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Systematic interactome mapping of acute lymphoblastic leukemia cancer gene products reveals EXT-1 tumor suppressor as a Notch1 and FBWX7 common interactor
© The Author(s). 2016
Received: 20 January 2016
Accepted: 19 May 2016
Published: 26 May 2016
Perturbed genotypes in cancer can now be identified by whole genome sequencing of large number of diverse tumor samples, and observed gene mutations can be used for prognosis and classification of cancer subtypes. Although mutations in a few causative genes are directly linked to key signaling pathways perturbation, a global understanding of how known cancer genes drive oncogenesis in human is difficult to assess.
We collected available information about mutated genes in Acute Lymphoblastic Leukemia (ALL). Validated human protein interactions (PPI) were collected from IntAct, HPRD and BioGRID interactomics databases, or obtained using yeast two-hybrid screening assay.
We have mapped interconnections between 116 cancer census gene products associated with ALL. Combining protein-protein interactions data and cancer-specific gene mutations information, we observed that 63 ALL-gene products are interconnected and identified 37 human proteins interacting with at least 2 ALL-gene products. We highlighted exclusive and coexistence genetic alterations in key signaling pathways including the PI3K/AKT and the NOTCH pathways. We then used different cell lines and reporter assay systems to validate the involvement of EXT1 in the Notch pathway.
We propose that novel ALL-gene candidates can be identified based on their functional association with well-known cancer genes. We identified EXT1, a gene not previously linked to ALL via mutations, as a common interactor of NOTCH1 and FBXW7 regulating the NOTCH pathway in an FBXW7-dependend manner.
The identification of genes responsible for oncogenesis is a major goal in cancer research. These genes are mostly defined as “altered genes directly promoting malignant progression”. After three decades of molecular cancer research, different strategies have been used to define the cancer genetic landscape. The catalogue of somatic mutations in cancer (COSMIC) (http://cancer.sanger.ac.uk) is a comprehensive resource of somatic mutations in human cancer samples curated from published studies and cancer genomes sequencing . As of May 31, 2015, COSMIC reports a set of 572 genes, called the cancer gene census, for which mutations are associated with cancer development. In cancer samples, it is challenging to analyze and prioritize sequential mutation accumulation events, which occur in oncogenes and tumor suppressors genes. The mutations that provide a selective growth advantage in any step of tumorigenesis (initiation, clonal expansion, tumor formation) are known as driver mutations. Out of 572 cancer gene census, about 140 genes including 71 tumor suppressors and 54 oncogenes are well-accepted as cancer driver genes because mutations in those genes promote tumorigenesis . These numbers are not static and should increase as more cancer genomes are sequenced. Other approaches are being used to identify novel candidate cancer genes including Genome Wide Association Studies (GWAS) for the identification of cancer-associated loci , in vivo transposon mutagenesis screens in mice “sleeping beauty technology”, for genes potentially implicated in tumorigenesis [4–6], and protein-protein interactions screens of gene products targeted by oncogenic viruses [7–9]. Integrating information from all the above resources in a “guilt-by-association” model that also considers interacting partners of cancer-associated gene products allowed prioritization of ~ 3000 genes potentially associated with cancer . However, analyzing variations of mutations in time and space in different cancer types and subtypes (e.g., what driver genes are important for what cancer type at what stage) has been challenging. Few studies led to the discovery of a number of genes implicated in specific tumor types. As an example, children medulloblastoma tumor samples exhibit an average of 11 gene alterations compared to 55–121 in adult tumors , whilst lung and colorectal cancers require only 3 driver gene mutations . In liquid tumors such as leukemia and lymphomas, it believed that, one of the most prevalent category of mutations involving cancer driver genes are chromosomal rearrangements such as BCR-ABL1 in chronic mylogenous leukemia (CML) , fusions involving nucleopins 98 and 124 and MLL gene fusions in acute myelogenous leukemia (AML) [2, 14, 15], and TEL-AML1 and TCF3-PBX1 in acute lymphoblastic leukemia (ALL) [16–18]. These gene fusions alone are often insufficient and may require additional genetic perturbations for leukemogenesis [19, 20].
