Deep sequencing reveals clonal evolution patterns and mutation events associated with relapse in B-cell lymphomas

Background

Molecular mechanisms associated with frequent relapse of diffuse large B-cell lymphoma (DLBCL) are poorly defined. It is especially unclear how primary tumor clonal heterogeneity contributes to relapse. Here, we explore unique features of B-cell lymphomas - VDJ recombination and somatic hypermutation - to address this question.

Results

We performed high-throughput sequencing of rearranged VDJ junctions in 14 pairs of matched diagnosis-relapse tumors, among which 7 pairs were further characterized by exome sequencing. We identify two distinctive modes of clonal evolution of DLBCL relapse: an early-divergent mode in which clonally related diagnosis and relapse tumors diverged early and developed in parallel; and a late-divergent mode in which relapse tumors developed directly from diagnosis tumors with minor divergence. By examining mutation patterns in the context of phylogenetic information provided by VDJ junctions, we identified mutations in epigenetic modifiers such as KMT2D as potential early driving events in lymphomagenesis and immune escape alterations as relapse-associated events.

Conclusions

Altogether, our study for the first time provides important evidence that DLBCL relapse may result from multiple, distinct tumor evolutionary mechanisms, providing rationale for therapies for each mechanism. Moreover, this study highlights the urgent need to understand the driving roles of epigenetic modifier mutations in lymphomagenesis, and immune surveillance factor genetic lesions in relapse.

Electronic supplementary material

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Gene bionetworks involved in the epigenetic transgenerational inheritance of altered mate preference: environmental epigenetics and evolutionary biology

Background

Mate preference behavior is an essential first step in sexual selection and is a critical determinant in evolutionary biology. Previously an environmental compound (the fungicide vinclozolin) was found to promote the epigenetic transgenerational inheritance of an altered sperm epigenome and modified mate preference characteristics for three generations after exposure of a gestating female.

Results

The current study investigated gene networks involved in various regions of the brain that correlated with the altered mate preference behavior in the male and female. Statistically significant correlations of gene clusters and modules were identified to associate with specific mate preference behaviors. This novel systems biology approach identified gene networks (bionetworks) involved in sex-specific mate preference behavior. Observations demonstrate the ability of environmental factors to promote the epigenetic transgenerational inheritance of this altered evolutionary biology determinant.

Conclusions

Combined observations elucidate the potential molecular control of mate preference behavior and suggests environmental epigenetics can have a role in evolutionary biology.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-377) contains supplementary material, which is available to authorized users.     

Glycomic profiling of carcinoembryonic antigen isolated from human tumor tissue

Background

Carcinoembryonic antigen (CEA) is a protein commonly found in human serum, with elevated CEA levels being linked to the progression of a wide range of tumors. It is currently used as a biomarker for malign tumors such as lung cancer and colorectal cancer [Urol Oncol: Semin Orig Invest 352: 644–648, 2013 and Lung Cancer 80: 45-49, 2013]. However, due to its low specificity in clinical applications, CEA can be used for monitoring only, rather than tumor diagnosis. The function of many glycoproteins is critically dependent on their glycosylation pattern, which in turn has the potential to serve in tumor detection. However, little is known about the detailed glycan patterns of CEA.

Methods

To determine these patterns, we isolated and purified CEA proteins from human tumor tissues using immunoaffinity chromatography. The glycan patterns of CEA were then analyzed using a Matrix-Assisted Laser Desorption/Ionization-Time of Flight-Mass Spectrometry³ (MALDI-TOF-MS³) approach.

Results

We identified 61 glycoforms in tumor tissue, where CEA is upregulated. These glycosylation entities were identified as bi-antennary, tri-antennary and tetra-antennary structures carrying sialic acid and fucose residues, and include a multitude of glycans previously not reported for CEA.

Conclusion

Our findings should facilitate a more precise tumor prediction than currently possible, ultimately resulting in improved tumor diagnosis and treatment.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-015-9088-3) contains supplementary material, which is available to authorized users.     

Interactively illustrating polymerization using three-level model fusion

Background

Research in cell biology is steadily contributing new knowledge about many aspects of physiological processes, both with respect to the involved molecular structures as well as their related function. Illustrations of the spatio-temporal development of such processes are not only used in biomedical education, but also can serve scientists as an additional platform for in-silico experiments.

Results

In this paper, we contribute a new, three-level modeling approach to illustrate physiological processes from the class of polymerization at different time scales. We integrate physical and empirical modeling, according to which approach best suits the different involved levels of detail, and we additionally enable a form of interactive steering, while the process is illustrated. We demonstrate the suitability of our approach in the context of several polymerization processes and report from a first evaluation with domain experts.

Conclusion

We conclude that our approach provides a new, hybrid modeling approach for illustrating the process of emergence in physiology, embedded in a densely filled environment. Our approach of a complementary fusion of three systems combines the strong points from the different modeling approaches and is capable to bridge different spatial and temporal scales.

Electronic supplementary material

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Development of synchronous VHL syndrome tumors reveals contingencies and constraints to tumor evolution

Background

Genomic analysis of multi-focal renal cell carcinomas from an individual with a germline VHL mutation offers a unique opportunity to study tumor evolution.

