Dr Qing Zhong

Conjoint Senior Lecturer
Children's Medical Research Institute(CMRI)


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Biographical details

Dr Qing Zhong is Group Leader for Cancer Data Science at CMRI and a conjoint Senior Lecturer at the University of Sydney.He completed his undergraduate study incomputer science and a PhD in biochemistry and computer science at ETH (Swiss Federal Institute of Technology) in Zurich, Switzerland.

He was awarded the ProMedica Stiftung for his post-doctoral training at University of Zurich, and was then appointed as a senior data scientist at University Hospital Zurich (USZ). Prior to joining CMRI, Dr Zhong was responsible for the analysis of biomedical data at the USZ and implemented a clinical big data system for consolidating genomic, clinical and demographic information into a unified model for precision and data-driven medicine. His research interests include big cancer data analysis, machine learning, and computational biology.

Research interests

ProCan (the ACRF International Centre for the Proteome of Human Cancer) is a world-first initiative developed and launched in September 2016 by Professors Phil Robinson and Roger Reddel, and established with a $10 million grant from the Australian Cancer Research Foundation (ACRF). Equipped with six SCIEX mass spectrometers and a super computer (700TB / 480 cores), ProCan processes tumour samples through a proteomic method, SWATH-MS, which allows fast mass spectrometric conversion of small amounts of tissue (biopsy level) into a single, permanent digital file representing the quantitative proteome of the sample. One of the goals of ProCan is to measure thousands of proteins in about 70,000 cancers of all types with known treatment outcome and correlate tumour proteotypes with clinical phenotypes. A recent $41 million funding boost from the Commonwealth and State governments, along with significant funding from Cancer Council NSW, will help us to achieve Phase Two, which will employ advanced computer analysis and bioinformatics techniques to compare the protein data with other information available. Phase two is critical if ProCan is to be used to predict the most effective cancer treatments for each individual.

The Cancer Data Science Group, led by Dr. Qing Zhong, aims to develop novel computational tools and sophisticated machine learning algorithms to achieve this goal. Other major focuses of the group are big proteogenomic data mining and management; the genome-proteome association analysis and multi-omic data integration for studying cancer; development of advanced statistical tools to account for batch effects caused by large-scale, high throughput proteomics, and implementation of big data-driven, evidence-based computational tools to achieve predictive, preventive, personalised medicine.

Selected grants

2018

  • Proteomics data platform for collaborative research and treatment-decision support; Reddel R, Zhong Q, Balleine R, Harnett P, Dalla-Pozza L, Robinson P; Cancer Council New South Wales/Infrastructure Development Grant.

Selected publications

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Journals

  • Zhong, Q., Guo, T., Rechsteiner, M., Ruschoff, J., Rupp, N., Fankhauser, C., Saba, K., Mortezavi, A., Poyet, C., et al (2017). A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients. Scientific Data, 4, 1-9. [More Information]
  • Dai, X., Gan, W., Li, X., Wang, S., Zhang, W., Huang, L., Liu, S., Zhong, Q., Guo, J., et al (2017). Prostate cancer-associated SPOP mutations confer resistance to BET inhibitors through stabilization of BRD4. Nature Medicine, 23(9), 1063-1071. [More Information]
  • Zhong, Q., Ruschoff, J., Guo, T., Gabrani, M., Schuffler, P., Rechsteiner, M., Liu, Y., Fuchs, T., Rupp, N., Fankhauser, C., et al (2016). Image-based computational quantification and visualization of genetic alterations and tumour heterogeneity. Scientific Reports, 6(7), 1-12. [More Information]
  • Groner, A., Cato, L., de Tribolet-Hardy, J., Bernasocchi, T., Janouskova, H., Melchers, D., Houtman, R., Cato, A., Tschopp, P., Zhong, Q., et al (2016). TRIM24 Is an Oncogenic Transcriptional Activator in Prostate Cancer. Cancer Cell, 29(6), 846-858. [More Information]
  • Poyet, C., Hermanns, T., Zhong, Q., Drescher, E., Eberli, D., Burger, M., Hofstaedter, F., Hartmann, A., Stohr, R., et al (2015). Positive fibroblast growth factor receptor 3 immunoreactivity is associated with low-grade non-invasive urothelial bladder cancer. Oncology Letters, 10(5), 2753-2760. [More Information]
  • Ikenberg, K., Valtcheva, N., Brandt, S., Zhong, Q., Wong, C., Noske, A., Rechsteiner, M., Rueschoff, J., Caduff, R., Dellas, A., et al (2014). KPNA2 is overexpressed in human and mouse endometrial cancers and promotes cellular proliferation. Journal of Pathology, 234(2), 239-252. [More Information]
  • Zhong, Q., Busetto, A., Fededa, J., Buhmann, J., Gerlich, D. (2012). Unsupervised modeling of cell morphology dynamics for time-lapse microscopy. Nature Methods, 9(7), 711-713. [More Information]

2017

  • Zhong, Q., Guo, T., Rechsteiner, M., Ruschoff, J., Rupp, N., Fankhauser, C., Saba, K., Mortezavi, A., Poyet, C., et al (2017). A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients. Scientific Data, 4, 1-9. [More Information]
  • Dai, X., Gan, W., Li, X., Wang, S., Zhang, W., Huang, L., Liu, S., Zhong, Q., Guo, J., et al (2017). Prostate cancer-associated SPOP mutations confer resistance to BET inhibitors through stabilization of BRD4. Nature Medicine, 23(9), 1063-1071. [More Information]

2016

  • Zhong, Q., Ruschoff, J., Guo, T., Gabrani, M., Schuffler, P., Rechsteiner, M., Liu, Y., Fuchs, T., Rupp, N., Fankhauser, C., et al (2016). Image-based computational quantification and visualization of genetic alterations and tumour heterogeneity. Scientific Reports, 6(7), 1-12. [More Information]
  • Groner, A., Cato, L., de Tribolet-Hardy, J., Bernasocchi, T., Janouskova, H., Melchers, D., Houtman, R., Cato, A., Tschopp, P., Zhong, Q., et al (2016). TRIM24 Is an Oncogenic Transcriptional Activator in Prostate Cancer. Cancer Cell, 29(6), 846-858. [More Information]

2015

  • Poyet, C., Hermanns, T., Zhong, Q., Drescher, E., Eberli, D., Burger, M., Hofstaedter, F., Hartmann, A., Stohr, R., et al (2015). Positive fibroblast growth factor receptor 3 immunoreactivity is associated with low-grade non-invasive urothelial bladder cancer. Oncology Letters, 10(5), 2753-2760. [More Information]

2014

  • Ikenberg, K., Valtcheva, N., Brandt, S., Zhong, Q., Wong, C., Noske, A., Rechsteiner, M., Rueschoff, J., Caduff, R., Dellas, A., et al (2014). KPNA2 is overexpressed in human and mouse endometrial cancers and promotes cellular proliferation. Journal of Pathology, 234(2), 239-252. [More Information]

2012

  • Zhong, Q., Busetto, A., Fededa, J., Buhmann, J., Gerlich, D. (2012). Unsupervised modeling of cell morphology dynamics for time-lapse microscopy. Nature Methods, 9(7), 711-713. [More Information]

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