The co-evolution of the genome and epigenome in colorectal cancer.

Colorectal malignancies are a leading cause of cancer-related death1 and have undergone extensive genomic study2,3. However, DNA mutations alone do not fully explain malignant transformation4-7. Here we investigate the co-evolution of the genome and epigenome of colorectal tumours at single-clone resolution using spatial multi-omic profiling of individual glands. We collected 1,370 samples from 30 primary cancers and 8 concomitant adenomas and generated 1,207 chromatin accessibility profiles, 527 whole genomes and 297 whole transcriptomes. We found positive selection for DNA mutations in chromatin modifier genes and recurrent somatic chromatin accessibility alterations, including in regulatory regions of cancer driver genes that were otherwise devoid of genetic mutations. Genome-wide alterations in accessibility for transcription factor binding involved CTCF, downregulation of interferon and increased accessibility for SOX and HOX transcription factor families, suggesting the involvement of developmental genes during tumourigenesis. Somatic chromatin accessibility alterations were heritable and distinguished adenomas from cancers. Mutational signature analysis showed that the epigenome in turn influences the accumulation of DNA mutations. This study provides a map of genetic and epigenetic tumour heterogeneity, with fundamental implications for understanding colorectal cancer biology.

Phenotypic plasticity and genetic control in colorectal cancer evolution.

Genetic and epigenetic variation, together with transcriptional plasticity, contribute to intratumour heterogeneity1. The interplay of these biological processes and their respective contributions to tumour evolution remain unknown. Here we show that intratumour genetic ancestry only infrequently affects gene expression traits and subclonal evolution in colorectal cancer (CRC). Using spatially resolved paired whole-genome and transcriptome sequencing, we find that the majority of intratumour variation in gene expression is not strongly heritable but rather 'plastic'. Somatic expression quantitative trait loci analysis identified a number of putative genetic controls of expression by cis-acting coding and non-coding mutations, the majority of which were clonal within a tumour, alongside frequent structural alterations. Consistently, computational inference on the spatial patterning of tumour phylogenies finds that a considerable proportion of CRCs did not show evidence of subclonal selection, with only a subset of putative genetic drivers associated with subclone expansions. Spatial intermixing of clones is common, with some tumours growing exponentially and others only at the periphery. Together, our data suggest that most genetic intratumour variation in CRC has no major phenotypic consequence and that transcriptional plasticity is, instead, widespread within a tumour.

Health Inequity and Time From Pediatric Stroke Onset to Arrival.

BACKGROUND: Time from stroke onset to hospital arrival determines treatment and impacts outcome. Structural, socioeconomic, and environmental factors are associated with health inequity and onset-to-arrival in adult stroke. We aimed to assess the association between health inequity and onset-to-arrival in a pediatric comprehensive stroke center. METHODS: A retrospective observational study was conducted on a consecutive cohort of children (>28 days-18 years) diagnosed with acute arterial ischemic stroke (AIS) between 2004 and 2019. Neighborhood-level material deprivation was derived from residential postal codes and used as a proxy measure for health inequity. Patients were stratified by level of neighborhood-level material deprivation, and onset-to-arrival was categorized into 3 groups: <6, 6 to 24, and >24 hours. Association between neighborhood-level material deprivation and onset-to-arrival was assessed in multivariable ordinal logistic regression analyses adjusting for sociodemographic and clinical factors. RESULTS: Two hundred and twenty-nine children were included (61% male; median age [interquartile range] at stroke diagnosis 5.8-years [1.1-11.3]). Over the 16-year study period, there was an increase in proportion of children diagnosed with AIS living in the most deprived neighborhoods and arriving at the emergency room within 6 hours (P=0.01). Among Asian patients, a higher proportion lived in the most deprived neighborhoods (P=0.02) and level of material deprivation was associated with AIS risk factors (P=0.001). CONCLUSIONS: Our study suggests an increase in pediatric stroke in deprived neighborhoods and certain communities, and earlier arrival times to the emergency room over time. However, whether these changes are due to an increase in incidence of childhood AIS or increased awareness and diagnosis is yet to be determined. The association between AIS risk factors and material deprivation highlights the intersectionality of clinical factors and social determinants of health. Finally, whether material deprivation impacts onset-to-arrival is likely complex and requires further examination.

The role of memory in non-genetic inheritance and its impact on cancer treatment resistance.

