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Background: Metastasis is the leading cause of cancer related deaths in breast cancer patients. Lymphovascular invasion represents one of the earliest stages of metastasis wherein the cells are introduced to a very different and distinct microenvironment. Methods: We leveraged spatial techniques developed for limited specimens in archival tissue to study patient matched cross-sectional tumor samples from different stages of breast neoplasia including normal breast, ductal carcinoma in situ (DCIS), primary invasive carcinoma (IBC), lymphovascular invasion (LVI) and regional lymph node metastasis. We selected a set of 21 patients with ER+ breast cancer to generate cross-sectional samples of each of these stages, for a total of 331 samples. The areas of LVI were identified by a combination of H&E review and immunohistochemistry for podoplanin. We performed smart-3SEQ for gene expression profiling and light pass whole genome sequencing for DNA copy number alterations. Results: We profiled the spectrum of neoplasia for transcriptome-wide gene expression. Principal component analysis of all 252 DCIS, LVI, IBC, or metastasis samples using the top 500 genes with the highest variance demonstrated that clustering was roughly based on the diagnostic stage (i.e. DCIS, LVI, IBC, or metastasis). Differential gene expression profiling identified thousands of genes increased or decreased in expression across the transitional stages with the largest change in gene expression being the transition from normal breast to DCIS, dominated by gene expression down regulation. We next performed NMF clustering on 62 samples of LVI from 18 cases and identified two patterns of gene expression which define two subgroups. Gene ontology analysis revealed that one cluster was associated with increased proliferation and metabolism, whereas the second cluster was dominated by an immune response. When we analyzed the immune and proliferative LVI subgroups separately, we found that the immune profiles in the patient matched IBC and LVI samples from the LVI Immune cluster were similar, whereas the immune profiles in the patient matched IBC and LVI samples from the Proliferative cluster were significantly different. At the LVI stage, all immune cell populations estimated by CibersortX were decreased in the Proliferative LVI cluster. These changes were validated using immunofluorescence for proliferation (Ki67), T cells (CD3) and macrophages (CD68) on the same samples. Using the LVI centroids, we built a model that could predict the same clusters in the METABRIC IBC. Kaplan-Meier analysis showed a significant difference between groups, with the Proliferative-like IBC group having a worse prognosis than the Immune-like IBC group. Conclusions: We observed a dichotomy at the LVI stage with a more proliferative cluster that may escape the immune response and an immune cluster which has a microenvironment with a similar pattern to its primary IBC. The recognition of two groups of LVI, differing in immune association and proliferation, raises the possibility that the risk of metastasis could be different in these two groups, leading to different biological pathways of progression. Citation Format: Belén Rivero-Gutiérrez, Diego Mallo, Almudena Espín-Pérez, Sujay Vennam, Chunfang Zhu, Sushama Varma, Greg Scott, Joseph Foley, E Shelley Hwang, Carlo Maley, Robert West. Characterization of the lymphovascular invasion microenvironment reveals immune response dichotomy [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD2-09.

Ductal carcinoma in situ (DCIS) is the most common form of preinvasive breast cancer and, despite treatment, a small fraction (5–10%) of DCIS patients develop subsequent invasive disease. A fundamental biologic question is whether the invasive disease arises from tumor cells in the initial DCIS or represents new unrelated disease. To address this question, we performed genomic analyses on the initial DCIS lesion and paired invasive recurrent tumors in 95 patients together with single-cell DNA sequencing in a subset of cases. Our data show that in 75% of cases the invasive recurrence was clonally related to the initial DCIS, suggesting that tumor cells were not eliminated during the initial treatment. Surprisingly, however, 18% were clonally unrelated to the DCIS, representing new independent lineages and 7% of cases were ambiguous. This knowledge is essential for accurate risk evaluation of DCIS, treatment de-escalation strategies and the identification of predictive biomarkers.

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.

