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)

Simulate bulk sampling

Description

Simulate bulk sequencing data by taking a local sample from the tumor and computing the variant allele frequencies of the various mutations.

Usage

bulkSample(tumor, pos, cube.length = 5, threshold = 0.05, coverage = 0)

Arguments

Details

A local region of the tumor is sampled by constructing a cube with side length cube.length around the center point pos. Each cell within the cube is sampled, and the reported quantity is variant (or mutation) allele frequency. Lattice sites without cells are assumed to be normal tissue, and thus the reported MAF may be less than 1.0 even if the mutation is present in all cancerous cells.

If coverage is non-zero then deep sequencing can be simulated. For a chosen coverage C, it is known that the number of times the base is read follows a Pois(C) distribution (see Illumina's website). Let d be the true coverage sampled from this distribution. Then the estimated VAF is drawn from a Bin(d,p)/d distribution.

Note that cube.length is required to be an odd integer (in order to have a well-defined center point).

Value

A data frame with 1 row and columns corresponding to the mutations. The entries are the mutation allele frequency.

Author(s)

Phillip B. Nicol

References

K. Chkhaidze, T. Heide, B. Werner, M. Williams, W. Huang, G. Caravagna, T. Graham, and A. Sottoriva. Spatially con- strained 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. Lander ES, Waterman MS.(1988) Genomic mapping by fingerprinting random clones: a mathematical analysis, Genomics 2(3): 231-239.

Examples

set.seed(116776544, kind = "Mersenne-Twister", normal.kind = "Inversion")
out <- simulateTumor(max_pop = 1000)
df <- bulkSample(tumor = out, pos = c(0,0,0))

2D cross section of the simulated tumor

Description

2D cross section of the simulated tumor.

Usage

plotSlice(tumor, slice.dim = "x", level = 0, plot.type = "normal")

Arguments

Value

None.

Author(s)

Phillip B. Nicol

Create a linear chain graph to describe the order of mutations

Description

A helper function for simulateTumor() which returns to the user the edge list for a linear chain.

Usage

progressionChain(n)

Arguments

Value

A matrix with 4 columns and n-1 rows which will be accepted as input to simulateTumor().

Author(s)

Phillip B. Nicol <philnicol740@gmail.com>

Examples

G <- progressionChain(3)

Define the progression of mutations from an igraph object

Description

A helper function for simulateTumor() which returns to the user the edge list for a DAG which is defined as an igraph object.

Usage

progressionDAG_from_igraph(iG)

Arguments

Value

A matrix with 4 columns which contains the edges of the graph as well as the rate of crossing each edge and the selective advantage/disadvantage obtained by crossing each edge.

Author(s)

Phillip B. Nicol <philnicol740@gmail.com>

Simulate multi-region bulk sampling

Description

Simulate bulk sequencing data by taking a local sample from the tumor and computing the variant allele frequencies of the various mutations.

Usage

randomBulkSamples(
  tumor,
  nsamples,
  cube.length = 5,
  threshold = 0.05,
  coverage = 0
)

Arguments

Details

This is the same as bulkSample(), except multiple samples are taken with random center points.

Value

A data frame with nsamples rows and columns corresponding to the mutations. The entries are the mutation allele frequency.

Author(s)

Phillip B. Nicol

Examples

out <- simulateTumor(max_pop = 1000)
df <- randomBulkSamples(tumor = out, nsamples = 5, cube.length = 5, threshold = 0.05)

Simulate fine needle aspiration

Description

Simulate a sampling procedure which takes a fine needle through the simulated tumor and reports the mutation allele frequency of the sampled cells.

Usage

randomNeedles(tumor, nsamples, threshold = 0.05, coverage = 0)

Arguments

Details

This sampling procedure is inspired by Chkhaidze et. al. (2019) and simulates fine needle aspiration. A random one-dimensional cross-section of the tumor is chosen, and the cells within this cross section are sampled, reporting mutation allele frequency.

Author(s)

Phillip B. Nicol

References

K. Chkhaidze, T. Heide, B. Werner, M. Williams, W. Huang, G. Caravagna, T. Graham, and A. Sottoriva. Spatially con- strained 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.

Examples

out <- simulateTumor(max_pop = 1000)
df <- randomNeedles(tumor = out, nsamples = 5)

Simulate single cell sequencing data

Description

Simulate single cell sequencing data by random selecting cells from the tumor.

Usage

randomSingleCells(tumor, ncells, fpr = 0, fnr = 0)

Arguments

Details

The procedure is exactly the same as singleCell() except that it allows multiple cells to be sequenced at once (chosen randomly throughout the entire tumor).

Value

A data frame with sample names on the row and mutation ID on the column. A 1 indicates that the mutation is present in the cell and a 0 indicates the mutation is not present.

Author(s)

Phillip B. Nicol <philnicol740@gmail.com>

Examples

out <- simulateTumor(max_pop = 1000)
df <- randomSingleCells(tumor = out, ncells = 5, fnr = 0.1) 

Spatial simulation of tumor growth

Description

Simulate the spatial growth of a tumor with a multi-type branching process on the three-dimensional integer lattice.

