Integration of transcriptomics, proteomics and loss-of-function screening reveals WEE1 as a target for combination with dasatinib against proneural glioblastoma

Abstract

Glioblastoma is characterized by a pronounced resistance to therapy with dismal prognosis. Transcriptomics classify glioblastoma into proneural (PN), mesenchymal (MES) and classical (CL) subtypes that show differential resistance to targeted therapies. The aim of this study was to provide a viable approach for identifying combination therapies in glioblastoma subtypes. Proteomics and phosphoproteomics were performed on dasatinib inhibited glioblastoma stem cells (GSCs) and complemented by an shRNA loss-of-function screen to identify genes whose knockdown sensitizes GSCs to dasatinib. Proteomics and screen data were computationally integrated with transcriptomic data using the SamNet 2.0 algorithm for network flow learning to reveal potential combination therapies in PN GSCs. In vitro viability assays and tumor spheroid models were used to verify the synergy of identified therapy. Further in vitro and TCGA RNA-Seq data analyses were utilized to provide a mechanistic explanation of these effects. Integration of data revealed the cell cycle protein WEE1 as a potential combination therapy target for PN GSCs. Validation experiments showed a robust synergistic effect through combination of dasatinib and the WEE1 inhibitor, MK-1775, in PN GSCs. Combined inhibition using dasatinib and MK-1775 propagated DNA damage in PN GCSs, with GCSs showing a differential subtype-driven pattern of expression of cell cycle genes in TCGA RNA-Seq data. The integration of proteomics, loss-of-function screens and transcriptomics confirmed WEE1 as a target for combination with dasatinib against PN GSCs. Utilizing this integrative approach could be of interest for studying resistance mechanisms and revealing combination therapy targets in further tumor entities.

Keywords: Computational integration; Dasatinib; Loss-of-function shRNA screen; Phosphoproteomics; WEE1.

Figure 1:. Integrated multi-omics approach to tackle resistance to dasatinib in proneural Glioblastoma stem-like cells (PN GCSs).

After stable isotope labeling with amino acids in cell culture (SILAC) and subsequent inhibition with dasatinib, PN and MES GSCs underwent phosphoproteomic analyses using mass spectrometry to study intracellular signal transduction pathways that are deregulated upon dasatinib treatment (upper part). This was complemented by a functional drop-out viability screen using a pooled barcoded shRNA library against 5000 human genes combined with dasatinib. This screen exposed several genes whose knockdown increased sensitivity to dasatinib inhibition and provided targets for combinatory drug inhibition (lower part). Both screen outputs were then integrated using SamNet 2.0, which also factored in gene expression data (middle part) to reveal possible combination therapy targets. MES = mesenchymal. DMSO = dimethyl sulfoxide. FC = fold-change. m/z = mass to charge ratio. IC₂₀ = inhibition concentration at which 80% of inhibited cells show viability.

Figure 2:. Phosphoproteomics reveal reduced PKA and cell-cycle signaling after dasatinib inhibition in MES GSCs.

(A) Scheme of the (phospho)proteomic screen. Initial analysis of ‘heavy’ samples revealed an incorporation rate of heavy proteins of over 95% of total captured proteins. (B) 3D Principal Component Analysis (PCA) of GSCs after dasatinib inhibition. Included are replicates of (n=3) day-4 and (n=3) day-12 time point samples. PC = Principal Component. PCA for proteomics data. (C) Identified peptides for each sample (GSC and time point). (D) PCA for phosphoproteomics data. (E) Identified phosphosites for each sample (GSC and time point). (F) Simplot showing pathways significantly downregulated in MES (NCH711d and NCH705) but not PN (NCH421k and NCH644) GSCs upon 1 μM dasatinib inhibition. (G) p-values of the top 10 significantly downregulated pathways in MES and not PN GSCs. (H) Boxplot of Protein Kinase A (PKA) targets. Blue = MES, green = PN. Included are 15 phosphosites considered to be PKA targets by PhosphositePlus or Scansite that showed significant (adj-p <0.05) deregulation in PN compared to MES GSCs upon dasatinib inhibition (two-sided t-test). (I) Boxplot of cell cycle proteins. Blue = MES, green = PN. Significantly differential phosphosites related to cell cycle pathways are shown on the x-axis. MES = mesenchymal, PN= proneural, GSC = GB stem-like cell. PC = principal component. DMSO = Dimethuylsulfoxide.