Intra-tumour heterogeneity is a leading cause of treatment failure and disease progression in cancer. While genetic mutations have long been accepted as a primary mechanism of generating this heterogeneity, the role of phenotypic plasticity is becoming increasingly apparent as a driver of intra-tumour heterogeneity. Consequently, understanding the role of this plasticity in treatment resistance and failure is a key component of improving cancer therapy. We develop a mathematical model of stochastic phenotype switching that tracks the evolution of drug-sensitive and drug-tolerant subpopulations to clarify the role of phenotype switching on population growth rates and tumour persistence. By including cytotoxic therapy in the model, we show that, depending on the strategy of the drug-tolerant subpopulation, stochastic phenotype switching can lead to either transient or permanent drug resistance. We study the role of phenotypic heterogeneity in a drug-resistant, genetically homogeneous population of non-small cell lung cancer cells to derive a rational treatment schedule that drives population extinction and avoids competitive release of the drug-tolerant sub-population. This model-informed therapeutic schedule results in increased treatment efficacy when compared against periodic therapy, and, most importantly, sustained tumour decay without the development of resistance.

Influence of sample size and analytic approach on stability and interpretation of brain-behavior correlations in task-related fMRI data.

Limited statistical power due to small sample sizes is a problem in fMRI research. Most of the work to date has examined the impact of sample size on task-related activation, with less attention paid to the influence of sample size on brain-behavior correlations, especially in actual experimental fMRI data. We addressed this issue using two large data sets (a working memory task, N = 171, and a relational processing task, N = 865) and both univariate and multivariate approaches to voxel-wise correlations. We created subsamples of different sizes and calculated correlations between task-related activity at each voxel and task performance. Across both data sets the magnitude of the brain-behavior correlations decreased and similarity across spatial maps increased with larger sample sizes. The multivariate technique identified more extensive correlated areas and more similarity across spatial maps, suggesting that a multivariate approach would provide a consistent advantage over univariate approaches in the stability of brain-behavior correlations. In addition, the multivariate analyses showed that a sample size of roughly 80 or more participants would be needed for stable estimates of correlation magnitude in these data sets. Importantly, a number of additional factors would likely influence the choice of sample size for assessing such correlations in any given experiment, including the cognitive task of interest and the amount of data collected per participant. Our results provide novel experimental evidence in two independent data sets that the sample size commonly used in fMRI studies of 20-30 participants is very unlikely to be sufficient for obtaining reproducible brain-behavior correlations, regardless of analytic approach.

Functional Connectivity within and beyond the Face Network Is Related to Reduced Discrimination of Degraded Faces in Young and Older Adults.

Degrading face stimuli reduces face discrimination in both young and older adults, but the brain correlates of this decline in performance are not fully understood. We used functional magnetic resonance imaging to examine the effects of degraded face stimuli on face and nonface brain networks and tested whether these changes would predict the linear declines seen in performance. We found decreased activity in the face network (FN) and a decrease in the similarity of functional connectivity (FC) in the FN across conditions as degradation increased but no effect of age. FC in whole-brain networks also changed with increasing degradation, including increasing FC between the visual network and cognitive control networks. Older adults showed reduced modulation of this whole-brain FC pattern. The strongest predictors of within-participant decline in accuracy were changes in whole-brain network FC and FC similarity of the FN. There was no influence of age on these brain-behavior relations. These results suggest that a systems-level approach beyond the FN is required to understand the brain correlates of performance decline when faces are obscured with noise. In addition, the association between brain and behavior changes was maintained into older age, despite the dampened FC response to face degradation seen in older adults.

Neural Correlates of Enhanced Memory for Meaningful Associations with Age.

Evidence suggests that age differences in associative memory are attenuated for associations that are consistent with prior knowledge. Such knowledge structures have traditionally been associated with the default network (DN), which also shows reduced modulation with age. In the present study, we investigated whether DN activity and connectivity patterns could account for this age-related effect. Younger and older adults underwent functional magnetic resonance imaging as they learned realistic and unrealistic prices of common grocery items. Both groups showed greater activity in the DN during the encoding of realistic, relative to unrealistic, prices. Moreover, DN activity at encoding and retrieval and its connectivity with an attention control network at encoding were associated with enhanced memory for realistic prices. Finally, older adults showed overactivation of control regions during retrieval of realistic prices relative to younger adults. Our findings suggest that DN activity and connectivity patterns (traditionally viewed as indicators of cognitive failure with age), and additional recruitment of control regions, might underlie older adults' enhanced memory for meaningful associations.

