Single-cell phylogenies reveal changes in the evolutionary rate within cancer and healthy tissues

Cell lineages accumulate somatic mutations during organismal development, potentially leading to pathological states. The rate of somatic evolution within a cell population can vary due to multiple factors, including selection, a change in the mutation rate, or differences in the microenvironment. Here, we developed a statistical test called the Poisson Tree (PT) test to detect varying evolutionary rates among cell lineages, leveraging the phylogenetic signal of single-cell DNA sequencing (scDNA-seq) data. We applied the PT test to 24 healthy and cancer samples, rejecting a constant evolutionary rate in 11 out of 15 cancer and five out of nine healthy scDNA-seq datasets. In six cancer datasets, we identified subclonal mutations in known driver genes that could explain the rate accelerations of particular cancer lineages. Our findings demonstrate the efficacy of scDNA-seq for studying somatic evolution and suggest that cell lineages often evolve at different rates within cancer and healthy tissues.

A Clonal Evolution Simulator for Planning Somatic Evolution Studies

Somatic evolution plays a key role in development, cell differentiation, and normal aging, but also in diseases such as cancer. Understanding mechanisms of somatic mutability and how they can vary between cell lineages will likely play a crucial role in biological discovery and medical applications. This need has led to a proliferation of new technologies for profiling single-cell variation, each with distinctive capabilities and limitations that can be leveraged alone or in combination with other technologies. The enormous space of options for assaying somatic variation, however, presents unsolved informatics problems with regard to selecting optimal combinations of technologies for designing appropriate studies for any particular scientific questions. Versatile simulation tools are needed to explore and optimize potential study designs if researchers are to deploy multiomic technologies most effectively. In this study, we present a simulator allowing for the generation of synthetic data from a wide range of clonal lineages, variant classes, and sequencing technology choices, intended to provide a platform for effective study design in somatic lineage analysis. Users can input various properties of the somatic evolutionary system, mutation classes, and biotechnology options, and then generate samples of synthetic sequence reads and their corresponding ground truth parameters for a given study design. We demonstrate the utility of the simulator for testing and optimizing study designs for various experimental queries.

Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes

Genome instability and aberrant alterations of transcriptional programs both play important roles in cancer. Single-cell RNA sequencing (scRNA-seq) has the potential to investigate both genetic and nongenetic sources of tumor heterogeneity in a single assay. Here we present a computational method, Numbat, that integrates haplotype information obtained from population-based phasing with allele and expression signals to enhance detection of copy number variations from scRNA-seq. Numbat exploits the evolutionary relationships between subclones to iteratively infer single-cell copy number profiles and tumor clonal phylogeny. Analysis of 22 tumor samples, including multiple myeloma, gastric, breast and thyroid cancers, shows that Numbat can reconstruct the tumor copy number profile and precisely identify malignant cells in the tumor microenvironment. We identify genetic subpopulations with transcriptional signatures relevant to tumor progression and therapy resistance. Numbat requires neither sample-matched DNA data nor a priori genotyping, and is applicable to a wide range of experimental settings and cancer types.

Uterine lavage identifies cancer mutations and increased TP53 somatic mutation burden in individuals with ovarian cancer

Current screening methods for ovarian cancer (OC) have failed to demonstrate a significant reduction in mortality. Uterine lavage combined with TP53 ultra-deep sequencing for the detection of disseminated OC cells has emerged as a promising tool, but this approach has not been tested for early-stage disease or non-serous histologies. In addition, lavages carry multiple background mutations, the significance of which is poorly understood. Uterine lavage was collected preoperatively in 34 patients undergoing surgery for suspected ovarian malignancy including 14 patients with benign disease and 20 patients with OC (6 non-serous and 14 high grade serous-like (serous)). Ultra-deep duplex sequencing (~3000x) with a panel of common OC genes identified the tumor mutation in 33% of non-serous (all early stage) and in 79% of serous cancers (including four early stage). In addition, all lavages carried multiple somatic mutations (average of 25 mutations per lavage), more than half of which corresponded to common cancer driver mutations. Driver mutations in KRAS, PIK3CA, PTEN, PPP2R1A and ARID1A presented as larger clones than non-driver mutations and with similar frequency in lavages from patients with and without OC, indicating prevalent somatic evolution in all patients. Driver TP53 mutations, however, presented as significantly larger clones and with higher frequency in lavages from individuals with OC, suggesting that TP53-specific clonal expansions are linked to ovarian cancer development. Our results demonstrate that lavages capture cancer cells, even from early-stage cancers, as well as other clonal expansions and support further exploration of TP53 mutation burden as a potential OC risk factor.

Homeostasis limits keratinocyte evolution

Human skin is riddled with mutations creating subclones of variable sizes. Some of these mutations are driver mutations, implicated in cancer development and progression, that appear to be under positive selection due to their relative sizes. We show how these driver and nondriver “passenger” mutations encode their history of division and loss within the tissue using a simple model combined with realistic mutation tracking. Using a three-dimensional in silico homeostatic epidermis model, we reveal that many mutations likely lack functional heterogeneity and are, instead, simply those that arise earlier in life within the basal layer. We use our model to reveal how functional differences conveyed by driver mutations could lead to a persistence phenotype while maintaining homeostasis.

