Highlighted projects

  • Title: Elucidating transcriptional rewiring on hematological malignancies via computational methods (LINKER)
    Funding Agency: European Commission, H2020-MSCA-IF-2019 Programme, Grant Agreement # 898356. Duration: 01/03/2020 - 28/02/2022. PI: Mikel Hernaez PhD
    Abstract: Genes and their corresponding pathways form networks that regulate various cellular functions that are critical in tumor development. These networks, coined Gene Regulatory Networks (GRNs), define the regulatory relationships among genes and provide a concise representation of the transcriptional regulatory landscape of the cell. Further, different phenotypes can lead to activation of different functional pathways by different global rewiring of the underlying GRNs. To uncover such transcriptional rewiring, in this project we will further advance and optimize a recent efficient computational method developed by Dr. Hernáiz, coined LINKER, aiming at uncovering GRNs from RNAseq data; and given those networks, develop efficient differential network analysis methods that will shed light into the regulatory rewiring associated with phenotype. As one of the key goals of this proposal is the translation of computational methods to advance clinical cancer knowledge, we will work with the hosting group to apply LINKER to uncover the transcriptional rewiring associated to hematological malignancies. Specifically, we will apply LINKER in a stepwise model first on available RNA-seq data from multiple myeloma and acute myeloblastic leukemia, and second to primary data from patients with these diseases provided by the hosting supervisor. Rewired GRNs between MM and normal BM plasma cells and between leukemic blast and normal hematopoietic progenitor cells, potentially implicated in the pathogenesis of the disease, will be functionally validated by state of the art gain and loss of function technologies (CRISPR/cas9). As data provided by the host institution includes clinical follow up from patients we will also examine the prognostic value of our identified GRN. I envision that LINKER will provide additional novel insights to our understanding of the key rewiring associated with these malignancies, increased by our ability to translate the discovered biomarkers to patient treatment.

  • Title: Novel methods to elucidate abiraterone resistance mechanisms using RNA-Seq data and xenograft models from CRPC patients
    Funding Agency: USA Department of Defense, FY19 DoD CDMRP PCRP
    Duration: 01/05/2020 - 30/04/2023. PI. Liewei Wang MD, PhD (Mayo Clinic) & MIkel Hernaez, PhD (CIMA University of Navarra)Marie Curie (LINKER), el del Departamento de Defensa
    Abstract: Prostate cancer is the most frequently diagnosed male cancer. Currently, treatment of patients with advanced- stage disease relies on hormone-like drugs such as Abiraterone, a recently approved new generation of hormonal therapy, which targets an enzyme involved in making androgen in the body. Even with the significant therapeutic advancement that has been brought into the clinic, advanced prostate cancer is still incurable and fatal. Approximately 40,000 men die from prostate cancer every year; most of them dying from advanced and metastatic disease. Therefore, it is critical to find new solutions via selective biomarkers that can enhance the effectiveness of the current treatments such as abiraterone.
    Approach: Our proposal will take advantage of a prospective clinical study, PROMOTE, designed to specifically address the above-mentioned challenges. We, investigators from UIUC and Mayo that include computer scientists, cancer biologist and pharmacologist, will make use of the data sets generated from PROMOTE, to develop novel computational methods to identify resistance mechanisms and potential therapies to overcome resistance. The PROMOTE study enrolled 91 patients with castration-resistant prostate cancer (CRPC), a stage at which patients have failed therapy with conventional hormonal treatment. Biopsy samples were collected before and after 12-weeks of abiraterone treatment. Genetic material (DNA and RNA) extracted from biopsy tissues were used for next-generation sequencing studies. Animal models were also generated using these biopsy samples to represent individual patient’s tumor. We have successfully obtained a dozen models that uniquely represent diversity of tumor biology. Specifically, we plan to develop, refine and apply two novel computational methods to help understand how genes are regulated differently between responders and non-responders, and to use that information to help prioritize and select appropriate drugs to overcome abiraterone resistance. Our strength is also significantly enhanced by having animal models generated from PROMOTE patients’ tumors to experimentally test the hypothesis generated from the computational analyses.
    Applicability: The outcome of this research has the potential to help patients who suffer from prostate cancer, and especially those who likely may not respond to standard hormonal therapy. Our proposed studies will result in clinically meaningful biomarkers to predict abiraterone response, as well as drugs that might help overcome abiraterone resistance. Furthermore, these results would help to build future clinical trials based on individual patient’s tumor biology so that we can select the most appropriate drugs for these patients.
    Advancing the field of prostate cancer: Our work will provide additional novel insights into our understanding of why and how patients become resistant to abiraterone. This knowledge is expected to identify many novel druggable targets and drugs that can be further tested in the clinic to help patients who do not respond to abiraterone, all of which could lead additional drug development and novel discovery science.

Mikel Hernáez

"Uncovering biomarkers via machine learning methods to improve diagnosis, prevention and treatment of diseases in the context of personalize medicine”,
Dr. Mikel Hernáez, Program Director.


Cristina López
Avda. Pío XII, 55
31008 Pamplona

(+34) 948 194 700 Ext. 6021