Figure 3:. Pooled shRNA drop-out viability screen reveals WEE1 to sensitize PN GB to dasatinib inhibition.

(A) shRNA screen workflow. (B) Principal Component Analysis (PCA) of GSCs in pooled shRNA screen. MES = shades of blue, PN = shades of green. A general overview of the different GSC subtypes and screening conditions is provided, with islands of different GSC subtypes labelled. (C) Top hits of pooled barcoded shRNA screen. Shown are the p-values and logCPM read values for each condition (CPM = counts per million). Genes are ranked according to the p-value (dark green: lowest; white: highest). Negative foldchanges are represented by red bars, positive foldchanges by blue bars. (D) Dot plot of foldchanges of all genes targeted by the DECIPHER^® screen. Shown is the difference in foldchange between the dasatinib and the DMSO screen in PN (y-axis) and MES (x-axis) GSCs. A PN hit is as close to 0 on the x-axis as possible and as negative on the y-axis as possible, provided it reaches significance (bigger dot sizes correspond to the lower p-values). Top hits from (C) are shown in red. FC = foldchange, Dasa= dasatinib. DMSO = Dimethyl sulfoxide

Figure 4:. Integration of phosphoproteomic data and DECIPHER^® drop-out viability screen shows WEE1 as a target for synergistic combinational therapy in PN GSCs.

(A) SamNet 2.0 directed graph. The graph begins with a source node (S) that has outgoing subtype-specific edges into nodes that represent proteins deregulated in response to dasatinib. These proteins are represented by rectangles (blue = MES-hit, green PN-hit, blue and green = deregulated in both. The gray nodes represent proteins not included in either screen, where each edge indicates a physical interaction between proteins. Since the interactions are bidirectional, the flow in the network can go either way. Diamonds at the end of the flow depict hits of the shRNA screen, with outgoing edges into the terminal sink node (T). (B) Cell cycle proteins connect vulnerabilities upon PN GSC dasatinib inhibition. Each node is divided into blue and green, depending on the PN (green) vs MES (blue) flow, with the size proportional to the node’s share in total flow. Label colors represent the type of node: red is a proteomic hit, yellow/orange is an shRNA hit, and black is a non-hit protein from the PPI. (C) Optimal network yields DNA Damage Proteins in MES GSCs. Each node is divided into blue and green, depending on the PN (green) vs MES (blue) flow, with the size proportional to the node’s share in total flow. Label colors represent the type of node as in B).

Figure 5:. Synergistic dasatinib and WEE1 inhibition in PN GCSs.