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 sculpting of somatic mutational landscapes by evolutionary forces and their impacts on aging-related disease

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.

Gattaca: Base-Pair Resolution Mutation Tracking for Somatic Evolution Studies using Agent-based Models

Research over the past two decades has made substantial inroads into our understanding of somatic mutations. Recently, these studies have focused on understanding their presence in homeostatic tissue. In parallel, agent-based mechanistic models have emerged as an important tool for understanding somatic mutation in tissue; yet no common methodology currently exists to provide base-pair resolution data for these models. Here, we present Gattaca as the first method for introducing and tracking somatic mutations at the base-pair resolution within agent-based models that typically lack nuclei. With nuclei that incorporate human reference genomes, mutational context, and sequence coverage/error information, Gattaca is able to realistically evolve sequence data, facilitating comparisons between in silico cell tissue modeling with experimental human somatic mutation data. This user-friendly method, incorporated into each in silico cell, allows us to fully capture somatic mutation spectra and evolution.

Cells with Cancer-associated Mutations Overtake Our Tissues as We Age

BACKGROUND

To shed light on the earliest events in oncogenesis, there is growing interest in understanding the mutational landscapes of normal tissues across ages. In the last decade, next-generation sequencing of human tissues has revealed a surprising abundance of cells with what would be considered oncogenic mutations.

AIMS

We performed meta-analysis on previously published sequencing data on normal tissues to categorize mutations based on their presence in cancer and showcase the quantity of cells with cancer-associated mutations in cancer-free individuals.

METHODS AND RESULTS

We analyzed sequencing data from these studies of normal tissues to determine the prevalence of cells with mutations in three different categories across multiple age groups: 1) mutations in genes designated as drivers, 2) mutations that are in the Cancer Gene Census (CGC), and 3) mutations in the CGC that are considered pathogenic. As we age, the percentage of cells in all three levels increase significantly, reaching over 50% of cells having oncogenic mutations for multiple tissues in the older age groups. The clear enrichment for these mutations, particularly at older ages, likely indicates strong selection for the resulting phenotypes. Combined with an estimation of the number of cells in tissues, we calculate that most older, cancer-free individuals possess at least a 100 billion cells that harbor at least one oncogenic mutation, presumably emanating from a fitness advantage conferred by these mutations that promotes clonal expansion.

CONCLUSIONS

These studies of normal tissues have highlighted the specific drivers of clonal expansion and how frequently they appear in us. Their high prevalence throughout cancer-free individuals necessitates reconsideration of the oncogenicity of these mutations, which could shape methods of detection, prevention and treatment of cancer, as well as of the potential impact of these mutations on tissue function and our health.

PolyG-DS: An ultrasensitive polyguanine tract-profiling method to detect clonal expansions and trace cell lineage

Polyguanine tracts (PolyGs) are short guanine homopolymer repeats that are prone to accumulating mutations when cells divide. This feature makes them especially suitable for cell lineage tracing, which has been exploited to detect and characterize precancerous and cancerous somatic evolution. PolyG genotyping, however, is challenging because of the inherent biochemical difficulties in amplifying and sequencing repetitive regions. To overcome this limitation, we developed PolyG-DS, a next-generation sequencing (NGS) method that combines the error-correction capabilities of duplex sequencing (DS) with enrichment of PolyG loci using CRISPR-Cas9-targeted genomic fragmentation. PolyG-DS markedly reduces technical artifacts by comparing the sequences derived from the complementary strands of each original DNA molecule. We demonstrate that PolyG-DS genotyping is accurate, reproducible, and highly sensitive, enabling the detection of low-frequency alleles (<0.01) in spike-in samples using a panel of only 19 PolyG markers. PolyG-DS replicated prior results based on PolyG fragment length analysis by capillary electrophoresis, and exhibited higher sensitivity for identifying clonal expansions in the nondysplastic colon of patients with ulcerative colitis. We illustrate the utility of this method for resolving the phylogenetic relationship among precancerous lesions in ulcerative colitis and for tracing the metastatic dissemination of ovarian cancer. PolyG-DS enables the study of tumor evolution without prior knowledge of tumor driver mutations and provides a tool to perform cost-effective and easily scalable ultra-accurate NGS-based PolyG genotyping for multiple applications in biology, genetics, and cancer research.

CellCoal: Coalescent Simulation of Single-Cell Sequencing Samples

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.

Increased stem cell proliferation in atherosclerosis accelerates clonal hematopoiesis

Clonal hematopoiesis, a condition in which individual hematopoietic stem cell clones generate a disproportionate fraction of blood leukocytes, correlates with higher risk for cardiovascular disease. The mechanisms behind this association are incompletely understood. Here, we show that hematopoietic stem cell division rates are increased in mice and humans with atherosclerosis. Mathematical analysis demonstrates that increased stem cell proliferation expedites somatic evolution and expansion of clones with driver mutations. The experimentally determined division rate elevation in atherosclerosis patients is sufficient to produce a 3.5-fold increased risk of clonal hematopoiesis by age 70. We confirm the accuracy of our theoretical framework in mouse models of atherosclerosis and sleep fragmentation by showing that expansion of competitively transplanted Tet2^-/- cells is accelerated under conditions of chronically elevated hematopoietic activity. Hence, increased hematopoietic stem cell proliferation is an important factor contributing to the association between cardiovascular disease and clonal hematopoiesis.