Usage

simulateTumor(
  max_pop = 250000,
  div_rate = 0.25,
  death_rate = 0.18,
  mut_rate = 0.01,
  driver_prob = 0.003,
  selective_adv = 1.05,
  disease_model = NULL,
  verbose = TRUE
)

Arguments

Details

The model is based upon Waclaw et. al. (2015), although the simulation algorithm used is different. A growth of a cancerous tumor is modeled using an exponential birth-death process on the three-dimensional integer lattice. Each cell is given a birth rate b and a death rate d such that the time until cell division or cell death is exponentially distributed with parameters b and d, respectively. A cell can replicate if at least one of the six sites adjacent to it is unoccupied. Each time cell replication occurs, both daughter cells receive Pois(u) genetic alterations. Each alteration is a driver mutation with some probability du. A cell with k driver mutations is given birth rate bs^k. The simulation begins with a single cell at the origin at time t = 0.

The model is simulated using a Gillespie algorithm. See the package vignette for details on how the algorithm is implemented.

Value

A list with components

• cell_ids - A data frame containing the information for the simulated cells. (x,y,z) position, allele ID number (note that 0 is the wild-type allele), number of genetic alterations, and Euclidean distance from origin are included.

• muts - A data frame consisting of the mutation ID number, the count of the mutation within the population, and the mutation allele frequency (which is the count divided by N).

• phylo_tree - A data frame giving all of the information necessary to determine the order of mutations. The parent of a mutation is defined to be the most recent mutation that precedes it. Since the ID 0 corresponds to the initial mutation, 0 does not have any parents and is thus the root of the tree.

• genotypes - A data frame containing the information about the mutations that make up each allele. The i-th row of this data frame corresponds to the allele ID i-1. The positive numbers in each row correspond to the IDs of the mutations present in that allele, while a -1 is simply a placeholder and indicates no mutation. The count column gives the number of cells which have the specific allele.

• color_scheme - A vector containing an assignment of a color to each allele.

• drivers - A vector containing the ID numbers for the driver mutations.

• time - The simulated time (in days).

• params - The parameters used for the simulation.

Author(s)

Phillip B. Nicol <philnicol740@gmail.com>

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.

D. Gillespie. Exact stochastic simulation of coupled chemical reactions. The Journal of Physical Chemistry, volume 81, pages 2340-2361, 1970.

Examples

out <- simulateTumor(max_pop = 1000)
#Take a look at mutants in order of decreasing MAF
sig_muts <- out$muts[order(out$muts$MAF, decreasing = TRUE),]

#Specify the disease model
out <- simulateTumor(max_pop = 1000, disease_model = progressionChain(3))

Simulate single cell sequencing data

Description

Simulate single cell sequencing data by selecting a cell at a specified position

Usage

singleCell(tumor, pos, noise = 0)

Arguments

Details

This function selects the cell at pos (error if no cell at specified position exists) and returns the list of mutations present in the cell. Due to technological artifacts, the false negative rate can be quite higher (10-20 percent). To account for this, the noise parameter introduces false negatives into the data set at the specified rate.

Value

A data frame with 1 row and columns corresponding to the mutations present in the cell. A 1 indicates that the mutation is detected while a 0 indicates the mutation is not detected.

Author(s)

Phillip B. Nicol <philnicol740@gmail.com>

References

K. Jahn, J. Kupiers and N. Beerenwinkel. Tree inference for single-cell data. Genome Biology, volume 17, 2016. https://doi.org/10.1186/s13059-016-0936-x.

Examples

set.seed(1126490984)
out <- simulateTumor(max_pop = 1000)
df <- singleCell(tumor = out, pos = c(0,0,0), noise = 0.1)

Quantify the spatial distribution of mutants

Description

Provides a summary the spatial distribution of mutants within the simulated tumor.

Usage

spatialDistribution(tumor, N = 500, cutoff = 0.01, make.plot = TRUE)

Arguments

Details

The genotype of a cell can be interpreted as a binary vector where the i-th component is 1 if mutation i is present in the cell and is 0 otherwise. Then a natural comparison of the similarity between two cells is the Jaccard index J(A,B) = |I(A,B)|/|U(A,B)|, where I(A,B) is the intersection of A and B and U(A,B) is the union. This function estimates the Jaccard index as a function of Euclidean distance between the cells by randomly sampling N pairs of cells.

Value

A list with the following components

• mean_mutant - A data frame with 2 columns giving the mean number of mutants as a function of Euclidean distance from the lattice origin (Euclid. distance rounded to nearest integer).

• mean_driver - The same as mean_mutant except for driver mutations only. Will be NULL if no drivers are present in the simulated tumor.

• jaccard A data frame with two columns giving mean jaccard index as a function of Euclidean distance between pairs of cells (rounded to nearest integer).

Author(s)

Phillip B. Nicol

Examples

set.seed(1126490984)
out <- simulateTumor(max_pop = 1000, driver_prob = 0.1)
sp <- spatialDistribution(tumor = out, make.plot = FALSE)

Interactive visualization of the simulated tumor

Description

Interactive visualization of the simulated tumor using the rgl package (if available).

Usage

visualizeTumor(tumor, plot.type = "normal", background = "black", axes = FALSE)

Arguments

Details

If rgl is installed, then the plots will be interactive. If rgl is unavailable, static plots will be created with scatterplot3d. Since plotting performance with scatterplot3d is reduced, it is strongly recommended that rgl is installed for optimal use of this function.

Value

None.

Author(s)

Phillip B. Nicol