Emergence of a multilocus mutator genotype in mutator Escherichia coli experimental populations under repeated lethal selection.

Mutator alleles, which confer increased mutation rates, are known to spontaneously emerge and "hitchhike" to fixation in evolving asexual populations. Theory predicts that in an evolving asexual mutator population, a second mutator allele may spontaneously arise and hitchhike to fixation. Here, we describe an empirical test of the hypothesis of repeated hitchhiking. The starting population was a clonal strain of mutL-Escherichia coli whose mutation rate was 100-fold higher than wild type. We exposed the mutL- strain to a series of three antibiotics in increasing order of selective strength: fosfomycin, rifampicin, and streptomycin. Two independent replicates of the experiment were performed. As predicted, elevated mutation rates and enrichment for multilocus mutators (which bear more than one mutator allele) were observed in the end point populations of both experiments. DNA sequencing revealed an identical spontaneous 1-bp insertion in the mutator gene mutT in both end point populations. In the multilocus mutators, the causal relationship between the mutT- mutations and the increase in mutation rate was supported with mutT+ plasmid complementation tests. Surprisingly, when the experiment was repeated with the antibiotics deployed in decreasing order of selective strength, enrichment for multilocus mutators was not observed. Our data support the likelihood that the mutT- mutations rose to fixation in both populations, consistent with the hypothesis of repeated mutator hitchhiking. The escalation of mutation rates in asexual populations is relevant to multiple biological scenarios, including antibiotic resistance, host-pathogen interactions, and carcinogenesis.

Protein destabilization underlies pathogenic missense mutations in ARID1B.

ARID1B is a SWI/SNF subunit frequently mutated in human Coffin-Siris syndrome (CSS) and it is necessary for proliferation of ARID1A mutant cancers. While most CSS ARID1B aberrations introduce frameshifts or stop codons, the functional consequence of missense mutations found in ARID1B is unclear. We here perform saturated mutagenesis screens on ARID1B and demonstrate that protein destabilization is the main mechanism associated with pathogenic missense mutations in patients with Coffin-Siris Syndrome.

CRISPR Screening Identifies Mechanisms of Resistance to KRASG12C and SHP2 Inhibitor Combinations in Non-Small Cell Lung Cancer.

Although KRASG12C inhibitors show clinical activity in patients with KRAS G12C mutated non-small cell lung cancer (NSCLC) and other solid tumor malignancies, response is limited by multiple mechanisms of resistance. The KRASG12C inhibitor JDQ443 shows enhanced preclinical antitumor activity combined with the SHP2 inhibitor TNO155, and the combination is currently under clinical evaluation. To identify rational combination strategies that could help overcome or prevent some types of resistance, we evaluated the duration of tumor responses to JDQ443 ± TNO155, alone or combined with the PI3Kα inhibitor alpelisib and/or the cyclin-dependent kinase 4/6 inhibitor ribociclib, in xenograft models derived from a KRASG12C-mutant NSCLC line and investigated the genetic mechanisms associated with loss of response to combined KRASG12C/SHP2 inhibition. Tumor regression by single-agent JDQ443 at clinically relevant doses lasted on average 2 weeks and was increasingly extended by the double, triple, or quadruple combinations. Growth resumption was accompanied by progressively increased KRAS G12C amplification. Functional genome-wide CRISPR screening in KRASG12C-dependent NSCLC lines with distinct mutational profiles to identify adaptive mechanisms of resistance revealed sensitizing and rescuing genetic interactions with KRASG12C/SHP2 coinhibition; FGFR1 loss was the strongest sensitizer, and PTEN loss the strongest rescuer. Consistently, the antiproliferative activity of KRASG12C/SHP2 inhibition was strongly enhanced by PI3K inhibitors. Overall, KRAS G12C amplification and alterations of the MAPK/PI3K pathway were predominant mechanisms of resistance to combined KRASG12C/SHP2 inhibitors in preclinical settings. The biological nodes identified by CRISPR screening might provide additional starting points for effective combination treatments. SIGNIFICANCE: Identification of resistance mechanisms to KRASG12C/SHP2 coinhibition highlights the need for additional combination therapies for lung cancer beyond on-pathway combinations and offers the basis for development of more effective combination approaches. See related commentary by Johnson and Haigis, p. 4005.