The special issue on cancer and evolution: Lessons learned

This special issue of evolutionary applications focused on the evolution of cancer has provided a wealth of different viewpoints and results from leaders in the field. Together, these papers emphasize the importance of a broad perspective in order to understand why we and other animals get cancer, how it evolves within an individual, and what we can do about it. We can no longer take reductionist approaches that consider only the cancer cells and their genes. Instead, we need to understand how millions of years of evolution have guided strategies that shape cancer risk, why cancer risk varies across different animals, how cancer risk can vary in a population and be influenced by ecology (and influence this ecology), and of course how cancers evolve within us and the evolutionarily informed strategies to counter their impact. My goal here will be to "bring it all home," providing a refresher of lessons learned with added kibitzing.

Germline ancestry influences the evolutionary disease course in lung adenocarcinomas

Precision medicine relies on targeting specific somatic alterations present in a patient's tumor. However, the extent to which germline ancestry may influence the somatic burden of disease has received little attention. We estimated the genetic ancestry of non-small-cell lung cancer (NSCLC) patients and performed an in-depth analysis of the influence of genetic ancestry on the evolutionary disease course. Compared with European Americans (EA), African Americans (AA) with lung adenocarcinoma (LUAD) were found to be significantly younger and smoke significantly less. However, LUADs from AAs exhibited a significantly higher somatic mutation burden, with a more pronounced tobacco carcinogen footprint and increased frequencies of alterations affecting cancer genes. Conversely, no significant differences were observed between lung squamous cell carcinomas (LUSC) from EAs and AAs. Our results suggest germline ancestry influences the somatic evolution of LUAD but not LUSC.

The three dimensions of somatic evolution: Integrating the role of genetic damage, life-history traits, and aging in carcinogenesis

Tumors result from genetic and epigenetic alterations that change cellular survival and differentiation probabilities, promoting clonal dominance. Subsequent genetic and selection processes in tumors allow cells to lose their tissue fidelity and migrate to other parts of the body, turning tumors into cancer. However, the relationship between genetic damage and cancer is not linear, showing remarkable and sometimes seemingly counterintuitive patterns for different tissues and across animal taxa. In the present paper, we attempt to integrate our understanding of somatic evolution and cancer as a product of three major orthogonal processes: occurrence of somatic mutations, evolution of species-specific life-history traits, and physiological aging. Patterns of cancer risk have been shaped by selective pressures experienced by animal populations over millions of years, influencing and influenced by selection acting on traits ranging from mutation rate to reproductive strategies to longevity. We discuss how evolution of species shapes their cancer profiles alongside and in connection with other evolving life-history traits and how this process explains the patterns of cancer incidence we observe in humans and other animals.

Integrating genetic and nongenetic drivers of somatic evolution during carcinogenesis: The biplane model

The multistep transition from a normal to a malignant cellular phenotype is often termed "somatic evolution" caused by accumulating random mutations. Here, we propose an alternative model in which the initial genetic state of a cancer cell is the result of mutations that occurred throughout the lifetime of the host. However, these mutations are not carcinogenic because normal cells in multicellular organism cannot ordinarily evolve. That is, proliferation and death of normal cells are controlled by local tissue constraints typically governed by nongenomic information dynamics in the cell membrane. As a result, the cells of a multicellular organism have a fitness that is identical to the host, which is then the unit of natural selection. Somatic evolution of a cell can occur only when its fate becomes independent of host constraints. Now, survival, proliferation, and death of individual cells are dependent on Darwinian dynamics. This cellular transition from host-defined fitness to self-defined fitness may, consistent with the conventional view of carcinogenesis, result from mutations that render the cell insensitive to host controls. However, an identical state will result when surrounding tissue cannot exert control because of injury, inflammation, aging, or infection. Here, all surviving cells within the site of tissue damage default to self-defined fitness functions allowing them to evolve so that the mutations accumulated over the lifetime of the host now serve as the genetic heritage of an evolutionary unit of selection. Furthermore, tissue injury generates a new ecology cytokines and growth factors that might promote proliferation in cells with prior receptor mutations. This model integrates genetic and nongenetic dynamics into cancer development and is consistent with both clinical observations and prior experiments that divided carcinogenesis to initiation, promotion, and progression steps.

Measuring the distribution of fitness effects in somatic evolution by combining clonal dynamics with dN/dS ratios

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.

Choice of control tissue impacts designation of germline variants in a cohort of papillary thyroid carcinoma patients

BACKGROUND

The term germline is commonly used to refer to any non-tumor control sample analyzed in tumor-normal paired sequencing experiments. Blood is the most commonly utilized control, and variants found in both tumor and blood are considered germline. However, somatic variants accumulate within an organism from embryogenesis throughout life. The resultant mosaicism is extensive and calls into question the assumption that blood, or any somatic tissue, represents the germline. Misclassification of germline and somatic variants has critical consequences for individual patient care and enormous impact on our health care system, given potential screening, counseling, and treatment implications of misidentifying germline variants.

