Tamayo, P. | Search bioRxiv

bioRxiv2018

Modelling bistable tumour population dynamics to design effective treatment strategies

Despite recent advances in targeted drugs and immunotherapy, cancer remains \"the emperor of all maladies\" due to inevitable emergence of resistance. Drug resistance is thought to be driven by mutations and/or dynamic plasticity that deregulate pathway activities and regulatory programs of a highly heterogeneous tumour. In this study, we propose a modelling framework to simulate population dynamics of heterogeneous tumour cells with reversible drug resistance. Drug sensitivity of a tumour cell is determined by its internal states, which are demarcated by coordinated activities of multiple interconnected oncogenic pathways. Transitions between cellular states depend on the effects of targeted drugs and regulatory relations between the pathways. Under this framework, we build a simple model to capture drug resistance characteristics of BRAF-mutant melanoma, where two cell states are described by two mutually inhibitory - main and alternative - pathways. We assume that cells with an activated main pathway are proliferative yet sensitive to the BRAF inhibitor, and cells with an activated alternative pathway are quiescent but resistant to the drug. We describe a dynamical process of tumour growth under various drug regimens using the explicit solution of mean-field equations. Based on these solutions, we compare efficacy of three treatment strategies: static treatments with continuous and constant dosages, periodic treatments with regular intermittent phases and drug holidays, and treatments derived from optimal control theory (OCT). Based on these analysis, periodic treatments outperform static treatments with a considerable margin, while treatments based on OCT outperform the best periodic treatment. Our results provide insights regarding optimal cancer treatment modalities for heterogeneous tumours, and may guide the development of optimal therapeutic strategies to circumvent drug resistance and due to tumour plasticity.

cancer biology↗

bioRxiv2018

Visualizing single-cell RNA-seq datasets with Similarity Weighted Nonnegative Embedding (SWNE)

High throughput single-cell gene expression profiling has enabled the characterization of novel cell types and developmental trajectories. Visualizing these datasets is crucial to biological interpretation, and the most popular method is t-Stochastic Neighbor embedding (t-SNE), which visualizes local patterns better than other methods, but often distorts global structure, such as distances between clusters. We developed Similarity Weighted Nonnegative Embedding (SWNE), which enhances interpretation of datasets by embedding the genes and factors that separate cell states alongside the cells on the visualization, captures local structure better than t-SNE and existing methods, and maintains fidelity when visualizing global structure. SWNE uses nonnegative matrix factorization to decompose the gene expression matrix into biologically relevant factors, embeds the cells, genes and factors in a 2D visualization, and uses a similarity matrix to smooth the embeddings. We demonstrate SWNE on single cell RNA-seq data from hematopoietic progenitors and human brain cells.

bioinformatics↗