Computational systems biology

Developing predictive models for precision medicine

Tumor cells exhibit a high degree of variability in terms of morphology, phenotype, metastatic potential and underlying molecular profile. This heterogeneity is present not only across different patients (inter-tumor heterogeneity) but also within the same tumor (intra-tumor heterogeneity) and has emerged as an inherent property of cancer.

Identifying the sources of heterogeneity and its implications in clinical outcomes, such as response to therapy or ability to metastasize, has become a cornerstone for the development of effective disease management strategies.

Cancer cell hetergeneity

Four levels of tumor heterogeneity.

Courtesy of Florian Markowetz, posted on Science B-Sides.

Mapping cell subpopulations

Our goal is to characterize the map of breast cancer cell subpopulations during disease progression and unravel changes in key signaling pathways and their links to metastasis.

We are focusing on triple-negative breast cancer, a rare, yet aggressive, subtype that accounts for ∼16% of all breast cancers.

Mass cytometry

To study both intra- and extra-tumor heterogeneity, single-cell experimental approaches are necessary. Mass cytometry (CyTOF) is a proteomic technology that allows the simultaneous quantification of dozens of proteins at single-cell resolution.

Our team is exploiting CyTOF data to extract the relevant information using sophisticated machine learning and modeling approaches.

Collaboration

Our team is part of the MetastasiX project, a SystemsX collaborative research project studying breast cancer heterogeneity from a systems biology perspective.

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Our goals

Extract biological information from high-dimensional data

For this task, we are developing

  • unsupervised learning approach to discover novel cell subpopulations
  • supervised approaches to compare subpopulations and identify proteins responsible for disease progression or differential response to treatment
  • network methodologies to elucidate signaling pathways relevant to disease progression and metastasis.

Develop methods tailored to the challenges of single-cell data

Single-cell techniques are largely influenced by confounding factors, with the cell cycle and cell volume being the dominant ones.

In our team, we are working on computational approaches to account for this hidden source of variability.

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Funding

Marianna Rapsomaniki

Marianna Rapsomaniki

IBM Research scientist

We gratefully acknowledge generous funding from


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