PATIENTS AND METHODS

Whole-exome sequencing was performed on six separate specimens from each of two patients with papillary thyroid carcinoma, and three specimens each from eight additional patients forming a validation cohort. Tumor variants were compared with each individual non-tumor control and with composite control sets generated as approximations of true germline. For the index patient, parental blood was also sequenced to assess whether patient-only samples could approximate a trio-derived germline.

RESULTS

Using different non-tumor control tissues results in altered germline-somatic designation of tumor variants. In patient 1, 82% of variants are labeled germline using blood control, compared with 75.8%, 61.5%, and 49.6% using lymph node, thyroid, and thymus, respectively. In patient 2, the thyroid control resulted in the greatest percentage of germline calls (70.0%), followed by thymus (56.0%), lymph node (50.1%), and blood (44.1%). Composite control sets built from multiple samples can approximate the germline, even in the absence of parental DNA.

CONCLUSIONS

Misclassification of germline-somatic origin has potential consequences for patient care, informing screening, trial eligibility, prophylactic interventions, and family planning. This study demonstrates the need for caution in interpreting germline-somatic designation if these data are to inform clinical decisions and suggests that improved design of controls can overcome current limitations.

A compartment size-dependent selective threshold limits mutation accumulation in hierarchical tissues

Renewed tissues of multicellular organism accumulate mutations that lead to aging and cancer. To mitigate these effects, self-renewing tissues produce cells along differentiation hierarchies, which have been shown to suppress somatic evolution both by limiting the number of cell divisions, and thus reducing mutational load, and by differentiation “washing out” mutations. Our analytical results reveal the existence of a third mechanism: a compartment size-dependent threshold in proliferative advantage, below which mutations cannot persist, but are rapidly expelled from the tissue by differentiation. In sufficiently small compartments, the resulting selective barrier can greatly slow down somatic evolution and reduce the risk of cancer by preventing the accumulation of mutations even if even they confer substantial proliferative advantage.

Cancer is a genetic disease fueled by somatic evolution. Hierarchical tissue organization can slow somatic evolution by two qualitatively different mechanisms: by cell differentiation along the hierarchy “washing out” harmful mutations and by limiting the number of cell divisions required to maintain a tissue. Here we explore the effects of compartment size on somatic evolution in hierarchical tissues by considering cell number regulation that acts on cell division rates such that the number of cells in the tissue has the tendency to return to its desired homeostatic value. Introducing mutants with a proliferative advantage, we demonstrate the existence of a third fundamental mechanism by which hierarchically organized tissues are able to slow down somatic evolution. We show that tissue size regulation leads to the emergence of a threshold proliferative advantage, below which mutants cannot persist. We find that the most significant determinant of the threshold selective advantage is compartment size, with the threshold being higher the smaller the compartment. Our results demonstrate that, in sufficiently small compartments, even mutations that confer substantial proliferative advantage cannot persist, but are expelled from the tissue by differentiation along the hierarchy. The resulting selective barrier can significantly slow down somatic evolution and reduce the risk of cancer by limiting the accumulation of mutations that increase the proliferation of cells.

Somatic mutations and cell identity linked by Genotyping of Transcriptomes

Defining the transcriptomic identity of malignant cells is challenging in the absence of surface markers that distinguish cancer clones from one another, or from admixed non-neoplastic cells. To address this challenge, here we developed Genotyping of Transcriptomes (GoT), a method to integrate genotyping with high-throughput droplet-based single-cell RNA sequencing. We apply GoT to profile 38,290 CD34+ cells from patients with CALR-mutated myeloproliferative neoplasms to study how somatic mutations corrupt the complex process of human haematopoiesis. High-resolution mapping of malignant versus normal haematopoietic progenitors revealed an increasing fitness advantage with myeloid differentiation of cells with mutated CALR. We identified the unfolded protein response as a predominant outcome of CALR mutations, with a considerable dependency on cell identity, as well as upregulation of the NF-κB pathway specifically in uncommitted stem cells. We further extended the GoT toolkit to genotype multiple targets and loci that are distant from transcript ends. Together, these findings reveal that the transcriptional output of somatic mutations in myeloproliferative neoplasms is dependent on the native cell identity.

An unbiased in vitro screen for activating epidermal growth factor receptor mutations

Cancer tissues harbor thousands of mutations, and a given oncogene may be mutated at hundreds of sites, yet only a few of these mutations have been functionally tested. Here, we describe an unbiased platform for the functional characterization of thousands of variants of a single receptor tyrosine kinase (RTK) gene in a single assay. Our in vitro screen for activating mutations (iSCREAM) platform enabled rapid analysis of mutations conferring gain-of-function RTK activity promoting clonal growth. The screening strategy included a somatic model of cancer evolution and utilized a library of 7,216 randomly mutated epidermal growth factor receptor (EGFR) single-nucleotide variants that were tested in murine lymphoid Ba/F3 cells. These cells depend on exogenous interleukin-3 (IL-3) for growth, but this dependence can be compensated by ectopic EGFR overexpression, enabling selection for gain-of-function EGFR mutants. Analysis of the enriched mutants revealed EGFR A702V, a novel activating variant that structurally stabilized the EGFR kinase dimer interface and conferred sensitivity to kinase inhibition by afatinib. As proof of concept for our approach, we recapitulated clinical observations and identified the EGFR L858R as the major enriched EGFR variant. Altogether, iSCREAM enabled robust enrichment of 21 variants from a total of 7,216 EGFR mutations. These findings indicate the power of this screening platform for unbiased identification of activating RTK variants that are enriched under selection pressure in a model of cancer heterogeneity and evolution.

