Dynamic Rearrangement of Cell States Detected by Systematic Screening of Sequential Anticancer Treatments

Signaling networks are nonlinear and complex, involving a large ensemble of dynamic interaction states that fluctuate in space and time. However, therapeutic strategies, such as combination chemotherapy, rarely consider the timing of drug perturbations. If we are to advance drug discovery for complex diseases, it will be essential to develop methods capable of identifying dynamic cellular responses to clinically relevant perturbations. Here, we present a Bayesian dose-response framework and the screening of an oncological drug matrix, comprising 10,000 drug combinations in melanoma and pancreatic cancer cell lines, from which we predict sequentially effective drug combinations. Approximately 23% of the tested combinations showed high-confidence sequential effects (either synergistic or antagonistic), demonstrating that cellular perturbations of many drug combinations have temporal aspects, which are currently both underutilized and poorly understood

Histone deacetylase 9 promotes endothelial to mesenchymal transition and an unfavorable atherosclerotic plaque phenotype

Endothelial-mesenchymal transition (EndMT) is associated with various cardiovascular diseases and in particular with atherosclerosis and plaque instability. However, the molecular pathways that govern EndMT are poorly defined. Specifically, the role of epigenetic factors and histone deacetylases (HDACs) in controlling EndMT and the atherosclerotic plaque phenotype remains unclear. Here, we identified histone deacetylation, specifically that mediated by HDAC9 (a class IIa HDAC), as playing an important role in both EndMT and atherosclerosis. Using in vitro models, we found class IIa HDAC inhibition sustained the expression of endothelial proteins and mitigated the increase in mesenchymal proteins, effectively blocking EndMT. Similarly, ex vivo genetic knockout of Hdac9 in endothelial cells prevented EndMT and preserved a more endothelial-like phenotype. In vivo, atherosclerosis-prone mice with endothelial-specific Hdac9 knockout showed reduced EndMT and significantly reduced plaque area. Furthermore, these mice displayed a more favorable plaque phenotype, with reduced plaque lipid content and increased fibrous cap thickness. Together, these findings indicate that HDAC9 contributes to vascular pathology by promoting EndMT. Our study provides evidence for a pathological link among EndMT, HDAC9, and atherosclerosis and suggests that targeting of HDAC9 may be beneficial for plaque stabilization or slowing the progression of atherosclerotic disease

Tracing clonal cell plasticity in the microenvironment of pancreatic ductal adenocarcinoma

The pancreas may regenerate without stem cells by induction of cellular plasticity between differentiated cells. Although essential for the remarkable capacity for pancreas regeneration, this property entails a vulnerability to cancer through acinar-to-ductal metaplasia and dedifferentiation by epithelial-mesenchymal transition, which contributes in largely unknown ways to metastasis in pancreatic cancer. As with most solid tumours, pancreatic ductal adenocarcinoma develops at the interface of cellular plasticity, genomic instability, and tumour microenvironments. However, current methodologies of analysing tumour tissue are unable to measure these modalities jointly. Here, by developing a method for epitope barcoding measured by cyclical imaging of antibody binding with confocal microscopes, this thesis explores the utility of multicolour cell labelling to investigate how polyclonal cancer evolution relates to epithelial-mesenchymal transition. The assembled barcodes consist of combinations of epitopes attached to a fluorophore and a nuclear-localisation signal, which enables cell sorting, quantitative image analysis, and effective decoding by statistical analysis. Using subcutaneous mouse models of pancreatic cancer, this thesis demonstrates that isogenic epitope labelling can detect epithelial clonal expansion events co-localised with fibroblasts in the tumour microenvironment. The epitope barcode constructs were stably expressed in mouse tumours for at least 2-3 weeks, proving feasibility of multicolour lineage tracing with cyclical imaging. By amending the barcodes with 8bp tags sequenced with single-cell RNA-seq, transcriptomic scores for epithelial-mesenchymal transition showed evidence for selectional acquisition of epithelial lineages during subcutaneous colonisation. Such labelled isogenic cell populations were amenable to Luria–Delbrück fluctuation analysis detecting lineages with epigenetic inheritance of cell states. Altogether, epitope barcoding and cyclical imaging form a compelling experimental strategy for investigating the clonality of transdifferentiation in the microenvironment of pancreatic cancer.Anglo-Danish Society. Augustinus Fonden

