Background

Human tumors are complex mixtures of cells harboring different molecular profiles, which result from the activity of both exogenous and endogenous mutational processes, as well as interactions with immune cells. This intra-tumour heterogeneity results from the accumulation of unique sets of somatic mutations in individual subclones and drives fundamental aspects of tumour biology. Therefore, decoding the patterns of clonal evolution in human cancers and assessing the phenotypic changes induced by somatic alterations at the single-cell level is essential to understand the molecular underpinnings of malignant transformation, tumour evolution, metastatic potential, and resistance to therapy.

Project

Computational approaches quantify tumour evolution, chart fitness landscapes and map genotype to phenotype

This collaborative project between the groups of Christina Curtis (Stanford) and Isidro Cortés-Ciriano (EMBL-EBI) builds on the teams’ collective expertise in computational modeling of tumor evolution and in characterizing structural variation, leverages longitudinal patient cohorts and organoid models of human cancers. In particular, this proposal will focus on the development of novel analytic techniques to measure evolutionary parameters, to chart cellular fitness landscapes and to map genotype to phenotype in human organoids. Additionally, efforts will focus on resolving complex patterns of structural variation using long and short read sequencing data and orthogonal techniques. collectively, these approaches will provide unprecedented and quantitative insights into mechanisms of human tumor progression.

References:

^{https://www.nature.com/articles/s41588-020-0668-4
https://www.nature.com/articles/s41588-020-0628-z
https://www.nature.com/articles/s41588-019-0423-x
https://www.nature.com/articles/s41588-019-0576-7}

Find out more:

Interested in finding out more about working at the interface of metabolism and organelle biology? Get in touch, we would love to hear from you!