Knocking down ERCC1 protein expression sensitizes cells to CPT-11.

<p>(A and B) MDA-MB-231 cells were transfected with 4 non-overlapping siRNA oligos targeting ERCC1 as well as a negative control siRNA using Dharmafect. (A) Forty-eight hours later, cell lysates were prepared, SDS-PAGE separated the lysates, and immunoblotting was performed using anti-ERCC1 antibodies. (B) Cells were placed in serum free media for 18 hours and pulsed with BrdU to measure DNA synthesis. Cells were fixed, permeabilized with 2N HCl, neutralized with borate buffer, and blocked with 20% goat serum. BrdU incorporation was detected using anti-BrdU alexa fluor 624. BrdU positive cells were counted as a fraction of 100 cells counted/each of four fields/coverslip. Each experiment was performed in duplicate at least three times. * p-value = 0.022.</p

Measuring γH2AX phosphorylation as a biomarker for response to PARP and topoisomerase inhibitors.

<p>Biopsies from MX-1 human tumor xenografts after 4 and 24 hours of CPT-11 (40mg/kg) and/or ABT-888 (5mg/kg) treatment were taken and centrifuged onto a glass slide. Cells were fixed, permeabilized, blocked overnight, and incubated with anti γ-H2AX antibody. Slides were washed with PBS followed by staining with FITC-conjugated secondary antibody. Following PBS washing, the slides were incubated with DAPI, washed in PBS, and mounted. The results were visualized and documented using the fluorescent setting of a Leica CTR5500 microscope and quantified using OpenLab software. Each experiment was repeated three times representing the bars in the graph.</p

The combination of PARP and topoisomerase inhibitors is an effective combination for TNBC <i>in vivo</i>.

<p>MX-1 breast cancer xenografts were implanted between the front and hind leg of nude mice. Tumor volume (in mm³) was measured with calipers every two days, and body weight was taken bi-weekly. When tumors reached a measurable burden (~63 mm³), the indicated treatments were started. CPT-11 was given IV every 7 days in 5 doses for a total dosage of 225 mg/kg. ABT-888 was given PO twice a day from days 9 to 20 and again from days 23 to 28 for a total dosage of 240 mg/kg. Mean tumor volume (± standard error [SE] of the mean) is plotted over time, separately for each of the four drug treatment groups.</p

BRCA mutated TNBC cell lines express high levels of PARP1 and are sensitive to PARP inhibition.

<p>(A) Cells were treated with increasing concentrations of ABT-888 over a 5 day incubation period. MTT assays were used to assess cell viability. The fraction of surviving cells was used to calculate the IC₅₀ values for ABT-888 for each cell line by sigmoidal dose response curve analyses (GraphPad Prism). IC₅₀ values calculated from three independent experiments performed in triplicate were graphed for each cell line. (B) PARP1 protein expression levels were evaluated from cell lysates collected from cells growing in log phase. Equal protein was separated by SDS-PAGE, transferred to PVDF, and immunoblotted using anti-PARP1 antibodies. β-actin protein levels were used as a loading control.</p

Summary ABT-888 and CPT-11 combination treatment in TNBC.

<p>Combinatorial index values (CI) were determined using Calcusyn software from the cell viability data. CI values less than 1 indicate synergy. Each experiment was repeated three times in triplicate.</p><p>Summary ABT-888 and CPT-11 combination treatment in TNBC.</p

Presentation_1_Network Rewiring in Cancer: Applications to Melanoma Cell Lines and the Cancer Genome Atlas Patients.PPTX

<p>Genes do not work in isolation, but rather as part of networks that have many feedback and redundancy mechanisms. Studying the properties of genetic networks and how individual genes contribute to overall network functions can provide insight into genetically-mediated disease processes. Most analytical techniques assume a network topology based on normal state networks. However, gene perturbations often lead to the rewiring of relevant networks and impact relationships among other genes. We apply a suite of analysis methodologies to assess the degree of transcriptional network rewiring observed in different sets of melanoma cell lines using whole genome gene expression microarray profiles. We assess evidence for network rewiring in melanoma patient tumor samples using RNA-sequence data available from The Cancer Genome Atlas. We make a distinction between “unsupervised” and “supervised” network-based methods and contrast their use in identifying consistent differences in networks between subsets of cell lines and tumor samples. We find that different genes play more central roles within subsets of genes within a broader network and hence are likely to be better drug targets in a disease state. Ultimately, we argue that our results have important implications for understanding the molecular pathology of melanoma as well as the choice of treatments to combat that pathology.</p

Table_1_Network Rewiring in Cancer: Applications to Melanoma Cell Lines and the Cancer Genome Atlas Patients.XLSX

<p>Genes do not work in isolation, but rather as part of networks that have many feedback and redundancy mechanisms. Studying the properties of genetic networks and how individual genes contribute to overall network functions can provide insight into genetically-mediated disease processes. Most analytical techniques assume a network topology based on normal state networks. However, gene perturbations often lead to the rewiring of relevant networks and impact relationships among other genes. We apply a suite of analysis methodologies to assess the degree of transcriptional network rewiring observed in different sets of melanoma cell lines using whole genome gene expression microarray profiles. We assess evidence for network rewiring in melanoma patient tumor samples using RNA-sequence data available from The Cancer Genome Atlas. We make a distinction between “unsupervised” and “supervised” network-based methods and contrast their use in identifying consistent differences in networks between subsets of cell lines and tumor samples. We find that different genes play more central roles within subsets of genes within a broader network and hence are likely to be better drug targets in a disease state. Ultimately, we argue that our results have important implications for understanding the molecular pathology of melanoma as well as the choice of treatments to combat that pathology.</p

Table_2_Network Rewiring in Cancer: Applications to Melanoma Cell Lines and the Cancer Genome Atlas Patients.XLSX

<p>Genes do not work in isolation, but rather as part of networks that have many feedback and redundancy mechanisms. Studying the properties of genetic networks and how individual genes contribute to overall network functions can provide insight into genetically-mediated disease processes. Most analytical techniques assume a network topology based on normal state networks. However, gene perturbations often lead to the rewiring of relevant networks and impact relationships among other genes. We apply a suite of analysis methodologies to assess the degree of transcriptional network rewiring observed in different sets of melanoma cell lines using whole genome gene expression microarray profiles. We assess evidence for network rewiring in melanoma patient tumor samples using RNA-sequence data available from The Cancer Genome Atlas. We make a distinction between “unsupervised” and “supervised” network-based methods and contrast their use in identifying consistent differences in networks between subsets of cell lines and tumor samples. We find that different genes play more central roles within subsets of genes within a broader network and hence are likely to be better drug targets in a disease state. Ultimately, we argue that our results have important implications for understanding the molecular pathology of melanoma as well as the choice of treatments to combat that pathology.</p