Dr. Eva Colas (Principal researcher)
Dr. Cristian Moiola (Post-doctoral researcher)
Dr. Eva Coll (PhD student)
Dr. Carlos López (PhD student)
Dr. Carina Masferrer (PhD student)
Dr. Beatriz Villafranca (PhD student)
Dr. Melek Denizli (Technician)
Dr. Marta Rebull (Technician)
Dr. Aida Soler (Technician)
Dr. Antonio Gil-Moreno (Principal researcher)
Dra. Silvia Cabrera (Clinical associated investigator)
Dr. Angel García (Clinical associated investigator)
Dr. Armando Reques (Clinical associated investigator)
Dra. Elena Suárez (Clinical associated investigator)
Dra. Mireia Armengol (Clinical associated investigator/resident)
IMPLEMENTATION OF MOLECULAR DIAGNOSTICS IN ENDOMETRIAL CANCER
Early detection is directly related to an increased survival for endometrial cancer patients. Diagnosis of endometrial cancer initiates with the presence of symptoms, i.e. an abnormal vaginal bleeding, and 1 of every 10 women with this symptom will have an endometrial cancer. In order to improve early detection, we aim to improve the process of diagnosis by implementing molecular markers in proximal bodily fluids. Thus, we expect to minimize invasiveness whilst increase accuracy on the newly developed methods of diagnosis. In this research line, the group has two ongoing projects:
Proteomic biomarkers for an improved diagnosis in uterine aspirates.
Exploitation of pap-smears as an untapped source of biomarkers of endometrial cancer.
PREVENTING DISTANT AND LOCAL DISSEMINATION OF ENDOMETRIAL CANCER CELLS
Once a patient is diagnosed, the presence of prognostic factors are used to classify patients according to their risk of recurrence, which directly affects their clinical management. However, up to 20% of patients diagnosed at early stages, and up to 50% of patients diagnosed in advanced stages will suffer a relapse without means of predicting it. We aim to understand the mechanisms of local and distant tumour dissemination to develop strategies that prevent tumour relapse. Moreover, we aim to design tools to better estimate the risk of recurrence of endometrial cancer patients. In this research line, the group has two ongoing projects:
Understanding the molecular mechanisms associated to local and distant dissemination via studying the MELF component and the exosomes derived from the tumor.
Deciphering biomarkers of recurrence in endometrial cancer.
DEVELOPING INDIVIDUALIZED TREATMENT FOR ENDOMETRIAL CANCER PATIENTS
Nowadays, adjuvant treatment of metastatic or recurrent endometrial cancer patients is restricted to few therapeutic options, mostly based on radiotherapy and conventional chemotherapy regimens. Advances in understanding the molecular landscape of endometrial cancer have arisen the development of targeted therapies. However, responses to these targeted therapies are modest, probably due to the underestimation of the heterogeneity found in cancer, misrepresentation of low frequent subtypes of cancer, as well as the lack of clinically relevant models to perform pre-clinical studies, among other issues. The group aims to develop clinically relevant models and design experimental treatments in order to increase the response rate of patients. In this research line, the group has 2 ongoing projects:
The U-CAN platform of endometrial cancer PDX models to perform preclinical studies.
Identification of new experimental therapies.
MOLECULAR DIAGNOSIS AND RECURRENCE PREDICTION IN ENDOMETRIOSIS
Endometriosis is an estrogen dependent disease that affects approximately 15% of women in reproductive age. Although endometriosis is considered a benign disease, characterized by the presence of uterine tissue outside the uterus, it causes strong pain and, often, infertility. So far, the gold standard method to diagnose endometriosis is laparoscopy, an invasive technique and there are no methods for early diagnose of the disease. In addition, patients undergoing surgery as a definitive treatment of endometriosis might recur without means of predicting it. In this research line, the group has 2 ongoing projects:
Assess the potential of angiogenic factors to diagnose and predict recurrence in endometriosis.
Identification of biomarkers to predict recurrence in endometrioma using a proteomic and transcriptomic approach.