Intra-tumoral heterogeneity (ITH) could represent clonal evolution where subclones with greater fitness confer more malignant phenotypes and invasion constitutes an evolutionary bottleneck. Alternatively, ITH could represent branching evolution with invasion of multiple subclones. The two models respectively predict a hierarchy of subclones arranged by phenotype, or multiple subclones with shared phenotypes. We delineate these modes of invasion by merging ancestral, topographic, and phenotypic information from 12 human colorectal tumors (11 carcinomas, 1 adenoma) obtained through saturation microdissection of 325 small tumor regions. The majority of subclones (29/46, 60%) share superficial and invasive phenotypes. Of 11 carcinomas, 9 show evidence of multiclonal invasion, and invasive and metastatic subclones arise early along the ancestral trees. Early multiclonal invasion in the majority of these tumors indicates the expansion of co-evolving subclones with similar malignant potential in absence of late bottlenecks and suggests that barriers to invasion are minimal during colorectal cancer growth., Invasion is a critical step in tumor development. Here, in colorectal cancer, the authors show that multiclonal invasion of the muscularis mucosae is pervasive, suggesting that invasive capacity is not a significant bottleneck in the evolution of the disease.

Ductal carcinoma in situ (DCIS) is the most common form of preinvasive breast cancer and, despite treatment, a small fraction (5-10%) of DCIS patients develop subsequent invasive disease. A fundamental biologic question is whether the invasive disease arises from tumor cells in the initial DCIS or represents new unrelated disease. To address this question, we performed genomic analyses on the initial DCIS lesion and paired invasive recurrent tumors in 95 patients together with single-cell DNA sequencing in a subset of cases. Our data show that in 75% of cases the invasive recurrence was clonally related to the initial DCIS, suggesting that tumor cells were not eliminated during the initial treatment. Surprisingly, however, 18% were clonally unrelated to the DCIS, representing new independent lineages and 7% of cases were ambiguous. This knowledge is essential for accurate risk evaluation of DCIS, treatment de-escalation strategies and the identification of predictive biomarkers.

Background: Heterogeneity is a hallmark of human cancers that is apparent both between and within individual tumors. Intra-tumor heterogeneity provides the genetic fuel for natural selection in clonal evolution and cancer progression. Tumors with high levels of genetic heterogeneity are hypothesized to be more likely to progress to invasion and metastasis Methods: We measured the mutational loads from separate areas of pure DCIS and compared this to genetic heterogeneity in DCIS lesions that co-exist with invasive cancer, as a surrogate for progression. Cases of pure DCIS and DCIS diagnosed concurrent with invasive cancer were identified. Two areas of DCIS from each case a minimum of 0.8 cm apart and control tissues were macro-dissected and the DNA extracted from FFPE samples. To analyze the data, we developed new bioinformatics methods that allowed analysis of small amounts of degraded DNA extracted from FFPE samples across multiple regions. Our bioinformatics pipeline was optimized on a series of 28 independent technical replicates of the same DNA sample sequenced twice, as training tools to find the best filtering parameters. Results: Whole exome sequencing was performed on two geospatially separated blocks for each case (41 pure DCIS and 30 DCIS adjacent to invasive disease). Minimum coverage for inclusion in this study was 40X over at least 50% of the exome. We used the ratio of private mutations (only in 1 area) to public (found in both areas) mutations as a measure of intra-tumor heterogeneity. Overall mutational load of DCIS was not a significant predictor to progression; however notably, DCIS adjacent to invasive disease patients had a higher ratio of private/public mutations (heterogeneity) in coding domains (Mann-Whitney p=0.016. Functional analysis of mutated coding genes (DAVID 6.8) shows a statistically significant enrichment in signal transduction, olfactory receptors (G-protein-coupled receptors) and cell-matrix interactions in both tumor types, after FDR correction. DCIS adjacent to invasive disease had an enrichment of mutated genes involved in additional cellular functions such as microtubule activity (fold enrichment =7.6, FDR=0.002), protein-protein interactions (fold enrichment =3.65, FDR=5.11E-04) and extracellular matrix remodeling (fold enrichment =8.3, FDR=0.02). Conclusion: We present an approach to measure clonal heterogeneity using a bulk sequencing strategy applied to geospatially distinct foci of DCIS. Our findings suggest that functional heterogeneity may play an important evolutionary role as a driver for invasive progression. Citation Format: Fortunato A, Mallo D, King L, Hardman T, Hall A, Marks JR, Hwang S, Maley CC. Genetic heterogeneity of DCIS is a predictor of invasive cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-06-12.