We previously showed that protein-protein interactions (PPI) data could be used for interpretation of expression profiles in cancer samples in order to identify and prioritize target genes and pathways . Here, we used PPI mapping strategies to explore information on cancer genes frequently mutated in ALL. We highlighted mutated hub proteins interconnected in an ALL-cancer gene products network and identified novel interacting partners that link key ALL-cancer driver gene products [21, 22].
Databases and literature PPI curation
Information about genes containing mutations in their coding regions was retrieved from the COSMIC database, evaluated; organized and selected genes were submitted for experimental analysis. To establish a catalog of genes and mutations associated with Acute Lymphoblastic Leukemia (ALL), we used the version 71 of COSMIC, previously downloaded to a local server and we extracted data only related to ALL. We developed and implemented a procedure that automatically collects information and check the consistency of changes with the coding sequences and find the corresponding positions on clones from the human ORFeome (http://horfdb.dfci.harvard.edu/).
The retrieved information include details provided at either nucleotide or protein level (mutation syntax), sample id (portion of a tumor being examined for mutations), tissue from which the sample originated, histological classification of the sample and the Pubmed id of the article that published the study. For each gene tested for PPIs using the yeast two-hybrid, we identified mutations positions on the ORFeome clone by sequence alignment (BLAST) then we verified if the protein sequence has undergone modifications as described by mutation syntax.
Human PPIs were collected and verified from the different interactomics databases IntAct , HPRD  and BioGRID . Only physical PPIs validated at least in two independent references or by two methods were considered as confident and maintained for the analysis.
Network data analyses and visualization
Network analyses and visualization of protein-protein interactions were carried out with Cytoscape software, which is a free software for visualizing, modeling and analyzing molecular and genetic interaction networks. Due to its features, Cytoscape and its plugins provide a powerful tool kit allowing to answer specific biological questions using large amounts of cellular network and molecular profiling information . In our maps, the nodes represent proteins that are connected with edges representing pairwise interactions extracted from interaction databases and from our Y2H experimental assay.
Cell culture and transfection
HEK293, HeLa and HeLa Notch1∆E-eGFP cells were cultured in DMEM supplemented with 10 % fetal bovine serum (FBS), 2 mM glutamine and penicillin/streptomycin. The same medium was used for U2OS Tet-on flp-in cells bearing isogenic transgenes encoding Notch1-Gal4. As for K562-control and K562 expressing Dll4 cells, they were grown and maintained in RPMI 1640 supplemented with 15 % FBS and antibiotics. T-ALL cell lines were grown and maintained in RMPI containing 10–20 % FBS and supplemented with antibiotics.
HEK293 cells were DNA-transfected with polyethylenimine (PEI) purchased from Sigma, reagent was dissolved in water at 1 mg/ml and preserved at −80 °C. Transfection with PEI was performed on HEK cells cultured in DMEM at 80 % confluence. Medium was changed before transfection and cells were collected 24 h post-transfection.
HeLa cells and HeLaN1ΔE-eGFP cells were DNA-transfected with lipofectamine 2000 reagent (Invitrogen) according to manufacturer’s instructions and collected 24 h post-transfection.
SiRNA transfection was performed with Calcium Phosphate using ProFection Mammalian Transfection kit from Promega according to manufacturer’s instructions on cells cultured in DMEM at 40–50 % confluence. Medium was changed 24 h later and cells were collected 48 h post-transfection.
For experiments involving both DNA and siRNA transfections, siRNA-transfection was performed according to manufacturer’s instructions and 24 h later after changing medium cells were transfected with DNA using lipofectamin 2000 reagent (Invitrogen) and cells were collected 24 h post DNA-transfection.
For proteasomal degradation inhibition, cells were treated with 10 μg/ml MG132 for 6 h before being collected.
siFBXW7: 5′- GCAUAGAUUUUAUGGUAAtt-3′
siCtrl: 5′- GGCUGCUUCUAUGAUUAUGtt-3′
Total RNA was extracted using GeneJET RNA Purification Kit (Thermo scientific), DNaseI-treated on the column (Thermo Scientific) and reverse-transcribed with random primers (Thermo scientific). qPCR was performed using SYBER Green detection from Roche and run on Lightcycler 480 (Roche). mRNA quantification was performed relative to GAPDH housekeeping gene. Relative expression levels were calculated for each gene using the ΔΔCt method.
