving high-risk (defined as progression or myeloma-related death within 18 months). Here, we performed preclinical validation of potential novel drug targets to confirm the utility of in silico guided target discovery in high-risk MM. Methods TTK (CFI402257, BAY-1217389), PLK4 (CFI400945, Centrinone), MELK (OTSSP167) and CDK1 (CPG71514) inhibitors were studied in a panel of human MM cell lines (n=11) for their activity in cell viability, cell growth, cell cycle, apoptosis, colony formation, drug combination and co-culture experiments. PKMYT1, TTK and PLK4 were targeted with doxycycline-inducible shRNAs. Analysis of gene expression (GEP) data (GSE24080) was used to link candidate genes to certain MM subgroups. Results The network of 17 genes driving high-risk disease contained eight kinases that serve as attractive drug targets (AURKA, NEK2, CDK1, BUB1B, MELK, TTK, PKMYT1, and PLK4), all of them involved in cell cycle regulation. Accordingly, expression levels of all kinases (except PKMYT1) were enriched in the GEP-defined proliferation associated subgroup of MM and thus linked to poor outcome. To study the interconnectedness of the individual network genes we first investigated the impact of previously reported CDK1 and MELK inhibitors on other network members. This demonstrated rapid loss of CDK1, NEK2, MELK, PKMYT1 and FOXM1 protein levels. We then selected TTK and PLK4 as putative novel MM targets with available inhibitors undergoing clinical testing in solid tumors. Protein and mRNA expression of both genes was confirmed in all MM cell lines. Two selective compounds per gene were used for preclinical studies. All four inhibitors significantly reduced MM cell viability and single dose IC70 treatment impaired cell growth up to 10 days (60-98% reduction, P<0.01). This growth inhibitory effect was confirmed with inducible shRNAs. Mechanistically, growth impairment was linked to G2M cell cycle arrest followed by the accumulation of polyploid cells (15-90% of cells 72h post treatment) which is in line with the role of both genes in chromosome segregation. The formation of aberrant mitoses led to the induction of apoptosis 3-5 days post treatment (≤20% viable cells at day 5 post treatment with 3/4 inhibitors) and was accompanied by the presence of active caspase 3 and cleaved PARP. Importantly, the activity of these drugs persisted in the presence of BMSCs and showed potent activity in colony formation assays (DMSO: 168±15 and 131±57, BAY-1217389: 39±29 and 41±3, CFI-400945: 19±26 and 30±6 colonies in KMS12BM and OPM2 cells at day 14, P<0.05). Drug combination studies pointed to favorable activity in combination with dexamethasone and lenalidomide. Furthermore, confirmatory TTK knockdown with two independent shRNAs sensitized MM cells to dexamethasone. Finally, PKMYT1 was chosen as putative target based on its role as major driver of high-risk in our model. We transduced MM cells with three doxycycline-inducible PKMYT1-targeting shRNAs and observed an impressive impact on myeloma cell growth upon doxycycline induction compared to non-targeting control shRNA (up to 85% reduction at day 10, P<0.01). Furthermore, PKMYT1 knockdown led to the induction of apoptosis in all MM cell lines tested. Based on these encouraging results we currently perform in-depth in silico and in vitro analyses of the underlying PKMYT1 signaling network. Detailed results of these sub-studies will be presented at the meeting. Conclusions Our results confirm the utility of computational based modelling of high-risk disease. This strategy not only revealed a genetic network closely associated to adverse prognosis, but also enabled the identification of so far unnoticed drug targets. Importantly, inhibitors of TTK and PLK4 are already in clinical testing and thus enable rapid clinical translation of our findings to MM patients in need of alternative therapeutic options. Disclosures Gruber: GNS Healthcare: Employment. Furchtgott:GNS Healthcare: Employment. Raut:GNS Healthcare: Employment. Wuest:GNS Healthcare: Employment. Runge:GNS Healthcare: Employment. Khalil:GNS Healthcare: Employment. Munshi:OncoPep: Other: Board of director. Hayete:GNS Healthcare: Employment. Ludwig:Amgen: Research Funding, Speakers Bureau; BMS: Speakers Bureau; Takeda: Research Funding, Speakers Bureau; Cilag-Janssen: Speakers Bureau; Celgene: Speakers Bureau.



2020 ◽  
Vol 19 (5) ◽  
pp. 300-300 ◽  
Author(s):  
Sorin Avram ◽  
Liliana Halip ◽  
Ramona Curpan ◽  
Tudor I. Oprea




2013 ◽  
Vol 14 (9) ◽  
pp. 952-958 ◽  
Author(s):  
Hepan Tan ◽  
Xiaoxia Ge ◽  
Lei Xie


2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Marie O. Pohl ◽  
Jessica von Recum-Knepper ◽  
Ariel Rodriguez-Frandsen ◽  
Caroline Lanz ◽  
Emilio Yángüez ◽  
...  


Author(s):  
Eamonn Morrison ◽  
Patty Wai ◽  
Andri Leonidou ◽  
Philip Bland ◽  
Saira Khalique ◽  
...  


BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Christos Dimitrakopoulos ◽  
Sravanth Kumar Hindupur ◽  
Marco Colombi ◽  
Dritan Liko ◽  
Charlotte K. Y. Ng ◽  
...  

Abstract Background Genetic aberrations in hepatocellular carcinoma (HCC) are well known, but the functional consequences of such aberrations remain poorly understood. Results Here, we explored the effect of defined genetic changes on the transcriptome, proteome and phosphoproteome in twelve tumors from an mTOR-driven hepatocellular carcinoma mouse model. Using Network-based Integration of multi-omiCS data (NetICS), we detected 74 ‘mediators’ that relay via molecular interactions the effects of genetic and miRNA expression changes. The detected mediators account for the effects of oncogenic mTOR signaling on the transcriptome, proteome and phosphoproteome. We confirmed the dysregulation of the mediators YAP1, GRB2, SIRT1, HDAC4 and LIS1 in human HCC. Conclusions This study suggests that targeting pathways such as YAP1 or GRB2 signaling and pathways regulating global histone acetylation could be beneficial in treating HCC with hyperactive mTOR signaling.



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