esiCancer

Darlan Conterno Minussi, Bernardo Henz, Mariana dos Santos Oliveira, Eduardo C. Filippi-Chiela, Manuel M. Oliveira, Guido Lenz

What is esiCancer?

esiCancer is a cell-autonomous software tool to model evolution of cancer. This program uses real genomic information of tumors to construct a biallelic genome and applies stochastic events, such as mutations, indels and other alterations to this genome. Each event can produce several alterations to the probability of division, death, event rate per division, and/or maximal divisions, which impact the population of cells over time.

esiCancer has a user friendly interface and produces .cvs outputs that can be easily analyzed by computer professionals or amateurs alike.

Compiling the code

Source code is written in C++, using Qt library for interface design. For compiling the code you will need to: (1) have a compiler installed (we suggest GCC or MinGW, but any compiler should work fine); (2) have the QT library, which can be downloaded from the QT website. We strongly recommend the use of QtCreator (installed with QT), as we provide the .pro project file. Any problem compiling the code, please let me know.

Start simulating clonal evolution

In the esiCancer interface, set the parameters and load the esiTable.csv. By clicking in Automatic Runs, the program will simulate several runs (according to the values on the spinners), generating output files. By modifying the parameters, you can test different hypothesis, checking the outcome quickly.

Parameters

The parameters are described next:

	Parameter	Action
Simulation parameters	Seed	Controls the pseudorandom number generator
	Number of cells	Starting number of cells
Standard Values	Probability of division	Initial probability of division for all cells
	Probability of death	Initial probability of death for all cells
	Maximum divisions	Number of divisions that a cell can go through
	Events per division	Number of events to be inserted into each cell at each division
Automatic runs	Parameter to iterate	Parameter that will be automatically iterated
	Minimum value	Starting iterated parameter value
	Maximum value	Ending iterated parameter value
	Increment	Defines the increment to be used from minimum to maximum
	Output Files	Defines the format of output files
Stop Conditions	Number of generations	Number of maximum generations allowed per run
	Number of Cells	Maximum number of cells
	Mutated Cells	Maximum number of mutated cells
Tumor Environment	Maximal Tumor Growth Rate	Maximal population growth rate allowed per generation
Manual Events	Generation	Generation that event rule is added
	Percentage	Percent of cells that is added event rule
	Gene-event Name	Gene-event that is added event rule (according to esiTable)
	Allels Changed	Number of alleles affected by event rule(mono or bi-allelic)

esiTable Parameters

The esiTable parameters are described next:

	Parameter Meaning	Action
genome size	Genome Size	Sum of the oncogenome and the normal genome
GENES	Gene Name	Name of a given gene
EVENTS	Evet Name	Name of a given event
PROBEVENT	Probability of Event	Probability of a specific event to occur (Prob = PROBEVENT / genome size)
DOMINANCE	Dominance	Determines the impact of a specific event if it occurs in one allele.
DIVFUNC	Function for division rate	Determines if the event adds or multiplies a specific value to its division rate.
DIVRATE	Division Rate	Determines the impact of a specific event on division rate.
DEFUNC	Function for death rate	Determines if the event adds or multiplies a specific value to its death rate.
DEATHRATE	Death Rate	Determines the impact of a specific event on death rate.
MUTFUNC	Function for mutation rate	Determines if the event adds or multiplies a specific value to its event per division rate.
MUT	Mutation Rate	Determines the impact of a specific event on the number of events per division rate.
MAXDIVFUNC	Function for maximum division rate	Determines if the event adds or multiplies a specific value to its maximum number of division rate.
MAXDIVRATE	Maximum Division Rate	Determines the impact of a specific event on the maximum number of divisions rate.
MEFUNC	Function for microenvironment	Determines if the event adds or multiplies a specific value to MTGR.
MICROENVIROMENT	Microenvironment	Determines the impact of a specific event on MTGR.

interactionTable Parameters

The interactionTable parameters are described next:

	Parameter Meaning	Action
Gene_Before	Gene Name	Defines the gene name affected first.
Event_Before	Event Name	Defines the event name affected first.
Gene_After	Gene Name	Defines the gene name affected after the first.
Event_After	Event Name	Defines the event name affected after the first.
DIVMOD	Division rate modifier	Determines the impact of interaction on division rate.
DEMOD	Death rate modifier	Determines the impact of interaction on death rate.
MAXDIVMOD	Maximum division rate modifier	Determines the impact of interaction on maximum division rate.
Link	Gene-Event Link	Determines if an event affects more than one gene (1 = affect; 0 = does not affect).

Output Files

When simulating different runs on Automatic Runs, our program generates output files informing important info about the runs. Each output file presents different data about the runs:

Output	Data presented
_seed_ancestralResults.csv	Returns the number of descendants cells for each initial cell per generation.
_seed_eventsMutationResults.csv	Returns the number of cells affected on each event in one or two alleles in each generation.
_seed_genesMutationResults.csv	Returns the number of cells affected on each gene in one or two alleles in each generation.
_seed_parameters.txt	Returns the input parameters (seed, Number of Cells, Proliferation Rate, Death Rate, Maximum Division, Events per Division).
_automatics_ancestralResults.csv	Returns the number of descendants cells for each initial cell in the final population for each seed. Also, returns the total number of divisions for each seed.
_automatics_eventsMutationResults.csv	Returns the number of cells affected on each event in one or two alleles in the final population for each seed.
_automatics_genesMutationResults.csv	Returns the number of cells affected on each gene in one or two alleles in the final population for each seed.
_automatics_sequenceEachCell.csv	Returns the single cell genes in the two alleles as two independent lists in a binary form (1 = affected; 0 = not affected).

Documentation

We haven't created a documentation for all classes and fucntions of the code, but a friendly documentation can be found here.

Running into issues?

Contact Bernardo Henz bernardohenz@gmail.com

Other informations can be obtained from www.ufrgs.br/labsinal/esiCancer.