Chk1 inhibition accelerates bendamustine-induced cell death.

<p><b>A.</b> Clonogenic assays were performed to assess cell survival following BDM and Chk1 inhibition. HeLa cells were treated with BDM (50 or 200 µM) for 24 h. Cells were grown for ∼10 days before being fixed and stained. The data presented are the mean absorbance value (O. D. 595 nm) relative to untreated cells, which is set to 100%. Each bar graph represents the average of 3 individual experiments performed in triplicate ± SD. †P<0.05 or *P<0.0001 or vs. untreated cells. <b>B.</b> Cell viability assessed by MTS assay was performed. HeLa cells were treated with BDM (3.125–200 µM) for 24 h. After this time, appropriate wells were co-treated with UCN-01 (100 nM) or Chk2 inhibitor (100 nM) for an additional 24 h. Data presented is the mean of 3 individual experiments performed in triplicate. Cell viability is expressed as a percentage of untreated cells ± SD. *P<0.0001 vs. 200 µM BDM alone. <b>C.</b> The percentage of apoptotic cells following indicated drug treatments was determined using Guava Nexin Reagent™. Data presented is the average of 3 individual experiments ± SD. *P<0.01 vs. untreated viable cells; †P<0.01 vs. untreated apoptotic cells.</p

Bendamustine induces both repairable and irreparable DNA damage.

<p>HeLa cells were treated for 24 or 48 h continuous treatment with either 50 or 200 µM BDM or 24 h followed by 24 h in the absence of drugs. <b>A.</b> Immunofluorescence analysis was performed to identify γ-H2AX, 53BP1 or RPA foci. Quantification of the average fluorescence per nucleus (nucleus outlined) is shown on the right. For γ-H2AX: *P = 8.9×10⁻²⁸ vs. untreated 24 h; ** P = 2.1×10⁻³¹ vs. untreated 24 h; †P = 5.2×10⁻⁴⁵ vs. untreated 48 h. For 53BP1: *P = 4.3×10⁻²⁰ vs. untreated 24 h; ** P = 4.2×10⁻⁴¹ vs. untreated 24 h; †P = 2.0×10⁻⁵⁷ vs. untreated 48 h. For RPA: *P = 2.5×10⁻¹⁶ vs. untreated 24 h; ** P = 2.8×10⁻⁵² vs. untreated 24 h; †P = 3.3×10⁻⁵⁸ vs. untreated 48 h. <b>B.</b> Lysates were probed to determine p-Chk1 (Ser345). Total Chk1 and alpha tubulin were used to determine loading.</p

Overcoming bendamustine-induced checkpoint arrest via Chk1 inhibition forces cells into premature mitosis.

<p>HeLa cells stably expressing GFP∶histone H2B were used for live cell video-microscopy. <b>A.</b> Representative montage of cells progressing through mitosis after mock treatment (upper panel), BDM at 50 µM (middle) or 200 µM (lower) followed by UCN-01 addition. <b>B.</b> Mitotic cells were fixed for metaphase spreads and dispersed onto glass slides, allowed to dry and then stained with DAPI. Metaphases were visualized using fluorescence microscopy. Images shown are representative of metaphases observed under each experimental condition. <b>C.</b> Representative electron micrographs of mitotic cells generated from untreated, 50 µM or 200 µM BDM+UCN-01 treatments.</p

Cell cycle perturbations induced by bendamustine are a widespread phenomenon in cancer cell lines.

<p>HeLa, BXPC3, MCF7, OVCAR 5 and U2932 cells were treated with bendamustine at the indicated concentrations for 24 h. Cell cycle profiles were determined using FACS analysis.</p

Involvement of base excision repair in bendamustine-induced DNA damage.

<p><b>A.</b> Immunofluorescence analyses were performed to detect remaining γ-H2AX foci after 48 h continuous treatment with 50 µM BDM in the presence or absence of either methoxyamine (MX) (6 mM) or the DNA PK inhibitor NU7441 (10 µM). Representative images are shown (left) along with quantitative analysis (right). Average values ± SD are shown. *P<0.005 vs. BDM alone. <b>B.</b> DNA damage induction and repair was conducted in assessed MEFs (<i>Tdg</i>^+/+ and <i>Tdg</i>^−/−) after treatment with BDM at 50 or 200 µM BDM for 24 h or 48 h. Representative images are shown, with nuclei outlined (circles) based on DAPI staining. Average γ-H2AX signal per nucleus ± SD is quantified (right). 24 h: *p = 0.009, **p = 2.0×10⁻⁸, ***p = 4.7×10⁻¹⁸ vs. untreated WT; †p = 0.00015, **p = 2.8×10⁻¹¹, ***p = 5.2×10⁻¹⁰ vs. untreated TDG −/−. 48 h: ***p = 4.1×10⁻¹⁰ vs. untreated WT; †p = 0.009, **p = 1.4×10⁻¹⁶, ***p = 1.9×10⁻¹⁶ vs. untreated TDG −/−.</p

SAMNet network results.

<p>For displaying network results, we use the following graphical strategy. Node size scales with the flow passing through each node. Nodes are colored based on the proportion of flow they get from the two commodities: kinase hit (purple) and genetic hit (blue). Edge-width scales with the flow passing through each edge, and the color represents the commodity type (kinase or genetic hit). The node shape specifies whether the node is a TF (octagon), and whether it is an experimental input gene (square). A) Example subnetwork identified using SAMNet, involved in the DNA damage response. B) Strategy for identifying significant nodes from the network. We use two metrics to identify the most meaningful genes from the network: i) evidence score counting how many experimental inputs are connected to the gene and ii) a node p-value based on comparing the observed flow a node receives to the expected flow received in a set of 100 networks with random inputs. C) Subnetwork containing the significant nodes AKT1, MXD1, ZEB1 identified using our network method. D) Same as C), but containing significant nodes SHC1, ELK3, STAT5A, NCK1, PTPN1.</p

Profiling the transcriptional and epigenomic response to gemcitabine.

