|From The evolutionary history of lethal metastatic prostate cancer, Gundem et al, Nature, 520, 353–357 (2015). Clustering of mutations reveals widespread polyclonal seeding. a, For pairs of metastases, cancer cell fractions (CCF), that is, the fraction of cancer cells within a sample containing a mutation, are plotted for all mutations. Red density areas off the axes and with CCF less than 1 reveal the existence of mutation clusters present at subclonal levels in more than one metastatic site. Mutation clusters for each sample are indicated with circles coloured according to the subclone they correspond to. The centre of each circle is positioned at the CCF values of the subclone in the two samples. The clusters at (1,1) correspond to the mutations present in all the cells in both sites, while those on axes refer to sample-specific subclones. For example, light blue and dark green clusters absent from sample A are positioned on the y axis when H is compared to A but are moved to (0.60,0.08) and (0.60,0.88) when H is compared to K. b, Each subclone detected is represented as a set of colour-coded ovals across all organ sites. Each row represents a sample. The area of each oval is proportional to the CCF of the corresponding subclone. Subclonal mutation clusters are shown with solid borders. Oval plots are divided into three types: trunk (CCF=1 in all samples), leaf (specific to a single sample) and branch (present in >1 sample and either not found in all samples or subclonal in at least one). c, Phylogenetic tree showing the relationships between subclones in A22. Branch lengths are proportional to the number of mutations in each cluster. Branches are annotated with samples in which they are present and with oncogenic/putative oncogenic alterations assigned to that subclone. d, Subclone colour key.|
|From The evolutionary history of lethal metastatic prostate cancer, Gundem et al, Nature, 520, 353–357 (2015). Metastasis-to-metastasis seeding occurs either by a linear or by a branching pattern of spread. a-c, Body maps show the seeding of all tumour sites from A22 (a), A21 (b) and A24 (c). Seeding events are represented with arrows, with double-heads when seeding could be in either direction. When the sequence of events may be ordered from the acquisition of mutations, arrows are numbered chronologically. Subclones on branching clonal lineages are labelled with the same number but with different letters, for example, 4a & 4b.|
The focus of my research is cancer evolution and heterogeneity. Cancers are made up of a heterogeneous mix of cells, each bearing a different set of mutations in its DNA. We aim to characterise groups of cells, or ‘subclones’, according to their mutational profiles and to study the interaction between subclones.
Tumours are difficult to treat because they change over time, gaining mutations that enable them to metastasise to distant organs or that result in resistance to treatment. By comparing multiple samples, we can identify those mutations that cause relapse and progression. Using genetic markers, we can also track the spread of disease, giving us insights into the mechanisms and processes involved in cancer growth and metastasis.
Cancer is a complex disease and the analysis of large numbers of tumours is key to understanding the factors that determine their virulence. The International Cancer Genome Consortium (ICGC) has collected and whole-genome sequenced several thousand cancer samples. I co-lead the Pan-Cancer Working Group on Evolution and Heterogeneity, an international collaboration that is using the DNA sequences of 2800 of these cancer samples to study evolution and heterogeneity across more than 30 different cancer types, including prostate, breast, lung, oesophageal and ovarian cancers.
Detailed Molecular and Immune Marker Profiling of Archival Prostate Cancer Samples Reveals an Inverse Association between TMPRSS2:ERG Fusion Status and Immune Cell Infiltration
Rao SR. et al, (2020), The Journal of Molecular Diagnostics
Genomic copy number predicts oesophageal cancer years before transformation
Killcoyne S. et al, (2020)
The evolutionary history of 2,658 cancers.
Gerstung M. et al, (2020), Nature, 578, 122 - 128
Inferring structural variant cancer cell fraction.
Cmero M. et al, (2020), Nature communications, 11
Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig.
Rubanova Y. et al, (2020), Nature communications, 11