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Next step in precision oncology - Studying the suitability of microRNA expression pattern to predict individualized treatment in cancer

Applicant Dr. Alexander Wurm
Subject Area Hematology, Oncology
Cell Biology
Term from 2021 to 2025
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 452587293
 
Final Report Year 2025

Final Report Abstract

Cancer is a complex and varied disease and each tumor has its own unique biological characteristics. To improve treatment outcomes, researchers are working to identify specific weaknesses in cancer cells. These weaknesses, called vulnerabilities, can be eventually targeted with distinct drugs designed to attack the cancer while sparing healthy tissue. In this study, we developed a new approach to detect such vulnerabilities more accurately. The method is based on microRNAs (miRNAs). These are very small molecules in our cells that help regulate gene activity. They act like switches, controlling when and how strongly certain genes are turned on or off. We discovered that in many types of cancer, miRNAs that normally suppress cancer-related pathways are present at very low levels. This means that the tumor may be disabling its own natural safety mechanisms to allow cancer-promoting signals to stay active. Building on this insight, we created a method that uses the pattern of low miRNA expression to identify genes that are likely overactive and potentially treatable with specific drugs. These genes are known as druggable targets, meaning there are medications that can act on them. We tested and validated this method initially in colorectal cancer cells and models. We then applied it to other types of cancer using patient-derived samples in both laboratory and animal models. To assess its clinical relevance, we used patient data from nation-wide precision oncology trial which is led by the National Center for Tumor Diseases (NCT) and the German Cancer Consortium (DKTK). The goal of the trial is to find personalized treatments for patients based on the individual molecular profile of their tumor. Our approach was able to identify cancer vulnerabilities with high sensitivity and accuracy. It also added useful information to existing drug prediction strategies based on DNA and RNA sequencing. This new method has the potential to make cancer treatment more personalized, more targeted, and more effective. It may support future clinical decision-making and help guide therapy recommendations across a wide range of cancer types.

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