Results

We perform whole exome sequencing on four clear cell renal cell carcinomas removed from both kidneys of a patient with a germline VHL mutation. We report that tumors arising in this context are clonally independent and harbour distinct secondary events exemplified by loss of chromosome 3p, despite an identical genetic background and tissue microenvironment. We propose that divergent mutational and copy number anomalies are contingent upon the nature of 3p loss of heterozygosity occurring early in tumorigenesis. However, despite distinct 3p events, genomic, proteomic and immunohistochemical analyses reveal evidence for convergence upon the PI3K-AKT-mTOR signaling pathway. Four germline tumors in this young patient, and in a second, older patient with VHL syndrome demonstrate minimal intra-tumor heterogeneity and mutational burden, and evaluable tumors appear to follow a linear evolutionary route, compared to tumors from patients with sporadic clear cell renal cell carcinoma.

Conclusions

In tumors developing from a germline VHL mutation, the evolutionary principles of contingency and convergence in tumor development are complementary. In this small set of patients with early stage VHL-associated tumors, there is reduced mutation burden and limited evidence of intra-tumor heterogeneity.

Electronic supplementary material

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Deep sequencing of the X chromosome reveals the proliferation history of colorectal adenomas

Background

Mismatch repair deficient colorectal adenomas are composed of transformed cells that descend from a common founder and progressively accumulate genomic alterations. The proliferation history of these tumors is still largely unknown. Here we present a novel approach to rebuild the proliferation trees that recapitulate the history of individual colorectal adenomas by mapping the progressive acquisition of somatic point mutations during tumor growth.

Results

Using our approach, we called high and low frequency mutations acquired in the X chromosome of four mismatch repair deficient colorectal adenomas deriving from male individuals. We clustered these mutations according to their frequencies and rebuilt the proliferation trees directly from the mutation clusters using a recursive algorithm. The trees of all four lesions were formed of a dominant subclone that co-existed with other genetically heterogeneous subpopulations of cells. However, despite this similar hierarchical organization, the growth dynamics varied among and within tumors, likely depending on a combination of tumor-specific genetic and environmental factors.

Conclusions

Our study provides insights into the biological properties of individual mismatch repair deficient colorectal adenomas that may influence their growth and also the response to therapy. Extended to other solid tumors, our novel approach could inform on the mechanisms of cancer progression and on the best treatment choice.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0437-8) contains supplementary material, which is available to authorized users.     

Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data

Background

Network inference deals with the reconstruction of molecular networks from experimental data. Given N molecular species, the challenge is to find the underlying network. Due to data limitations, this typically is an ill-posed problem, and requires the integration of prior biological knowledge or strong regularization. We here focus on the situation when time-resolved measurements of a system’s response after systematic perturbations are available.

Results

We present a novel method to infer signaling networks from time-course perturbation data. We utilize dynamic Bayesian networks with probabilistic Boolean threshold functions to describe protein activation. The model posterior distribution is analyzed using evolutionary MCMC sampling and subsequent clustering, resulting in probability distributions over alternative networks. We evaluate our method on simulated data, and study its performance with respect to data set size and levels of noise. We then use our method to study EGF-mediated signaling in the ERBB pathway.

Conclusions

Dynamic Probabilistic Threshold Networks is a new method to infer signaling networks from time-series perturbation data. It exploits the dynamic response of a system after external perturbation for network reconstruction. On simulated data, we show that the approach outperforms current state of the art methods. On the ERBB data, our approach recovers a significant fraction of the known interactions, and predicts novel mechanisms in the ERBB pathway.

Electronic supplementary material

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DiscML: an R package for estimating evolutionary rates of discrete characters using maximum likelihood

Background

The study of discrete characters is crucial for the understanding of evolutionary processes. Even though great advances have been made in the analysis of nucleotide sequences, computer programs for non-DNA discrete characters are often dedicated to specific analyses and lack flexibility. Discrete characters often have different transition rate matrices, variable rates among sites and sometimes contain unobservable states. To obtain the ability to accurately estimate a variety of discrete characters, programs with sophisticated methodologies and flexible settings are desired.

Results

DiscML performs maximum likelihood estimation for evolutionary rates of discrete characters on a provided phylogeny with the options that correct for unobservable data, rate variations, and unknown prior root probabilities from the empirical data. It gives users options to customize the instantaneous transition rate matrices, or to choose pre-determined matrices from models such as birth-and-death (BD), birth-death-and-innovation (BDI), equal rates (ER), symmetric (SYM), general time-reversible (GTR) and all rates different (ARD). Moreover, we show application examples of DiscML on gene family data and on intron presence/absence data.

Conclusion

DiscML was developed as a unified R program for estimating evolutionary rates of discrete characters with no restriction on the number of character states, and with flexibility to use different transition models. DiscML is ideal for the analyses of binary (1s/0s) patterns, multi-gene families, and multistate discrete morphological characteristics.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-320) contains supplementary material, which is available to authorized users.     

