Joe YEONG Poh Sheng


SUMMARY
Joe Yeong is a Principal Investigator at the Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR). He is also concurrently the Director of Immunopathology at SingHealth Duke-NUS, and a Principal Investigator with the Department of Anatomical Pathology, Singapore General Hospital (SGH). 

Joe is listed in Stanford University’s list of “World’s Top 2% Scientist” in 2024. His main research focus is to understand and overcome the resistance of cancer immunotherapy,  by using advances technologies and AI. As an immuno-pathologist, his key vision is to bridge between immunologists and pathologists to better harness the advances of immunotherapy and further beyond. He is the pioneer in automation of quantitative multiplex immunohistochemistry, using clinical autostainers to study and quantitate tumour immune microenvironment in clinical samples, and also in spatial technologies. He has published more than 120 papers in this field and given more than 100 invited talks internationally. His works on cancer immunology are included in multiple National Medical Research Council (NMRC) funded studies as well as pharmaceutical industry sponsored projects. 

Apart from his extensive achievements in his field, Joe also contributes immeasurably in the scientific communities for cancer immunotherapy, spatial technologies, medical diagnostics, and academic publishing. He served as a committee member in the World Immunotherapy Council (WIC), Society for Immunotherapy of Cancer (SITC) and is one of the organisers for its 2019, 2023 & 2025 WIC Global Symposium as well as multiplex IF expert consensus meeting 2022. In 2023, Joe co-founded the WIC Asian Chapter for promoting tumor immunology and advancing cancer immunotherapy education, information and research across Asia. Subsequently in 2025, the WIC Asian chapter has expanded to the Asia Pacific (APAC) region.

In addition, Joe serves as Program Chair of one of the largest AI medical Imaging conferences, CLINICCAI-MICCAI. He also serves as a Secretary (Executive) in Singapore Society of Oncology – Cancer Immunotherapy Consortium, Co-lead in Education/Diagnostic of Singhealth Duke-NUS Cell Therapy Centre as well as Advisor (Spatial Technology), Cancer Discovery Hub, National Cancer Centre Singapore. 

Joe has editorial roles for Nature Springer, Elsevier, SLAS Technology (Journal), Frontiers, Pathogens and World Scientific (Chief Editor). He is also a regular reviewer for top journals such as JITC, Mod Path, Lancet and Nature.

Joe has completed multidisciplinary training for clinical (MBBS), immunology (PhD) and histopathology (British Royal College of Pathologist (RCPath) Fellowship), and possesses more than 10 years of translational research experience.


AWARDS & GRANTS

KEY AWARDS
  • 2025: Annual Chapter of Pathologists Research Award 
  • 2024: World Immunotherapy Council Asian Chapter, Research Contribution Award
  • 2024: MedTech Actuator 2024 Finalist
  • 2022: Top Cited Author 2020-2021, Wiley
  • 2021: APEC ASPIRE Prize Award finalist (Representing Singapore)
  • 2020: A*STAR Career Development Award
  • 2017: Best Oral Presentation, Asia-Pacific Primary Liver Cancer Meeting (APPLE)
  • 2017: Best Platform Presentation, Singhealth Pathology ACP Research Day
  • 2017: NUS Wong Hock Boon Society Outstanding Mentor Award
KEY GRANTS
  • 2024: National Medical Research Council Large Collaborative Grant (NMRC OF-LCG) (Theme PI: Joe Yeong)
  • 2023: Singapore-China Government Joint Innovation Call (Co-PI: Joe Yeong)
  • 2023: National Institute of Health (NIH, USA) R01 (Co-PI: Joe Yeong)
  • 2022: Industry Alignment Fund – Industry Collaboration Project (IAF-ICP) (Lead PI: Joe Yeong)
  • 2021: Industry Alignment Fund – Industry Collaboration Project (IAF-ICP) (Lead PI: Joe Yeong)

RESEARCH

Immunopathology
Our research focuses on integrative translational research that combines tumor immunology and pathology. We aim to apply immune markers in routine clinical practice to enhance diagnosis and prognosis, with a strong emphasis on immunotherapy and precision medicine.

A key area of investigation is understanding the mechanisms behind immunotherapy resistance, particularly in the context of anti-PD-1/PD-L1 therapies. By exploring these resistance pathways, we seek to develop strategies to improve treatment efficacy and patient outcomes.

Additionally, we work on the automation, standardization, and translation of tissue and cell-based diagnostic tests. This includes optimizing processes from sample processing to imaging and analysis, ensuring consistency and accuracy in diagnostic workflows.

Our research also extends to the application of artificial intelligence (AI) in immunopathology, drug discovery, and development. This includes innovations such as H&E 2.0, Personalized Medicine 2.0, Cytology 2.0, Single Cell 2.0, and Liquid Biopsy 2.0, all aimed at enhancing precision diagnostics and therapeutic decision-making.


PUBLICATIONS   

TECHNOLOGY DISCLOSURES
  • Antibodies optimised for immunohistochemistry (IHC) 
    A library of 74 antibodies optimised for immunohistochemistry (licensed to company)

  • Multiplex immunohistochemistry/immunofluorescence (mIHC/IF)
    A translational assay compared to conventional IHC, for testing in triple negative breast cancer (licensed to company)

  • Antibody-drug conjugate target protein expression prediction on haematoxylin and eosin-stained whole slide images
    Pipeline for predicting tumour biomarkers at the single cell level from H&E whole slide images via a series of deep learning models (licensed to company)

  • Integrating StyleGAN and deep learning for enhanced histomorphological analysis of CD8+ T-Cell tumor specificity
    An innovative approach to demystify the 'black-box' nature of deep learning models in medical imaging
  • Development of personalised cancer vaccine
    A novel way to package and deliver cancer vaccine
  • IHC-based clinical assay for EBC-129 ADC for cancer
    IHC assays for EBC-129 antigen, an antibody-drug conjugate (ADC), that can be used to select patients that will likely respond to treatment
  • CD8+ T-cell prediction with AI in histological images
    AI model trained to recognise CD8+ cells from H&E slides

  • Tumour PD-L1 protein expression prediction on haematoxylin and eosin-stained whole slide images
    Predicts the expression of PDL1 among tumor cells at the single cell level from H&E whole slide images

*Please contact A*STAR if you wish to collaborate or license these technologies.

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