Despite extraordinary efforts to profile cancer genomes, interpreting the vast amount of
genomic data in the light of cancer evolution remains challenging. Here we demonstrate that …

Subclonal architectures are prevalent across cancer types. However, the temporal evolutionary
dynamics that produce tumor subclones remain unknown. Here we measure clone …

Progress in defining genomic fitness landscapes in cancer, especially those defined by copy
number alterations (CNAs), has been impeded by lack of time-series single-cell sampling of …

Cancers accumulate mutations that lead to neoantigens, novel peptides that elicit an
immune response, and consequently undergo evolutionary selection. Here we establish how …

High-grade serous ovarian cancer (HGSOC) is an archetypal cancer of genomic instability 1
, 2 , 3 – 4 patterned by distinct mutational processes 5 , 6 , tumour heterogeneity 7 , 8 – 9 …

How cell-to-cell copy number alterations that underpin genomic instability 1 in human
cancers drive genomic and phenotypic variation, and consequently the evolution of cancer 2 , …

Most cancer genomic data are generated from bulk samples composed of mixtures of
cancer subpopulations, as well as normal cells. Subclonal reconstruction methods based on …

Objective IBD confers an increased lifetime risk of developing colorectal cancer (CRC), and
colitis-associated CRC (CA-CRC) is molecularly distinct from sporadic CRC (S-CRC). Here …

Cancers originate from somatic cells in the human body that have accumulated genetic
alterations. These mutations modify the phenotype of the cells, allowing them to escape the …

Quantification of the effect of spatial tumour sampling on the patterns of mutations detected
in next-generation sequencing data is largely lacking. Here we use a spatial stochastic …