CancerResource - updated database of cancer-relevant proteins, mutations and interacting drugs

Here, we present an updated version of CancerResource, freely available without registration at http://bioinformatics.charite.de/care. With upcoming information on target expression and mutations in patients’ tumors, the need for systems supporting decisions on individual therapy is growing. This knowledge is based on numerous, experimentally validated drug-target interactions and supporting analyses such as measuring changes in gene expression using microarrays and HTS-efforts on cell lines. To enable a better overview about similar drug-target data and supporting information, a series of novel information connections are established and made available as described in the following. CancerResource contains about 91 000 drug-target relations, more than 2000 cancer cell lines and drug sensitivity data for about 50 000 drugs. CancerResource enables the capability of uploading external expression and mutation data and comparing them to the database's cell lines. Target genes and compounds are projected onto cancer-related pathways to get a better overview about how drug-target interactions benefit the treatment of cancer. Features like cellular fingerprints comprising of mutations, expression values and drug-sensitivity data can promote the understanding of genotype to drug sensitivity associations. Ultimately, these profiles can also be used to determine the most effective drug treatment for a cancer cell line most similar to a patient's tumor cells

2D and 3D similarity landscape analysis identifies PARP as a novel off-target for the drug Vatalanib

Background Searching for two-dimensional (2D) structural similarities is a useful tool to identify new active compounds in drug-discovery programs. However, as 2D similarity measures neglect important structural and functional features, similarity by 2D might be underestimated. In the present study, we used combined 2D and three-dimensional (3D) similarity comparisons to reveal possible new functions and/or side-effects of known bioactive compounds. Results We utilised more than 10,000 compounds from the SuperTarget database with known inhibition values for twelve different anti-cancer targets. We performed all-against-all comparisons resulting in 2D similarity landscapes. Among the regions with low 2D similarity scores are inhibitors of vascular endothelial growth factor receptor (VEGFR) and inhibitors of poly ADP-ribose polymerase (PARP). To demonstrate that 3D landscape comparison can identify similarities, which are untraceable in 2D similarity comparisons, we analysed this region in more detail. This 3D analysis showed the unexpected structural similarity between inhibitors of VEGFR and inhibitors of PARP. Among the VEGFR inhibitors that show similarities to PARP inhibitors was Vatalanib, an oral “multi-targeted” small molecule protein kinase inhibitor being studied in phase-III clinical trials in cancer therapy. An in silico docking simulation and an in vitro HT universal colorimetric PARP assay confirmed that the VEGFR inhibitor Vatalanib exhibits off-target activity as a PARP inhibitor, broadening its mode of action. Conclusion In contrast to the 2D-similarity search, the 3D-similarity landscape comparison identifies new functions and side effects of the known VEGFR inhibitor Vatalanib

Statin and rottlerin small-molecule inhibitors restrict colon cancer progression and metastasis via MACC1

MACC1 (Metastasis Associated in Colon Cancer 1) is a key driver and prognostic biomarker for cancer progression and metastasis in a large variety of solid tumor types, particularly colorectal cancer (CRC). However, no MACC1 inhibitors have been identified yet. Therefore, we aimed to target MACC1 expression using a luciferase reporter-based high-throughput screening with the ChemBioNet library of more than 30,000 compounds. The small molecules lovastatin and rottlerin emerged as the most potent MACC1 transcriptional inhibitors. They remarkably inhibited MACC1 promoter activity and expression, resulting in reduced cell motility. Lovastatin impaired the binding of the transcription factors c-Jun and Sp1 to the MACC1 promoter, thereby inhibiting MACC1 transcription. Most importantly, in CRC-xenografted mice, lovastatin and rottlerin restricted MACC1 expression and liver metastasis. This is—to the best of our knowledge—the first identification of inhibitors restricting cancer progression and metastasis via the novel target MACC1. This drug repositioning might be of therapeutic value for CRC patients

PBMC from a healthy HLA-B*57:01 positive donor (Donor 1) where primed with abacavir at day 0, cultured for 14 days and then restimulated 1:10 with (A) HLA-B*57:01 single antigen line (C1R.B57), (B) with O/N abacavir treated C1R.B57 (C1R.B57.ABC) or (C) with O/N acyclovir treated C1R.B57 (C1R.B57.ACY).

<p>Antigen activated cells were detected by ICS for IFN-γ production and CD8+/ IFN-γ T-cells quantitated using flow cytometry. (D) PBMC from two healthy HLA-B*57:01 positive donors were either primed with abacavir (ABC primed), primed with acyclovir (ACY primed) or had no treatment (Control. PBMC were cultured for 14 days and then stimulated 1:10 with treated and untreated single antigen line, C1R.B57, as indicated.</p

The workflow of the virtual screening protocol for screening of similar drugs to abacavir.

<p>The workflow of the virtual screening protocol for screening of similar drugs to abacavir.</p

Effects of acyclovir (2 mg/mL) on the affinity of C-terminal residues for HLA-B*57:01.

<p>Values are represented as geometric mean with 95% CI of the fold difference between vehicle/acyclovir treatment. The experiment was run 6 times with each run performed in triplicates. Analyzed for statistical significance by column statistics; p < 0.05 was considered significant (*p < 0.05; **p < 0.01; ***p < 0.001). The most pronounced affinity increases for HLA-B*57:01 in the presence of 2 mg/mL of acyclovir were found for peptides with a cysteine, isoleucine and valine at the C-terminus.</p

Effects of abacavir and acyclovir on HLA-B*57:01 binding specificity.

<p>Specific peptides with a terminal isoleucine that showed an increased affinity for HLA-B*57:01 in the presence of abacavir were tested. Values are represented as geometric mean with 95% CI of two independent runs in triplicates, analyzed for statistical significance by Mann-Whitney U test comparing log IC₅₀ values vs. vehicle; p < 0.05 was considered significant (*p < 0.05; **p < 0.01; ***p < 0.001).</p

Effects of abacavir and acyclovir on HLA-B*57:01 binding specificity.

<p>Specific peptides with a terminal valine that showed an increased affinity for HLA-B*57:01 in the presence of abacavir were tested. Values are represented as geometric mean with 95% CI of two independent runs in triplicates, analyzed for statistical significance by Mann-Whitney U test comparing log IC₅₀ values vs. vehicle; p < 0.05 was considered significant (*p < 0.05; **p < 0.01; ***p < 0.001).</p