Expressed Barcoding Enables High-Resolution Tracking of the Evolution of Drug Tolerance.

For a majority of patients with non-small cell lung cancer with EGFR mutations, treatment with EGFR inhibitors (EGFRi) induces a clinical response. Despite this initial reduction in tumor size, residual disease persists that leads to disease relapse. Elucidating the preexisting biological differences between sensitive cells and surviving drug-tolerant persister cells and deciphering how drug-tolerant cells evolve in response to treatment could help identify strategies to improve the efficacy of EGFRi. In this study, we tracked the origins and clonal evolution of drug-tolerant cells at a high resolution by using an expressed barcoding system coupled with single-cell RNA sequencing. This platform enabled longitudinal profiling of gene expression and drug sensitivity in response to EGFRi across a large number of clones. Drug-tolerant cells had higher expression of key survival pathways such as YAP and EMT at baseline and could also differentially adapt their gene expression following EGFRi treatment compared with sensitive cells. In addition, drug combinations targeting common downstream components (MAPK) or orthogonal factors (chemotherapy) showed greater efficacy than EGFRi alone, which is attributable to broader targeting of the heterogeneous EGFRi-tolerance mechanisms present in tumors. Overall, this approach facilitates thorough examination of clonal evolution in response to therapy that could inform the development of improved diagnostic approaches and treatment strategies for targeting drug-tolerant cells. SIGNIFICANCE: The evolution and heterogeneity of EGFR inhibitor tolerance are identified in a large number of clones at enhanced cellular and temporal resolution using an expressed barcode technology coupled with single-cell RNA sequencing.

Cancer lineage-specific regulation of YAP responsive elements revealed through large-scale functional epigenomic screens.

YAP is a key transcriptional co-activator of TEADs, it regulates cell growth and is frequently activated in cancer. In Malignant Pleural Mesothelioma (MPM), YAP is activated by loss-of-function mutations in upstream components of the Hippo pathway, while, in Uveal Melanoma (UM), YAP is activated in a Hippo-independent manner. To date, it is unclear if and how the different oncogenic lesions activating YAP impact its oncogenic program, which is particularly relevant for designing selective anti-cancer therapies. Here we show that, despite YAP being essential in both MPM and UM, its interaction with TEAD is unexpectedly dispensable in UM, limiting the applicability of TEAD inhibitors in this cancer type. Systematic functional interrogation of YAP regulatory elements in both cancer types reveals convergent regulation of broad oncogenic drivers in both MPM and UM, but also strikingly selective programs. Our work reveals unanticipated lineage-specific features of the YAP regulatory network that provide important insights to guide the design of tailored therapeutic strategies to inhibit YAP signaling across different cancer types.

Exquisite Sensitivity to Dual BRG1/BRM ATPase Inhibitors Reveals Broad SWI/SNF Dependencies in Acute Myeloid Leukemia.

Various subunits of mammalian SWI/SNF chromatin remodeling complexes display loss-of-function mutations characteristic of tumor suppressors in different cancers, but an additional role for SWI/SNF supporting cell survival in distinct cancer contexts is emerging. In particular, genetic dependence on the catalytic subunit BRG1/SMARCA4 has been observed in acute myelogenous leukemia (AML), yet the feasibility of direct therapeutic targeting of SWI/SNF catalytic activity in leukemia remains unknown. Here, we evaluated the activity of dual BRG1/BRM ATPase inhibitors across a genetically diverse panel of cancer cell lines and observed that hematopoietic cancer cell lines were among the most sensitive compared with other lineages. This result was striking in comparison with data from pooled short hairpin RNA screens, which showed that only a subset of leukemia cell lines display sensitivity to BRG1 knockdown. We demonstrate that combined genetic knockdown of BRG1 and BRM is required to recapitulate the effects of dual inhibitors, suggesting that SWI/SNF dependency in human leukemia extends beyond a predominantly BRG1-driven mechanism. Through gene expression and chromatin accessibility studies, we show that the dual inhibitors act at genomic loci associated with oncogenic transcription factors, and observe a downregulation of leukemic pathway genes, including MYC, a well-established target of BRG1 activity in AML. Overall, small-molecule inhibition of BRG1/BRM induced common transcriptional responses across leukemia models resulting in a spectrum of cellular phenotypes. IMPLICATIONS: Our studies reveal the breadth of SWI/SNF dependency in leukemia and support targeting SWI/SNF catalytic function as a potential therapeutic strategy in AML.