Open reading frames (ORF) encoding Notch1 partners (tested for Protein complementation assay) were obtained from human ORFeome v5.1 (center of cancer systems biology: CCSB) as entry clones. Human NICD plasmid, was obtained from Addgene. FBXW7α expressing vector was kindly provided by Dr. E. Dejardin from the laboratory of molecular immunology and signal transduction—(GIGA-ULg). ORFs that were not available from the hORFeome V5.1 (BRAF, HRAS, ABL1, JAK2 and SMARCB1 and NOTCH1 genes), were purshassed from Genecopea and cloned by Gateway recombination technology (Invitrogen) using specific primers flanked with the following AttB1 and AttB2 Gateway sites: 5′- GGGGACAACTTTGTACAAAAAAGTTGGCATG-3′ (AttB1) and 5′- GGGGACAACTTTGTACAAGAAAGTTGA-3′ (AttB2). These constructions were verified by PCR and sequencing.
Inserts from pDONR223 were transferred by LR cloning (Invitrogen) into different destination vectors: pAD-destCYH and pDB-dest the Y2H expression vectors, and pDEST1899 (flag tag), pDEST491 (YFP-tag) and pDEST-mcherry for mammalian expression studies.
High-throughput yeast Two-hybrid (HT-Y2H)
We used the hORFeome version 5.1, a collection of human ORFs cloned from the Mammalian Gene Collection (MGC) resource, representing a resource of ORFs that can be transferred easily to any Gateway compatible destination vectors. This collection contains 15 483 ORFs representing almost half of the human genome. They are cloned into the pAD-dest-CYH and pDB-dest encoding the yeast Gal4 Activating and DNA-binding domains, respectively. The resulting individual clones were transferred into MATa Y8800 (pAD) and MATα Y8930 (pDB) S. cerevisiae strains. Twenty-one selected ALL-genes were screened for interactions with the hORFeome V5.1 as described in .
One pool of 21 AD of selected genes into Y8800 yeast strain was mated to each of the 15483 dB-ORFs Y8930 of the hORFeome v5.1 and each of ALL-genes-DB into Y8930 yeast strain was mated to 165 pools of 94 AD-ORFs of the hORFeome v5. One Y2H screening was performed in the reciprocal orientation, as described in . Positive colonies for the GAL1:: HIS3 and GAL1:: ADE2 selective markers but negative for autoactivation were selected for PCR-amplification (Zymolyase 20 T from Seikagaku Biobusiness, and Platinum® Taq DNA Polymerase from Invitrogen) and identification of interacting proteins by sequencing of the respective AD- and DB-ORFs.
Luciferase reporter assays
Cells were seeded in 24-well plates and transfected with 300 ng of either TP1 luciferase reporter plasmid (TP1-luc) or CBF1 reporter plasmid (CBF1-luc) and 30 ng of renilla Luciferase (R-Luc). Twenty-four hours post-transfection luciferase activity was measured in cell lysates.
U2OS N1-Gal4 cells they were transfected 300 ng of Gal4-firefly luciferase and 30 ng R-Luc reporter plasmid. After 24 h, K562 cells expressing Notch ligands DLL4 or K562 control cells were added to the transfected cells in the presence of tetracycline (2 μg/mL). After 24-h of coculture, luciferase activity was measured in cell lysates.
Cell lysis and luciferase assays were performed in triplicate using Dual-luciferase reporter assay system from Promega. Luciferase measurements were performed in 96 well plates using DLR automated machine. Firefly luciferase values were normalized to R-luc values and calculated ratio represent luciferase activity.
Immunofluorescence and confocal microscopy
HeLa Notch1∆E-eGFP cells were seeded onto coverslips in 24-well plates and transfected with 1 μg of EXT1-mcherry plasmid using lipofectamine2000 (Invotrogen). Twenty-four hours post-transfection, cells were washed in warm PBS, fixed in 3,7 % PBS-paraformaldehyde for 20 min at room temperature, washed 3 times with PBS, and mounted on glass coverslips using ProLong Gold Antifade montant with DAPI (life technologies).
Slides were examined by confocal microscopy using the Nikon A1R confocal system and images processed with the IMAGI software.