Pure ductal carcinoma in situ (DCIS) is being diagnosed more frequently through breast screening programmes and is associated with an increased risk of developing invasive breast cancer. We assessed the clonal relatedness of 143 cases of pure DCIS and their subsequent events using a combination of whole exome, targeted and copy number sequencing, supplemented by single cell analysis. Unexpectedly, 18% of all invasive events after DCIS were clonally unrelated to the primary DCIS. Single cell sequencing of selected pairs confirmed our findings. In contrast, synchronous DCIS and invasive disease (n=44) were almost always (93%) clonally related. This challenges the dogma that almost all invasive events after DCIS represent invasive transformation of the initial DCIS and suggests that DCIS could be an independent risk factor for developing invasive disease as well as a precursor lesion. Our findings support a paradigm shift that confirms a more complex role for DCIS than previously recognized, and that the future management of DCIS should take into account both the precursor and risk factor implications of this diagnosis.

Intra-tumoral heterogeneity (ITH) could represent clonal evolution where subclones with greater fitness confer more malignant phenotypes and invasion constitutes an evolutionary bottleneck. Alternatively, ITH could represent branching evolution with invasion of multiple subclones. The two models respectively predict a hierarchy of subclones arranged by phenotype, or multiple subclones with shared phenotypes. We delineated these modes of invasion by merging ancestral, topographic, and phenotypic information from 12 human colorectal tumors (11 carcinomas, 1 adenoma) obtained through saturation microdissection of 325 small tumor regions. The majority of subclones (29/46, 60%) shared superficial and invasive phenotypes. Of 11 carcinomas, 9 showed evidence of multiclonal invasion, and invasive and metastatic subclones arose early along the ancestral trees. Early multiclonal invasion in the majority of these tumors indicates the expansion of co-evolving subclones with similar malignant potential in absence of late bottlenecks, and suggests that barriers to invasion are minimal during colorectal cancer growth.

This abstract was not presented at the symposium.

The low risk of progression of Barrett’s esophagus (BE) to esophageal adenocarcinoma can lead to over-diagnosis and over-treatment of BE patients. This may be addressed through a better understanding of the dynamics surrounding BE malignant progression. Although genetic diversity has been characterized as a marker of malignant development, it is still unclear how BE arises and develops. Here we uncover the evolutionary dynamics of BE at crypt and biopsy levels in eight individuals, including four patients that experienced malignant progression. We assay eight individual crypts and the remaining epithelium by SNP array for each of 6–11 biopsies over 2 time points per patient (358 samples in total). Our results indicate that most Barrett’s segments are clonal, with similar number and inferred rates of alterations observed for crypts and biopsies. Divergence correlates with geographical location, being higher near the gastro-esophageal junction. Relaxed clock analyses show that genomic instability precedes and is enhanced by genome doubling. These results shed light on the clinically relevant evolutionary dynamics of BE., Clonal dynamics of Barrett’s esophagus (BE) leading to cancer are poorly understood. Here, they report BE segments are clonal, have frequent mutations at the gastro-esophageal junction, genomic instability precedes genome doubling/clonal expansion, and a correlation between inter- and intra-biopsy genetic diversity.

Intra-tumor heterogeneity drives neoplastic progression by supplying the fuel for natural selection among neoplastic cells. It also complicates screening and treatment of neoplasms. We hypothesize that the degree of intra-tumor heterogeneity in DCIS should predict which tumors are likely to become invasive and metastatic. We initiated a pilot project to test this hypothesis by comparing 9 cases of pure DCIS to 9 cases of DCIS with adjacent invasive disease. For each case, we sequenced the exome from two spatially distinct regions of DCIS as well as normal tissue taken from a lymph node with no tumor involvement. This required the development of new methods to extract high quality sequencing data from small amounts of DNA extracted from FFPE samples. We calculated the genetic divergence between the two tumor regions, defined as percent of the sequenced regions of the genome showing differences between the two samples that had sufficient sequencing coverage and quality scores for confident scoring. We also employed automated imaging analysis to score microenvironmental differences between the two tumor regions. These microenvironmental measures are based on ecological methods for measuring organismal interactions and habitats. We will present initial data on differences in phenotypic and genotypic intra-tumor heterogeneity comparing pure DCIS to DCIS associated with invasive breast cancer. Our methods can be readily translated to large tissue banks of FFPE samples from DCIS. Citation Format: Fortunato A, King L, Mallo D, Kovacheva V, Yuan Y, Boddy A, Graham T, Aktipis A, Mardis ER, Hall A, Marks JR, Hwang S, Maley CC. Genomic and microenvironmental intra-tumor heterogeneity in DCIS [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-05-30.