Systematic dissection of transcriptional regulatory networks by genome-scale and single-cell CRISPR screens.

Millions of putative transcriptional regulatory elements (TREs) have been cataloged in the human genome, yet their functional relevance in specific pathophysiological settings remains to be determined. This is critical to understand how oncogenic transcription factors (TFs) engage specific TREs to impose transcriptional programs underlying malignant phenotypes. Here, we combine cutting edge CRISPR screens and epigenomic profiling to functionally survey ≈15,000 TREs engaged by estrogen receptor (ER). We show that ER exerts its oncogenic role in breast cancer by engaging TREs enriched in GATA3, TFAP2C, and H3K27Ac signal. These TREs control critical downstream TFs, among which TFAP2C plays an essential role in ER-driven cell proliferation. Together, our work reveals novel insights into a critical oncogenic transcription program and provides a framework to map regulatory networks, enabling to dissect the function of the noncoding genome of cancer cells.

Application of simultaneous selective pressures slows adaptation.

Beneficial mutations that arise in an evolving asexual population may compete or interact in ways that alter the overall rate of adaptation through mechanisms such as clonal or functional interference. The application of multiple selective pressures simultaneously may allow for a greater number of adaptive mutations, increasing the opportunities for competition between selectively advantageous alterations, and thereby reducing the rate of adaptation. We evolved a strain of Saccharomyces cerevisiae that could not produce its own histidine or uracil for ~500 generations under one or three selective pressures: limitation of the concentration of glucose, histidine, and/or uracil in the media. The rate of adaptation was obtained by measuring evolved relative fitness using competition assays. Populations evolved under a single selective pressure showed a statistically significant increase in fitness on those pressures relative to the ancestral strain, but the populations evolved on all three pressures did not show a statistically significant increase in fitness over the ancestral strain on any single pressure. Simultaneously limiting three essential nutrients for a population of S. cerevisiae effectively slows the rate of evolution on any one of the three selective pressures applied, relative to the single selective pressure cases. We identify possible mechanisms for fitness changes seen between populations evolved on one or three limiting nutrient pressures by high-throughput sequencing. Adding multiple selective pressures to evolving disease like cancer and infectious diseases could reduce the rate of adaptation and thereby may slow disease progression, prolong drug efficacy and prevent deaths.

The Discovery of SWI/SNF Chromatin Remodeling Activity as a Novel and Targetable Dependency in Uveal Melanoma.

Uveal melanoma is a rare and aggressive cancer that originates in the eye. Currently, there are no approved targeted therapies and very few effective treatments for this cancer. Although activating mutations in the G protein alpha subunits, GNAQ and GNA11, are key genetic drivers of the disease, few additional drug targets have been identified. Recently, studies have identified context-specific roles for the mammalian SWI/SNF chromatin remodeling complexes (also known as BAF/PBAF) in various cancer lineages. Here, we find evidence that the SWI/SNF complex is essential through analysis of functional genomics screens and further validation in a panel of uveal melanoma cell lines using both genetic tools and small-molecule inhibitors of SWI/SNF. In addition, we describe a functional relationship between the SWI/SNF complex and the melanocyte lineage-specific transcription factor Microphthalmia-associated Transcription Factor, suggesting that these two factors cooperate to drive a transcriptional program essential for uveal melanoma cell survival. These studies highlight a critical role for SWI/SNF in uveal melanoma, and demonstrate a novel path toward the treatment of this cancer.

Genetic heterogeneity and clonal evolution during metastasis in breast cancer patient-derived tumor xenograft models.