Protein complementation assay (PCA)
NICD and FBXW7 were cloned in pN1Gluc vector and pN2Gluc vectors respectively (for Gaussia luciferase 1 and 2) using the Gateway cloning technology. HEK293 cells were seeded in 24 well-plates at a concentration 5.10 4 cell/well, then transfected with GL1 or/and GL2 plasmids and 24 h post-transfection, luciferase activity was measured on lysates transferred into 96-well plate using and automated machine DLR with Renilla luciferase substrate. Normalized luciferase ratio was calculated as follows: NLR = luciferase value GL1 + GL2/(luciferase value GL1 + luciferase value GL2). An interaction is considered positive or validated when NLR ≥ 3.5. Cell lysis and luciferase assays were performed in triplicate for each condition.
EXT1 silencing in zebrafish
Transgenic zebrafish line Tp1bglob:eGFP line  were maintained according to EU regulations on laboratory animals. Knockdown experiments were performed by injecting embryos at the one- to two-cell stage with 10 ng of single splice-blocking morpholino designed specifically for both EXT1 a and b orthologs.
Total RNA was extracted from HeLaN1ΔE-eGFP cells (siCTRL, siEXT1, siFBXW7), quantified and tested for RNA quality controlled using Agilent 2100 bioanalyzer using the Eukaryote Total RNA Nano assay. Total RNA strands were used to generate libraries and sequenced by HiSeq2000 sequencer.
Graph values are presented as mean +/− standard deviation, calculated on at least three independent experiments. Unless stated otherwise, significance was determined using a two-tailed Student’s t-test (comparison of means). P-value thresholds are depicted as follows; *: p < 0.05; **: p < 0.01; ***: p < 0.001 and ****: p < 0.0001.
Where ri is the rank ratio for result i, N is the number of genes used.
Results and Discussion
Mutations associated to ALL in cancer gene census
For each ALL-gene we extracted the number of samples as well as the number of distinct mutations. Figure 1b represents the top 20 frequently mutated genes among the 116 ALL-genes. Each gene that has at least 2 distinct mutations observed in at least 556 different samples, from a total of 36,909 ALL samples was examined. Seven genes were found mutated in more than 5 % of ALL samples, including genes encoding for FLT3 (9.9 %), NRAS (6.14 %), JAK2 (6.08 %), NOTCH1 (5.87 %), IKZF1 (5.67 %), KRAS (5.52 %) and PTPN11 (5.32 %) (Fig. 1b and c). We also ranked ALL-genes according to the number of distinct mutations found in ALL samples (Fig. 1b and d). The top ranked gene was NOTCH1 with 595 distinct mutations mostly found in its heterodimerization (HD) domain (63 % of mutations) and in its proline, glutamic acid, serine, threonine-rich (PEST) domain (27 % of mutations) (Additional file 2: Figure S1). Mutations in the HD domain that enhance NOTCH1 cleavage and nuclear translocation of the intracellular NOTCH1 protein (ICN), and mutations in the PEST domain that result in the stabilization of ICN, are gain-of-function mutations affecting the transcriptional activation of Notch1-target genes. The majority of these activating mutations were found in human T lymphocytes ALL (T-ALL) samples, as previously reported . Other highly mutated ALL-genes include PTEN (140 distinct mutations) WT1 (86 distinct mutations), TP53 (85 mutations), PAX5 (79 mutations), and IL7R (73 mutations) (Fig. 1d).
Interconnections between ALL-gene products
To analyze the connectivity between ALL-gene products, we collected protein-protein interactions (PPI) data from three databases: BioGRID , HPRD  and IntAct  and filtered all reported interactions between the 116 ALL-gene products. Figure 2a shows that 63 out 116 ALL-gene products are interconnected. We then prioritized ALL-gene products based on their degree of interconnectivity (Additional file 1: Table S2B). One of the top interconnected ALL-proteins is beta-catenin (encoded by CTNNB1 gene), which is a central hub in the Wnt/β-catenin signalling pathway and plays a crucial role normal haematopoiesis . It has been shown that 50–85 % of the childhood T-ALL patients overexpress β-catenin , further supporting our finding that β-catenin is an important hub in ALL. Other Wnt/β-catenin signaling pathway members such as APC, TCF3 and TCF7L2 interacting with β-catenin, are part of our ALL-gene product set and were previously found differentially expressed in T-ALL patients .
