Defining genetic intra-tumor heterogeneity: a chronological annotation of mutational pathways

Tumor heterogeneity is believed to be important in tumor progression and its response to therapies. However, despite numerous mutations being reported in human tumors, genetic intra-tumor heterogeneity remains poorly defined. We have developed a novel strategy to provide a chronological annotation of mutational events in a tumor. We used an endometrial tumor from a patient and transplanted it into athymic mice to create many tumor xenografts. While the patient tumor xenografts were initially responsive to raloxifene treatment, xenografts created with cancer cell clones isolated from the same patient tumor showed dramatic differences in response to raloxifene, indicating existence of intra-tumor heterogeneity with some subpopulations inherently resistant to the drug. A 250K single nucleotide polymorphism (SNP) array from Affymetrix was used to profile genotype changes on 3 xenografts and 10 single cells from another 10 xenografts. We found 797 SNP sites containing loss of heterozygosity (LOH) common to all these specimens, indicating that genetic mutations in these regions may contain the earliest genetic events in the original patient tumor. Based upon the genotype information from the 10 single cancer cells, we developed a phylogenetic tree using neighbor-joining method. We showed that there are at least 3 distinct subpopulations in the patient tumor. Additionally, the phylogenetic tree was used to determine the order of genetic events, thus providing a chronological annotation to genetic mutations. Our approach represents an important analytic strategy for defining genetic intra-tumor heterogeneity and providing chronological annotations to the genetic landscape revealed by future whole genome sequencing in tumors

Serial evolutionary networks of within-patient HIV-1 sequences reveal patterns of evolution of X4 strains

<p>Abstract</p> <p>Background</p> <p>The HIV virus is known for its ability to exploit numerous genetic and evolutionary mechanisms to ensure its proliferation, among them, high replication, mutation and recombination rates. Sliding MinPD, a recently introduced computational method <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>, was used to investigate the patterns of evolution of serially-sampled HIV-1 sequence data from eight patients with a special focus on the emergence of X4 strains. Unlike other phylogenetic methods, Sliding MinPD combines distance-based inference with a nonparametric bootstrap procedure and automated recombination detection to reconstruct the evolutionary history of longitudinal sequence data. We present serial evolutionary networks as a longitudinal representation of the mutational pathways of a viral population in a within-host environment. The longitudinal representation of the evolutionary networks was complemented with charts of clinical markers to facilitate correlation analysis between pertinent clinical information and the evolutionary relationships.</p> <p>Results</p> <p>Analysis based on the predicted networks suggests the following:: significantly stronger recombination signals (p = 0.003) for the inferred ancestors of the X4 strains, recombination events between different lineages and recombination events between putative reservoir virus and those from a later population, an early star-like topology observed for four of the patients who died of AIDS. A significantly higher number of recombinants were predicted at sampling points that corresponded to peaks in the viral load levels (p = 0.0042).</p> <p>Conclusion</p> <p>Our results indicate that serial evolutionary networks of HIV sequences enable systematic statistical analysis of the implicit relations embedded in the topology of the structure and can greatly facilitate identification of patterns of evolution that can lead to specific hypotheses and new insights. The conclusions of applying our method to empirical HIV data support the conventional wisdom of the new generation HIV treatments, that in order to keep the virus in check, viral loads need to be suppressed to almost undetectable levels.</p

A phylogenomic analysis of Marek's disease virus reveals independent paths to virulence in Eurasia and North America

Virulence determines the impact a pathogen has on the fitness of its host, yet current understanding of the evolutionary origins and causes of virulence of many pathogens is surprisingly incomplete. Here, we explore the evolution of Marek's disease virus (MDV), a herpesvirus commonly afflicting chickens and rarely other avian species. The history of MDV in the 20th century represents an important case study in the evolution of virulence. The severity of MDV infection in chickens has been rising steadily since the adoption of intensive farming techniques and vaccination programs in the 1950s and 1970s, respectively. It has remained uncertain, however, which of these factors is causally more responsible for the observed increase in virulence of circulating viruses. We conducted a phylogenomic study to understand the evolution of MDV in the context of dramatic changes to poultry farming and disease control. Our analysis reveals evidence of geographical structuring of MDV strains, with reconstructions supporting the emergence of virulent viruses independently in North America and Eurasia. Of note, the emergence of virulent viruses appears to coincide approximately with the introduction of comprehensive vaccination on both continents. The time-dated phylogeny also indicated that MDV has a mean evolutionary rate of ~1.6 × 10−5 substitutions per site per year. An examination of gene-linked mutations did not identify a strong association between mutational variation and virulence phenotypes, indicating that MDV may evolve readily and rapidly under strong selective pressures and that multiple genotypic pathways may underlie virulence adaptation in MDV

