The Proteogenomics of Prostate Cancer Radioresistance
Aug 21, 2024·,,,,,,,,,,,,
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Roni Haas
Gavin Frame
Shahbaz Khan
Beth K. Neilsen
Boon Hao Hong
Celestia P.X. Yeo
Takafumi N. Yamaguchi
Enya H.W. Ong
Wenyan Zhao
Benjamin Carlin
Eugenia L.L. Yeo
Kah Min Tan
Yuan Zhe Bugh
Chenghao Zhu
Rupert Hugh-White
Julie Livingstone
Dennis J.J. Poon
Pek Lim Chu
Yash Patel
Shu Tao
Vladimir Ignatchenko
Natalie J. Kurganovs
Geoff S. Higgins
Michelle R. Downes
Andrew Loblaw
Danny Vesprini
Amar U. Kishan
Melvin L.K. Chua
Thomas Kislinger
Paul C. Boutros
Stanley K. Liu
Abstract
Prostate cancer is frequently treated with radiotherapy. Unfortunately, aggressive radioresistant relapses can arise, and the molecular underpinnings of radioresistance are unknown. Modern clinical radiotherapy is evolving to deliver higher doses of radiation in fewer fractions (hypofractionation). We therefore analyzed genomic, transcriptomic, and proteomic data to characterize prostate cancer radioresistance in cells treated with both conventionally fractionated and hypofractionated radiotherapy. Independent of fractionation schedule, resistance to radiotherapy involved massive genomic instability and abrogation of DNA mismatch repair. Specific prostate cancer driver genes were modulated at the RNA and protein levels, with distinct protein subcellular responses to radiotherapy. Conventional fractionation led to a far more aggressive biomolecular response than hypofractionation. Testing preclinical candidates identified in cell lines, we revealed POLQ (DNA Polymerase Theta) as a radiosensitizer. POLQ-modulated radioresistance in model systems and was predictive of it in large patient cohorts. The molecular response to radiation is highly multimodal and sheds light on prostate cancer lethality.
Type
Publication
Cancer Research Communications

Authors
Research Assistant Professor
Chenghao Zhu is a Research Assistant Professor in the NCI-designated Cancer
Center at Sanford Burnham Prebys. His research focuses on developing
computational methods and software for proteogenomics and applying
proteogenomics to cancer diagnosis, prognosis, and clinico-epidemiologic
questions.