Generation of fusion protein EGFRvIII-HBcAg and its anti-tumor effect in vivo

The epidermal growth factor receptor variant III (EGFRvIII) is the most common variation of EGFR. Because it shows a high frequency in several different types of tumor and has not been detected in normal tissues, it is an ideal target for tumor specific therapy. In this study, we prepared EGFRvIII-HBcAg fusion protein. After immunization with fusion protein, HBcAg or PBS, the titers of antibody in BALB/c mice immunized with fusion protein reached 2.75 × 105. Western blot analysis demonstrated that the fusion protein had specific antigenicity against anti-EGFRvIII antibody. Further observation showed fusion protein induced a high frequency of IFN-γ-secreting lymphocytes. CD4+T cells rather than CD8+T cells were associated with the production of IFN-γ. Using Renca-vIII(+) cell as specific stimulator, we observed remarkable cytotoxic activity in splenocytes from mice immunized with fusion protein. Mice were challenged with Renca-vIII(+) cells after five times immunization. In fusion protein group, three of ten mice failed to develop tumor and all survived at the end of the research. The weight of tumors in fusion protein were obviously lighter than that in other two groups (t = 4.73, P = 0.044;t = 6.89, P = 0.040). These findings demonstrated that EGFRvIII-HBcAg fusion protein triggered protective responses against tumor expressing EGFRvIII

Cancer-cell intrinsic gene expression signatures overcome intratumoural heterogeneity bias in colorectal cancer patient classification

Stromal-derived intratumoural heterogeneity (ITH) has been shown to undermine molecular stratification of patients into appropriate prognostic/predictive subgroups. Here, using several clinically relevant colorectal cancer (CRC) gene expression signatures, we assessed the susceptibility of these signatures to the confounding effects of ITH using gene expression microarray data obtained from multiple tumour regions of a cohort of 24 patients, including central tumour, the tumour invasive front and lymph node metastasis. Sample clustering alongside correlative assessment revealed variation in the ability of each signature to cluster samples according to patient-of-origin rather than region-of-origin within the multi-region dataset. Signatures focused on cancer-cell intrinsic gene expression were found to produce more clinically useful, patient-centred classifiers, as exemplified by the CRC intrinsic signature (CRIS), which robustly clustered samples by patient-of-origin rather than region-of-origin. These findings highlight the potential of cancer-cell intrinsic signatures to reliably stratify CRC patients by minimising the confounding effects of stromal-derived ITH

Multi-cancer computational analysis reveals invasion-associated variant of desmoplastic reaction involving INHBA, THBS2 and COL11A1

<p>Abstract</p> <p>Background</p> <p>Despite extensive research, the details of the biological mechanisms by which cancer cells acquire motility and invasiveness are largely unknown. This study identifies an invasion associated gene signature shedding light on these mechanisms.</p> <p>Methods</p> <p>We analyze data from multiple cancers using a novel computational method identifying sets of genes whose coordinated overexpression indicates the presence of a particular phenotype, in this case high-stage cancer.</p> <p>Results</p> <p>We conclude that there is one shared "core" metastasis-associated gene expression signature corresponding to a specific variant of stromal desmoplastic reaction, present in a large subset of samples that have exceeded a threshold of invasive transition specific to each cancer, indicating that the corresponding biological mechanism is triggered at that point. For example this threshold is reached at stage IIIc in ovarian cancer and at stage II in colorectal cancer. Therefore, its presence indicates that the corresponding stage has been reached. It has several features, such as coordinated overexpression of particular collagens, mainly <it>COL11A1 </it>and other genes, mainly <it>THBS2 </it>and <it>INHBA</it>. The composition of the overexpressed genes indicates invasion-facilitating altered proteolysis in the extracellular matrix. The prominent presence in the signature of INHBA in all cancers strongly suggests a biological mechanism centered on activin A induced TGF-β signaling, because activin A is a member of the TGF-β superfamily consisting of an INHBA homodimer. Furthermore, we establish that the signature is predictive of neoadjuvant therapy response in at least one breast cancer data set.</p> <p>Conclusions</p> <p>Therefore, these results can be used for developing high specificity biomarkers sensing cancer invasion and predicting response to neoadjuvant therapy, as well as potential multi-cancer metastasis inhibiting therapeutics targeting the corresponding biological mechanism.</p

Physiochemical property space distribution among human metabolites, drugs and toxins

