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DTSTART;TZID=Australia/Sydney:20260907T130000
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SUMMARY:Single-cell spatial pharmacobiology identifies conserved stromal barriers to therapeutic antibody delivery in human solid tumors
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Guolan Lu\, Stanford University \n\n\n\nThis is an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\nThe development of effective antibody therapeutics has been hampered by a lack of methods to measure drug delivery and activity within tumors at single-cell resolution. Here we introduce single-cell spatial pharmacobiology (SSP)\, an experimental and analytical framework that integrates in situ imaging of a systemically infused\, fluorescently labeled therapeutic antibody with high-plex spatial proteomics to quantify antibody distribution\, target engagement and tumor microenvironment (TME) architecture. We applied SSP to tumor tissues from participants with head and neck squamous cell carcinoma and pancreatic ductal adenocarcinoma who received the antibody panitumumab-IRDye800 in phase 1 trials. SSP identified pronounced spatial heterogeneity in single-cell drug delivery and target engagement\, shaped by conserved stromal barriers\, including periostin-rich extracellular matrix assemblies and fibroblast-activation-protein-positive cancer-associated fibroblast neighborhoods\, which were associated with reduced antibody delivery in both tumor types. SSP measures drug–target–TME interactions in human tumors and can support studies of resistance mechanisms\, dosing strategies and discovery of spatial biomarkers for precision oncology. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Guolan Lu\n\n\n\nGuolan Lu is a tenure-track Assistant Professor at Stanford University. Her laboratory develops spatial multi-omics and artificial intelligence technologies to determine how tissue organization shapes disease progression and therapeutic response. During her postdoctoral training at Stanford with Eben Rosenthal and Garry Nolan\, she designed and led a first-in-human fluorescence-guided surgery study in pancreatic cancer and pioneered single-cell spatial pharmacobiology in human tumors. She received her PhD through the joint Biomedical Engineering program at Georgia Tech and Emory University. \n\n\n\nConnect with Guolan:X: @GuolanLu
URL:https://spds.sydney.edu.au/event/single-cell-spatial-pharmacobiology-identifies-conserved-stromal-barriers-to-therapeutic-antibody-delivery-in-human-solid-tumors/
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DTSTART;TZID=Australia/Sydney:20260914T130000
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DTSTAMP:20260903T020427Z
CREATED:20260902T015611Z
LAST-MODIFIED:20260903T020427Z
UID:5473-1789390800-1789394400@spds.sydney.edu.au
SUMMARY:Joint PhD student presentations
DESCRIPTION:This is a hybrid event with two presentations. In person venue: Carslaw Lecture Theatre 373Zoom link: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\nInterpretable analytical methods for characterising disease-associated phenotypic modulation of cellsSpeaker: Elijah Willie\, University of Sydney\n\n\n\nRealising the analytical potential of single-cell technologies requires computational frameworks that address fundamental challenges in characterising cellular heterogeneity and disease-associated phenotypes. High-throughput cytometry and spatial omics platforms have advanced substantially in their resolution and throughput\, enabling simultaneous measurement of dozens of parameters across millions of cells. However\, these technologies present analytical obstacles including technical artifacts\, batch effects\, and the complexity of spatial interactions that limit biological discovery and clinical application. Addressing challenges such as robust cell type identification\, cross-cohort transferability\, and spatial variance decomposition necessitates the development of innovative computational approaches. \n\n\n\nTo this end\, this work contributes to single-cell computational biology by (1) establishing a multiview framework that harmonizes correlation and distance metrics through ensemble learning\, overcoming similarity metric limitations in imaging cytometry to enable robust identification of cellular phenotypes\, (2) developing transferable deep learning approaches combining batch-agnostic normalization with hierarchically-structured feature selection to enable cross-institutional validation of cytometry-based biomarkers\, and (3) creating variance decomposition methods for spatial transcriptomics that quantify cell type-specific interactions while correcting for lateral spillover\, revealing how proximity modulates gene expression programs in breast cancer and melanoma. The frameworks presented here provide validated computational tools that advance single-cell analysis and establish foundations for future development of interpretable methods that characterise disease-associated cellular modulation. \n\n\n\n\n\n\n\nMultimodal learning for organ transplant diagnosticsSpeaker: Harry Robertson\, University of Sydney\n\n\n\nKidney transplantation remains the only solution for end-stage renal failure\, yet chronic immune injury and reactive monitoring limit long-term graft survival and patient quality of life. Current strategies to monitor patients are reactive and require invasive biopsies to diagnose allograft dysfunction. These biopsies are assessed by semi-quantitative criteria with known between-reader variability. This thesis develops 1) a blood test for allograft rejection and 2) a co-pilot for renal biopsy assessment. To develop our blood test\, we first assembled PROMAD\, the largest transcriptomic resource in the field\, spanning more than 150 studies and 12\,000 samples across kidney\, heart\, liver and lung transplantation. By developing TOP\, a novel transfer learning framework\, we produced an organ-agnostic blood test that predicts biopsy-proven rejection (AUC=0.81)\, outperforming the current standard-of-care. Next\, we developed RenalAgent an AI co-pilot that reads kidney biopsies\, scores lesions and produces evidence-linked reports. Across 15\,082 biopsies from Mass General Brigham (MGB)\, RenalAgent achieved high concordance with expert pathologists (mean kappa=0.73 across 16 tasks). In external cohorts from Australia\, Europe\, and Mt Sinai (US)\, RenalAgent demonstrated prognostic utility through longitudinal assessment\, predicting graft loss better than the current standard-of-care. Together\, these contributions aim to advance precision monitoring for transplant recipients. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nElijah Willie\n\n\n\nElijah studied computer science and molecular biology as an undergraduate at Simon Fraser University\, then completed a master’s in bioinformatics at the University of British Columbia. After a few years working as a bioinformatician\, he began his PhD at the University of Sydney in 2022 and completed it in 2025. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHarry Robertson\n\n\n\nHarry completed his undergraduate degree in applied medical science at the University of Sydney in 2021. During his PhD he received a Westpac Future Leaders Scholarship\, and in 2024 a Fulbright Future Scholarship to study under Prof. Faisal Mahmood at Harvard. His work has been published in Nature Medicine.
URL:https://spds.sydney.edu.au/event/statistical-bioinformatics-seminar/
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