Genetic heterogeneity within a tumor arises by clonal evolution, and patients with highly heterogeneous tumors are more likely to be resistant to therapy and have reduced survival. Clonal evolution also occurs when a subset of cells leave the primary tumor to form metastases, which leads to reduced genetic heterogeneity at the metastatic site. Although this process has been observed in human cancer, experimental models which recapitulate this process are lacking. Patient-derived tumor xenografts (PDX) have been shown to recapitulate the patient's original tumor's intra-tumor genetic heterogeneity, as well as its genomics and response to treatment, but whether they can be used to model clonal evolution in the metastatic process is currently unknown. Here, we address this question by following genetic changes in two breast cancer PDX models during metastasis. First, we discovered that mouse stroma can be a confounding factor in assessing intra-tumor heterogeneity by whole exome sequencing, thus we developed a new bioinformatic approach to correct for this. Finally, in a spontaneous, but not experimental (tail-vein) metastasis model we observed a loss of heterogeneity in PDX metastases compared to their orthotopic "primary" tumors, confirming that PDX models can faithfully mimic the clonal evolution process undergone in human patients during metastatic spreading.

PAX8 activates metabolic genes via enhancer elements in Renal Cell Carcinoma.

Transcription factor networks shape the gene expression programs responsible for normal cell identity and pathogenic state. Using Core Regulatory Circuitry analysis (CRC), we identify PAX8 as a candidate oncogene in Renal Cell Carcinoma (RCC) cells. Validation of large-scale functional genomic screens confirms that PAX8 silencing leads to decreased proliferation of RCC cell lines. Epigenomic analyses of PAX8-dependent cistrome demonstrate that PAX8 largely occupies active enhancer elements controlling genes involved in various metabolic pathways. We selected the ferroxidase Ceruloplasmin (CP) as an exemplary gene to dissect PAX8 molecular functions. PAX8 recruits histone acetylation activity at bound enhancers looping onto the CP promoter. Importantly, CP expression correlates with sensitivity to PAX8 silencing and identifies a subset of RCC cases with poor survival. Our data identifies PAX8 as a candidate oncogene in RCC and provides a potential biomarker to monitor its activity.

High mutation rates limit evolutionary adaptation in Escherichia coli.

Mutation is fundamental to evolution, because it generates the genetic variation on which selection can act. In nature, genetic changes often increase the mutation rate in systems that range from viruses and bacteria to human tumors. Such an increase promotes the accumulation of frequent deleterious or neutral alleles, but it can also increase the chances that a population acquires rare beneficial alleles. Here, we study how up to 100-fold increases in Escherichia coli's genomic mutation rate affect adaptive evolution. To do so, we evolved multiple replicate populations of asexual E. coli strains engineered to have four different mutation rates for 3000 generations in the laboratory. We measured the ability of evolved populations to grow in their original environment and in more than 90 novel chemical environments. In addition, we subjected the populations to whole genome population sequencing. Although populations with higher mutation rates accumulated greater genetic diversity, this diversity conveyed benefits only for modestly increased mutation rates, where populations adapted faster and also thrived better than their ancestors in some novel environments. In contrast, some populations at the highest mutation rates showed reduced adaptation during evolution, and failed to thrive in all of the 90 alternative environments. In addition, they experienced a dramatic decrease in mutation rate. Our work demonstrates that the mutation rate changes the global balance between deleterious and beneficial mutational effects on fitness. In contrast to most theoretical models, our experiments suggest that this tipping point already occurs at the modest mutation rates that are found in the wild.

Assessing the benefits of horizontal gene transfer by laboratory evolution and genome sequencing.