Another example is PIK3R1 with 8 partners including PIK3CA that interacts with additional 4 ALL-gene products. PI3K members are essential effectors in the PI3K/AKT/mTOR signaling pathway, which is activated in a number of ALL samples . Another example is ABL1 that interacts with 7 partners. The BCR-ABL1 fusion is the driver chromosomal rearrangement in chronic myeloid leukemia (CML)  and is also found in more than 20 % of ALL patients . Mutations in ABL1 gene were associated to different types of cancer .
Co-occurrence of mutations in ALL-genes
We then explored the relationship between interacting genes based on the occurrence of mutations in the same ALL samples. We showed that, in addition to biophysical interactions, several ALL-gene products are mutated in the same patient samples, suggesting several ways of deregulating cancer pathways (Fig. 2b). As shown on Fig. 2b, our analysis revealed that ALL samples could be classified into 4 distinct groups of affected pathways, based on co-occurrence of mutations in important cancer driver genes: PI3K/AKT and NOTCH pathways, JAK and RAS pathways, Wnt/β-catenin and the cell cycle, and the transcriptional regulation pathways. Interestingly, protein phosphatases PTEN and PTPN11, and proteins important in genome maintenance and chromatin modification P53 and CREB binding protein are centrals and connect with deregulated pathways through different set of mutations (Additional file 1: Table S3).
Combining all the above criteria: frequency of mutations in individual cancer genes in ALL samples, number of distinct mutations and their pattern in ALL-gene (Fig. 1 and Additional file 2: Figure S1), interconnections between ALL-gene products and co-occurrence of mutated genes in the same samples (Fig. 2), we prioritized ALL-genes and suggest that TP53, NOTCH1, CREBBP, PTEN, EGFR, JAK2, ABL1, PTPN11, CBL and EP300 are the top 10 ALL driver genes (Additional file 1: Table S4).
Functional associations between ALL-gene products and their partners in the human proteome
As suggested by other studies, interconnected proteins are more associated with common diseases than expected by chance  and the same cancer driver genes are often involved in different cancer types, as evidenced by several examples . To identify novel ALL-gene candidates through a “guilt-by-association” prediction, we prioritized ALL-gene products interactors using three criteria: (1) the number of ALL-gene products partners, (2) their implications in other types of cancer and (3) their expression in 24 common ALL cell lines. In total, we identified 37 ALL-gene products interactors that could be considered as ALL-associated candidates (Fig. 3c and d).
EXT1 is functionally associated with the notch pathway through its interaction with NOTCH1 and FBXW7
The sequencing of different cancer genomes allows identification and characterization of mutated genes in cancer samples. However, the development of genome-based therapies requires greater knowledge of the specific driver genes implicated in diverse cancer types and subtypes. In this study, we have analyzed mutated genes found in ALL samples collected in the Sanger COSMIC database. We proposed a list of genes more likely involved in ALL by combining the frequency of mutations in ALL samples, the number and pattern of distinct mutations, and the interconnectivity between their products that determine specific affected signaling pathways. We finally propose that novel ALL-gene candidates can be identified based on their functional association with well-known cancer genes. Finally, we demonstrated that EXT1, a tumor suppressor not previously linked to ALL, is involved in the regulation of the NOTCH pathway trough its dual interaction with NOTCH1 and FBXW7.
We thank the GIGA-Research technology platforms (Interactome, Genomics, imaging, Flow cytometry, viral vectors and Zebrafish facility) for technical assistance. We also thank the COSMIC database for data curation.
This work was supported by the Fonds National de la Recherche Scientifique (FNRS) grants # 7.4538.09/11/13 to JCT, the Fondation Belge contre le Cancer (FBC) grant # HE2010 to FD, and the Fonds Leon Fredericq to SD.
Availability of data and materials
All datasets supporting the conclusions of this article are included within the main manuscript or in supplemental files.
SD carried out interactomes screening and cell culture assays and drafted the manuscript. LJH collected mutations and interactomes data and performed network analysis. DK and AD carried out some cell culture experiments. NS, RK, FD and JCT directed the study. JCT wrote the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
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