Introduction. The natural history of preinvasive breast cancer, or ductal carcinoma in situ (DCIS) remains poorly understood. Overcoming this gap would allow risk-appropriate treatment for patients diagnosed with DCIS. We used a multiregional sequencing approach in combination with mathematical modeling to characterize the evolutionary dynamics of DCIS initiation and progression. Methods. We analyzed a cohort of 18 patients diagnosed with DCIS, either with (n=9) or without (n=9) synchronous invasive cancer. Based on whole exome sequencing, tumor-specific mutation panels were constructed, each targeting 29-75 mutations (median: 60). From each tumor, and using selective ultraviolet radiation fractionation (SURF), we microdissected small spots (encompassing 1-3 duct cross-sections) from 3-4 spatially separated microscope sections (mean slide separation: 1.25cm, range: 0.34-6.0cm). Spots were spatially registered and genotyped based on targeted sequencing of the tumor-specific mutation panels. For each tumor, we performed unsupervised clonal deconvolution of the spot genotypes (CloneFinder) and constructed phylogenetic subclone trees. To quantify the spatial patterns of subclonal mutations, we introduced a dispersion index (DI), ranging from low (DI=0%) to high (DI=100%). To provide a spatio-temporal context for the heterogeneity patterns we developed a family of stochastic mathematical models of DCIS initiation and progression. Thereby, we embedded the evolutionary dynamics of tumor cell expansion in the branching topology of mammary ductal trees. Results. A total of 485 microdissected spots (median per tumor: 23, range: 10-50) were spatially registered and sequenced (median depth: 9,000x). All tumors were multiclonal, containing a median of 5 subclones (range: 2-14). Surprisingly, the correlation between spatial and genomic distances of spots was low. Individual subclones were diffusely dispersed across tumors. DCIS with synchronous DCIS and invasive cancer (mixed DCIS) had a higher mutation dispersion (DI=84.7%) than those without (pure DCIS, DI=70.5%; p=0.03, Wilcoxon rank-sum test). Mixed DCIS also had a higher fraction of spots containing more than one subclone than pure DCIS (median: 30.4% vs 0%, p=0.03). Among 7 mixed DCIS with invasive spots, 5 showed evidence of multiclonal invasion, that is more than one invading subclones were found in both in situ and invasive regions of the tumor. Mathematical modeling analyses show that the observed spatial patterns of genetic heterogeneity are consistent with a single expansion of mixing subclones across the ductal tree architecture. Conclusions. Our findings provide novel insights into the early growth and invasion dynamics of DCIS lesions. Further, we identified potential evolutionary markers for the delineation between indolent (pure) and aggressive (mixed) DCIS. This constitutes an important step towards identification of patients with low-risk DCIS who could be appropriately managed with less aggressive treatment. Citation Format: Marc D. Ryser, Inmaculada C. Sorribes, Matthew Greenwald, Ethan Wu, Allison Hall, Diego Mallo, Lorraine M. King, Timothy Hardman, Lunden Simpson, Carlo C. Maley, Jeffrey R. Marks, Darryl Shibata, E. Shelley Hwang. Inferring the evolutionary dynamics of ductal carcinoma in situ through multi-regional sequencing and mathematical modeling [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PR02.

We present here a fast and flexible software–SimPhy–for the simulation of multiple gene families evolving under incomplete lineage sorting, gene duplication and loss, horizontal gene transfer—all three potentially leading to the species tree/gene tree discordance—and gene conversion. SimPhy implements a hierarchical phylogenetic model in which the evolution of species, locus and gene trees is governed by global and local parameters (e.g., genome-wide, species-specific, locus-specific), that can be fixed or be sampled from a priori statistical distributions. SimPhy also incorporates comprehensive models of substitution rate variation among lineages (uncorrelated relaxed clocks) and the capability of simulating partitioned nucleotide, codon and protein multilocus sequence alignments under a plethora of substitution models using the program INDELible. We validate SimPhy's output using theoretical expectations and other programs, and show that it scales extremely well with complex models and/or large trees, being an order of magnitude faster than the most similar program (DLCoal-Sim). In addition, we demonstrate how SimPhy can be useful to understand interactions among different evolutionary processes, conducting a simulation study to characterize the systematic overestimation of the duplication time when using standard reconciliation methods. SimPhy is available at https://github.com/adamallo/SimPhy, where users can find the source code, pre-compiled executables, a detailed manual and example cases.