Epigenetic evolution and lineage histories of chronic lymphocytic leukaemia

Genetic and epigenetic intra-tumoral heterogeneity cooperate to shape the evolutionary course of cancer¹. Chronic lymphocytic leukaemia (CLL) is a highly informative model for cancer evolution as it undergoes substantial genetic diversification and evolution after therapy^2,3. The CLL epigenome is also an important disease-defining feature^4,5, and growing populations of cells in CLL diversify by stochastic changes in DNA methylation known as epimutations⁶. However, previous studies using bulk sequencing methods to analyse the patterns of DNA methylation were unable to determine whether epimutations affect CLL populations homogeneously. Here, to measure the epimutation rate at single-cell resolution, we applied multiplexed single-cell reduced-representation bisulfite sequencing to B cells from healthy donors and patients with CLL. We observed that the common clonal origin of CLL results in a consistently increased epimutation rate, with low variability in the cell-to-cell epimutation rate. By contrast, variable epimutation rates across healthy B cells reflect diverse evolutionary ages across the trajectory of B cell differentiation, consistent with epimutations serving as a molecular clock. Heritable epimutation information allowed us to reconstruct lineages at high-resolution with single-cell data, and to apply this directly to patient samples. The CLL lineage tree shape revealed earlier branching and longer branch lengths than in normal B cells, reflecting rapid drift after the initial malignant transformation and a greater proliferative history. Integration of single-cell bisulfite sequencing analysis with single-cell transcriptomes and genotyping confirmed that genetic subclones mapped to distinct clades, as inferred solely on the basis of epimutation information. Finally, to examine potential lineage biases during therapy, we profiled serial samples during ibrutinib-associated lymphocytosis, and identified clades of cells that were preferentially expelled from the lymph node after treatment, marked by distinct transcriptional profiles. The single-cell integration of genetic, epigenetic and transcriptional information thus charts the lineage history of CLL and its evolution with therapy.

Selective Pressures on Human Cancer Genes along the Evolution of Mammals

Cancer is a disease driven by both somatic mutations that increase survival and proliferation of cell lineages and the evolution of genes associated with cancer risk in populations. Several genes associated with cancer in humans, hereafter cancer genes, show evidence of germline positive selection among species. Taking advantage of a large collection of mammalian genomes, we systematically looked for signatures of germline positive selection in 430 cancer genes available in COSMIC. We identified 40 cancer genes with a robust signal of positive selection in mammals. We found evidence for fewer selective constraints—higher number of non-synonymous substitutions per non-synonymous site to the number of synonymous substitutions per synonymous site (dN/dS)—and higher incidence of positive selection—more positively selected sites—in cancer genes bearing germline and recessive mutations that predispose to cancer. This finding suggests a potential association between relaxed selection, positive selection, and risk of hereditary cancer. On the other hand, we did not find significant differences in terms of tissue or gene type. Human cancer genes under germline positive selection in mammals are significantly enriched in the processes of DNA repair, with high presence of Fanconi anaemia/Breast Cancer A (FA/BRCA) pathway components and T cell proliferation genes. We also show that the inferred positively selected sites in the two genes with the strongest signal of positive selection, i.e., BRCA2 and PTPRC, are in regions of functional relevance, which could be relevant to cancer susceptibility.

A Population Phylogenetic View of Mitochondrial Heteroplasmy

The mitochondrion has recently emerged as an active player in myriad cellular processes. Additionally, it was recently shown that >200 diseases are known to be linked to variants in mitochondrial DNA or in nuclear genes interacting with mitochondria. This has reinvigorated interest in its biology and population genetics. Mitochondrial heteroplasmy, or genotypic variation of mitochondria within an individual, is now understood to be common in humans and important in human health. However, it is still not possible to make quantitative predictions about the inheritance of heteroplasmy and its proliferation within the body, partly due to the lack of an appropriate model. Here, we present a population-genetic framework for modeling mitochondrial heteroplasmy as a process that occurs on an ontogenetic phylogeny, with genetic drift and mutation changing heteroplasmy frequencies during the various developmental processes represented in the phylogeny. Using this framework, we develop a Bayesian inference method for inferring rates of mitochondrial genetic drift and mutation at different stages of human life. Applying the method to previously published heteroplasmy frequency data, we demonstrate a severe effective germline bottleneck comprised of the cumulative genetic drift occurring between the divergence of germline and somatic cells in the mother, and the separation of germ layers in the offspring. Additionally, we find that the two somatic tissues we analyze here undergo tissue-specific bottlenecks during embryogenesis, less severe than the effective germline bottleneck, and that these somatic tissues experience little additional genetic drift during adulthood. We conclude with a discussion of possible extensions of the ontogenetic phylogeny framework and its possible applications to other ontogenetic processes in addition to mitochondrial heteroplasmy.

