Associate Professor

Stanley Liu

PhD, MD, University of Toronto

Sunnybrook Health Sciences Centre
2075 Bayview Avenue, Room T2 169, Toronto, Ontario Canada M4N 3M5
Research Interests
Cancer Diagnosis and Therapy, Cancer Mechanisms and Models

At A Glance

  • Translational research lab focusing on improving the outcomes of prostate cancer patients through patient-derived predictive models, non-invasive biomarkers, and immune therapy
  • Utilize molecular and cellular assays, preclinical cancer models and clinical cancer patient samples
  • Extensive collaborations with basic researchers, bioinformaticians and clinical colleagues

Short Bio

Stanley Liu is a Clinician-scientist, Radiation Oncologist and Associate Professor within the Departments of Radiation Oncology and Medical Biophysics at the University of Toronto. He completed his PhD at the University of Toronto, Department of Medical Biophysics, followed by his MD training and a radiation oncology residency at the University of Toronto. He completed his post-doctoral fellowship at the University of Oxford, UK, supported by a Terry Fox fellowship and an ASCO Young Investigator Award. His research lab at Sunnybrook is focused on advancing outcomes for cancer patients by researching therapy resistance and biomarkers of response. He also treats patients with genitourinary malignancies at the Sunnybrook Odette Cancer Centre.

Research Synopsis

Patient-derived cell lines for personalized therapy prediction
The treatment of metastatic prostate cancer has undergone a transformation with several large randomized clinical trials demonstrating a survival benefit with the addition of newer androgen axis targeting agents (ARATs), chemotherapy and radiotherapy.  A major clinical limitation is our inability to accurately predict disease response in the context of an individual patient due to the lack of predictive biomarkers. This is further hindered by the lack of research models that represent the clinical heterogeneity of metastatic prostate cancer, including ethnic diversity, which impedes ongoing research.  We have successfully grown prostate cancer cells isolated from prostate core biopsies in our lab, and are determining their sensitivity to clinically approved treatments. We believe that this approach will provide clinically-actionable information to guide therapy prediction while facilitating in depth studies about the underlying biology of advanced prostate cancer.

Leveraging patient immune response in recurrent and aggressive prostate cancer
Once prostate cancer metastasizes to distant sites, it is considered incurable. Hormone therapy is the standard treatment for metastatic disease; a subset of these men may have a further survival benefit from radiotherapy. However, there is currently a lack of biomarkers for early prediction of patient response post radiotherapy. We published our findings in which radiotherapy response in high-risk prostate cancer patients displayed a distinct circulating immune cell signature that may have potential as an early predictive biomarker. To further investigate this, we are measuring the activation of immune cells in patients who have spread of their cancer beyond the prostate, before and after radiation treatment. This will allow us to determine if high dose radiation to the prostate activates the immune system and reduces the rate of cancer progression. This work allows exploration of biomarkers for early prediction of response to radiation treatment using immune system activation.  We are also investigating the potential of leveraging immune checkpoint therapy in radiorecurrent prostate cancer.

microRNA as biomarkers to improve cancer detection and management
microRNA are detectable in patient biofluids (e.g., blood, urine, saliva), in addition to tumor, and they are inherently stable, making them excellent biomarkers. We believe that urinary microRNA are an ideal source of potential biomarkers since urine is readily obtainable and non-invasive. We believe that microRNA may also be used as predictive biomarkers to identify more aggressive forms of prostate cancer. Thus, we are determining whether testing for specific microRNAs obtained after a prostate exam, predicts for aggressive prostate cancer. If proven, this may allow the early identification of patients with aggressive prostate cancer so that appropriate treatment decisions can be made.

Recent Publications

  • Kurganovs N, Wang H, Huang X, Ignatchenko V, Macklin A, Khan S, Downes MR, Boutros PC, Liu SK*, Kislinger T*. A proteomic investigation of isogenic radiation resistant prostate cancer cell lines.  Proteomics Clinical Applications. 15(5):d2100037, 2021.
  • Wang H, Mendez L, Morton G, Loblaw A, Mesci A, Chung H, Chan S, Huang X, Downes M, Vesprini D, Liu SK. Immune cell profiling in Gleason 9 prostate cancer patients treated with brachytherapy versus external beam radiotherapy: an exploratory study. Radiotherapy and Oncology.  2021; 155:80-85.
  • Ray J, Haughey C, Hoey C, Jeon J, Murphy R, Dura-Perez L, McCabe N, Downes M, Jain S, Boutros PC, Mills IG, Liu SK. miR-191 promotes radiation resistance of prostate cancer through interaction with RXRA. Cancer Letters 473:107-117, 2020
  • Jeon J, Olkov-Mitsel O, Xie H, Yao C, Zhao F, Jahangiri S, Cuizon C, Scarcello S, Jeyapala R, Watson JD, Fraser M, Ray J, Commisso K, Loblaw A, Fleshner NE, Bristow RG, Downes M, Vesprini D, Liu SK*, Bapat B*, Boutros PC*. Temporal stability and prognostic biomarker potential of the prostate cancer urine transcriptome.  Journal of the National Institute of Cancer 2020; 1;112(3):247-255.
  • Hoey C, Musa A, Ghiam AF, Vesprini D, Huang X, Commisso K, Commisso A, Fokas M, Loblaw DA, He HH, Liu SK.  Circulating miRNAs as non-invasive biomarkers to predict aggressive prostate cancer after radical prostatectomy. J Transl Med 17:173, 2019. 
  • Hoey C, Ray J, Jeon J, Huang X, Taeb S, Ylanko J, Andrews DW, Boutros PC, Liu SK. MiRNA-106a and prostate cancer radioresistance: a novel role for LITAF in ATM regulation. Molecular Oncology. 2018 Aug;12(8):1324-1341.

Graduate Students

Gavin Frame
Eric Han Zhi Wang
Jessica Wright