Jürgen C. Becker

Prof. Dr. Dr. Jürgen C. Becker is a leading academic and researcher in translational skin cancer, affiliated with the University of Duisburg-Essen's Biological Faculty and the German Cancer Research Center (DKFZ). He leads the Translational Skin Cancer Research group at the West German Cancer Center (WTZ), part of the German Cancer Consortium (DKTK), focusing on bridging laboratory discoveries with clinical applications in melanoma, Merkel cell carcinoma, and cutaneous lymphomas.

His research is centered on understanding the tumor microenvironment, immunotherapy response mechanisms, and tumor heterogeneity. Utilizing advanced technologies such as single-cell multiomics, spatial transcriptomics, and deep learning, his team investigates biomarkers for diagnosis, prognosis, and treatment response. He is actively involved in clinical trials like ADMEC-O and IMMUNED, evaluating adjuvant immunotherapies in Merkel cell carcinoma and melanoma.

The recent publications highlight a strong trend in immunotherapy, molecular mechanisms of resistance, viral oncology, and epigenetic regulation in skin cancers. His work spans from basic molecular studies to phase 2 clinical trials, demonstrating a comprehensive translational approach. Key themes include T-cell responses, DNA methylation, BCL-xL targeting, and the role of the microbiome and endogenous retroviruses in cancer progression.

Scientific Awards:

• No scientific awards explicitly mentioned in the provided text.

Advising and Grants:
As a group leader and principal investigator, Prof. Becker mentors researchers and leads funded projects within DKTK and EU/NRW initiatives. He is involved in low-interventional clinical trials (LIT) and translational programs such as ‘FieldCancerization’, ‘CemiFirst’, and ‘MCC TRIM’, indicating significant grant support and collaborative leadership.

Labs and Teams:
He leads the Translational Skin Cancer Research group at WTZ, collaborating with national and international teams. His lab employs cutting-edge methods including spatial transcriptomics, single-cell sequencing, and patient-derived models, supported by robust bioinformatics and deep learning approaches.