Genetic diversity both between and within individual tumors constitutes a challenge to personalized cancer medicine. Intra-tumor heterogeneity provides the genetic fuel for natural selection in clonal evolution and cancer progression. Tumors with high levels of genetic heterogeneity are hypothesized to be more likely to demonstrate aggressive behavior and progress to invasion and metastasis. We analyzed the mutational loads from separate areas of pure DCIS and compared this to genetic heterogeneity in DCIS lesions found adjacent to invasive and metastatic cancer. Two spatially distinct areas of DCIS from each case were macro-dissected and the DNA extracted from FFPE samples. To analyze the data, we developed new bioinformatics methods that allowed analysis of small amounts of degraded DNA extracted from FFPE samples across multiple regions. Our bioinformatics pipeline was optimized on a series of 28 independent technical replicates of the same DNA sample sequenced twice, as training tools to find the best filtering parameters. Whole exome sequencing was performed on each of the two geospatially separated samples for each case. Minimum coverage for inclusion in this study was 40X over at least 50% of the exome. We used the ratio of private mutations (only in 1 area) to public (found in both areas) mutations as a measure of intra-tumor heterogeneity. We present an approach to measure clonal heterogeneity using a bulk sequencing strategy applied to geospatially distinct foci of DCIS. We found statistically significant difference between DCIS adjacent to invasive disease and metastatic patients' genetic divergence (t-test, p=0.013). Our findings suggest that genetic and functional heterogeneity may play an important evolutionary role as a driver for invasive progression. Citation Format: Angelo Fortunato, Diego Mallo, Lorraine King, Timothy Hardman, Allison Hall, Jeffrey R. Marks, Shelley Shelley Hwang, Carlo C. Maley. Genetic and functional heterogeneity of DCIS as predictors of invasive cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2502.

Introduction. Ductal carcinoma in situ (DCIS) of the breast has a limited propensity to progress to invasive breast cancer. However, due to a lack of markers that predict the risk of progression to invasive cancer, many DCIS patients may receive unnecessary surgery. Here, we explore whether the multiclonal intratumoral heterogeneity commonly observed in invasive breast cancers is also present in the precursor DCIS, and whether it differs between pure DCIS (without concurrent invasion) and mixed DCIS (with concurrent invasion). Methods. A total of 19 tumors (10 pure, 9 mixed) were analyzed. Based on whole exome sequencing, tumor-specific mutation panels were identified and contained a median of 60 point mutations per tumor (range: 35-80). From each tumor, 3-4 histologic sections were obtained, with a mean distance between slides of 1.26cm (range: 0.34-6.0cm). Individual DCIS ducts were microdissected from the sections using selective ultraviolet radiation fractionation (SURF); median number of ducts per tumor was 23 (range: 10-50). For each duct, the tumor specific mutation panel was assayed using targeted deep sequencing (median depth: 9,000x). Clonal deconvolution within each tumor was performed using the software package CloneFinder. Individual glands were classified as multiclonal if they contained 2 or more tumor subclones. In addition, the following evolutionary measures were computed for each tumor: (i) clone diversity as the mean pairwise Hamming distance between all ducts; (ii) clonal mixing as the number of clones that were present in more than one section. Comparisons between pure and mixed cases were performed using a two-sided Wilcoxon rank sum test. Results. All DCIS lesions were multiclonal. The number of subclones per tumor ranged from 2 to 9 overall, with a median of 5 and 6 subclones in pure and mixed DCIS, respectively (p-value for difference=.1). The median fraction of multiclonal glands per tumor was higher in mixed vs. pure DCIS (10% vs. 0%; p=.03). Median subclone diversity was higher in mixed vs. pure DCIS (1.48 vs. 1.04; p=.03), and the fraction of mixing subclones trended higher in mixed DCIS (median: 63%, range: 38-100%) compared to pure DCIS (median: 37%, range: 0-80%; p=.1). Subclones were spatially extensive: 18/19 tumors had at least one subclone that spanned across the analyzed sections, covering a mean distance of 2.5cm. Conclusions. All analyzed DCIS cases were multiclonal and individual subclones were found to span large regions of the tumor. Despite small sample sizes we found differences between pure and mixed DCIS. In particular, mixed DCIS harbored a higher fraction of multiclonal glands and a higher subclone diversity between individual ducts. Our findings characterize the evolutionary dynamics of breast cancer initiation and may provide evolutionary markers that distinguish indolent (pure) and progressive (mixed) disease. As such, this line of work has the potential to risk-stratify DCIS lesions. Citation Format: Marc D. Ryser, Inmaculada C. Sorribes, Matthew Greenwald, Allison Hall, Diego Mallo, Lorraine M. King, Timothy Hardman, Lunden Simpson, Carlo C. Maley, Jeffrey R. Marks, Darryl Shibata, E. Shelley Hwang. Ductal carcinoma in situ is a multiclonal disease [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2683.