The 2019 mathematical oncology roadmap.

Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology-defined here simply as the use of mathematics in cancer research-complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.

Steering Evolution with Sequential Therapy to Prevent the Emergence of Bacterial Antibiotic Resistance.

The increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. Here, we consider a simple model of evolution in asexually reproducing populations which considers adaptation as a biased random walk on a fitness landscape. This model associates the global properties of the fitness landscape with the algebraic properties of a Markov chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies. Using this formalism, we analyze 15 empirical fitness landscapes of E. coli under selection by different ß-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be either hindered or promoted by different sequences of drug application. Specifically, we demonstrate that the majority, approximately 70%, of sequential drug treatments with 2-4 drugs promote resistance to the final antibiotic. Further, we derive optimal drug application sequences with which we can probabilistically 'steer' the population through genotype space to avoid the emergence of resistance. This suggests a new strategy in the war against antibiotic-resistant organisms: drug sequencing to shepherd evolution through genotype space to states from which resistance cannot emerge and by which to maximize the chance of successful therapy.

A low-cost, open-source evolutionary bioreactor and its educational use.

A morbidostat is a bioreactor that uses antibiotics to control the growth of bacteria, making it well-suited for studying the evolution of antibiotic resistance. However, morbidostats are often too expensive to be used in educational settings. Here we present a low-cost morbidostat called the EVolutionary biorEactor (EVE) that can be built by students with minimal engineering and programming experience. We describe how we validated EVE in a real classroom setting by evolving replicate Escherichia coli populations under chloramphenicol challenge, thereby enabling students to learn about bacterial growth and antibiotic resistance.

Reconfiguration and dedifferentiation of functional networks during cognitive control across the adult lifespan.

Healthy aging is accompanied by reduced cognitive control and widespread alterations in the underlying brain networks; but the extent to which large-scale functional networks in older age show reduced specificity across different domains of cognitive control is unclear. Here we use cov-STATIS (a multi-table multivariate technique) to examine similarity of functional connectivity during different domains of cognitive control-inhibition, initiation, shifting, and working memory-across the adult lifespan. We report two major findings: (1) Functional connectivity patterns during initiation, inhibition, and shifting were more similar in older ages, particularly for control and default networks, a pattern consistent with dedifferentiation of the neural correlates associated with cognitive control; and (2) Networks exhibited age-related reconfiguration such that frontal, default, and dorsal attention networks were more integrated whereas sub-networks of somato-motor system were more segregated in older age. Together these findings offer new evidence for dedifferentiation and reconfiguration of functional connectivity underlying different aspects of cognitive control in normal aging.

Dataset of functional connectivity during cognitive control for an adult lifespan sample.

We provide functional connectivity matrices generated during functional magnetic resonance imaging (fMRI) during different tasks of cognitive control in healthy aging adults. These data can be used to replicate the primary results from the related manuscript: Reconfiguration and dedifferentiation of functional networks during cognitive control across the adult lifespan (Rieck et al., 2021). One-hundred-forty-four participants (ages 20-86) were scanned on a Siemens 3T MRI scanner while they were completing tasks to measure functional activity during inhibition, initiation, shifting, and working memory. Estimates of functional connectivity (quantified with timeseries correlations) between different brain regions were computed using three different brain atlases: Schaefer 100 parcel 17 network atlas (Schaefer et al., 2018; Yeo et al., 2011), Power 229 node 10 network atlas (Power et al., 2011), and Schaefer 200 parcel 17 network atlas (Schaefer et al., 2018; Yeo et al., 2011). The resulting functional connectivity correlation matrices are provided as text files with this article. Cov-STATIS (Abdi et al., 2012; a multi-table multivariate statistical technique; https://github.com/HerveAbdi/DistatisR) was used to examine similarity between functional connectivity during the different domains of cognitive control. The effect of aging on these functional connectivity patterns was also examined by computing measures of "task differentiation" and "network segregation." This dataset also provides supplemental analyses from the related manuscript (Rieck et al., 2021) to replicate the primary age findings with additional brain atlases. Cognitive neuroscience researchers can benefit from these data by further investigating the age effects on functional connectivity during tasks of cognitive control, in addition to examining the impact of different brain atlases on functional connectivity estimates. These data can also be used for the development of other multi-table and network-based statistical methods in functional neuroimaging.

