Welcome to the
IdISBa Cancer Epigenetics Lab

Contact us at: idisba.epigenetics@gmail.com

Cancer Epigenetics

We are the Cancer Epigenetics Lab, at the Cancer Cell Biology Group. Our lab is focused on studying epigenetic alterations involved in aggressive cancer traits. Our projects are based on the combination of bioinformatics tools, such as Machine Learning and Artificial Intelligence, and wet lab involving genome editing and multi-omics profiling.

Our goal is to translate basic research into the beachside, thus, improving the patients' outcomes.

We are mainly focused on two aggressive forms of cancer, glioblastoma and triple-negative breast cancer.


One of our main goals is the study of the deadliest brain cancer, Glioblastoma. This cancer is characterized for its dismal prognosis, due to resistance to current treatments (temozolomide) and the presence of a persistent population of cells (Glioma Stem Cells).

We focus on these two aspects of this disease, using bioinformatics and epigenetics to unravel the mechanisms of resistance to treatment of the Glioma Stem Cells.

Triple Negative Breast Cancer

Triple Negative Breast Cancer (TNBC) is characterized by the absence of ER, PR and HER2 markers, and is the subtype of Breast Cancer with the lowest survival, due to the lack of specific treatments for this tumors.

​In the last years immunotherapy has emerged as a potential treatment for TNBC, but it is not effective in all patients. One of our main objectives is to identify biomarkers that allow the stratification of patients which will respond to immunotherapy and patients which will not.

Our lab collaborates with UCLA in the discovery of novel chemoresistance mechanisms

We will generate cell models of triple-negative breast cancer using CRISPR/Cas9 to unveil the implication of epigenetic mechanisms in chemoresistance.

More info here

Pere Llinàs has been granted with a Sara Borrell postdoctoral grant

This grant may allow Pere Llinàs to continue his current investigation in TNBC, focused on the study of metastasis, chemoresistance and immunotherapy.

More info here

Our group has been awarded with the Impetus Call, which will support the first research project of Pere Llinàs-Arias as a principal investigator

This project aims to generate an epigenetic-based score to predict the metastasic risk in TNBC.

Machine Learning-Based Epigenetic Classifiers for Axillary Staging of Patients with ER-Positive Early-Stage Breast Cancer.

Orozco JIJ, Le J, Ensenyat-Mendez M, Baker JL, Weidhaas J, Klomhaus A, Marzese DM, DiNome ML.

Ann Surg Oncol. 2022 Jul 16. doi: 10.1245/s10434-022-12143-6. Online ahead of print.

PMID: 35842534

Glioblastoma Embryonic-like Stem Cells Exhibit Immune-Evasive Phenotype

Sesé B, Íñiguez-Muñoz S, Ensenyat-Mendez M, Llinàs-Arias P, Ramis G, Orozco JIJ, Fernández de Mattos S, Villalonga P and Marzese DM

Cancers (Basel). 2022

Epigenetic Signatures Predict Pathologic Nodal Stage in Breast Cancer Patients with Estrogen Receptor-Positive, Clinically Node-Positive Disease.

Ensenyat-Mendez M, Rünger D, Orozco JIJ, Le J, Baker JL, Weidhaas J, Marzese DM, DiNome ML.

Ann Surg Oncol. 2022 Aug;29(8):4716-4724. doi: 10.1245/s10434-022-11684-0. Epub 2022 Apr 9.

PMID: 35397740

LCOR mediates interferon-independent tumor immunogenicity and responsiveness to immune-checkpoint blockade in triple-negative breast cancer.

Pérez-Núñez I, Rozalén C, Palomeque JÁ, Sangrador I, Dalmau M, Comerma L, Hernández-Prat A, Casadevall D, Menendez S, Liu DD, Shen M, Berenguer J, Ruiz IR, Peña R, Montañés JC, Albà MM, Bonnin S, Ponomarenko J, Gomis RR, Cejalvo JM, Servitja S, Marzese DM, Morey L, Voorwerk L, Arribas J, Bermejo B, Kok M, Pusztai L, Kang Y, Albanell J, Celià-Terrassa T.

Nat Cancer. 2022 Mar;3(3):355-370. doi: 10.1038/s43018-022-00339-4. Epub 2022 Mar 17.

PMID: 35301507

Establishing Novel Molecular Subtypes of Appendiceal Cancer

Garland-Kledzik M, Scholer A, Ensenyat-Mendez M, Orozco JIJ, Khader A, Santamaria-Barria J, Fischer T, Pigazzi A, Marzese DM.

Ann Surg Oncol. 2022 Mar;29(3):2118-2125. doi: 10.1245/s10434-021-10945-8. Epub 2021 Oct 30.

PMID: 34718915


We are grateful to the following funding entities
for supporting our research:

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