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Background Genetic alterations of somatic cells can drive non-malignant clone formation and promote cancer initiation. However, the link between these processes remains unclear and hampers our understanding of tissue homeostasis and cancer development. Results Here, we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and non-cancer somatic evolution in 122 cancer types and 12 non-cancer tissues. Mapping the alterations of these genes in 7953 pan-cancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and non-cancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation. Conclusions Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and non-cancer somatic drivers, their literature support, and properties are accessible in the Network of Cancer Genes and Healthy Drivers resource at http://www.network-cancer-genes.org/ .

Our understanding of cancer has greatly advanced since Nordling [Nordling CO (1953) Br J Cancer 7(1):68–72] and Armitage and Doll [Armitage P, Doll R (1954) Br J Cancer 8(1):1–12] put forth the multistage model of carcinogenesis. However, a number of observations remain poorly understood from the standpoint of this paradigm in its contemporary state. These observations include the similar age-dependent exponential rise in incidence of cancers originating from stem/progenitor pools differing drastically in size, age-dependent cell division profiles, and compartmentalization. This common incidence pattern is characteristic of cancers requiring different numbers of oncogenic mutations, and it scales to very divergent life spans of mammalian species. Also, bigger mammals with larger underlying stem cell pools are not proportionally more prone to cancer, an observation known as Peto’s paradox. Here, we present a number of factors beyond the occurrence of oncogenic mutations that are unaccounted for in the current model of cancer development but should have significant impacts on cancer incidence. Furthermore, we propose a revision of the current understanding for how oncogenic and other functional somatic mutations affect cellular fitness. We present evidence, substantiated by evolutionary theory, demonstrating that fitness is a dynamic environment-dependent property of a phenotype and that oncogenic mutations should have vastly different fitness effects on somatic cells dependent on the tissue microenvironment in an age-dependent manner. Combined, this evidence provides a firm basis for understanding the age-dependent incidence of cancers as driven by age-altered systemic processes regulated above the cell level.

Background Somatic mutations in healthy tissues contribute to aging, neurodegeneration, and cancer initiation, yet they remain largely uncharacterized. Results To gain a better understanding of the genome-wide distribution and functional impact of somatic mutations, we leverage the genomic information contained in the transcriptome to uniformly call somatic mutations from over 7500 tissue samples, representing 36 distinct tissues. This catalog, containing over 280,000 mutations, reveals a wide diversity of tissue-specific mutation profiles associated with gene expression levels and chromatin states. For example, lung samples with low expression of the mismatch-repair gene MLH1 show a mutation signature of deficient mismatch repair. In addition, we find pervasive negative selection acting on missense and nonsense mutations, except for mutations previously observed in cancer samples, which are under positive selection and are highly enriched in many healthy tissues. Conclusions These findings reveal fundamental patterns of tissue-specific somatic evolution and shed light on aging and the earliest stages of tumorigenesis.

Antibodies are created and refined by somatic evolution in B cell populations, which endows the human immune system with the ability to recognize and eliminate diverse pathogens. However, the evolutionary processes that sculpt antibody repertoires remain poorly understood. Here, using an unbiased repertoire-scale approach, we show that the population genetic signatures of evolution are evident in human B cell lineages and reveal how antibodies evolve somatically. We measured the dynamics and genetic diversity of B cell responses in five adults longitudinally before and after influenza vaccination using high-throughput antibody repertoire sequencing. We identified vaccine-responsive B cell lineages that carry signatures of selective sweeps driven by positive selection, and discovered that they often display evidence for selective sweeps favoring multiple subclones. We also found persistent B cell lineages that exhibit stable population dynamics and carry signatures of neutral drift. By exploiting the relationship between B cell fitness and antibody binding affinity, we demonstrate the potential for using phylogenetic approaches to identify antibodies with high binding affinity. This quantitative characterization reveals that antibody repertoires are shaped by an unexpectedly broad spectrum of evolutionary processes and shows how signatures of evolutionary history can be harnessed for antibody discovery and engineering.

The distribution of fitness effects (DFE) defines how new mutations spread through an evolving population. The ratio of non-synonymous to synonymous mutations (dN/dS) has become a popular method to detect selection in somatic cells. However the link, in somatic evolution, between dN/dS values and fitness coefficients is missing. Here we present a quantitative model of somatic evolutionary dynamics that determines the selective coefficients of individual driver mutations from dN/dS estimates. We then measure the DFE for somatic mutant clones in ostensibly normal oesophagus and skin. We reveal a broad distribution of fitness effects, with the largest fitness increases found for TP53 and NOTCH1 mutants (proliferative bias 1–5%). This study provides the theoretical link between dN/dS values and selective coefficients in somatic evolution, and measures the DFE of mutations in human tissues.

