Measures of genetic diversification in somatic tissues at bulk and single-cell resolution.

Intra-tissue genetic heterogeneity is universal to both healthy and cancerous tissues. It emerges from the stochastic accumulation of somatic mutations throughout development and homeostasis. By combining population genetics theory and genomic information, genetic heterogeneity can be exploited to infer tissue organization and dynamics in vivo. However, many basic quantities, for example the dynamics of tissue-specific stem cells remain difficult to quantify precisely. Here, we show that single-cell and bulk sequencing data inform on different aspects of the underlying stochastic processes. Bulk-derived variant allele frequency spectra (VAF) show transitions from growing to constant stem cell populations with age in samples of healthy esophagus epithelium. Single-cell mutational burden distributions allow a sample size independent measure of mutation and proliferation rates. Mutation rates in adult hematopietic stem cells are higher compared to inferences during development, suggesting additional proliferation-independent effects. Furthermore, single-cell derived VAF spectra contain information on the number of tissue-specific stem cells. In hematopiesis, we find approximately 2 × 105 HSCs, if all stem cells divide symmetrically. However, the single-cell mutational burden distribution is over-dispersed compared to a model of Poisson distributed random mutations. A time-associated model of mutation accumulation with a constant rate alone cannot generate such a pattern. At least one additional source of stochasticity would be needed. Possible candidates for these processes may be occasional bursts of stem cell divisions, potentially in response to injury, or non-constant mutation rates either through environmental exposures or cell-intrinsic variation.

Mutation divergence over space in tumour expansion.

Mutation accumulation in tumour evolution is one major cause of intra-tumour heterogeneity (ITH), which often leads to drug resistance during treatment. Previous studies with multi-region sequencing have shown that mutation divergence among samples within the patient is common, and the importance of spatial sampling to obtain a complete picture in tumour measurements. However, quantitative comparisons of the relationship between mutation heterogeneity and tumour expansion modes, sampling distances as well as the sampling methods are still few. Here, we investigate how mutations diverge over space by varying the sampling distance and tumour expansion modes using individual-based simulations. We measure ITH by the Jaccard index between samples and quantify how ITH increases with sampling distance, the pattern of which holds in various sampling methods and sizes. We also compare the inferred mutation rates based on the distributions of variant allele frequencies under different tumour expansion modes and sampling sizes. In exponentially fast expanding tumours, a mutation rate can always be inferred for any sampling size. However, the accuracy compared with the true value decreases when the sampling size decreases, where small sampling sizes result in a high estimate of the mutation rate. In addition, such an inference becomes unreliable when the tumour expansion is slow, such as in surface growth.

Adaptive therapy achieves long-term control of chemotherapy resistance in high grade ovarian cancer.

Drug resistance results in poor outcomes for most patients with metastatic cancer. Adaptive Therapy (AT) proposes to address this by exploiting presumed fitness costs incurred by drug-resistant cells when drug is absent, and prescribing dose reductions to allow fitter, sensitive cells to re-grow and re-sensitise the tumour. However, empirical evidence for treatment-induced fitness change is lacking. We show that fitness costs in chemotherapy-resistant ovarian cancer cause selective decline and apoptosis of resistant populations in low-resource conditions. Moreover, carboplatin AT caused fluctuations in sensitive/resistant tumour population size in vitro and significantly extended survival of tumour-bearing mice. In sequential blood-derived cell-free DNA and tumour samples obtained longitudinally from ovarian cancer patients during treatment, we inferred resistant cancer cell population size through therapy and observed it correlated strongly with disease burden. These data have enabled us to launch a multicentre, phase 2 randomised controlled trial (ACTOv) to evaluate AT in ovarian cancer.

Coordinated inheritance of extrachromosomal DNA species in human cancer cells.

