Resources

Code

TNBC-ICI: https://github.com/CancerEpigeneticsLab/TNBC-ICI

Code used to identify a minimum signature that can predict response to immunotherapy in triple-negative breast cancer patients. The resources also include a methodology to test new samples.

Publication (Pre-Print, accepted at Communications Medicine): https://www.researchsquare.com/article/rs-2284514/v1 

cGSCs: https://github.com/CancerEpigeneticsLab/cGSCs 

Scripts employed for the discovery of a Glioma Stem Cell (GSC) population called core-GSC (c-GSC) with embryonic-like features and immune-evasive phenotype, and for the validation of the model of induced-cGSCs (ic-GSCs)

Publication (Gold Open Access): https://www.mdpi.com/2072-6694/14/9/2070 

iGlioSub: https://github.com/CancerEpigeneticsLab/iGlioSub

Methodology used for the creation of iGlioSub, a machine learning-based classifier (Random Forest and Nearest Shrunken Centroid) which uses transcriptomic and epigenomic features to stratify GBM subtypes.

Publication (Gold Open Access): https://biodatamining.biomedcentral.com/articles/10.1186/s13040-021-00273-8 

EpiLN: https://github.com/CancerEpigeneticsLab/EpiLN

Methodology used for the creation of EpiLN, a machine learning-based classifier which employs DNA methylation features to identify breast cancer patients with lymph node metastasis.

Data

Transcriptomic and epigenomic data of ic-GSCs and GBM-DCs

https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10977/ (RNA-seq data)

https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10978/ (EPIC microarray data)

Protocols

Tumor microdissection protocol