Integration of pan-cancer transcriptomics with RPPA proteomics reveals mechanisms of epithelial-mesenchymal transition

<div><p>Integrating data from multiple regulatory layers across cancer types could elucidate additional mechanisms of oncogenesis. Using antibody-based protein profiling of 736 cancer cell lines, along with matching transcriptomic data, we show that pan-cancer bimodality in the amounts of mRNA, protein, and protein phosphorylation reveals mechanisms related to the epithelial-mesenchymal transition (EMT). Based on the bimodal expression of E-cadherin, we define an EMT signature consisting of 239 genes, many of which were not previously associated with EMT. By querying gene expression signatures collected from cancer cell lines after small-molecule perturbations, we identify enrichment for histone deacetylase (HDAC) inhibitors as inducers of EMT, and kinase inhibitors as mesenchymal-to-epithelial transition (MET) promoters. Causal modeling of protein-based signaling identifies putative drivers of EMT. In conclusion, integrative analysis of pan-cancer proteomic and transcriptomic data reveals key regulatory mechanisms of oncogenic transformation.</p></div

Bayesian networks of proteins and phosphosites inferred from pan-cancer cell lines identify drivers of EMT and correlate to tumor networks.

<p>All Bayesian network structures were inferred by a Fast Greedy Search algorithm. (<b>A</b>) Network centrality statistics of the directed causal graph over all measured proteins pertaining to the influence of proteins on cancer signaling. (<b>B</b>) In and out degree distributions of proteins and phosphosites from the inferred network. (<b>C</b>) Causal neighborhood (1st neighbors) of EMT markers E-cadherin, Rab25, and Claudin7. Tissue-specific correlations in support for each edge are shown as bars along the edges. The layout was determined using the hierarchical Sugiyama algorithm with all edges oriented downwards. (<b>D</b>) Network comparisons between cell line and tumor data. (<b>E</b>) Distribution of average connectivity in bootstrapped Bayesian networks. (<b>F</b>) Distribution of networks of bimodal coupling coefficients.</p

Bimodal protein expression and phosphorylation detected across cancer types associate with known oncogenic processes including EMT.

<p>(<b>A</b>) Two-component Gaussian mixture model fit to E-cadherin protein expression. The lines indicate the probability density contribution from the low (-) and high (+) expression components. The histogram represents the RPPA measurements for the cell lines. (<b>B</b>) By comparing a two- versus one-component fit using the Bayesian Information Criterion (BIC), 260 out of 450 RPPA measurements supported bimodal expression. (<b>C</b>) Heat map of the posterior probabilities of each cell line belonging to the low (-, blue) or high (+, red) mixture component for the top-20 most bimodal proteins. The posterior probabilities can be thought of as soft assignments for the cell lines to low or high expression. Shannon entropy of the tissues assigned to low and high expression quantify the tissue diversity giving rise to the bimodal fits. (<b>D</b>) Overview of classification approach of proteins in terms of bimodality, tissue diversity (Shannon entropy), and frequency of cell lines assigned to the fitted distributions. (<b>E</b>) Significant GO terms for common bimodal proteins that were not found to be significant for non-bimodal proteins (p < 0.05, Benjamini-Hochberg).</p

Pan-cancer bimodal coupling between E-cadherin protein expression and genome-wide transcripts defines an EMT signature, predicting EMT- and MET-inducing small-molecules.

<p>(<b>A</b>) Top-25 transcripts in CCLE with the strongest positive and top 24 negative bimodal coupling coefficients (r_b) to E-cadherin protein expression. Red squares indicate previous EMT signature genes in non-small cell lung carcinoma published by Byers <i>et al</i>. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005911#pcbi.1005911.ref051" target="_blank">51</a>]. To define an EMT signature, we considered transcripts with |r_b| > 0.5, resulting in 215 epithelial and 24 mesenchymal markers. (<b>B</b>) Distribution of bimodal coupling coefficients, showing that E-cadherin coupling coefficients are shifted towards negative values compared to all measured proteins. (<b>C</b>) Overlap of EMT signature with previously published transcriptomic EMT signatures. The ‘mesenchymal’ bar plot is for the inversely correlated (coupled) genes and the ‘epithelial’ for the positively correlated genes. (<b>D</b>-<b>E</b>) Gene set enrichment analysis of epithelial part of the EMT signature. The TF enrichment analysis used ChIP-seq data to predict TFs involved in the regulation of the epithelial genes. The pie charts indicate the fraction of the signature genes associated with significantly enriched terms or TFs. (<b>F</b>) Small-molecule perturbations predicted to induce EMT and MET based on L1000 cell line data and the L1000CDS² method. The top-50 signatures are shown with results from multiple cell lines or concentrations aggregated by boxplots. PK: protein kinase.</p