The chromosomal theory of inheritance has dominated human genetics, including cancer genetics. Genes on the same chromosome segregate together while genes on different chromosomes assort independently, providing a fundamental tenet of Mendelian inheritance. Extrachromosomal DNA (ecDNA) is a frequent event in cancer that drives oncogene amplification, dysregulated gene expression and intratumoral heterogeneity, including through random segregation during cell division. Distinct ecDNA sequences, herein termed ecDNA species, can co-exist to facilitate intermolecular cooperation in cancer cells. However, how multiple ecDNA species within a tumor cell are assorted and maintained across somatic cell generations to drive cancer cell evolution is not known. Here we show that cooperative ecDNA species can be coordinately inherited through mitotic co-segregation. Imaging and single-cell analyses show that multiple ecDNAs encoding distinct oncogenes co-occur and are correlated in copy number in human cancer cells. EcDNA species are coordinately segregated asymmetrically during mitosis, resulting in daughter cells with simultaneous copy number gains in multiple ecDNA species prior to any selection. Computational modeling reveals the quantitative principles of ecDNA co-segregation and co-selection, predicting their observed distributions in cancer cells. Finally, we show that coordinated inheritance of ecDNAs enables co-amplification of specialized ecDNAs containing only enhancer elements and guides therapeutic strategies to jointly deplete cooperating ecDNA oncogenes. Coordinated inheritance of ecDNAs confers stability to oncogene cooperation and novel gene regulatory circuits, allowing winning combinations of epigenetic states to be transmitted across cell generations.

Hepatocytes undergo punctuated expansion dynamics from a periportal stem cell niche in normal human liver.

BACKGROUND & AIMS: While normal human liver is thought to be generally quiescent, clonal hepatocyte expansions have been observed, though neither their cellular source nor their expansion dynamics have been determined. Knowing the hepatocyte cell of origin, and their subsequent dynamics and trajectory within the human liver will provide an important basis to understand disease-associated dysregulation. METHODS: Herein, we use in vivo lineage tracing and methylation sequence analysis to demonstrate normal human hepatocyte ancestry. We exploit next-generation mitochondrial sequencing to determine hepatocyte clonal expansion dynamics across spatially distinct areas of laser-captured, microdissected, clones, in tandem with computational modelling in morphologically normal human liver. RESULTS: Hepatocyte clones and rare SOX9+ hepatocyte progenitors commonly associate with portal tracts and we present evidence that clones can lineage-trace with cholangiocytes, indicating the presence of a bipotential common ancestor at this niche. Within clones, we demonstrate methylation CpG sequence diversity patterns indicative of periportal not pericentral ancestral origins, indicating a portal to central vein expansion trajectory. Using spatial analysis of mitochondrial DNA variants by next-generation sequencing coupled with mathematical modelling and Bayesian inference across the portal-central axis, we demonstrate that patterns of mitochondrial DNA variants reveal large numbers of spatially restricted mutations in conjunction with limited numbers of clonal mutations. CONCLUSIONS: These datasets support the existence of a periportal progenitor niche and indicate that clonal patches exhibit punctuated but slow growth, then quiesce, likely due to acute environmental stimuli. These findings crucially contribute to our understanding of hepatocyte dynamics in the normal human liver. IMPACT AND IMPLICATIONS: The liver is mainly composed of hepatocytes, but we know little regarding the source of these cells or how they multiply over time within the disease-free human liver. In this study, we determine a source of new hepatocytes by combining many different lab-based methods and computational predictions to show that hepatocytes share a common cell of origin with bile ducts. Both our experimental and computational data also demonstrate hepatocyte clones are likely to expand in slow waves across the liver in a specific trajectory, but often lie dormant for many years. These data show for the first time the expansion dynamics of hepatocytes in normal liver and their cell of origin enabling the accurate measurment of changes to their dynamics that may lead to liver disease. These findings are important for researchers determining cancer risk in human liver.

In-situ growth of covalent organic framework on stainless steel needles as solid-phase microextraction probe coupled with electrospray ionization mass spectrometry for rapid and sensitive determination of tricyclic antidepressants in biosamples.

