Structural Characterization of Macromolecular Assemblies
Signatures for the prediction of recurrence and metastasis in Endometrial Cancers. Stop unnecessary Lymphadenectomy!
In collaboration with Dr. R. Rovira, MD and the Gynecologic Oncology and Advanced Laparoscopic Unit (Hospital de la Santa Creu i Sant Pau) and Dr. V. Céspedes, PhD (Institut de Recerca del Hospital de la Santa Creu i Sant Pau)
Endometrial Cancer (EC) is the most frequent type of gynecological cancer in developed countries and a very painful disease. In general, EC has high survival rates if diagnosed in its early stages. Unfortunately, its symptoms are often overlooked, leading to a late diagnosis and in consequence, poor survival rate. Approximately one patient out of five already has the cancer in an advanced stage when detected and these cases have a poor prognosis and a greater probability of relapse two or three years after the treatment. Lymphadenectomy is normally used in patients with cancer. However, routine staining reveals that for instance, only 20-25% of EC patients are diagnosed with nodal involvement. Therefore 70% of the patients are exposed to the morbidity and other post-operative complications related to lymphadenectomy without its therapeutic benefit. Moreover, 25% of node-negative EC recurs, thereby suggesting that some types of EC (and other gynecological tumors) are not detected with this treatment and indicating that there is a need to improve the diagnosis and treatment protocols currently used in the public health system. To tackle our objectives, we have put together a multidisciplinary team combining surgeons and clinicians (gynecological oncologists and oncologists) working in the “la Santa Creu i Sant Pau” Hospital, biochemists, molecular biologists, informaticians (our contribution), and the patients themselves—the key players in this project.
The hypothesis of this work is that, in addition to all aspects considered in the present protocols of diagnosis, some genetic, transcriptomic and clinical differences might exist among patients, which can help predict the risk of tumor recurrence. Our aim is to determine the probability of relapse and metastasis of Endometrial Cancer (EC) patients, using information obtained from biopsies and machine learning techniques. These predictions will help develop a diagnostic tool to improve the treatment of patients with the most aggressive types of EC, thus having a positive impact on their quality of life and survival. Accelerating the identification for high-risk patients will result in more effective therapeutic interventions, delivered before the tumor spreads and gets out of control. We also plan to develop a system to grade patients currently considered low-risk. In this case, the aim is to restrict lymphadenectomy only to those patients for which this procedure might be beneficial, leading to optimal and less painful treatments for patients suffering from non-aggressive tumors.