https://github.com/bhklab/epistromaimmune
https://github.com/bhklab/epistromaimmune
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/bhklab/epistromaimmune
- Owner: bhklab
- Created: 2016-07-11T17:38:15.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2017-06-19T15:38:28.000Z (almost 8 years ago)
- Last Synced: 2025-03-27T14:05:30.886Z (2 months ago)
- Language: R
- Size: 24.1 MB
- Stars: 2
- Watchers: 4
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# EpiStromaImmune
Abstract
--------
Engaging the immune system promises to be key for optimal cancer therapy, especially in hard-to-treat triple-negative breast cancer (TNBC). Using laser capture microdissection-coupled expression profiling, we identify three tumor-associated immune microenvironments with distinct CD8+ T cell localization and outcome. Approximately 25% of TNBCs possess an immunoreactive microenvironment, defined by enhanced infiltration of granzyme B+/CD8+ T cells into the tumor bed and a type I interferon signature. These display elevated expression of multiple immune checkpoint inhibitors but are associated with good outcome. In contrast, TNBCs with an “immune-cold” microenvironment restrict CD8+ T cells to tumor margins, possess elevated expression of the immunosuppressive marker B7-H4, and exhibit signatures of activated stroma and worse outcome. A third immunomodulatory microenvironment, also associated with worse outcome, is enriched for IL-17-producing cells and neutrophils and exhibits stromal localisation of CD8+ T cells and PD-L1. These distinct immune microenvironments have implications for TNBC patient stratification for immunotherapies.
Citation
--------
To be publsihed.
Full Reproducibility of the Analysis Results
--------------------------------------------
We describe below how to fully reproduce the figures and tables reported in the paper
1. Set up the software environment
2. Run the R scripts
3. Generate figures
Set up the software environment
-------------------------------
We developed and tested our analysis pipeline using R running on linux and Mac OSX platforms. The following is a copy of `sessionInfo()` from the development environment in R
```
R version 3.1.3 (2015-03-09)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.10.2 (Yosemite)
attached base packages:
[1] grid parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] BiocInstaller_1.16.5 moments_0.14 GSA_1.03 VennDiagram_1.6.16 futile.logger_1.4.1 PharmacoGx_1.1.5 Hmisc_3.17-2 ggplot2_2.1.0
[9] Formula_1.2-1 lattice_0.20-33 gplots_2.17.0 xlsx_0.5.7 stringi_1.0-1 affy_1.44.1 Biobase_2.26.0 BiocGenerics_0.12.1
[17] xlsxjars_0.6.1 rJava_0.9-8 pvclust_2.0-0 piano_1.6.2 genefu_1.16.0 biomaRt_2.22.0 mclust_5.1 survcomp_1.16.0
[25] prodlim_1.5.7 survival_2.38-3 RCytoscape_1.16.0 XMLRPC_0.3-0 graph_1.44.1
loaded via a namespace (and not attached):
[1] acepack_1.3-3.3 affyio_1.34.0 amap_0.8-14 AnnotationDbi_1.28.2 bitops_1.0-6 bootstrap_2015.2 caTools_1.17.1 cluster_2.0.3
[9] colorspace_1.2-6 corrplot_0.73 DBI_0.3.1 digest_0.6.9 downloader_0.4 foreign_0.8-66 futile.options_1.0.0 gdata_2.17.0
[17] GenomeInfoDb_1.2.5 gridExtra_2.2.1 gtable_0.2.0 gtools_3.4.2 igraph_1.0.1 IRanges_2.0.1 KernSmooth_2.23-15 lambda.r_1.1.7
[25] latticeExtra_0.6-28 lava_1.4.1 limma_3.22.7 lsa_0.73.1 magicaxis_1.9.4 magrittr_1.5 marray_1.44.0 MASS_7.3-45
[33] munsell_0.4.3 nnet_7.3-12 plotrix_3.6-1 plyr_1.8.3 preprocessCore_1.28.0 RColorBrewer_1.1-2 Rcpp_0.12.3 RCurl_1.95-4.8
[41] relations_0.6-6 rmeta_2.16 rpart_4.1-10 RSQLite_1.0.0 S4Vectors_0.4.0 scales_0.4.0 sets_1.0-16 slam_0.1-32
[49] sm_2.2-5.4 SnowballC_0.5.1 splines_3.1.3 stats4_3.1.3 SuppDists_1.1-9.2 survivalROC_1.0.3 tools_3.1.3 XML_3.98-1.4
[57] zlibbioc_1.12.0
```
All these packages are available on [CRAN](http://cran.r-project.org) or [Bioconductor](http://www.bioconductor.org)
all necessary packages have `library()` calls within the R scripts themselves, or the script assumes a previous script has been run and thus should have loaded nessesary packages.
Running R Scripts
-------------------------------
CoreDenScripts-
"CoreDen-GSEA-BT-IPA.R": Pathway significantly associated with core density phenotype on the bulktumor data, using Gene Set Enrichment Analysis (GSEA) as implemented in the Piano R package //
"CoreDen-IPA-PathwayScores.R": To obtain Metagene Signatures for the Core Density phenotype
EpiDenScripts-
"GSEA-BT-EpiDen-IPA": Pathway significantly associated with EpiDen phenotype on bulktumor data using Gene Set Enrichment Analysis (GSEA) as implemented in the Piano R package
"EpiDen-IPA-PathwayScores": To obtain Metagene Signatures for the EpiDen phenotype
KM plot-
"KMPlot-Rody.R": Prognostic value of combine on the Rody dataset (GSE31519)
Step1: Combine Immune and Fibrosis and split the patients by 60H and 40L
Step2: Combine Interferon and Cholesterol and split by 50H and 50L
Kappa statistic-
"Kappa-CDMetasigs.R": This code is used to calculate the Kappa statistic for each metasignature along with the combined Immune+Fibrosis
"Kappa-EDMetasigs.R": This code is used to calculate the Kappa statistic for each metasignature along with the combined Cholesterol+Interferon