Tricyclic antidepressants (TCAs) including amitriptyline (AT), doxepin (DOX) and nortriptyline (NT) are the first-line drugs for the clinical treatment of depression; however, monitoring TCA concentrations in biological fluids and tissues is necessary to improve therapeutic effect and determine the cause of death in patients. It is of great significance to develop a rapid and sensitive method for real-time monitoring of TCAs in various biosamples. In this work, we fabricated a novel covalent organic framework (COF) based solid-phase microextraction (SPME) probe by an in-situ step-by-step strategy, which was obtained by sequentially modifying 1,3,5-tri (4-aminophenyl) benzene (TPB) and 2, 5-divinylbenzaldehyde (DVA) on the surface of polydopamine layer. The TPB-DVA-COF-SPME probe possessed high specific surface area (1244 m2·g - 1), regular pores (3.23 nm), good hydrophobicity and stability, resulting in efficient enrichment for TCAs. Furthermore, the combination of TPB-DVA-COF-SPME probe and ambient electrospray ionization mass spectrometry system (ESI/MS) was firstly proposed for rapid and sensitive determination of TCAs in biosamples. As a result, the developed method exhibited low limits of detection (LODs) (0.1-0.5 µg∙L - 1), high enrichment factors (39-218), and low relative standard deviations (RSDs) for one probe (1.2-3.8%) and probe-to-probe (2.0-3.7%). Benefiting from these outstanding performance, TPB-DVA-COF-SPME probe was further successfully applied to biosamples (i.e., serum, liver, kidney, and brain) with excellent reusability, indicating the promising applicability of the TPB-DVA-COF-SPME-ESI/MS as a powerful tool for drug monitoring.

First passage time analysis of spatial mutation patterns reveals sub-clonal evolutionary dynamics in colorectal cancer.

The signature of early cancer dynamics on the spatial arrangement of tumour cells is poorly understood, and yet could encode information about how sub-clones grew within the expanding tumour. Novel methods of quantifying spatial tumour data at the cellular scale are required to link evolutionary dynamics to the resulting spatial architecture of the tumour. Here, we propose a framework using first passage times of random walks to quantify the complex spatial patterns of tumour cell population mixing. First, using a simple model of cell mixing we demonstrate how first passage time statistics can distinguish between different pattern structures. We then apply our method to simulated patterns of mutated and non-mutated tumour cell population mixing, generated using an agent-based model of expanding tumours, to explore how first passage times reflect mutant cell replicative advantage, time of emergence and strength of cell pushing. Finally, we explore applications to experimentally measured human colorectal cancer, and estimate parameters of early sub-clonal dynamics using our spatial computational model. We infer a wide range of sub-clonal dynamics, with mutant cell division rates varying between 1 and 4 times the rate of non-mutated cells across our sample set. Some mutated sub-clones emerged after as few as 100 non-mutant cell divisions, and others only after 50,000 divisions. The majority were consistent with boundary driven growth or short-range cell pushing. By analysing multiple sub-sampled regions in a small number of samples, we explore how the distribution of inferred dynamics could inform about the initial mutational event. Our results demonstrate the efficacy of first passage time analysis as a new methodology in spatial analysis of solid tumour tissue, and suggest that patterns of sub-clonal mixing can provide insights into early cancer dynamics.

A Novel, Simple, and Low-Cost Approach for Machine Learning Screening of Kidney Cancer: An Eight-Indicator Blood Test Panel with Predictive Value for Early Diagnosis.

Clear cell renal cell carcinoma (ccRCC) accounts for more than 90% of all renal cancers. The five-year survival rate of early-stage (TNM 1) ccRCC reaches 96%, while the advanced-stage (TNM 4) is only 23%. Therefore, early screening of patients with renal cancer is essential for the treatment of renal cancer and the long-term survival of patients. In this study, blood samples of patients were collected and a pre-defined set of blood indicators were measured. A random forest (RF) model was established to predict based on each indicator in the blood, and was trained with all relevant indicators for comprehensive predictions. In our study, we found that there was a high statistical significance (p < 0.001) for all indicators of healthy individuals and early cancer patients, except for uric acid (UA). At the same time, ccRCC also presented great differences in most blood indicators between males and females. In addition, patients with ccRCC had a higher probability of developing a low ratio of albumin (ALB) to globulin (GLB) (AGR < 1.2). Eight key indicators were used to classify and predict renal cell carcinoma. The area under the receiver operating characteristic (ROC) curve (AUC) of the eight-indicator model was as high as 0.932, the sensitivity was 88.2%, and the specificity was 86.3%, which are acceptable in many applications, thus realising early screening for renal cancer by blood indicators in a simple blood-draw physical examination. Furthermore, the composite indicator prediction method described in our study can be applied to other clinical conditions or diseases, where multiple blood indicators may be key to enhancing the diagnostic potential of screening strategies.

