SITH-package {SITH}

Visualize and analyze intratumor heterogeneity using a spatial model of tumor growth

Description

The SITH (spatial model of intratumor heterogeneity) package implements a lattice based spatial model of tumor growth and mutation. Interactive 3D visualization of the tumor are possible using rgl. Additional functions for visualization and investigating the spatial distribution of mutants are provided. SITH also provides functions to simulate single cell sequencing and bulk sampling data sets from the simulated tumor.

Background

On-lattice models of tumor growth and mutation are computationally efficient and provide a simple setting to study how spatially constrained growth impacts intratumor heterogeneity. While this model has been studied extensively in literature (see Waclaw (2015), Chkhaidze (2019), Opasic (2019)), existing software is either not publicly available or inconvenient to use with R.

The motivation for creating the SITH package was to provide a spatial simulator that is both easy to use and can be used entirely with R. The core function in the package is simulateTumor(), which wraps a C++ implementation of the model into R using the Rcpp package. Once the results of the simulation are saved as an R object, SITH provides several other useful functions for studying this model.

See the package vignette for more information on the model and the algorithm used.

Author(s)

Phillip B. Nicol

References

B. Waclaw, I. Bozic, M. Pittman, R. Hruban, B. Vogelstein and M. Nowak. A spatial model predicts that dispersal and cell turnover limit intratumor heterogeneity. Nature, pages 261-264, 2015. https://doi.org/10.1038/nature14971.

K. Chkhaidze, T. Heide, B. Werner, M. Williams, W. Huang, G. Caravagna, T. Graham, and A. Sottoriva. Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data. PLOS Computational Biology, 2019. https://doi.org/10.1371/journal.pcbi.1007243.

L. Opasic, D. Zhou, B. Wener, D. Dingli and A. Traulsen. How many samples are needed to infer truly clonal mutations from heterogeneous tumours? BMC Cancer, https://doi.org/10.1186/s12885-019-5597-1.

Examples

#Simulate tumor growth
out <- simulateTumor()
  
#3d interactive visualization using rgl 
visualizeTumor(out)
#or see regions with lots of mutants
visualizeTumor(out, plot.type = "heat")
  
#get a summary on the spatial dist. of mutants
sp <- spatialDistribution(out)
  
#simulate single cell sequencing
Scs <- randomSingleCells(tumor = out, ncells = 5, fnr = 0.1)
  
#simulate bulk sampling
Bulks <- randomBulkSamples(tumor = out, nsamples = 5)