Mapping the breast cancer metastatic cascade onto ctDNA using genetic and epigenetic clonal tracking.

Circulating tumour DNA (ctDNA) allows tracking of the evolution of human cancers at high resolution, overcoming many limitations of tissue biopsies. However, exploiting ctDNA to determine how a patient's cancer is evolving in order to aid clinical decisions remains difficult. This is because ctDNA is a mix of fragmented alleles, and the contribution of different cancer deposits to ctDNA is largely unknown. Profiling ctDNA almost invariably requires prior knowledge of what genomic alterations to track. Here, we leverage on a rapid autopsy programme to demonstrate that unbiased genomic characterisation of several metastatic sites and concomitant ctDNA profiling at whole-genome resolution reveals the extent to which ctDNA is representative of widespread disease. We also present a methylation profiling method that allows tracking evolutionary changes in ctDNA at single-molecule resolution without prior knowledge. These results have critical implications for the use of liquid biopsies to monitor cancer evolution in humans and guide treatment.

Measuring single cell divisions in human tissues from multi-region sequencing data.

Both normal tissue development and cancer growth are driven by a branching process of cell division and mutation accumulation that leads to intra-tissue genetic heterogeneity. However, quantifying somatic evolution in humans remains challenging. Here, we show that multi-sample genomic data from a single time point of normal and cancer tissues contains information on single-cell divisions. We present a new theoretical framework that, applied to whole-genome sequencing data of healthy tissue and cancer, allows inferring the mutation rate and the cell survival/death rate per division. On average, we found that cells accumulate 1.14 mutations per cell division in healthy haematopoiesis and 1.37 mutations per division in brain development. In both tissues, cell survival was maximal during early development. Analysis of 131 biopsies from 16 tumours showed 4 to 100 times increased mutation rates compared to healthy development and substantial inter-patient variation of cell survival/death rates.

Exploiting evolutionary steering to induce collateral drug sensitivity in cancer.

Drug resistance mediated by clonal evolution is arguably the biggest problem in cancer therapy today. However, evolving resistance to one drug may come at a cost of decreased fecundity or increased sensitivity to another drug. These evolutionary trade-offs can be exploited using 'evolutionary steering' to control the tumour population and delay resistance. However, recapitulating cancer evolutionary dynamics experimentally remains challenging. Here, we present an approach for evolutionary steering based on a combination of single-cell barcoding, large populations of 108-109 cells grown without re-plating, longitudinal non-destructive monitoring of cancer clones, and mathematical modelling of tumour evolution. We demonstrate evolutionary steering in a lung cancer model, showing that it shifts the clonal composition of the tumour in our favour, leading to collateral sensitivity and proliferative costs. Genomic profiling revealed some of the mechanisms that drive evolved sensitivity. This approach allows modelling evolutionary steering strategies that can potentially control treatment resistance.

Subclonal reconstruction of tumors by using machine learning and population genetics.

Most cancer genomic data are generated from bulk samples composed of mixtures of cancer subpopulations, as well as normal cells. Subclonal reconstruction methods based on machine learning aim to separate those subpopulations in a sample and infer their evolutionary history. However, current approaches are entirely data driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in the analysis if evolution is not accounted for, and this is exacerbated with multi-sampling of the same tumor. We present a novel approach for model-based tumor subclonal reconstruction, called MOBSTER, which combines machine learning with theoretical population genetics. Using public whole-genome sequencing data from 2,606 samples from different cohorts, new data and synthetic validation, we show that this method is more robust and accurate than current techniques in single-sample, multiregion and longitudinal data. This approach minimizes the confounding factors of nonevolutionary methods, thus leading to more accurate recovery of the evolutionary history of human cancers.

Colorectal cancer residual disease at maximal response to EGFR blockade displays a druggable Paneth cell-like phenotype.