The evolutionary dynamics of extrachromosomal DNA in human cancers.

Oncogene amplification on extrachromosomal DNA (ecDNA) is a common event, driving aggressive tumor growth, drug resistance and shorter survival. Currently, the impact of nonchromosomal oncogene inheritance-random identity by descent-is poorly understood. Also unclear is the impact of ecDNA on somatic variation and selection. Here integrating theoretical models of random segregation, unbiased image analysis, CRISPR-based ecDNA tagging with live-cell imaging and CRISPR-C, we demonstrate that random ecDNA inheritance results in extensive intratumoral ecDNA copy number heterogeneity and rapid adaptation to metabolic stress and targeted treatment. Observed ecDNAs benefit host cell survival or growth and can change within a single cell cycle. ecDNA inheritance can predict, a priori, some of the aggressive features of ecDNA-containing cancers. These properties are facilitated by the ability of ecDNA to rapidly adapt genomes in a way that is not possible through chromosomal oncogene amplification. These results show how the nonchromosomal random inheritance pattern of ecDNA contributes to poor outcomes for patients with cancer.

Clonal Transitions and Phenotypic Evolution in Barrett's Esophagus.

BACKGROUND & AIMS: Barrett's esophagus (BE) is a risk factor for esophageal adenocarcinoma but our understanding of how it evolves is poorly understood. We investigated BE gland phenotype distribution, the clonal nature of phenotypic change, and how phenotypic diversity plays a role in progression. METHODS: Using immunohistochemistry and histology, we analyzed the distribution and the diversity of gland phenotype between and within biopsy specimens from patients with nondysplastic BE and those who had progressed to dysplasia or had developed postesophagectomy BE. Clonal relationships were determined by the presence of shared mutations between distinct gland types using laser capture microdissection sequencing of the mitochondrial genome. RESULTS: We identified 5 different gland phenotypes in a cohort of 51 nondysplastic patients where biopsy specimens were taken at the same anatomic site (1.0-2.0 cm superior to the gastroesophageal junction. Here, we observed the same number of glands with 1 and 2 phenotypes, but 3 phenotypes were rare. We showed a common ancestor between parietal cell-containing, mature gastric (oxyntocardiac) and goblet cell-containing, intestinal (specialized) gland phenotypes. Similarly, we have shown a clonal relationship between cardiac-type glands and specialized and mature intestinal glands. Using the Shannon diversity index as a marker of gland diversity, we observed significantly increased phenotypic diversity in patients with BE adjacent to dysplasia and predysplasia compared to nondysplastic BE and postesophagectomy BE, suggesting that diversity develops over time. CONCLUSIONS: We showed that the range of BE phenotypes represents an evolutionary process and that changes in gland diversity may play a role in progression. Furthermore, we showed a common ancestry between gastric and intestinal-type glands in BE.

LiquidCNA: Tracking subclonal evolution from longitudinal liquid biopsies using somatic copy number alterations.

Cell-free DNA (cfDNA) measured via liquid biopsies provides a way for minimally invasive monitoring of tumor evolutionary dynamics during therapy. Here we present liquidCNA, a method to track subclonal evolution from longitudinally collected cfDNA samples sequenced through cost-effective low-pass whole-genome sequencing. LiquidCNA utilizes somatic copy number alteration (SCNA) to simultaneously genotype and quantify the size of the dominant subclone without requiring B-allele frequency information, matched-normal samples, or prior knowledge on the genetic identity of the emerging clone. We demonstrate the accuracy of liquidCNA in synthetically generated sample sets and in vitro mixtures of cancer cell lines. In vivo application in patients with metastatic lung cancer reveals the progressive emergence of a novel tumor subpopulation. LiquidCNA is straightforward to use, is computationally inexpensive, and enables continuous monitoring of subclonal evolution to understand and control-therapy-induced resistance.