<p>Abstract</p> <p>Background</p> <p>The current approach to screen for drug-like molecules is to sieve for molecules with biochemical properties suitable for desirable pharmacokinetics and reduced toxicity, using predominantly biophysical properties of chemical compounds, based on empirical rules such as Lipinski's "rule of five" (Ro5). For over a decade, Ro5 has been applied to combinatorial compounds, drugs and ligands, in the search for suitable lead compounds. Unfortunately, till date, a clear distinction between drugs and non-drugs has not been achieved. The current trend is to seek out drugs which show metabolite-likeness. In identifying similar physicochemical characteristics, compounds have usually been clustered based on some characteristic, to reduce the search space presented by large molecular datasets. This paper examines the similarity of current drug molecules with human metabolites and toxins, using a range of computed molecular descriptors as well as the effect of comparison to clustered data compared to searches against complete datasets.</p> <p>Results</p> <p>We have carried out statistical and substructure functional group analyses of three datasets, namely human metabolites, drugs and toxin molecules. The distributions of various molecular descriptors were investigated. Our analyses show that, although the three groups are distinct, present-day drugs are closer to toxin molecules than to metabolites. Furthermore, these distributions are quite similar for both clustered data as well as complete or unclustered datasets.</p> <p>Conclusion</p> <p>The property space occupied by metabolites is dissimilar to that of drugs or toxin molecules, with current drugs showing greater similarity to toxins than to metabolites. Additionally, empirical rules like Ro5 can be refined to identify drugs or drug-like molecules that are clearly distinct from toxic compounds and more metabolite-like. The inclusion of human metabolites in this study provides a deeper insight into metabolite/drug/toxin-like properties and will also prove to be valuable in the prediction or optimization of small molecules as ligands for therapeutic applications.</p

Synergy between trastuzumab and pertuzumab for human epidermal growth factor 2 (Her2) from colocalization: an in silico based mechanism

10.1186/bcr2888Breast Cancer Research133-BCRR

Colon cancer subtypes: Concordance, effect on survival and selection of the most representative preclinical models

Multiple gene-expression-based subtypes have been proposed for the molecular subdivision of colon cancer in the last decade. We aimed to cross-validate these classifiers to explore their concordance and their power to predict survival. A gene-chip-based database comprising 2,166 samples from 12 independent datasets was set up. A total of 22 different molecular subtypes were re-trained including the CCHS, CIN25, CMS, ColoGuideEx, ColoGuidePro, CRCassigner, MDA114, Meta163, ODXcolon, Oncodefender, TCA19, and V7RHS classifiers as well as subtypes established by Budinska, Chang, DeSousa, Marisa, Merlos, Popovici, Schetter, Yuen, and Watanabe (first authors). Correlation with survival was assessed by Cox proportional hazards regression for each classifier using relapse-free survival data. The highest efficacy at predicting survival in stage 2-3 patients was achieved by Yuen (p = 3.9e-05, HR = 2.9), Marisa (p = 2.6e-05, HR = 2.6) and Chang (p = 9e-09, HR = 2.35). Finally, 61 colon cancer cell lines from four independent studies were assigned to the closest molecular subtype. © 2016 The Author(s)

The von Hippel-Lindau Tumor Suppressor Protein Promotes c-Cbl-Independent Poly-Ubiquitylation and Degradation of the Activated EGFR

Somatic mutations or reduced expression of the von Hippel-Lindau (VHL) tumor suppressor occurs in the majority of the clear cell renal cell carcinoma (ccRCC) and is a causal factor for the pathogenesis of ccRCC. pVHL was reported to suppress the oncogenic activity of Epidermal Growth Factor Receptor (EGFR) by reducing the expression of the EGFR agonist TGF-α and by reducing the translation efficiency of EGFR itself. Furthermore, it was reported that pVHL down-regulates activated EGFR by promoting efficient lysosomal degradation of the receptor. These modes of negative regulation of EGFR by pVHL were dependent on Hypoxia Inducible Factor (HIF). In this study, we report that HIF was not the only factor stabilizing the activated EGFR in VHL-deficient ccRCC cells. Down-regulation of endogenous HIF in these cells had little effect on the turnover rates of the activated EGFR. Furthermore, neither pretreatment with lysomomal inhibitors pretreatment nor down-regulation of c-Cbl, a major E3 ubiquitin ligase that targets the activated EGFR for lysosomal degradation, significantly increased the stabilities of EGFR in VHL-expressing ccRCC cells. In contrast, pretreatment with proteasomal inhibitors extended EGFR lifetime and led to similar EGFR half-lives in VHL-expressing and VHL-deficient ccRCC cells. Down-regulation of c-Cbl in VHL-deficient ccRCC cells revealed that the c-Cbl and pVHL collaborated to down-regulate the activated EGFR. Finally, we found that pVHL promoted the poly-ubiquitylation of the activated EGFR, and this function was c-Cbl-independent. Thus these results indicate that pVHL limits EGFR signaling by promoting c-Cbl-independent poly-ubiquitylation of the activated receptor, which likely results in its degradation by proteasome