To alleviate the over-diagnosis and overtreatment of premalignant conditions we need to predict their progression to cancer, and therefore, the dynamics of an evolutionary process. However, monitoring evolutionary processes in vivo is extremely challenging. Computer simulations constitute an attractive alternative, allowing us to study these dynamics based on a set of evolutionary parameters.We introduce CryptSim, a simulator of crypt evolution inspired by Barrett’s esophagus. We detail the most relevant computational strategies it implements, and perform a simulation study showing that the interaction between neighboring crypts may play a crucial role in carcinogenesis.

Motivation A reconciliation is an annotation of the nodes of a gene tree with evolutionary events—for example, speciation, gene duplication, transfer, loss, etc.—along with a mapping onto a species tree. Many algorithms and software produce or use reconciliations but often using different reconciliation formats, regarding the type of events considered or whether the species tree is dated or not. This complicates the comparison and communication between different programs. Results Here, we gather a consortium of software developers in gene tree species tree reconciliation to propose and endorse a format that aims to promote an integrative—albeit flexible—specification of phylogenetic reconciliations. This format, named recPhyloXML, is accompanied by several tools such as a reconciled tree visualizer and conversion utilities. Availability and implementation http://phylariane.univ-lyon1.fr/recphyloxml/.

The opisthokonts are one of the major super-groups of eukaryotes. It comprises two major clades: 1) the Metazoa and their unicellular relatives and 2) the Fungi and their unicellular relatives. There is, however, little knowledge of the role of opisthokont microbes in many natural environments, especially among non-metazoan and non-fungal opisthokonts. Here we begin to address this gap by analyzing high throughput 18S rDNA and 18S rRNA sequencing data from different European coastal sites, sampled at different size fractions and depths. In particular, we analyze the diversity and abundance of choanoflagellates, filastereans, ichthyosporeans, nucleariids, corallochytreans and their related lineages. Our results show the great diversity of choanoflagellates in coastal waters as well as a relevant role of the ichthyosporeans and the uncultured marine opisthokonts (MAOP). Furthermore, we describe a new lineage of marine fonticulids (MAFO) that appears to be abundant in sediments. Therefore, our work points to a greater potential ecological role for unicellular opisthokonts than previously appreciated in marine environments, both in water column and sediments, and also provides evidence of novel opisthokont phylogenetic lineages. This study highlights the importance of high throughput sequencing approaches to unravel the diversity and distribution of both known and novel eukaryotic lineages.

Evolution by natural selection is the conceptual foundation for nearly every branch of biology and increasingly also for biomedicine and medical research. In cancer biology, evolution explains how populations of cells in tumors change over time. It is a fundamental question whether this evolutionary process is driven primarily by natural selection and adaptation or by other evolutionary processes such as founder effects and drift. In cancer biology, as in organismal evolutionary biology, there is controversy about this question and also about the use of adaptation through natural selection as a guiding framework for research. In this review, we discuss the differences and similarities between evolution among somatic cells versus evolution among organisms. We review what is known about the parameters and rate of evolution in neoplasms, as well as evidence for adaptation. We conclude that adaptation is a useful framework that accurately explains the defining characteristics of cancer. Further, convergent evolution through natural selection provides the only satisfying explanation both for how a group of diverse pathologies have enough in common to usefully share the descriptive label of "cancer" and for why this convergent condition becomes life-threatening.