Proteins and phosphosites with coupled bimodality form network communities associated with EMT and intermediate transitions.

<p>(<b>A</b>) Pan-cancer protein communities detected by Spearman’s correlation of the posterior probabilities of cell lines having low or high expression (|r_b| > 0.3). Only RPPA measurements associated with bimodal fits with high tissue diversity were included. Network communities were detected by calculating the leading non-negative eigenvector according to Newman’s method. Only edges within identified communities are shown, colored by the magnitude of the bimodal coupling coefficients. The size and color of the nodes represent the fitted mixing parameters from the Gaussian mixture models, quantifying whether underlying switches are common or rare in cancer cell lines. Each community was manually named according to plausible biological mechanisms by conducting a literature search for their protein members. Asterisks (*) indicate proteins with reported mechanisms linked to EMT. (<b>B</b>) Proposed interpretation of a two-step transition from the endothelial–E, to the mesenchymal–M states through two identified modules: EMT1 and EMT3. (<b>C</b>) Supporting protein expression data showing that Claudin7 and E-cadherin are correlated. Cell lines are colored by the tissue of origin (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005911#pcbi.1005911.g003" target="_blank">Fig 3</a> for tissue labels).</p

Pan-cancer cell line data from CCLE transcriptomic and reverse phase protein arrays (RPPA) cluster by tissue of origin and E-cadherin expression but not by prior metastasis classification.

<p>(<b>A</b>) Overlap of available RPPA and CCLE data with regard to cancer cell lines (left), measured transcripts and proteins (middle), and proteins measured for both basal expression and phosphorylation levels (right). The colored areas indicate data used to calculate and compare Euclidean distances between cell lines. (<b>B</b>) t-SNE plots of overlapping cancer cell lines based on protein, transcript, and equally weighted combined data. Each point represents a cell line and is colored by the tissue of origin (top), E-cadherin expression (middle), or tumor classification (bottom). NS: not specified. (<b>C</b>) Comparing pairwise distances between all cell lines using a linear model at the mRNA or protein levels. The red points show the top-100 highest residuals of cell line pairs, and the blue points the top-100 lowest residuals. (<b>D</b>) Dendrograms of breast cancer cell lines mapped for transcriptomic and RPPA data. The leaves of the trees were arranged to minimize the number of crossing lines between leaves of the two trees. L1-5 represents clusters found within the luminal subtype of breast cancer cell lines.</p

Bimodal protein expression and phosphorylation detected across cancer types associate with known oncogenic processes including EMT.

<p>(<b>A</b>) Two-component Gaussian mixture model fit to E-cadherin protein expression. The lines indicate the probability density contribution from the low (-) and high (+) expression components. The histogram represents the RPPA measurements for the cell lines. (<b>B</b>) By comparing a two- versus one-component fit using the Bayesian Information Criterion (BIC), 260 out of 450 RPPA measurements supported bimodal expression. (<b>C</b>) Heat map of the posterior probabilities of each cell line belonging to the low (-, blue) or high (+, red) mixture component for the top-20 most bimodal proteins. The posterior probabilities can be thought of as soft assignments for the cell lines to low or high expression. Shannon entropy of the tissues assigned to low and high expression quantify the tissue diversity giving rise to the bimodal fits. (<b>D</b>) Overview of classification approach of proteins in terms of bimodality, tissue diversity (Shannon entropy), and frequency of cell lines assigned to the fitted distributions. (<b>E</b>) Significant GO terms for common bimodal proteins that were not found to be significant for non-bimodal proteins (p < 0.05, Benjamini-Hochberg).</p