A simple model-based approach to inferring and visualizing cancer mutation signatures

Yuichi Shiraishi, Georg Tremmel, Satoru Miyano, Matthew Stephens

 

Recent advances in sequencing technologies have enabled the production of massive amounts of data on somatic mutations from cancer genomes. These data have led to the detection of characteristic patterns of somatic mutations or ``mutation signatures'' at an unprecedented resolution, with the potential for new insights into the causes and mechanisms of tumorigenesis. Here we present new methods for modelling, identifying and visualizing such mutation signatures. Our methods greatly simplify mutation signature models compared with existing approaches, reducing the number of parameters by orders of magnitude even while increasing the contextual factors (e.g. the number of flanking bases) that are accounted for. This improves both sensitivity and robustness of inferred signatures. We also provide a new intuitive way to visualize the signatures, analogous to the use of sequence logos to visualize transcription factor binding sites. We illustrate our new method on somatic mutation data from urothelial carcinoma of the upper urinary tract, and a larger dataset from 30 diverse cancer types. The results illustrate several important features of our methods, including the ability of our new visualization tool to clearly highlight the key features of each signature, the improved robustness of signature inferences from small sample sizes, and more detailed inference of signature characteristics such as strand biases and sequence context effects at the base two positions 5' to the mutated site. The overall framework of our work is based on probabilistic models that are closely connected with ``mixed-membership models'' which are widely used in population genetic admixture analysis, and in machine learning for document clustering. We argue that recognizing these relationships should help improve understanding of mutation signature extraction problems, and suggests ways to further improve the statistical methods. Our methods are implemented in an R package pmsignature (https://github.com/friend1ws/pmsignature) and a web application available at https://friend1ws.shinyapps.io/pmsignature_shiny/.

 

http://biorxiv.org/content/early/2015/05/27/019901

 

Cancer therapeutic potential of combinatorial immuno- and vaso-modulatory interventions

Cancer therapeutic potential of combinatorial immuno- and vaso-modulatory interventions 

H. Hatzikirou, J. C. L. Alfonso, S. Muhle, C. Stern, S. Weiss, M. Meyer-Hermann

Currently, most of the basic mechanisms governing tumor-immune system interactions, in combination with modulations of tumor-associated vasculature, are far from being completely understood. Here, we propose a mathematical model of vascularized tumor growth, where the main novelty is the modelling of the interplay between functional tumor vasculature and effector recruitment dynamics. Parameters are calibrated on the basis of different in vivo Rag1-/- and wild-type (WT) BALB/c murine tumor growth experiments. The model analysis supports that vasculature normalization can be a plausible and effective strategy to treat cancer when combined with appropriate immuno-stimulation. We find that improved levels of functional vasculature, potentially mediated by vascular normalization or stress alleviation strategies, can provide beneficial outcomes in terms of tumor burden reduction and control. Normalization of tumor blood vessels opens a therapeutic window of opportunity to augment the anti- tumor immune responses, as well as to reduce the intratumoral immunosuppression and hypoxia due to vascular abnormalities. The potential success of normalizing tumor vasculature closely depends on the effector cell recruitment dynamics and tumor sizes. Furthermore, an arbitrary increase of initial effector cell concentration does not necessarily imply tumor control, and we evidence the existence of an optimal effector concentration range for tumor shrinkage. Based on these findings, we suggest a theory-driven therapeutic proposal that optimally combines immune- and vaso-modulatory interventions.

http://arxiv.org/abs/1505.05670

A non-local model for cancer stem cells and the tumor growth paradox

Iacopo Borsi, Antonio Fasano, Mario Primicerio, Thomas Hillen

The "tumor growth paradox" refers to the observation that incomplete treatment of cancers can enhance their growth. As shown here and elsewhere, the existence of cancer stem cells (CSC) can explain this effect. CSC are less sensitive to treatments, hence any stress applied to the tumor selects for CSC, thereby increasing the fitness of the tumor. In this paper we use a mathematical model to understand the role of CSC in the progression of cancer. Our model is a rather general system of integro-differential equations for tumor growth and tumor spread. Such a model has never been analysed, and we prove results on local and global existence of solutions, their uniqueness and their boundedness. We show numerically that this model exhibits the tumor growth paradox for all parameters tested. This effect becomes more relevant for small renewal rate of the CSC.

http://biorxiv.org/content/early/2015/05/21/019604

 

A stochastic individual-based model for immunotherapy of cancer

Martina Baar, Loren Coquille, Hannah Mayer, Michael Hölzel, Meri Rogava, Thomas Tüting, Anton Bovier 

 

We propose an extension of a standard stochastic individual-based model in population dynamics which broadens the range of biological applications. Our primary motivation is modelling of immunotherapy for malignant tumors. The main characteristics of the model are distinguishing phenotype and genotype, including environment-dependent transitions between phenotypes that do not affect the genotype, and the introduction of a competition term which lowers the reproduction rate of an individual in addition to the usual term that increases its death rate. We prove that this stochastic process converges in the limit of large populations to a deterministic limit which is the solution to a system of quadratic differential equations. We illustrate the new setup by using it to model various phenomena arising in immunotherapy. Our aim is twofold: on the one hand, we show that the interplay of genetic mutations and phenotypic switches on different timescales as well as the occurrence of metastability phenomena raise new mathematical challenges. On the other hand, we argue why understanding purely stochastic events (which cannot be obtained with deterministic systems) may help to understand the resistance of tumors to various therapeutic approaches and may have non-trivial consequences on tumor treatment protocols and demonstrate this through numerical simulations.

http://arxiv.org/abs/1505.00452