Heena Dalal is a Postdoctoral Bioinformatician in Prof. Anita Grigoriadis’ group at King’s College London within the Breast Cancer Now Unit. She leads computational analysis and methodological development for spatial and multimodal tumour profiling within a large translational immuno-oncology programme in triple-negative breast cancer (TNBC). Her research sits at the interface of spatial biology, machine learning and therapeutic discovery and is driven by a central question: how can spatial multi-omics data be converted into mechanistic and predictive models that inform treatment strategy?

Her work is organised into three primary areas:

• Spatial tumour–immune system modelling: Development of computational frameworks that model the tumour microenvironment as an interacting ecosystem rather than isolated cell populations. Using spatial transcriptomics and imaging-derived data, this includes defining immune activation and evasion programmes, mapping functional immune niches, and quantifying cellular neighbourhood interactions that determine treatment response. The work establishes biologically actionable tumour states relevant to therapeutic intervention.
• Predictive modelling of treatment response: Development of predictive models to identify determinants of sensitivity and resistance to therapy from high-dimensional molecular data. By modelling receptor-ligand signalling, cellular crosstalk, and tumour state transitions, this approach derives robust stratification features linking mechanism to outcome and supporting biomarker and target prioritisation strategies.
• Multimodal data integration for translational discovery: Design and implementation of scalable computational pipelines integrating spatial transcriptomics, single-cell sequencing, digital pathology, and proteomic measurements.

The emphasis is on interpretability and decision-relevant outputs, enabling translation of complex datasets into experimentally testable hypotheses and therapeutic insights.

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