(A) Dose-response viability plot with matrix plot showing concentrations with the most synergistic area and (B) Synergy score (δ) based on the ZIP (zero interaction potency) model after dasatinib and MK-1775 treatment depicting synergistic effects of dasatinib and MK-1775 in PN NCH644. GSCs were treated with different concentrations of dasatinib (1.25 – 20 μM) and MK-1775 (0.375 – 6 μM) for 48 h. Cell viability was determined via CellTiter-Glo^® assay (n=3). Red denotes synergism, green denotes antagonism (n=3). (C) Propidium-iodide (PI) staining 48h (black) and 96h (gray) after treatment of PN NCH644 GSCs with the combination dose showing the highest synergistic score (5μM dasatinib, 0.75μM MK-1775), with controls using vehicle (DMSO: Dimethylsulfoxide) and single treatments of dasatinib and the WEE1 inhibitor MK-1775 (n=3). (D) Dose-response viability plot with matrix plot and E) Synergy score (δ) based on the ZIP model MES NCH705. (F) PI staining 48h (black) and 96h (gray) after treatment of PN NCH644 GSCs with the combination dose showing the highest synergistic score with different controls (n=3). (G) Depiction of the most optimal combination concentrations with calculated synergy scores as determined by the ZIP and BLISS independence model in all GSCs. (H) Experimental set-up and treatment intervals of the NCH644 cerebral spheroid co-culture experiment with 5 μM dasatinib, 0.75 μM MK-1775 and a combination. (I) Luminescence intensity of photons emitted from each spheroid as a surrogate measure for response to 96h treatment in four groups, measured at treatment start (day 0) followed by measurements every 96h for a total of 12 days. Six spheroids were used per condition, p-values for different time points are indicated under the graph, two-way ANOVA corrected with Tukey’s test for multiple comparisons was used. (J) Quantification of anti-GFP and (K) Anti-nestin staining signals upon different treatment modalities as indicated at day 4 after treatment. Signal was normalized to number of cells (based on DAPI). Error bars depict standard error of the mean (SEM), *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001, n.s.: not significant, one-way ANOVA corrected with Tukey’s test for multiple comparisons. (L) Representative images after day 4 of treatment showing GFP and DAPI signals in co-culture spheroids under different treatment conditions. Co-culture spheroids were stained with anti-GFP marking tumor cells (green) and DAPI was used as a DNA marker (blue) to correct for different co-culture spheroid sizes. 6 spheroids were used per condition and 2–3 slices per spheroid.

Figure 6:. PN GCSs show a different WEE-1 mediated cell-cycle regulation pattern.

(A) Western blot showing WEE1 protein levels in astrocytes and PN (NCH421k, NCH644) and MES (NCH705, NCH711d) GSC subtypes. β-Actin served as a loading control. Band intensities are shown, normalized to β-Actin and then to astrocytes (B) WEE1 mRNA expression in GB compared to normal brain tissue (data from [50], unpaired student’s t-test, ****p<0.0001). (C) Boxplots showing TCGA GB RNA-Seq WEE1 expression in MES, CL and PN GB patient samples ***p<0.001, **p<0.01. (D) mRNA levels of WEE1 in the MES and PN GSC subtypes from gene expression profiling data (GEO accession: GSE159609). E) Western blot showing protein levels of WEE1, CDK1, and P-CDK in DMSO (−/−), dasatinib (5 μM) and MK-1775 (0.75 μM) single or in combination treated PN NCH644 and MES NCH705 cells (48 hours), α-tubulin served as a loading control. Band intensities are shown, normalized to α-tubulin and then to the PN DMSO samples. (F) Heatmap with unsupervised clustering analysis of the TCGA GB RNA-Seq data patient cohort reclassified according to the signatures (n = 265), showing 3 clustering groups with patterns of subtype-dependent clustering upon calling genes of GOBP Cell Cycle signature. Patient samples are distributed horizontally, with colors denoting GB subtype, genes from the gene set are listed vertically. (G) Uniform Manifold Approximation and Projection (UMAP) plot showing TCGA GB RNA-Seq patient sample distribution of different subtypes (MES, CL, PN) upon calling genes of the GOBP Cell Cycle signature. (H) Assessment of cell cycle phases by using PI-staining as measured by FACS in PN (NCH644) after treatment with DMSO (−/−), 5 μM dasatinib, 0.75 μM MK-1775 or the combination for 48 hours, data shown after excluding live cells (subG1 phase was removed). Significances are displayed for DMSO treatment comparisons (mean ± SEM, p-values are indicated in the graph, two-way ANOVA corrected with Tukey’s test for multiple comparisons, *<0.05, **<0.01***<0.001, **** <0.0001, NCH644 n=3. (I) (Left) Representative Western blot showing protein levels of γH2Ax in PN NCH644 and MES NCH705 GCSs 48h after dasatinib (5 μM) and MK-1775 (0.75 μM) combination or single-treatments or with DMSO (−/−), β-Actin served as a loading control. MES= mesenchymal, PN= proneural, CL= classical. (Right) Quantification of band intensities from three independent experiments, mean ± SD, one-way ANOVA corrected with Tukey’s test for multiple comparisons, *<0.05, ***<0.001.