BACKGROUND: Recombination is widespread across the tree of life, because it helps purge deleterious mutations and creates novel adaptive traits. In prokaryotes, it often takes the form of horizontal gene transfer from a donor to a recipient bacterium. While such transfer is widespread in natural communities, its immediate fitness benefits are usually unknown. We asked whether any such benefits depend on the environment, and on the identity of donor and recipient strains. To this end, we adapted Escherichia coli to two novel carbon sources over several hundred generations of laboratory evolution, exposing evolving populations to various DNA donors. RESULTS: At the end of these experiments, we measured fitness and sequenced the genomes of 65 clones from 34 replicate populations to study the genetic changes associated with adaptive evolution. Furthermore, we identified candidate de novo beneficial mutations. During adaptive evolution on the first carbon source, 4-Hydroxyphenylacetic acid (HPA), recombining populations adapted better, which was likely mediated by acquiring the hpa operon from the donor. In contrast, recombining populations did not adapt better to the second carbon source, butyric acid, even though they suffered fewer extinctions than non-recombining populations. The amount of DNA transferred, but not its benefit, strongly depended on the donor-recipient strain combination. CONCLUSIONS: To our knowledge, our study is the first to investigate the genomic consequences of prokaryotic recombination and horizontal gene transfer during laboratory evolution. It shows that the benefits of recombination strongly depend on the environment and the foreign DNA donor.

Effect of Population Size and Mutation Rate on the Evolution of RNA Sequences on an Adaptive Landscape Determined by RNA Folding.

The dynamics of populations evolving on an adaptive landscape depends on multiple factors, including the structure of the landscape, the rate of mutations, and effective population size. Existing theoretical work often makes ad hoc and simplifying assumptions about landscape structure, whereas experimental work can vary important parameters only to a limited extent. We here overcome some of these limitations by simulating the adaptive evolution of RNA molecules, whose fitness is determined by the thermodynamics of RNA secondary structure folding. We study the influence of mutation rates and population sizes on final mean population fitness, on the substitution rates of mutations, and on population diversity. We show that evolutionary dynamics cannot be understood as a function of mutation rate µ, population size N, or population mutation rate Nµ alone. For example, at a given mutation rate, clonal interference prevents the fixation of beneficial mutations as population size increases, but larger populations still arrive at a higher mean fitness. In addition, at the highest population mutation rates we study, mean final fitness increases with population size, because small populations are driven to low fitness by the relatively higher incidence of mutations they experience. Our observations show that mutation rate and population size can interact in complex ways to influence the adaptive dynamics of a population on a biophysically motivated fitness landscape.

Growthcurver: an R package for obtaining interpretable metrics from microbial growth curves.

BACKGROUND: Plate readers can measure the growth curves of many microbial strains in a high-throughput fashion. The hundreds of absorbance readings collected simultaneously for hundreds of samples create technical hurdles for data analysis. RESULTS: Growthcurver summarizes the growth characteristics of microbial growth curve experiments conducted in a plate reader. The data are fitted to a standard form of the logistic equation, and the parameters have clear interpretations on population-level characteristics, like doubling time, carrying capacity, and growth rate. CONCLUSIONS: Growthcurver is an easy-to-use R package available for installation from the Comprehensive R Archive Network (CRAN). The source code is available under the GNU General Public License and can be obtained from Github (Sprouffske K, Growthcurver sourcecode, 2016).

How Archiving by Freezing Affects the Genome-Scale Diversity of Escherichia coli Populations.

In the experimental evolution of microbes such as Escherichia coli, many replicate populations are evolved from a common ancestor. Freezing a population sample supplemented with the cryoprotectant glycerol permits later analysis or restarting of an evolution experiment. Typically, each evolving population, and thus each sample archived in this way, consists of many unique genotypes and phenotypes. The effect of archiving on such a heterogeneous population is unknown. Here, we identified optimal archiving conditions for E. coli. We also used genome sequencing of archived samples to study the effects that archiving has on genomic population diversity. We observed no allele substitutions and mostly small changes in allele frequency. Nevertheless, principal component analysis of genome-scale allelic diversity shows that archiving affects diversity across many loci. We showed that this change in diversity is due to selection rather than drift. In addition, ∼1% of rare alleles that occurred at low frequencies were lost after treatment. Our observations imply that archived populations may be used to conduct fitness or other phenotypic assays of populations, in which the loss of a rare allele may have negligible effects. However, caution is appropriate when sequencing populations restarted from glycerol stocks, as well as when using glycerol stocks to restart or replay evolution. This is because the loss of rare alleles can alter the future evolutionary trajectory of a population if the lost alleles were strongly beneficial.