Multiregional Tumor Trees Are Not Phylogenies

Tumor samples most often comprise a mixture of different cell lineages. Multiregional trees built from bulk mutational profiles do not consider this heterogeneity and can potentially lead to erroneous evolutionary inferences, including biased timing of somatic mutations, spurious parallel mutation events, and/or incorrect chronological ordering of metastatic events.

Mutations in Cancer Cause Gain of Cysteine, Histidine, and Tryptophan at the Expense of a Net Loss of Arginine on the Proteome Level

Accumulation of somatic mutations is critical for the transition of a normal cell to become cancerous. Mutations cause amino acid substitutions that change properties of proteins. However, it has not been studied as to what extent the composition and accordingly chemical properties of the cell proteome is altered as a result of the increased mutation load in cancer. Here, we analyzed data on amino acid substitutions caused by mutations in about 2000 protein coding genes from the Cancer Cell Line Encyclopedia that contains information on nucleotide and amino acid alterations in 782 cancer cell lines, and validated the analysis with information on amino acid substitutions for the same set of proteins in the Catalogue of Somatic Mutations in Cancer (COSMIC; v78) in circa 18,000 tumor samples. We found that nonsynonymous single nucleotide substitutions in the analyzed proteome subset ultimately result in a net gain of cysteine, histidine, and tryptophan at the expense of a net loss of arginine. The extraordinary loss of arginine may be attributed to some extent to composition of its codons as well as to the importance of arginine in the functioning of prominent tumor suppressor proteins like p53.

The Somatic Nature of Cancer Allows It to Affect Highly Constrained Genes

Cancer is special among genetic disorders in two major ways: first, cancer is a disease of the most basic of cellular functions, such as cell proliferation, differentiation, and the maintenance of genomic integrity. Second, in contrast to most genetic disorders that are mediated by germline (hereditary) mutations, cancer is largely a somatic disease. Here we show that these two traits are not detached and that it is the somatic nature of cancer that allows it to affect the most basic of cellular functions. We begin by demonstrating that cancer genes are both more functionally central (as measured by their patterns of expression and protein interaction) and more evolutionarily constrained than non-cancer genetic disease genes. We then compare genes that are only modified somatically in cancer (hereinafter referred to as “somatic cancer genes”) to those that can also be modified in a hereditary manner, contributing to cancer development (hereinafter referred to as “hereditary cancer genes”). We show that both somatic and hereditary cancer genes are much more functionally central than genes contributing to non-cancer genetic disorders. At the same time, hereditary cancer genes are only as constrained as non-cancer hereditary disease genes, while somatic cancer genes tend to be much more constrained in evolution. Thus, it appears that it is the somatic nature of cancer that allows it to modify the most constrained genes and, therefore, affect the most basic of cellular functions.

Stochastic modeling reveals an evolutionary mechanism underlying elevated rates of childhood leukemia

Young children have higher rates of leukemia than young adults. This fact represents a fundamental conundrum, because hematopoietic cells in young children should have fewer mutations (including oncogenic ones) than such cells in adults. Here, we present the results of stochastic modeling of hematopoietic stem cell (HSC) clonal dynamics, which demonstrated that early HSC pools were permissive to clonal evolution driven by drift. We show that drift-driven clonal expansions cooperate with faster HSC cycling in young children to produce conditions that are permissive for accumulation of multiple driver mutations in a single cell. Later in life, clonal evolution was suppressed by stabilizing selection in the larger young adult pools, and it was driven by positive selection at advanced ages in the presence of microenvironmental decline. Overall, our results indicate that leukemogenesis is driven by distinct evolutionary forces in children and adults.

Toward an evolutionary model of cancer: Considering the mechanisms that govern the fate of somatic mutations

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.

Inferring the Origin of Metastases from Cancer Phylogenies

Determining the evolutionary history of metastases is a key problem in cancer biology. Several recent studies have presented inferences regarding the origin of metastases based on phylogenies of cancer lineages. Many of these studies have concluded that the observed monophyly of metastatic subclones favored metastasis-to-metastasis spread ("a metastatic cascade" rather than parallel metastases from the primary tumor). In this article, we argue that identifying a monophyletic clade of metastatic subclones does not provide sufficient evidence to unequivocally establish a history of metastatic cascades. In the absence of a complete phylogeny of the subclones within the primary tumor, a scenario of parallel metastatic events from the primary tumor is an equally plausible interpretation. Future phylogenetic studies on the origin of metastases should obtain a complete phylogeny of subclones within the primary tumor. This complete phylogeny may be obtainable by ultra-deep sequencing and phasing of large sections or by targeted sequencing of many small, spatially heterogeneous sections, followed by phylogenetic reconstruction using well-established molecular evolutionary models. In addition to resolving the evolutionary history of metastases, a complete phylogeny of subclones within the primary tumor facilitates the identification of driver mutations by application of phylogeny-based tests of natural selection.

