Understanding disease outcome in cancer by integrating large-scale transcriptomic data
Understanding disease outcome in cancer by integrating large-scale transcriptomic data
Cancer remains one of the leading causes of death worldwide. While treatments such as chemotherapy, targeted therapy, and immunotherapy have made great progress, many patients still do not benefit from them. A key challenge is understanding why some patients respond well to treatment while others do not, as these differences are driven by a complex interplay of tumor-intrinsic and patient-specific factors.
In this thesis of Stefan Loipfinger, we applied advanced machine learning methods to analyze large collections of gene expression data from tumors and patient blood samples. These methods helped to uncover hidden biological patterns that capture specific processes related to cancer progression and treatment response.
Our studies showed that certain genetic alterations in tumors can affect the immune environment, potentially making tumors more resistant to immunotherapy. We have also identified gene expression patterns in the blood related to how well patients respond to immunotherapy. In breast and ovarian cancers, we found specific patterns associated with treatment benefit and survival, offering opportunities to refine risk assessment and guide therapy decisions. In metastatic breast cancer, we highlighted new molecular features that could contribute to early treatment resistance.
Overall, our research demonstrates how large-scale gene expression analysis can identify biological processes associated with cancer progression and treatment response. These findings open the door to more precise, personalized treatment strategies and point to new biological targets that could improve patient outcomes in the future.