<p>We measured changes in gene expression upon treatment of PANC1 cells with gemcitabine by RNA-seq. To understand the gene regulatory changes in response to gemcitabine treatment, we profiled DNaseI hypersensitivity for gemcitabine and vehicle-treated PANC1 cells. A) GO enrichment analysis for genes changing expression in response to gemcitabine (at the top in orange are genes up-regulated upon drug treatment, below in black are the genes down-regulated). B) Overlap between the genes that change expression when treated with gemcitabine (differentially expressed genes), genetic modifiers of the gemcitabine resistance (genetic hits) and targets of our hit kinase inhibitors that sensitize cells to gemcitabine (kinase hits). We find a modest overlap between the three sets, a trend observed before when comparing complementary high-throughput profiling approaches [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185650#pone.0185650.ref027" target="_blank">27</a>]. C) Construction of a TF-DNA regulatory network using DNaseI hypersensitivity data collected for cells treated and untreated with gemcitabine. We called peaks on the DNaseI data (combined with existing data for the same cell line from the ENCODE project), and then scanned each peak for TF binding sites using Transfac motif matrices. We assign a TF-gene regulatory interaction if we find a TF motif in a DNaseI peak that is within 5kb of the gene’s transcription start site. D) Top 50 transcription factors enriched in the promoters of differentially expressed genes. For each transcription factor, we performed a Fisher’s Exact Test to ask whether we see an overrepresentation of the transcription factor’s associated motifs in the promoters of genes changing expression in response to gemcitabine, compared to its presence in all promoters harboring a DNaseI peak. Note: we show here only those TFs with motifs in promoters of more than 100 differentially expressed genes.</p

General approach for data integration using the SAMNet algorithm.

<p>We used the SAMNet algorithm to model the cellular response to gemcitabine. SAMNet uses as inputs i) kinases that are predicted to be targeted by the eight gemcitabine-synergizing kinase inhibitors discovered, ii) genetic modifiers of gemcitabine efficacy (genetic hits), iii) genes changing in expression upon gemcitabine treatment, and iv) a TF-gene network based on TF motif matches in open chromatin regions in the promoters of the differentially expressed genes. The SAMNet algorithm then connects the input kinases and genetic hits to the differentially expressed genes through the protein-protein and the TF-gene interactomes in a constrained optimization setting. To distinguish networks anchored in kinase target hits from those anchored in genetic hits, we defined two optimization problems (or commodities) that are solved simultaneously. These are depicted as purple and blue.</p

A screen to identify kinase inhibitors that modify gemcitabine cytotoxicity in pancreatic cancer (PANC1 cell line).

<p>We performed a screen to identify kinase inhibitors that enhanced killing of PANC1 cells by a sublethal dose (20nM) of gemcitabine, with hits defined as those kinase inhibitors that reduce survival by > 30% when combined with gemcitabine versus a vehicle control. A) For a set of 160 kinase inhibitors, we compare growth of PANC1 cells treated with the kinase inhibitor alone (vehicle) with the growth when treated with the kinase inhibitor and gemcitabine (Gem), across 8 kinase inhibitor concentrations. We show examples of a kinase inhibitor that synergizes with gemcitabine and one that does not. B) Identification of kinase inhibitors that modify gemcitabine cytotoxicity. For each kinase inhibitor and each concentration tested, we compute the reduction in viability for cells treated with both a kinase inhibitor and gemcitabine, compared to cells treated with the inhibitor alone. We filter out kinase inhibitors that are toxic in the absence of gemcitabine. We set a threshold of 30% reduction in viability for calling hit kinase inhibitors (red line). Note: for each kinase inhibitor, we only plot here the concentration tested that yields the largest reduction in viability. C) Mapping of kinase inhibitors to their target kinases, based on in vitro profiling of kinase inhibitor specificity from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185650#pone.0185650.ref007" target="_blank">7</a>]. Rows are kinase inhibitors from the screen and columns are kinases. The values in the heatmap represent the percent activity of the kinase when treated with the kinase inhibitor, compared to when untreated. D) Comparison of effect sizes in the kinase screen vs. the genetic screen. For each target of our hit kinase inhibitors, we show its change in activity by the kinase inhibitor vs. the change in gemcitabine survival when the gene is inhibited by siRNA in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185650#pone.0185650.ref008" target="_blank">8</a>] labeled as “Genetic screen effect size”.</p

Additional file 1: of Targeting WEE1 to enhance conventional therapies for acute lymphoblastic leukemia

Table S1. Patient’s characteristic of gene expression cohort. Table S2. Patient’s characteristic ex vivo AZD-1775 treatment in single agent or in combination. Table S3. Quantitative analyses of G2/M checkpoint-related genes. Differential gene expression of 24 genes involved in the regulation of the G2/M checkpoint of primary leukemic cells in comparison to normal mononuclear cells (MNCs). In the table, the primary leukemic samples have been divided into three groups based on the ex vivo sensitivity to AZD-1775. Very good IC50 < 5uM; good IC50 < 10uM; poor IC50 > 10 uM. (PDF 253 kb