Exploiting existing bulk and single-cell RNAseq data to identify biomarkers of response to ICI.

• Leveraging large-scale transcriptomic datasets to uncover predictive signatures.

• Integrating single-cell resolution data with bulk expression profiles.

Assessment of the initial steps in gastric and esophageal cancer development, using new animal models and primary human samples.

• Single-cell RNA-seq, ATAC-seq, DNA-seq, and spatial transcriptomics data.

Long-standing collaborations on the genomic aspects of Esophageal Adenocarcinoma (EA).

• Characterizing the mutational landscape and clonal evolution of EA.

• Developing computational tools for early detection and risk stratification.

• Assessment of ecDNA in EA.

Causal inference from cancer studies, integrative scRNA analysis, tumor heterogeneity simulation, and deep learning for early detection.

• Estimation of causal effects and relationships from observational and interventional cancer data. 

• Statistical and ML methods for integrative analysis of scRNA and related experiments.

• Simulation methodology to study tumor heterogeneity, clonal development, SNVs and copy number aberrations.

• Statistical inference for duplication rates in sequence analysis. 

• Domain-knowledge informed deep learning for early detection of pancreatic cancer. 

We have been developing bioinformatics methods for copy number calling from shallow single-cell DNA (scDNA) sequencing technologies, particularly DLP+ (Laks, E. et al. (2019)  Cell, 179, pp. 1207-1221.e22. Available at: https://doi.org/10.1016/j.cell.2019.10.026.)

• Stochastic models for pile-ups from scDNA sequence data.

• Development of Songbird, a copy number calling framework See Wesley, B.K. et al. (2025) “Wavelet Based Whole Genome Doubling Aware Single Cell Copy Number Calling,” available at https://doi.org/10.64898/2025.12.18.693686.

A Cancer UK Grand Challenge project combining STPT, DLP+, spatial transcriptomics, and VR for 3D tumor analysis.

• Serial two-photon tomography (STPT) for comprehensive tissue imaging.

• DLP+ single-cell DNA sequencing for clonal structure analysis.

• Spatial transcriptomics for mapping gene expression in tissue context.

• Virtual reality tools for 3D tumor exploration.

• For a recent position paper, see Bressan D., IMAXT Cancer Grand Challenges Consortium, Walton N, Hannon G.J. (2025) “Cancer Research in the Age of Spatial Omics: Lessons from IMAXT,” Cancer Discovery, 15, pp. 16–21. Available at: https://doi.org/10.1158/2159-8290.CD-24-1686.

Stochastic models and statistical inference in population genetics, including ABC methods and applications of branching processes to cancer evolution.

• Approximate Bayesian Computation, sequential Monte Carlo Distributional Random Forests.

• Long-standing research into the theoretical foundations of population genetics.

Mathematical modeling of follicle stem cell (FSC) dynamics in the Drosophila ovary.

• Application of ABC-DRF, ABC-SMC-DRF.

• Multi-compartment birth-death process models.