INVESTIGATING INVASION IN DUCTAL CARCINOMA IN SITU WITH TOPOGRAPHICAL SINGLE CELL GENOME SEQUENCING

Synchronous Ductal Carcinoma in situ (DCIS-IDC) is an early stage breast cancer invasion in which it is possible to delineate genomic evolution during invasion because of the presence of both in situ and invasive regions within the same sample. While laser capture microdissection studies of DCIS-IDC examined the relationship between the paired in situ (DCIS) and invasive (IDC) regions, these studies were either confounded by bulk tissue or limited to a small set of genes or markers. To overcome these challenges, we developed Topographic Single Cell Sequencing (TSCS), which combines laser-catapulting with single cell DNA sequencing to measure genomic copy number profiles from single tumor cells while preserving their spatial context. We applied TSCS to sequence 1,293 single cells from 10 synchronous DCIS patients. We also applied deep-exome sequencing to the in situ, invasive and normal tissues for the DCIS-IDC patients. Previous bulk tissue studies had produced several conflicting models of tumor evolution. Our data support a multiclonal invasion model, in which genome evolution occurs within the ducts and gives rise to multiple subclones that escape the ducts into the adjacent tissues to establish the invasive carcinomas. In summary, we have developed a novel method for single cell DNA sequencing, which preserves spatial context, and applied this method to understand clonal evolution during the transition between carcinoma in situ to invasive ductal carcinoma

A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors

Increasingly complex in silico modeling approaches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models, such as those based on partial differential equations, are computationally affordable and allow large tumor sizes and long temporal windows to be studied, but miss the discrete nature of many key underlying cellular processes. Individual-based approaches provide a much more detailed description of tumors, but have difficulties when trying to handle full-sized real cancers. Thus, there exists a trade-off between the integration of macroscopic and microscopic information, now widely available, and the ability to attain clinical tumor sizes. In this paper we put forward a stochastic mesoscopic simulation framework that incorporates key cellular processes during tumor progression while keeping computational costs to a minimum. Our framework captures a physical scale that allows both the incorporation of microscopic information, tracking the spatio-temporal emergence of tumor heterogeneity and the underlying evolutionary dynamics, and the reconstruction of clinically sized tumors from high-resolution medical imaging data, with the additional benefit of low computational cost. We illustrate the functionality of our modeling approach for the case of glioblastoma, a paradigm of tumor heterogeneity that remains extremely challenging in the clinical setting.This research has been supported by grants awarded to VMPG by James S. Mc. Donnell Foundation, United States of America, 21st Century Science Initiative in Mathematical and Complex Systems Approaches for Brain Cancer (collaborative award 220020560) and Junta de Comunidades de Castilla-La Mancha, Spain (grant number SBPLY/17/180501/000154). VMPG and GFC thank the funding from Ministerio de Ciencia e Innovacion, Spain (grant number PID2019-110895RB-I00). This research has also been supported by a grant awarded to GFC and JBB by the Junta de Comunidades de Castilla-La Mancha, Spain (grant number SBPLY/19/180501/000211). AMR received support from Asociacion Pablo Ugarte (http://www.asociacionpablougarte.es). JJS received support from Universidad de Castilla-La Mancha (grant number 2020-PREDUCLM-15634). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Models of RNA virus evolution and their roles in vaccine design

Viruses are fast evolving pathogens that continuously adapt to the highly variable environments they live and reproduce in. Strategies devoted to inhibit virus replication and to control their spread among hosts need to cope with these extremely heterogeneous populations and with their potential to avoid medical interventions. Computational techniques such as phylogenetic methods have broadened our picture of viral evolution both in time and space, and mathematical modeling has contributed substantially to our progress in unraveling the dynamics of virus replication, fitness, and virulence. Integration of multiple computational and mathematical approaches with experimental data can help to predict the behavior of viral pathogens and to anticipate their escape dynamics. This piece of information plays a critical role in some aspects of vaccine development, such as viral strain selection for vaccinations or rational attenuation of viruses. Here we review several aspects of viral evolution that can be addressed quantitatively, and we discuss computational methods that have the potential to improve vaccine design

Exploiting natural selection to study adaptive behavior

The research presented in this dissertation explores different computational and modeling techniques that combined with predictions from evolution by natural selection leads to the analysis of the adaptive behavior of populations under selective pressure. For this thesis three computational methods were developed: EXPLoRA, EVORhA and SSA-ME. EXPLoRA finds genomic regions associated with a trait of interests (QTL) by explicitly modeling the expected linkage disequilibrium of a population of sergeants under selection. Data from BSA experiments was analyzed to find genomic loci associated with ethanol tolerance. EVORhA explores the interplay between driving and hitchhiking mutations during evolution to reconstruct the subpopulation structure of clonal bacterial populations based on deep sequencing data. Data from mixed infections and evolution experiments of E. Coli was used and their population structure reconstructed. SSA-ME uses mutual exclusivity in cancer to prioritize cancer driver genes. TCGA data of breast cancer tumor samples were analyzed.status: publishe

Evolution of oesophageal adenocarcinoma from metaplastic columnar epithelium without goblet cells in Barrett's oesophagus

Supported by the Dutch Cancer Society (KWF) and Cancer Research UK (CR-UK). This work was supported by Cancer Research UK (grant number A14895

Animal domestication in the era of ancient genomics

The domestication of animals led to a major shift in human subsistence patterns, from a hunter–gatherer to a sedentary agricultural lifestyle, which ultimately resulted in the development of complex societies. Over the past 15,000 years, the phenotype and genotype of multiple animal species, such as dogs, pigs, sheep, goats, cattle and horses, have been substantially altered during their adaptation to the human niche. Recent methodological innovations, such as improved ancient DNA extraction methods and next-generation sequencing, have enabled the sequencing of whole ancient genomes. These genomes have helped reconstruct the process by which animals entered into domestic relationships with humans and were subjected to novel selection pressures. Here, we discuss and update key concepts in animal domestication in light of recent contributions from ancient genomics