Eavesdropping on altered cell-to-cell signaling in cancer by secretome profiling

In the past decade, cumulative clinical experiences with molecular targeted therapies and immunotherapies for cancer have promoted a shift in our conceptual understanding of cancer. This view shifted from viewing solid tumors as a homogeneous mass of malignant cells to viewing tumors as heterogeneous structures that are dynamically shaped by intercellular interactions among the variety of stromal, immune, and malignant cells present within the tumor microenvironment. As in any dynamic system, identifying how cells communicate to maintain homeostasis and how this communication is altered during oncogenesis are key hurdles for developing therapies to restore normal tissue homeostasis. Here, I discuss tissues as dynamic systems, using the mammary gland as an example, and the evolutionary concepts applied to oncogenesis. Drawing from these concepts, I present 2 competing hypotheses for how intercellular communication might be altered during oncogenesis. As an initial test of these competing hypotheses, a recent secretome comparison between normal human mammary and HER2+ breast cancer cell lines suggested that the particular proteins secreted by the malignant cells reflect a convergent evolutionary path associated with oncogenesis in a specific anatomical niche, despite arising in different individuals. Overall, this study illustrates the emerging power of secretome proteomics to probe, in an unbiased way, how intercellular communication changes during oncogenesis.

Stress, genomic adaptation, and the evolutionary trade-off

Cells are constantly exposed to various internal and external stresses. The importance of cellular stress and its implication to disease conditions have become popular research topics. Many ongoing investigations focus on the sources of stress, their specific molecular mechanisms and interactions, especially regarding their contributions to many common and complex diseases through defined molecular pathways. Numerous molecular mechanisms have been linked to endoplasmic reticulum stress along with many unexpected findings, drastically increasing the complexity of our molecular understanding and challenging how to apply individual mechanism-based knowledge in the clinic. A newly emergent genome theory searches for the synthesis of a general evolutionary mechanism that unifies different types of stress and functional relationships from a genome-defined system point of view. Herein, we discuss the evolutionary relationship between stress and somatic cell adaptation under physiological, pathological, and somatic cell survival conditions, the multiple meanings to achieve adaptation and its potential trade-off. In particular, we purposely defocus from specific stresses and mechanisms by redirecting attention toward studying underlying general mechanisms.

Inferring alterations in cell-to-cell communication in HER2+ breast cancer using secretome profiling of three cell models

Challenges in demonstrating durable clinical responses to molecular-targeted therapies have sparked a re-emergence in viewing cancer as an evolutionary process. In somatic evolution, cellular variants are introduced through a random process of somatic mutation and are selected for improved fitness through a competition for survival. In contrast to Darwinian evolution, cellular variants that are retained may directly alter the fitness competition. If cell-to-cell communication is important for selection, the biochemical cues secreted by malignant cells that emerge should be altered to bias this fitness competition. To test this hypothesis, we compared the proteins secreted in vitro by two human HER2+ breast cancer cell lines (BT474 and SKBR3) relative to a normal human mammary epithelial cell line (184A1) using a proteomics workflow that leveraged two-dimensional gel electrophoresis (2DE) and MALDI-TOF mass spectrometry. Supported by the 2DE secretome maps and identified proteins, the two breast cancer cell lines exhibited secretome profiles that were similar to each other and, yet, were distinct from the 184A1 secretome. Using protein-protein interaction and pathway inference tools for functional annotation, the results suggest that all three cell lines secrete exosomes, as confirmed by scanning electron microscopy. Interestingly, the HER2+ breast cancer cell line exosomes are enriched in proteins involved in antigen-processing and presentation and glycolytic metabolism. These pathways are associated with two of the emerging hallmarks of cancer: evasion of tumor immunosurveillance and deregulating cellular energetics.

Dynamics of growth factor production in monolayers of cancer cells and evolution of resistance to anticancer therapies

Tumor heterogeneity is well documented for many characters, including the production of growth factors, which improve tumor proliferation and promote resistance against apoptosis and against immune reaction. What maintains heterogeneity remains an open question that has implications for diagnosis and treatment. While it has been suggested that therapies targeting growth factors are robust against evolved resistance, current therapies against growth factors, like antiangiogenic drugs, are not effective in the long term, as resistant mutants can evolve and lead to relapse. We use evolutionary game theory to study the dynamics of the production of growth factors by monolayers of cancer cells and to understand the effect of therapies that target growth factors. The dynamics depend on the production cost of the growth factor, on its diffusion range and on the type of benefit it confers to the cells. Stable heterogeneity is a typical outcome of the dynamics, while a pure equilibrium of nonproducer cells is possible under certain conditions. Such pure equilibrium can be the goal of new anticancer therapies. We show that current therapies, instead, can be effective only if growth factors are almost completely eliminated and if the reduction is almost immediate.

The ecology of human papillomavirus-induced epithelial lesions and the role of somatic evolution in their progression

BACKGROUND

Human papillomavirus (HPV) infection frequently induces hyperproliferation of epithelial cells, leading to both benign (warts) and malignant tumors (cervical cancer). We seek to understand how HPV may achieve these changes by modulating cellular population dynamics. Furthermore, HPV-induced lesion progression is generally understood as a series of molecular changes. As a complement to this approach, we investigate the role of phenotypic changes produced by natural selection during lesion progression.