Blockade of epidermal growth factor receptor (EGFR) causes tumor regression in some patients with metastatic colorectal cancer (mCRC). However, residual disease reservoirs typically remain even after maximal response to therapy, leading to relapse. Using patient-derived xenografts (PDXs), we observed that mCRC cells surviving EGFR inhibition exhibited gene expression patterns similar to those of a quiescent subpopulation of normal intestinal secretory precursors with Paneth cell characteristics. Compared with untreated tumors, these pseudodifferentiated tumor remnants had reduced expression of genes encoding EGFR-activating ligands, enhanced activity of human epidermal growth factor receptor 2 (HER2) and HER3, and persistent signaling along the phosphatidylinositol 3-kinase (PI3K) pathway. Clinically, properties of residual disease cells from the PDX models were detected in lingering tumors of responsive patients and in tumors of individuals who had experienced early recurrence. Mechanistically, residual tumor reprogramming after EGFR neutralization was mediated by inactivation of Yes-associated protein (YAP), a master regulator of intestinal epithelium recovery from injury. In preclinical trials, Pan-HER antibodies minimized residual disease, blunted PI3K signaling, and induced long-term tumor control after treatment discontinuation. We found that tolerance to EGFR inhibition is characterized by inactivation of an intrinsic lineage program that drives both regenerative signaling during intestinal repair and EGFR-dependent tumorigenesis. Thus, our results shed light on CRC lineage plasticity as an adaptive escape mechanism from EGFR-targeted therapy and suggest opportunities to preemptively target residual disease.

Model genotype-phenotype mappings and the algorithmic structure of evolution.

Cancers are complex dynamic systems that undergo evolution and selection. Personalized medicine approaches in the clinic increasingly rely on predictions of tumour response to one or more therapies; these predictions are complicated by the inevitable evolution of the tumour. Despite enormous amounts of data on the mutational status of cancers and numerous therapies developed in recent decades to target these mutations, many of these treatments fail after a time due to the development of resistance in the tumour. The emergence of these resistant phenotypes is not easily predicted from genomic data, since the relationship between genotypes and phenotypes, termed the genotype-phenotype (GP) mapping, is neither injective nor functional. We present a review of models of this mapping within a generalized evolutionary framework that takes into account the relation between genotype, phenotype, environment and fitness. Different modelling approaches are described and compared, and many evolutionary results are shown to be conserved across studies despite using different underlying model systems. In addition, several areas for future work that remain understudied are identified, including plasticity and bet-hedging. The GP-mapping provides a pathway for understanding the potential routes of evolution taken by cancers, which will be necessary knowledge for improving personalized therapies.

Antibiotic collateral sensitivity is contingent on the repeatability of evolution.

Antibiotic resistance represents a growing health crisis that necessitates the immediate discovery of novel treatment strategies. One such strategy is the identification of collateral sensitivities, wherein evolution under a first drug induces susceptibility to a second. Here, we report that sequential drug regimens derived from in vitro evolution experiments may have overstated therapeutic benefit, predicting a collaterally sensitive response where cross-resistance ultimately occurs. We quantify the likelihood of this phenomenon by use of a mathematical model parametrised with combinatorially complete fitness landscapes for Escherichia coli. Through experimental evolution we then verify that a second drug can indeed stochastically exhibit either increased susceptibility or increased resistance when following a first. Genetic divergence is confirmed as the driver of this differential response through targeted and whole genome sequencing. Taken together, these results highlight that the success of evolutionarily-informed therapies is predicated on a rigorous probabilistic understanding of the contingencies that arise during the evolution of drug resistance.

The Spatiotemporal Evolution of Lymph Node Spread in Early Breast Cancer.

Purpose: The most significant prognostic factor in early breast cancer is lymph node involvement. This stage between localized and systemic disease is key to understanding breast cancer progression; however, our knowledge of the evolution of lymph node malignant invasion remains limited, as most currently available data are derived from primary tumors.Experimental Design: In 11 patients with treatment-naïve node-positive early breast cancer without clinical evidence of distant metastasis, we investigated lymph node evolution using spatial multiregion sequencing (n = 78 samples) of primary and lymph node deposits and genomic profiling of matched longitudinal circulating tumor DNA (ctDNA).Results: Linear evolution from primary to lymph node was rare (1/11), whereas the majority of cases displayed either early divergence between primary and nodes (4/11) or no detectable divergence (6/11), where both primary and nodal cells belonged to a single recent expansion of a metastatic clone. Divergence of metastatic subclones was driven in part by APOBEC. Longitudinal ctDNA samples from 2 of 7 subjects with evaluable plasma taken perioperatively reflected the two major evolutionary patterns and demonstrate that private mutations can be detected even from early metastatic nodal deposits. Moreover, node removal resulted in disappearance of private lymph node mutations in ctDNA.Conclusions: This study sheds new light on a crucial evolutionary step in the natural history of breast cancer, demonstrating early establishment of axillary lymph node metastasis in a substantial proportion of patients. Clin Cancer Res; 24(19); 4763-70. ©2018 AACR.