A personalised medicine approach for ponatinib-resistant chronic myeloid leukaemia.

BACKGROUND: Chronic myeloid leukaemia (CML) is characterised by the presence of a fusion driver oncogene, BCR-ABL1, which is a constitutive tyrosine kinase. Tyrosine kinase inhibitors (TKIs) are the central treatment strategy for CML patients and have significantly improved survival rates, but the T315I mutation in the kinase domain of BCR-ABL1 confers resistance to all clinically approved TKIs, except ponatinib. However, compound mutations can mediate resistance even to ponatinib and remain a clinical challenge in CML therapy. Here, we investigated a ponatinib-resistant CML patient through whole-genome sequencing (WGS) to identify the cause of resistance and to find alternative therapeutic targets. PATIENTS AND METHODS: We carried out WGS on a ponatinib-resistant CML patient and demonstrated an effective combination therapy against the primary CML cells derived from this patient in vitro. RESULTS: Our findings demonstrate the emergence of compound mutations in the BCR-ABL1 kinase domain following ponatinib treatment, and chromosomal structural variation data predicted amplification of BCL2. The primary CD34(+) CML cells from this patient showed increased sensitivity to the combination of ponatinib and ABT-263, a BCL2 inhibitor with a negligible effect against the normal CD34(+) cells. CONCLUSION: Our results show the potential of personalised medicine approaches in TKI-resistant CML patients and provide a strategy that could improve clinical outcomes for these patients

A blood gene expression marker of early Alzheimer's disease.

PublishedJournal ArticleResearch Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov'tA marker of Alzheimer's disease (AD) that can accurately diagnose disease at the earliest stage would significantly support efforts to develop treatments for early intervention. We have sought to determine the sensitivity and specificity of peripheral blood gene expression as a diagnostic marker of AD using data generated on HT-12v3 BeadChips. We first developed an AD diagnostic classifier in a training cohort of 78 AD and 78 control blood samples and then tested its performance in a validation group of 26 AD and 26 control and 118 mild cognitive impairment (MCI) subjects who were likely to have an AD-endpoint. A 48 gene classifier achieved an accuracy of 75% in the AD and control validation group. Comparisons were made with a classifier developed using structural MRI measures, where both measures were available in the same individuals. In AD and control subjects, the gene expression classifier achieved an accuracy of 70% compared to 85% using MRI. Bootstrapping validation produced expression and MRI classifiers with mean accuracies of 76% and 82%, respectively, demonstrating better concordance between these two classifiers than achieved in a single validation population. We conclude there is potential for blood expression to be a marker for AD. The classifier also predicts a large number of people with MCI, who are likely to develop AD, are more AD-like than normal with 76% of subjects classified as AD rather than control. Many of these people do not have overt brain atrophy, which is known to emerge around the time of AD diagnosis, suggesting the expression classifier may detect AD earlier in the prodromal phase. However, we accept these results could also represent a marker of diseases sharing common etiology.InnoMed, European Union of the Sixth Framework programAlzheimer’s Research UKJohn and Lucille van Geest FoundationNIHRBiomedical Research Centre for Mental Health, South London and Maudsley NHS Foundation TrustInstitute of Psychiatry Kings College LondonNIA/NIH RC

Intrinsic Structural Disorder Confers Cellular Viability on Oncogenic Fusion Proteins

Chromosomal translocations, which often generate chimeric proteins by fusing segments of two distinct genes, represent the single major genetic aberration leading to cancer. We suggest that the unifying theme of these events is a high level of intrinsic structural disorder, enabling fusion proteins to evade cellular surveillance mechanisms that eliminate misfolded proteins. Predictions in 406 translocation-related human proteins show that they are significantly enriched in disorder (43.3% vs. 20.7% in all human proteins), they have fewer Pfam domains, and their translocation breakpoints tend to avoid domain splitting. The vicinity of the breakpoint is significantly more disordered than the rest of these already highly disordered fusion proteins. In the unlikely event of domain splitting in fusion it usually spares much of the domain or splits at locations where the newly exposed hydrophobic surface area approximates that of an intact domain. The mechanisms of action of fusion proteins suggest that in most cases their structural disorder is also essential to the acquired oncogenic function, enabling the long-range structural communication of remote binding and/or catalytic elements. In this respect, there are three major mechanisms that contribute to generating an oncogenic signal: (i) a phosphorylation site and a tyrosine-kinase domain are fused, and structural disorder of the intervening region enables intramolecular phosphorylation (e.g., BCR-ABL); (ii) a dimerisation domain fuses with a tyrosine kinase domain and disorder enables the two subunits within the homodimer to engage in permanent intermolecular phosphorylations (e.g., TFG-ALK); (iii) the fusion of a DNA-binding element to a transactivator domain results in an aberrant transcription factor that causes severe misregulation of transcription (e.g. EWS-ATF). Our findings also suggest novel strategies of intervention against the ensuing neoplastic transformations

Cryptic splicing events in the iron transporter ABCB7 and other key target genes in SF3B1-mutant myelodysplastic syndromes.