METHODS

We develop and numerically analyze spatially and evolutionarily explicit mathematical models of HPV-induced epithelial lesions.

RESULTS

Infection of basal cells is a requirement for persistent infection. Increasing the maximum tissue density at which HPV-infected cells can divide and decreasing infected cell migration rate leads to large increases in the number of HPV-infected cells, and consequently, virions shed from the skin surface per day. Evolution by natural selection in an autonomous population of cells leads to tissue changes that are qualitatively similar to those observed during lesion progression.

CONCLUSIONS

HPV modulates cell population dynamics, which can be characterized by specific ecological parameters. As an unintended consequence, this ecological strategy of the virus may be successfully co-opted by autonomous host cells and play a role in lesion progression.

Autophagy on acid

The microenvironment of solid tumors tends to be more acidic (6.5-7.0) than surrounding normal (7.2-7.4) tissue. Chaotic vasculature, oxygen limitation and major metabolic changes all contribute to the acidic microenvironment. We have previously proposed that low extracellular pH (pHe) plays a critical role in the development and progression of solid tumors. While extracellular acidosis is toxic to most normal cells, cancer cells can adapt and survive under this harsh condition. In this study, we focused on identifying survival strategies employed by cancer cells when challenged with an acidic pHe (6.6-6.7) either acutely or for many generations. While acutely acidic cells did not grow, those acclimated over many generations grew at the same rate as control cells. We observed that these cells induce autophagy in response to acidosis both acutely and chronically, and that this adaptation appears to be necessary for survival. Inhibition of autophagy in low pH cultured cells results in cell death. Histological analysis of tumor xenografts reveals a strong correlation of LC3 protein expression in regions projected to be acidic. Furthermore, in vivo buffering experiments using sodium bicarbonate, previously shown to raise extracellular tumor pH, decreases LC3 protein expression in tumor xenografts. These data imply that autophagy can be induced by extracellular acidosis and appears to be chronically employed as a survival adaptation to acidic microenvironments.

Investigating prostate cancer tumour-stroma interactions: clinical and biological insights from an evolutionary game

BACKGROUND

Tumours are made up of a mixed population of different types of cells that include normal structures as well as ones associated with the malignancy, and there are multiple interactions between the malignant cells and the local microenvironment. These intercellular interactions, modulated by the microenvironment, effect tumour progression and represent a largely under-appreciated therapeutic target. We use observations of primary tumour biology from prostate cancer to extrapolate a mathematical model. Specifically, it has been observed that in prostate cancer three disparate cellular outcomes predominate: (i) the tumour remains well differentiated and clinically indolent--in this case the local stromal cells may act to restrain the growth of the cancer; (ii) early in its genesis the tumour acquires a highly malignant phenotype, growing rapidly and displacing the original stromal population (often referred to as small cell prostate cancer)--these less common aggressive tumours are relatively independent of the local microenvironment and (iii) the tumour co-opts the local stroma--taking on a classic stromagenic phenotype where interactions with the local microenvironment are critical to the cancer growth.

METHODS

We present an evolutionary game theoretical construct that models the influence of tumour-stroma interactions in driving these outcomes. We consider three characteristic and distinct cellular populations: stromal cells, tumour cells that are self-reliant in terms of microenvironmental factors and tumour cells that depend on the environment for resources, but can also co-opt stroma.

RESULTS

Using evolutionary game theory we explore a number of different scenarios that elucidate the impact of tumour-stromal interactions on the dynamics of prostate cancer growth and progression, and how different treatments in the metastatic setting can affect different types of tumours.

CONCLUSION

The tumour microenvironment has a crucial role in selecting the traits of the tumour cells that will determine prostate cancer progression. Equally important treatments like hormone therapy affect the selection of these cancer phenotypes making it very important to understand how they impact prostate cancer's somatic evolution.

Cancer research meets evolutionary biology

There is increasing evidence that Darwin's theory of evolution by natural selection provides insights into the etiology and treatment of cancer. On a microscopic scale, neoplastic cells meet the conditions for evolution by Darwinian selection: cell reproduction with heritable variability that affects cell survival and replication. This suggests that, like other areas of biological and biomedical research, Darwinian theory can provide a general framework for understanding many aspects of cancer, including problems of great clinical importance. With the availability of raw molecular data increasing rapidly, this theory may provide guidance in translating data into understanding and progress. Several conceptual and analytical tools from evolutionary biology can be applied to cancer biology. Two clinical problems may benefit most from the application of Darwinian theory: neoplastic progression and acquired therapeutic resistance. The Darwinian theory of cancer has especially profound implications for drug development, both in terms of explaining past difficulties, and pointing the way toward new approaches. Because cancer involves complex evolutionary processes, research should incorporate both tractable (simplified) experimental systems, and also longitudinal observational studies of the evolutionary dynamics of cancer in laboratory animals and in human patients. Cancer biology will require new tools to control the evolution of neoplastic cells.