Peto's paradox revisited: theoretical evolutionary dynamics of cancer in wild populations.

If the occurrence of cancer is the result of a random lottery among cells, then body mass, a surrogate for cells number, should predict cancer incidence. Despite some support in humans, this assertion does not hold over the range of different natural animal species where cancer incidence is known. Explaining the so-called 'Peto's paradox' is likely to increase our understanding of how cancer defense mechanisms are shaped by natural selection. Here, we study how body mass may affect the evolutionary dynamics of tumor suppressor gene (TSG) inactivation and oncogene activation in natural animal species. We show that the rate of TSG inactivation should evolve to lower values along a gradient of body mass in a nonlinear manner, having a threshold beyond which benefits to adaptive traits cannot overcome their costs. We also show that oncogenes may be frequently activated within populations of large organisms. We then propose experimental settings that can be employed to identify protection mechanisms against cancer. We finally highlight fundamental species traits that natural selection should favor against carcinogenesis. We conclude on the necessity of comparing genomes between populations of a single species or genomes between species to better understand how evolution has molded protective mechanisms against cancer development and associated mortality.

An evolutionary explanation for the presence of cancer nonstem cells in neoplasms.

Contrary to conventional views that assume all cells in a neoplasm can propagate the tumor, the cancer stem cell hypothesis posits that only a fraction of the cells (the cancer stem cells) can act as tumor-propagating cells, while most of the tumor is composed of cells with limited replication potential. Here, we offer an evolutionary approach to this controversy. We used several evolutionary, computational models to investigate cancer cell dynamics and conditions consistent with the stem cell hypothesis. Our models predict that if selection acts at the cell level, neoplasms should be primarily comprised of cancer stem cells, in contrast to experimental data indicating that neoplasms contain large fractions of cancer nonstem cells. We explore several solutions explaining the paradoxical existence of cancer nonstem cells in neoplasms, including the possibility that selection acts at the level of multicellular proliferative units.

Cancer in light of experimental evolution.

Cancer initiation, progression, and the emergence of therapeutic resistance are evolutionary phenomena of clonal somatic cell populations. Studies in microbial experimental evolution and the theoretical work inspired by such studies are yielding deep insights into the evolutionary dynamics of clonal populations, yet there has been little explicit consideration of the relevance of this rapidly growing field to cancer biology. Here, we examine how the understanding of mutation, selection, and spatial structure in clonal populations that is emerging from experimental evolution may be applicable to cancer. Along the way, we discuss some significant ways in which cancer differs from the model systems used in experimental evolution. Despite these differences, we argue that enhanced prediction and control of cancer may be possible using ideas developed in the context of experimental evolution, and we point out some prospects for future research at the interface between these traditionally separate areas.

Solving the puzzle of metastasis: the evolution of cell migration in neoplasms.

BACKGROUND: Metastasis represents one of the most clinically important transitions in neoplastic progression. The evolution of metastasis is a puzzle because a metastatic clone is at a disadvantage in competition for space and resources with non-metastatic clones in the primary tumor. Metastatic clones waste some of their reproductive potential on emigrating cells with little chance of establishing metastases. We suggest that resource heterogeneity within primary tumors selects for cell migration, and that cell emigration is a by-product of that selection. METHODS AND FINDINGS: We developed an agent-based model to simulate the evolution of neoplastic cell migration. We simulated the essential dynamics of neoangiogenesis and blood vessel occlusion that lead to resource heterogeneity in neoplasms. We observed the probability and speed of cell migration that evolves with changes in parameters that control the degree of spatial and temporal resource heterogeneity. Across a broad range of realistic parameter values, increasing degrees of spatial and temporal heterogeneity select for the evolution of increased cell migration and emigration. CONCLUSIONS: We showed that variability in resources within a neoplasm (e.g. oxygen and nutrients provided by angiogenesis) is sufficient to select for cells with high motility. These cells are also more likely to emigrate from the tumor, which is the first step in metastasis and the key to the puzzle of metastasis. Thus, we have identified a novel potential solution to the puzzle of metastasis.