CoRe.PanCancer_ADaM: Execute ADaM at the Pan-cancer level

In DepMap-Analytics/CoRe: CoRe R package

Execute ADaM at the Pan-cancer level

Description

Execute ADaM at PanCancer level.

Usage

CoRe.PanCancer_ADaM(pancan_depMat,
                          tissues_ctypes,
                          clannotation = NULL,
                          display=TRUE,
                          ntrials=1000,
                          verbose=TRUE,
                          TruePositives)

Arguments

pancan_depMat	A binary dependency matrix derived from screening (ideally 100s of) cell-lines from multiple tissue lineages and where rows are genes and columns are cell-lines/samples, with a 1 in position [i,j] indicating that the inactivation of the i-th gene exerts a significant loss of fitness in the j-th cell-line/sample.
tissues_ctypes	Vector of strings with tissue/cancer type names of interest. These should be compatible with the cell model annotations of the Cell Model Passports [2] (downloadable through the function CoRe.download_AnnotationModel).
clannotation	Data frame containing the Cancer cell lines' annotations, derived from the cell model passports [2] (downloadable through the function CoRe.download_AnnotationModel).
display	Boolean, default is TRUE. Should bar plots of the dependency profiles be plotted.
ntrials	Integer, default =1000. How many times to randomly perturb the dependency matrix in order to generate null distributions of number of fitness genes across fixed number of cell lines.
verbose	Boolean, default is TRUE. Should the computation progress be monitored.
TruePositives	Vector of gene symbols to be used as prior known essential genes by the ADaM algorithm.

Details

This function executes ADaM on every tissue in cascade to identify Cancer Type specific Core Fitness genes, then iterates the procedure as detailed in [1] to identify a set of Pan-cancer core fitness genes.

Value

PanCancer_CF_genes

A vector of string with predicted PanCancer Core Fitness Genes' symbols.

Author(s)

C. Pacini, E. Karakoc, A. Vinceti & F. Iorio

References

[1] Behan FM, Iorio F, Picco G, Gonçalves E, Beaver CM, Migliardi G, et al. Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. Nature. 2019;568:511–6.

[2] Van der Meer D, Barthorpe S, Yang W, et al. Cell Model Passports-a hub for clinical, genetic and functional datasets of preclinical cancer models. Nucleic Acids Res. 2019;47(D1):D923–D929.

[3] Hart T, Chandrashekhar M, Aregger M, Steinhart Z, Brown KR, MacLeod G, Mis M, Zimmermann M, Fradet-Turcotte A, Sun S, Mero P, Dirks P, Sidhu S, Roth FP, Rissland OS, Durocher D, Angers S, Moffat J. High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities. Cell. 2015 Dec 3;163(6):1515-26. doi: 10.1016/j.cell.2015.11.015. Epub 2015 Nov 25. PMID: 26627737.

[4] Dwane L, Behan FM, Gonçalves E, Lightfoot H, Yang W, van der Meer D, Shepherd R, Pignatelli M, Iorio F, Garnett MJ. Project Score database: a resource for investigating cancer cell dependencies and prioritizing therapeutic targets. Nucleic Acids Res. 2021 Jan 8;49(D1):D1365-D1372.

See Also

CoRe.CS_ADaM CoRe.ADaM CoRe.download_AnnotationModel

Examples

# Identifying pan-cancer core-fitness genes with the ADaM model, as
# described in Behan et al 2019, i.e. performing analyses at individual
# tissues/cancer-type level then iterating the proceudre at pan-cancer level

## Downloading binary dependency matrix
## for > 300 cancer cell lines from Project Score [1,4]
BinDepMat<-CoRe.download_BinaryDepMatrix()

## Defining tissues/cancer-types that should be considered in the
## first phase of ADaM executions
tissues_ctypes<-c("Haematopoietic and Lymphoid",
                  "Ovary",
                  "Peripheral Nervous System",
                  "Central Nervous System",
                  "Pancreas",
                  "Head and Neck",
                  "Bone",
                  "Lung",
                  "Large Intestine",
                  "Esophagus",
                  "Endometrium",
                  "Stomach",
                  "Breast")

## Downloading cell line annotations from the Cell Model Passports [2]
clannotation<-
  CoRe.download_AnnotationModel('https://cog.sanger.ac.uk/cmp/download/model_list_latest.csv.gz') ## dataset from [2]

## Downloading a set of priori known essential genes to be used as true positives from [3] and manually
## curated as detailed in [1]
data(curated_BAGEL_essential)

## Execute ADaM at the pancancer level
PanCancer_CF_genes<-
  CoRe.PanCancer_ADaM(pancan_depMat = BinDepMat,
                      tissues_ctypes = tissues_ctypes,
                      clannotation = clannotation,
                      TruePositives = curated_BAGEL_essential,
                      display = FALSE)


## Inspect output
PanCancer_CF_genes

DepMap-Analytics/CoRe documentation built on July 6, 2022, 8:01 a.m.