The concept of homology lies at the root of evolutionary biology. Since the seminal work of Fitch (1970), three main categories of homology relationships have been defined at the molecular level: orthology, paralogy, and xenology. In brief, if two gene copies arose by duplication they are paralogs, whereas if they arose through speciation they are orthologs. If one of them was transferred from a contemporaneous species, we call them xenologs (Supplementary Fig. S1 in Supplementary Material online, available at http://dx.doi.org/10.5061/dryad.87k57; see Gray and Fitch (1983); Fitch (2000)). Indeed, these terms were coined under a phylogenetic framework in which species were represented by single individuals, and as such they have remained very much intact during the last four decades—although particular cases within these categories have received specific names (Mindell and Meyer 2001). However, advances in sequencing technology have changed the field, and it is now very common to collect data sets containing multiple gene loci and/or multiple individuals per species. In general, such genome-wide data sets not only have unveiled extensive phylogenomic incongruence (Jeffroy et al. 2006; Salichos and Rokas 2013) but have brought back to the spotlight the consideration of how ancestral polymorphisms sort within populations (Edwards 2009). Altogether, phylogenomic data make imperative the explicit distinction between organismal and gene histories. Let us consider phylogenetic relationships at three different levels: species, loci, and gene copies (Fig. 1). The distinction between species/population trees and gene trees has been known for decades (Goodman et al. 1979; Pamilo and Nei 1988; Takahata 1989), whereas the introduction of locus trees into these models is very recent (Rasmussen and Kellis 2012). In brief, a species tree depicts the evolutionary history of the sampled organisms. In this case, the nodes represent speciation events, connected by branches that reflect the population history along these periods, and where their widths represent effective population size (Ne) and their lengths represent time (usually in years or number of generations). Apart from speciations, only evolutionary processes that affect species as a whole are represented at this level, like hybridization. Note that species trees are equivalent to population trees when the organismal units of interest are conspecific populations. In this case, the nodes of the population trees represent isolation events. In general, we will refer to “species” as any diverging, interbreeding group of individuals regardless of its taxonomic rank. On the other hand, a locus tree represents the evolutionary history of the sampled loci for a given gene family (see Rasmussen and Kellis 2012). Since the loci exist inside individuals evolving as part of a population, the locus tree is embedded within the species tree. In a locus tree, the nodes depict either genetic divergence due to speciation in the embedding species tree or locus-level events such as duplication, losses, or horizontal gene transfers, whereas the branch lengths and widths represent time and Ne, respectively. Here, we assume that the locuslevel events get immediately fixed in the population, so these Ne are equivalent to those in the species tree and are the same for every locus. Finally, a gene tree represents the evolutionary history of the sampled gene copies that evolve inside the locus tree. Gene tree nodes indicate coalescent events, which looking forward in time correspond to the process of DNA replication and divergence, and that can occur around the speciation time, well before (deep coalescence) or afterwards (migration in population trees). The branches of the gene tree usually represent amount of substitutions per site, and can also represent number of generations or other measures of time. Importantly, these three historical layers do not necessarily coincide. True species/population trees can differ from true locus trees due to gene duplications, losses, and/or horizontal gene transfers, whereas true gene trees can differ from their embedding locus and species trees if there is incomplete lineage sorting (ILS) (Maddison 1997; Page and Charleston 1997) (and migration in the case of population trees). In this regard

The unprecedented amount of data resulting from next-generation sequencing has opened a new era in phylogenetic estimation. Although large datasets should, in theory, increase phylogenetic resolution, massive, multilocus datasets have uncovered a great deal of phylogenetic incongruence among different genomic regions, due both to stochastic error and to the action of different evolutionary process such as incomplete lineage sorting, gene duplication and loss and horizontal gene transfer. This incongruence violates one of the fundamental assumptions of the DNA barcoding approach, which assumes that gene history and species history are identical. In this review, we explain some of the most important challenges we will have to face to reconstruct the history of species, and the advantages and disadvantages of different strategies for the phylogenetic analysis of multilocus data. In particular, we describe the evolutionary events that can generate species tree—gene tree discordance, compare the most popular methods for species tree reconstruction, highlight the challenges we need to face when using them and discuss their potential utility in barcoding. Current barcoding methods sacrifice a great amount of statistical power by only considering one locus, and a transition to multilocus barcodes would not only improve current barcoding methods, but also facilitate an eventual transition to species-tree-based barcoding strategies, which could better accommodate scenarios where the barcode gap is too small or inexistent.This article is part of the themed issue ‘From DNA barcodes to biomes’.