Recent studies have revealed that normal human tissues accumulate many somatic mutations. In particular, human skin is riddled with mutations, with multiple subclones of variable sizes. Driver mutations are frequent and tend to have larger subclone sizes, suggesting selection. To begin to understand the histories encoded by these complex somatic mutations, we incorporated genomes into a simple agent-based skin-cell model whose prime directive is homeostasis. In this model, stem-cell survival is random and dependent on proximity to the basement membrane. This simple homeostatic skin model recapitulates the observed log-linear distributions of somatic mutations, where most mutations are found in increasingly smaller subclones that are typically lost with time. Hence, neutral mutations are “passengers” whose fates depend on the random survival of their stem cells, where a rarer larger subclone reflects the survival and spread of mutations acquired earlier in life. The model can also maintain homeostasis and accumulate more frequent and larger driver subclones if these mutations (NOTCH1 and TP53) confer relatively higher persistence in normal skin or during tissue damage (sunlight). Therefore, a relatively simple model of epithelial turnover indicates how observed passenger and driver somatic mutations could accumulate without violating the prime directive of homeostasis in normal human tissues.

The extent to which normal (nonmalignant) cells of the body can evolve through mutation and selection during the lifetime of the organism has been a major unresolved issue in evolutionary and developmental studies. On the one hand, stable multicellular individuality seems to depend on genetic homogeneity and suppression of evolutionary conflicts at the cellular level. On the other hand, the example of clonal selection of lymphocytes indicates that certain forms of somatic mutation and selection are concordant with the organism-level fitness. Recent DNA sequencing and tissue physiology studies suggest that in addition to adaptive immune cells also neurons, epithelial cells, epidermal cells, hematopoietic stem cells and functional cells in solid bodily organs are subject to evolutionary forces during the lifetime of an organism. Here we refer to these recent studies and suggest that the expanding list of somatically evolving cells modifies idealized views of biological individuals as radically different from collectives.

Our capacity to study individual cells has enabled a new level of resolution for understanding complex biological systems such as multicellular organisms or microbial communities. Not surprisingly, several methods have been developed in recent years with a formidable potential to investigate the somatic evolution of single cells in both healthy and pathological tissues. However, single-cell sequencing data can be quite noisy due to different technical biases, so inferences resulting from these new methods need to be carefully contrasted. Here, I introduce CellCoal, a software tool for the coalescent simulation of single-cell sequencing genotypes. CellCoal simulates the history of single-cell samples obtained from somatic cell populations with different demographic histories and produces single-nucleotide variants under a variety of mutation models, sequencing read counts, and genotype likelihoods, considering allelic imbalance, allelic dropout, amplification, and sequencing errors, typical of this type of data. CellCoal is a flexible tool that can be used to understand the implications of different somatic evolutionary processes at the single-cell level, and to benchmark dedicated bioinformatic tools for the analysis of single-cell sequencing data. CellCoal is available at https://github.com/dapogon/cellcoal.

Aging represents the major risk factor for the development of cancer and many other diseases. Recent findings show that normal tissues become riddled with expanded clones that are frequently driven by cancer‐associated mutations in an aging‐dependent fashion. Additional studies show how aged tissue microenvironments promote the initiation and progression of malignancies, while young healthy tissues actively suppress the outgrowth of malignant clones. Here, we discuss conserved mechanisms that eliminate poorly functioning or potentially malignant cells from our tissues to maintain organismal health and fitness. Natural selection acts to preserve tissue function and prevent disease to maximize reproductive success but these mechanisms wane as reproduction becomes less likely. The ensuing age‐dependent tissue decline can impact the shape and direction of clonal somatic evolution, with lifestyle and exposures influencing its pace and intensity. We also consider how aging‐ and exposure‐dependent clonal expansions of “oncogenic” mutations might both increase cancer risk late in life and contribute to tissue decline and non‐malignant disease. Still, we can marvel at the ability of our bodies to avoid cancers and other diseases despite the accumulation of billions of cells with cancer‐associated mutations. Pathogenic clonal expansions can promote and be promoted by inflammation, and contribute to multiple diseases of aging. While these clones can sometimes directly contribute to malignant disease, as clearly demonstrated for leukemias with clonal hematopoiesis mutations, evidence also reveals how clonal expansions can contribute indirectly to cancers and non‐malignant diseases such as through the promotion of inflammation.