Self-assembly synthes is of trypsin-immobilized monolithic microreactor for fast and efficient proteolysis.

Fast and highly efficient digestion of proteins is essential for high-throughput proteomic analysis. Herein, a facile approach was developed for self-assembly preparation of trypsin-immobilized capillary monolithic column and its application as an immobilized enzyme microreactor (IMER) for fast and highly efficient proteolysis was described. The performance of the trypsin-immobilized monolithic enzyme microreactor was evaluated by in-situ digestion of model proteins. The results showed that the trypsin-immobilized monolithic enzyme microreactor had much higher tryptic digestion efficiency than the free trypsin in solution, where the coverage of peptide sequences by mass spectrometry (MS)-based analysis could bear comparison with the free one, while the digestion time was dramatically shortened from 12 h to 16 s. Furthermore, the trypsin-immobilized monolithic enzyme microreactor also exhibited good practicability to complex human serum sample, in which the total of 45 peptides from human serum albumin (HSA) matched with sequence coverage of 75% were precisely identified. The successful application demonstrated the promising potential of the trypsin-immobilized capillary monolithic column as the IMER in high-throughput proteomic analysis.

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.

Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data.

Quantification of the effect of spatial tumour sampling on the patterns of mutations detected in next-generation sequencing data is largely lacking. Here we use a spatial stochastic cellular automaton model of tumour growth that accounts for somatic mutations, selection, drift and spatial constraints, to simulate multi-region sequencing data derived from spatial sampling of a neoplasm. We show that the spatial structure of a solid cancer has a major impact on the detection of clonal selection and genetic drift from both bulk and single-cell sequencing data. Our results indicate that spatial constrains can introduce significant sampling biases when performing multi-region bulk sampling and that such bias becomes a major confounding factor for the measurement of the evolutionary dynamics of human tumours. We also propose a statistical inference framework that incorporates spatial effects within a growing tumour and so represents a further step forwards in the inference of evolutionary dynamics from genomic data. Our analysis shows that measuring cancer evolution using next-generation sequencing while accounting for the numerous confounding factors remains challenging. However, mechanistic model-based approaches have the potential to capture the sources of noise and better interpret the data.

Life History Trade-Offs in Tumors.

PURPOSE: In this paper, we provide an overview of a life history theory and how it applies to cancer evolution. RECENT FINDINGS: We review the literature on trade-offs in tumors, focusing on the trade-offs among cellular proliferation, survival, and motility. Trade-offs are critical natural constraints for almost all evolutionary processes. Many ecological studies show that trade-offs among these cellular functions maintain a genetic diversity. In addition, these trade-offs are not fixed, but rather can shift depending on the ecological circumstances in the microenvironment. This can lead to selection for the cellular capacity to respond to these differing microenvironments in ways that promote the fitness of the cancer cell. We relate these life history trade-offs to the recently developed Evo-Eco indexes and discuss how life history theory can help refine our measures of tumor evolution and ecology. SUMMARY: Life history theory provides a framework for understanding how the spatial and temporal variability in the tumor microenvironment-in particular resources and threats-affect trade-offs among cell survival, cell proliferation, and cell migration. We discuss how these trade-offs can potentially be leveraged in cancer therapy to increase the effectiveness of treatment.

Robust RNA-based in situ mutation detection delineates colorectal cancer subclonal evolution.

Intra-tumor heterogeneity (ITH) is a major underlying cause of therapy resistance and disease recurrence, and is a read-out of tumor growth. Current genetic ITH analysis methods do not preserve spatial context and may not detect rare subclones. Here, we address these shortfalls by developing and validating BaseScope-a novel mutation-specific RNA in situ hybridization assay. We target common point mutations in the BRAF, KRAS and PIK3CA oncogenes in archival colorectal cancer samples to precisely map the spatial and morphological context of mutant subclones. Computational modeling suggests that subclones must arise sufficiently early, or carry a considerable fitness advantage, to form large or spatially disparate subclones. Examples of putative treatment-resistant cells isolated in small topographical areas are observed. The BaseScope assay represents a significant technical advance for in situ mutation detection that provides new insight into tumor evolution, and could have ramifications for selecting patients for treatment.