Nonsense Mediated Decay Resistant Mutations Are a Source of Expressed Mutant Proteins in Colon Cancer Cell Lines with Microsatellite Instability

BACKGROUND: Frameshift mutations in microsatellite instability high (MSI-High) colorectal cancers are a potential source of targetable neo-antigens. Many nonsense transcripts are subject to rapid degradation due to nonsense-mediated decay (NMD), but nonsense transcripts with a cMS in the last exon or near the last exon-exon junction have intrinsic resistance to nonsense-mediated decay (NMD). NMD-resistant transcripts are therefore a likely source of expressed mutant proteins in MSI-High tumours. METHODS: Using antibodies to the conserved N-termini of predicted mutant proteins, we analysed MSI-High colorectal cancer cell lines for examples of naturally expressed mutant proteins arising from frameshift mutations in coding microsatellites (cMS) by immunoprecipitation and Western Blot experiments. Detected mutant protein bands from NMD-resistant transcripts were further validated by gene-specific short-interfering RNA (siRNA) knockdown. A genome-wide search was performed to identify cMS-containing genes likely to generate NMD-resistant transcripts that could encode for antigenic expressed mutant proteins in MSI-High colon cancers. These genes were screened for cMS mutations in the MSI-High colon cancer cell lines. RESULTS: Mutant protein bands of expected molecular weight were detected in mutated MSI-High cell lines for NMD-resistant transcripts (CREBBP, EP300, TTK), but not NMD-sensitive transcripts (BAX, CASP5, MSH3). Expression of the mutant CREBBP and EP300 proteins was confirmed by siRNA knockdown. Five cMS-bearing genes identified from the genome-wide search and without existing mutation data (SFRS12IP1, MED8, ASXL1, FBXL3 and RGS12) were found to be mutated in at least 5 of 11 (45%) of the MSI-High cell lines tested. CONCLUSION: NMD-resistant transcripts can give rise to expressed mutant proteins in MSI-High colon cancer cells. If commonly expressed in primary MSI-High colon cancers, MSI-derived mutant proteins could be useful as cancer specific immunological targets in a vaccine targeting MSI-High colonic tumours

In silico approach to screen compounds active against parasitic nematodes of major socio-economic importance

Infections due to parasitic nematodes are common causes of morbidity and fatality around the world especially in developing nations. At present however, there are only three major classes of drugs for treating human nematode infections. Additionally the scientific knowledge on the mechanism of action and the reason for the resistance to these drugs is poorly understood. Commercial incentives to design drugs that are endemic to developing countries are limited therefore, virtual screening in academic settings can play a vital role is discovering novel drugs useful against neglected diseases. In this study we propose to build robust machine learning model to classify and screen compounds active against parasitic nematodes.A set of compounds active against parasitic nematodes were collated from various literature sources including PubChem while the inactive set was derived from DrugBank database. The support vector machine (SVM) algorithm was used for model development, and stratified ten-fold cross validation was used to evaluate the performance of each classifier. The best results were obtained using the radial basis function kernel. The SVM method achieved an accuracy of 81.79% on an independent test set. Using the model developed above, we were able to indentify novel compounds with potential anthelmintic activity.In this study, we successfully present the SVM approach for predicting compounds active against parasitic nematodes which suggests the effectiveness of computational approaches for antiparasitic drug discovery. Although, the accuracy obtained is lower than the previously reported in a similar study but we believe that our model is more robust because we intentionally employed stringent criteria to select inactive dataset thus making it difficult for the model to classify compounds. The method presents an alternative approach to the existing traditional methods and may be useful for predicting hitherto novel anthelmintic compounds.12 page(s