Compounds rescuing the effect of disulfiram.

<p>Compounds rescuing the effect of disulfiram.</p

Combinatorial high-throughput cell viability screen to identify disulfiram modulating compounds.

<p>Loess-normalized CellTiter-Glo results with 3357 compounds screened in the absence (y-axis) and presence (x-axis) of disulfiram (EC₅₀ 90 nM) in VCaP prostate cancer cells. Each dot represents result obtained with one compound. Data points qualifying as disulfiram sensitizing (squares below the trendline) and rescuing (triangles above the trendline) compounds are indicated. Result with sunitinib is indicated by an arrow.</p

Disulfiram-sunitinib combination reduces PC-3 spheroid growth and invasion.

<p>A) Cell morphology in 3D spheroid assay in response to compound exposures. B) The area of cells in the Incucyte images (% of total area) in response to compound treatments. Asterisks indicate the statistical significance: *, P<0.05.</p

Disulfiram-sunitinib co-exposure reduces AR signalling in VCaP prostate cancer cells.

<p>The expression of A) AR, B) PSA, C) ERG, D) MYC mRNA in response to disulfiram (DSF) and sunitinib (Su) exposures alone and in combination. E) AR and PSA protein expression in response to compound exposures alone and in combination. F) Quantification of AR protein expression in response to 6- and 24 h compound exposures. Asterisks indicate the statistical significance: *, P<0.05; **, P<0.01; and ***, P<0.005.</p

Disulfiram-sunitinib combination reduces E-cadherin expression.

<p>E-cadherin (red) and F-actin (yellow) expressions in response to compound treatments. DNA was stained with DAPI (blue).</p

The effect of disulfiram and sunitinib cotreatment on PC-3 cell migration.

<p>Cells were automatically imaged once every hour after wound scratching. Wound closure effect was calculated as wound confluence in response 6-, 12- and 24 h exposures of the compounds A) Wound healing in response to compound exposures for 24 hours. Black area represents the wound edges in the beginning of the assay. B) Quantification of cells entering the wound area. Asterisks indicate the statistical significance: *, P<0.05; **, P<0.01; and ***, P<0.005.</p

Sunitinib shows synergism with disulfiram in prostate cancer cells.

<p>A) Cell morphology in response to disulfiram (1 µM) and sunitinib (5 µM) exposures alone and in combination. B) Presentation of combination index (CI) and fraction of cells affected (Fa) by compound exposures in different concentrations (500 nM, 1 µM, 5 µM and 10 µM) in VCaP prostate cancer cells. CI values for each concentration: 500 µM: 0.92, 1 µM: 0.19, 5 µM: 0.21, 10 µM: 0.40. C) Caspase 3/7 activities in response to compound exposures. Asterisks indicate the statistical significance: ***, P<0.005.</p

Compounds sensitizing the effect of disulfiram.

<p>Compounds sensitizing the effect of disulfiram.</p

Illustration of disulfiram (DSF), sunitinib and disulfiram-sunitinib co-exposure induced effects on prostate cell viability.

<p>Relative cell viability results in A) VCaP and B) PC-3 prostate cancer cells as well as in non-malignant C) RWPE-1 and D) EP156T cells. Asterisks indicate the statistical significance: *, P<0.05; **, P<0.01; and ***, P<0.005.</p

Reanalysis of RNA-Sequencing Data Reveals Several Additional Fusion Genes with Multiple Isoforms

<div><p>RNA-sequencing and tailored bioinformatic methodologies have paved the way for identification of expressed fusion genes from the chaotic genomes of solid tumors. We have recently successfully exploited RNA-sequencing for the discovery of 24 novel fusion genes in breast cancer. Here, we demonstrate the importance of continuous optimization of the bioinformatic methodology for this purpose, and report the discovery and experimental validation of 13 additional fusion genes from the same samples. Integration of copy number profiling with the RNA-sequencing results revealed that the majority of the gene fusions were promoter-donating events that occurred at copy number transition points or involved high-level DNA-amplifications. Sequencing of genomic fusion break points confirmed that DNA-level rearrangements underlie selected fusion transcripts. Furthermore, a significant portion (>60%) of the fusion genes were alternatively spliced. This illustrates the importance of reanalyzing sequencing data as gene definitions change and bioinformatic methods improve, and highlights the previously unforeseen isoform diversity among fusion transcripts.</p> </div