The splicing factor SF3B1 is the most frequently mutated gene in myelodysplastic syndromes (MDS), and is strongly associated with the presence of ring sideroblasts (RS). We have performed a systematic analysis of cryptic splicing abnormalities from RNA sequencing data on hematopoietic stem cells (HSCs) of SF3B1-mutant MDS cases with RS. Aberrant splicing events in many downstream target genes were identified and cryptic 3' splice site usage was a frequent event in SF3B1-mutant MDS. The iron transporter ABCB7 is a well-recognized candidate gene showing marked downregulation in MDS with RS. Our analysis unveiled aberrant ABCB7 splicing, due to usage of an alternative 3' splice site in MDS patient samples, giving rise to a premature termination codon in the ABCB7 mRNA. Treatment of cultured SF3B1-mutant MDS erythroblasts and a CRISPR/Cas9-generated SF3B1-mutant cell line with the nonsense-mediated decay (NMD) inhibitor cycloheximide showed that the aberrantly spliced ABCB7 transcript is targeted by NMD. We describe cryptic splicing events in the HSCs of SF3B1-mutant MDS, and our data support a model in which NMD-induced downregulation of the iron exporter ABCB7 mRNA transcript resulting from aberrant splicing caused by mutant SF3B1 underlies the increased mitochondrial iron accumulation found in MDS patients with RS

Discovering cancer genes by integrating network and functional properties

<p>Abstract</p> <p>Background</p> <p>Identification of novel cancer-causing genes is one of the main goals in cancer research. The rapid accumulation of genome-wide protein-protein interaction (PPI) data in humans has provided a new basis for studying the topological features of cancer genes in cellular networks. It is important to integrate multiple genomic data sources, including PPI networks, protein domains and Gene Ontology (GO) annotations, to facilitate the identification of cancer genes.</p> <p>Methods</p> <p>Topological features of the PPI network, as well as protein domain compositions, enrichment of gene ontology categories, sequence and evolutionary conservation features were extracted and compared between cancer genes and other genes. The predictive power of various classifiers for identification of cancer genes was evaluated by cross validation. Experimental validation of a subset of the prediction results was conducted using siRNA knockdown and viability assays in human colon cancer cell line DLD-1.</p> <p>Results</p> <p>Cross validation demonstrated advantageous performance of classifiers based on support vector machines (SVMs) with the inclusion of the topological features from the PPI network, protein domain compositions and GO annotations. We then applied the trained SVM classifier to human genes to prioritize putative cancer genes. siRNA knock-down of several SVM predicted cancer genes displayed greatly reduced cell viability in human colon cancer cell line DLD-1.</p> <p>Conclusion</p> <p>Topological features of PPI networks, protein domain compositions and GO annotations are good predictors of cancer genes. The SVM classifier integrates multiple features and as such is useful for prioritizing candidate cancer genes for experimental validations.</p

Human Cancer Protein-Protein Interaction Network: A Structural Perspective

Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network). The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%). We illustrate the interface related affinity properties of two cancer-related hub proteins: Erbb3, a multi interface, and Raf1, a single interface hub. The results reveal that affinity of interactions of the multi-interface hub tends to be higher than that of the single-interface hub. These findings might be important in obtaining new targets in cancer as well as finding the details of specific binding regions of putative cancer drug candidates

Predicting cancer involvement of genes from heterogeneous data

<p>Abstract</p> <p>Background</p> <p>Systematic approaches for identifying proteins involved in different types of cancer are needed. Experimental techniques such as microarrays are being used to characterize cancer, but validating their results can be a laborious task. Computational approaches are used to prioritize between genes putatively involved in cancer, usually based on further analyzing experimental data.</p> <p>Results</p> <p>We implemented a systematic method using the PIANA software that predicts cancer involvement of genes by integrating heterogeneous datasets. Specifically, we produced lists of genes likely to be involved in cancer by relying on: (i) protein-protein interactions; (ii) differential expression data; and (iii) structural and functional properties of cancer genes. The integrative approach that combines multiple sources of data obtained positive predictive values ranging from 23% (on a list of 811 genes) to 73% (on a list of 22 genes), outperforming the use of any of the data sources alone. We analyze a list of 20 cancer gene predictions, finding that most of them have been recently linked to cancer in literature.</p> <p>Conclusion</p> <p>Our approach to identifying and prioritizing candidate cancer genes can be used to produce lists of genes likely to be involved in cancer. Our results suggest that differential expression studies yielding high numbers of candidate cancer genes can be filtered using protein interaction networks. </p

A Chemocentric Approach to the Identification of Cancer Targets

A novel chemocentric approach to identifying cancer-relevant targets is introduced. Starting with a large chemical collection, the strategy uses the list of small molecule hits arising from a differential cytotoxicity screening on tumor HCT116 and normal MRC-5 cell lines to identify proteins associated with cancer emerging from a differential virtual target profiling of the most selective compounds detected in both cell lines. It is shown that this smart combination of differential in vitro and in silico screenings (DIVISS) is capable of detecting a list of proteins that are already well accepted cancer drug targets, while complementing it with additional proteins that, targeted selectively or in combination with others, could lead to synergistic benefits for cancer therapeutics. The complete list of 115 proteins identified as being hit uniquely by compounds showing selective antiproliferative effects for tumor cell lines is provided

Osteoclast stimulation factor 1 (Ostf1) KNOCKOUT increases trabecular bone mass in mice

Osteoclast stimulation factor 1 (OSTF1) is an SH3-domain containing protein that was initially identified as a factor involved in the indirect activation of osteoclasts. It has been linked to spinal muscular atrophy in humans through its interaction with SMN1, and is one of six genes deleted in a human developmental microdeletion syndrome. To investigate the function of OSTF1, we generated an Ostf1 knockout mouse model, with exons 3 and 4 of Ostf1 replaced by a LacZ orf. Extensive X-Gal staining was performed to examine the developmental and adult expression pattern, followed by phenotyping. We show widespread expression of the gene in the vasculature of most organs and in a number of cell types in adult and embryonic mouse tissues. Furthermore, whilst SHIRPA testing revealed no behavioural defects, we demonstrate increased trabecular mass in the long bones, confirming a role for OSTF1 in bone development