The Proteogenomics of Prostate Cancer Radioresistance

Aug 21, 2024·
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
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
· 0 min read
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
publications
Chenghao Zhu
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.