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We analyse sequencing data from multiple bulk tumour samples to investigate intra-tumour heterogeneity.
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Dr Simone Zaccaria
Group Lead
Cancer Research UK Career Development Fellow
Email: s.zaccaria@ucl.ac.uk
Cancer arises from an evolutionary process where different kinds of somatic mutations are accumulated in the genome of different subpopulations of cells. Such process yields heterogeneous tumours, where distinct subpopulation of cells, or clones, have different complements of somatic mutations. As different tumour clones may have different behaviours, for example different responses to treatment, the identification of somatic mutations from DNA sequencing data have a critical impact on cancer prognosis and diagnosis.
Most cancer sequencing studies perform DNA sequencing of one or more bulk tumour samples, obtained from multiple regions of a primary tumor, matched primary and metastases, or longitudinal samples. However, the inference of somatic mutations from DNA sequencing data of bulk tumour samples is challenging because each samples is a mixture of thousands to millions of different cells from different tumour clones. In such mixtures the signal from the observed sequencing reads is a superposition of the signals from normal cells and distinct tumor clones, which share the same clonal mutations but are distinguished by different subclonal mutations.
Our lab focuses on the design and development of computational methods to deconvolve, or separate, this mixed signal into the individual components arising from each of these clones. Moreover, we design algorithms to investigate spatial heterogeneity from multiple bulk tumour samples and reconstruct tumour phylogenetic trees that describe the history of tumour evolution.
Relevant publications
Zaccaria S, Raphael BJ. Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data. Nature Communications 11, 4301 (2020)
Zaccaria S, El-Kebir M, Klau GW & Raphael BJ. Phylogenetic copy-number factorization of multiple tumor samples. Journal of Computational Biology, 25(7): 689-708 (2018). Accepted and presented at RECOMB 2017.
Tumour heterogeneity in prostate
In connection with the DoMore! Project, an app was created to demonstrate 3D reconstruction of prostate cancer. This app illustrates the extent to which a single tumour can yield widely varied results, and shows why a high number of samples is needed to determine the true nature of a tumour.
Further useful videos
- Challenges posed by intratumor heterogeneity - IMPAKT 2012 Breast Cancer Conference from Professor Charles Swanton
- Introduction to Cancer Bioinformatics I: Inferring Genomic Variation from Tumor Sequencing Data - from the Simons Institute with Professor Ben Raphael and Niko Beerenwinkel
- Lung Cancer Evolution and Immune Escape - from LabRoots with Dr Nicholas McGranahan
- Computational Analysis of Somatic Mutations in Cancer - from Professor Ben Raphael at the Computational Genomics Summer Institute 2016
- Inferring Intra-Tumor Heterogeneity from Whole-Genome/Exome Sequencing Data - from Layla Oesper
- Quantifying the dynamics of tumor progression - from Dr Christina Curtis at Stanford Big Data 2015
- Tumor Phylogeny Inference Using Tree-Constrained Important Sampling - from Gryte Satas at ISMB/ECCB 2017
- Reconstruction of clonal trees and tumor composition from multi-sample sequencing data - from Mohammed El-Kebir at ISMB/ECCB 2015