Whole-genome analyses of human medulloblastomas show that the dominant clone at relapse is present as a rare subclone at primary diagnosis.

During the last decades, gene trees have been often interpreted as species phylogenies. However, the extensive gene tree discordance found in multi-locus datasets has put into question this interpretation, and a variety of new methods that explicitly consider species trees have been proposed in recent years. Some of these explicitly consider evolutionary processes that can lead to true gene tree discordance, namely incomplete lineage sorting, gene duplication and loss and horizontal gene transfer. Choosing the most appropriate species tree method for the data at hand is not straightforward due to different data prerequisites, model assumptions, analytical strategies and computational implementations. Key Concepts: We could think of at least three different phylogenetic layers corresponding to species trees, locus trees and gene trees. These depict, respectively, the history of the sampled species, loci and genes copies. Traditional phylogenetic inference has focused on the reconstruction of gene trees, assumed to be accurate proxies for species history. True species, locus and gene trees can be incongruent due to the effect of evolutionary processes like incomplete lineage sorting, gene duplication and loss and horizontal gene transfer. This incongruence might appear larger due to estimation error. Extensive gene tree incongruence unveiled in multi-locus datasets has encouraged the development of methods that explicitly reconstruct species trees. The supermatrix approach combines loci into a super-alignment and estimates the corresponding supergene tree. Supertree methods combine gene trees to obtain an estimate of the species tree. Other methods co-estimate gene and species trees in a single step using full probabilistic models. Different species tree methods require distinct data specifications, mainly related with the consideration of paralogs, number of sampled species, missing taxa and number of loci. Rooting and reconstruction uncertainty must be carefully considered before choosing a species tree method. Keywords: supermatrix; supertree; incomplete lineage sorting; gene duplication and loss; horizontal gene transfer; hybridisation; multispecies coalescent; reconciliation

The opisthokonts are one of the major super groups of eukaryotes. It comprises two major clades: (i) the Metazoa and their unicellular relatives and (ii) the Fungi and their unicellular relatives. There is, however, little knowledge of the role of opisthokont microbes in many natural environments, especially among non-metazoan and non-fungal opisthokonts. Here, we begin to address this gap by analysing high-throughput 18S rDNA and 18S rRNA sequencing data from different European coastal sites, sampled at different size fractions and depths. In particular, we analyse the diversity and abundance of choanoflagellates, filastereans, ichthyosporeans, nucleariids, corallochytreans and their related lineages. Our results show the great diversity of choanoflagellates in coastal waters as well as a relevant representation of the ichthyosporeans and the uncultured marine opisthokonts (MAOP). Furthermore, we describe a new lineage of marine fonticulids (MAFO) that appears to be abundant in sediments. Taken together, our work points to a greater potential ecological role for unicellular opisthokonts than previously appreciated in marine environments, both in water column and sediments, and also provides evidence of novel opisthokont phylogenetic lineages. This study highlights the importance of high-throughput sequencing approaches to unravel the diversity and distribution of both known and novel eukaryotic lineages.

Current phylogenomic data sets highlight the need for species tree methods able to deal with several sources of gene tree/species tree incongruence. At the same time, we need to make most use of all available data. Most species tree methods deal with single processes of phylogenetic discordance, namely, gene duplication and loss, incomplete lineage sorting (ILS) or horizontal gene transfer. In this manuscript, we address the problem of species tree inference from multilocus, genome-wide data sets regardless of the presence of gene duplication and loss and ILS therefore without the need to identify orthologs or to use a single individual per species. We do this by extending the idea of Maximum Likelihood (ML) supertrees to a hierarchical Bayesian model where several sources of gene tree/species tree disagreement can be accounted for in a modular manner. We implemented this model in a computer program called guenomu whose inputs are posterior distributions of unrooted gene tree topologies for multiple gene families, and whose output is the posterior distribution of rooted species tree topologies. We conducted extensive simulations to evaluate the performance of our approach in comparison with other species tree approaches able to deal with more than one leaf from the same species. Our method ranked best under simulated data sets, in spite of ignoring branch lengths, and performed well on empirical data, as well as being fast enough to analyze relatively large data sets. Our Bayesian supertree method was also very successful in obtaining better estimates of gene trees, by reducing the uncertainty in their distributions. In addition, our results show that under complex simulation scenarios, gene tree parsimony is also a competitive approach once we consider its speed, in contrast to more sophisticated models.