Plenary Lectures
Professor
Steven Xi Chen- Professor of Biostatistics
Department of Public Health Sciences
University of Miami Miller School of Medicine, United States
Translational Bioinformatics for Precision Oncology: Multi-Omics Characterization and Therapeutic Strategies in Triple-Negative Breast Cancer
Triple-negative breast cancer (TNBC) lacks estrogen, progesterone, and HER2 receptors, making it one of the most aggressive and treatment-resistant breast cancer subtypes. In a landmark study, our team used large-scale transcriptome profiling to define the first robust molecular subtypes of TNBC. Building on that foundation, we have integrated genomic, transcriptomic, epigenomic, and proteomic data from multiple cohorts to further refine these subtypes and identify potential therapeutic opportunities for each subtype.
This presentation will trace the journey from that initial subtype discovery to today’s multi-omics and single-cell genomics framework. I will outline the computational analyses behind these findings, from data harmonization and machine-learning classification to validation in patient-derived models and early-phase trials. Along the way, I will highlight how collaborative, open-source tools are accelerating the move from “big data” to bedside decision-making. Together, our studies show how integrative analytics can transform TNBC from a single clinical label into a set of actionable, biologically informed disease states, laying the groundwork for more precise and effective therapies.
Professor
Jasmine Foo- Professor
School of Mathematics
University of Minnesota, United States
Mathematics of Cancer Evolution
Tumors can be viewed as a complex, heterogeneous ecosystems that can often evade therapy by evolving rapidly. The microenvironment of a tumor, consisting of drugs, signaling factors, oxygen and stromal cells is increasingly recognised as a critical mediator of tumor evolution and therapy response. In this talk I will present some examples of how mathematical models can be leveraged to understand the role of the tumor microenvironment in tumor response to therapy.
Professor
Neville E. Sanjana- Full Professor of Biology, Neuroscience and Physiology
Department of Biology
New York University, United States
Programmable Biology:
Deciphering Genome Function to Understand Human Health
I will present recent work from our group to programme better cell therapies using genome-scale overexpression and to understand the function of disease-associated noncoding variants using genetic perturbations and biobank-scale human genetic data.
The engineering of autologous patient T cells for adoptive cell therapies has revolutionised the treatment of several types of cancer. However, further improvements are needed to increase response and cure rates. We identified positive regulators of T cell functions through overexpression of around 12,000 barcoded human open reading frames (ORFs). The top-ranked genes increased the proliferation and activation of primary human CD4+ and CD8+ T cells and their secretion of key cytokines such as interleukin-2 and interferon-γ. In addition, we developed a single-cell genomics method (OverCITE-seq) for high-throughput quantification of the transcriptome and surface antigens in ORF-engineered T cells. Our results provide several strategies for improving next-generation T cell therapies by the induction of synthetic cell programmes.
Most variants associated with complex traits and diseases identified by genome-wide association studies (GWAS) map to noncoding regions of the genome with unknown effects. Using ancestrally diverse biobank-scale GWAS data, massively parallel CRISPR screens, and single cell transcriptomic and proteomic sequencing, we discovered 124 cis-target genes of 91 noncoding blood trait GWAS loci. Using precise variant insertion via base editing, we connected specific variants with gene expression changes. We also identified trans-effect networks of noncoding loci when cis target genes encoded transcription factors or microRNAs. Networks were themselves enriched for GWAS variants and demonstrated polygenic contributions to complex traits. This platform enables massively-parallel characterisation of the target genes and mechanisms of human noncoding variants in both cis and trans.
