The Life History of 21 Breast Cancers

Abstract

SUMMARY Cancer evolves dynamically as clonal expansions supersede one another driven by shifting selective pressures, mutational processes, and disrupted cancer genes. These processes mark the genome, such that a cancer’s life history is encrypted in the somatic mutations present. We developed algorithms to decipher this narrative and applied them to 21 breast cancers. Mutational processes evolve across a cancer’s lifespan, with many emerging late but contributing extensive genetic variation. Subclonal diversification is prominent, and most mutations are found in just a fraction of tumor cells. Every tumor has a dominant subclonal lineage, representing more than 50% of tumor cells. Minimal expansion of these subclones occurs until many hundreds to thousands of mutations have accumulated, implying the existence of long-lived, quiescent cell lineages capable of substantial proliferation upon acquisition of enabling genomic changes. Expansion of the dominant subclone to an appreciable mass may therefore represent the final rate-limiting step in a breast cancer’s development, triggering diagnosis.

Classical pathology and mutational load of breast cancer – integration of two worlds

“…Hypothesizing that the abundance of mutated genes is a measure of genetic heterogeneity of the tumour 34, we here provide evidence that particularly pathological grading of breast cancer is in fact a microscopic read‐out of tumour heterogeneity, which indicates different biological and clinical behaviour of the tumour. Several studies either sequencing tumour bulks or single tumour cells have demonstrated that breast cancer subtypes exhibit considerable spatial and temporal heterogeneity on the genetic level within the primary, during metastatic progression and in patient‐derived xenografts 24, 25, 26, 27, 28, 29, 30, 31. This genomic diversity within breast cancers is a result of but also facilitates cellular evolution 34 allowing the tumour to dynamically adapt to external or internal stimuli as originally conceptualized by Nowell in 1976 54.…”

“…Interestingly, recent genomic profiling studies 24, 25, 26, 27, 28, 29, 30, 31 suggested a substantial degree of intra‐ and inter‐tumour heterogeneity fuelling selection processes during evolution of breast cancer 32, 33, which may complicate prognostication as well as prediction and impede cancer precision medicine approaches 34. In this context, it is worth recalling that just as the molecular phenotype, the morphological phenotype of the tumour, including tumour grade and tumour size is essentially a result of accumulated genetic aberrations over time, thereby reflecting tumour evolution at the phenotypic level.…”

Navigating cancer network attractors for tumor-specific therapy

“…5). Relatively new techniques, such as single-cell and highdepth sequencing 70,71 , imaging 72 and cytometry time-of-flight 73 , could prove especially valuable for monitoring the number, properties and behavior of different tumor subclones (Fig. 2d).…”

PyClone: statistical inference of clonal population structure in cancer

“…Despite progress in measuring allele prevalence with deep sequencing [4][5][6][7][8] , statistical approaches to cluster deep digital sequencing of mutations into biologically relevant groupings remain under-developed, with poorly understood analytical assumptions. The allelic prevalence of a mutation is a compound measure of several factors: the proportion of contaminating normal cells, the proportion of tumour cells harbouring the mutation and the number of allelic copies of the mutation in each cell, plus uncharacterized sources of technical noise.…”