The distribution and mutagenesis of short coding INDELs from 1,128 whole exomes

Background

Identifying insertion/deletion polymorphisms (INDELs) with high confidence has been intrinsically challenging in short-read sequencing data. Here we report our approach for improving INDEL calling accuracy by using a machine learning algorithm to combine call sets generated with three independent methods, and by leveraging the strengths of each individual pipeline. Utilizing this approach, we generated a consensus exome INDEL call set from a large dataset generated by the 1000 Genomes Project (1000G), maximizing both the sensitivity and the specificity of the calls.

Results

This consensus exome INDEL call set features 7,210 INDELs, from 1,128 individuals across 13 populations included in the 1000 Genomes Phase 1 dataset, with a false discovery rate (FDR) of about 7.0%.

Conclusions

In our study we further characterize the patterns and distributions of these exonic INDELs with respect to density, allele length, and site frequency spectrum, as well as the potential mutagenic mechanisms of coding INDELs in humans.

Electronic supplementary material

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Unraveling the effect of genomic structural changes in the rhesus macaque - implications for the adaptive role of inversions

Background

By reshuffling genomes, structural genomic reorganizations provide genetic variation on which natural selection can work. Understanding the mechanisms underlying this process has been a long-standing question in evolutionary biology. In this context, our purpose in this study is to characterize the genomic regions involved in structural rearrangements between human and macaque genomes and determine their influence on meiotic recombination as a way to explore the adaptive role of genome shuffling in mammalian evolution.

Results

We first constructed a highly refined map of the structural rearrangements and evolutionary breakpoint regions in the human and rhesus macaque genomes based on orthologous genes and whole-genome sequence alignments. Using two different algorithms, we refined the genomic position of known rearrangements previously reported by cytogenetic approaches and described new putative micro-rearrangements (inversions and indels) in both genomes. A detailed analysis of the rhesus macaque genome showed that evolutionary breakpoints are in gene-rich regions, being enriched in GO terms related to immune system. We also identified defense-response genes within a chromosome inversion fixed in the macaque lineage, underlying the relevance of structural genomic changes in evolutionary and/or adaptation processes. Moreover, by combining in silico and experimental approaches, we studied the recombination pattern of specific chromosomes that have suffered rearrangements between human and macaque lineages.

Conclusions

Our data suggest that adaptive alleles – in this case, genes involved in the immune response – might have been favored by genome rearrangements in the macaque lineage.

Electronic supplementary material

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Population structure of mitochondrial genomes in Saccharomyces cerevisiae

Background

Rigorous study of mitochondrial functions and cell biology in the budding yeast, Saccharomyces cerevisiae has advanced our understanding of mitochondrial genetics. This yeast is now a powerful model for population genetics, owing to large genetic diversity and highly structured populations among wild isolates. Comparative mitochondrial genomic analyses between yeast species have revealed broad evolutionary changes in genome organization and architecture. A fine-scale view of recent evolutionary changes within S. cerevisiae has not been possible due to low numbers of complete mitochondrial sequences.

Results

To address challenges of sequencing AT-rich and repetitive mitochondrial DNAs (mtDNAs), we sequenced two divergent S. cerevisiae mtDNAs using a single-molecule sequencing platform (PacBio RS). Using de novo assemblies, we generated highly accurate complete mtDNA sequences. These mtDNA sequences were compared with 98 additional mtDNA sequences gathered from various published collections. Phylogenies based on mitochondrial coding sequences and intron profiles revealed that intraspecific diversity in mitochondrial genomes generally recapitulated the population structure of nuclear genomes. Analysis of intergenic sequence indicated a recent expansion of mobile elements in certain populations. Additionally, our analyses revealed that certain populations lacked introns previously believed conserved throughout the species, as well as the presence of introns never before reported in S. cerevisiae.

Conclusions

Our results revealed that the extensive variation in S. cerevisiae mtDNAs is often population specific, thus offering a window into the recent evolutionary processes shaping these genomes. In addition, we offer an effective strategy for sequencing these challenging AT-rich mitochondrial genomes for small scale projects.

Electronic supplementary material

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Increased rate of hair keratin gene loss in the cetacean lineage

Background

Hair represents an evolutionary innovation that appeared early on mammalian evolutionary history, and presumably contributed significantly to the rapid radiation of the group. An interesting event in hair evolution has been its secondary loss in some mammalian groups, such as cetaceans, whose hairless phenotype appears to be an adaptive response to better meet the environmental conditions. To determine whether different repertoire of keratin genes among mammals can potentially explain the phenotypic hair features of different lineages, we characterized the type I and II clusters of alpha keratins from eight mammalian species, including the hairless dolphin and minke whale representing the order Cetacea.

Results

We combined the available genomic information with phylogenetic analysis to conduct a comprehensive analysis of the evolutionary patterns of keratin gene clusters. We found that both type I and II gene clusters are fairly conserved among the terrestrial mammals included in this study, with lineage specific gene duplication and gene loss. Nevertheless, there is also evidence for an increased rate of pseudogenization in the cetacean lineage when compared to their terrestrial relatives, especially among the hair type keratins.

Conclusions

Here we present a comprehensive characterization of alpha-keratin genes among mammals and elucidate the mechanisms involved in the evolution of this gene family. We identified lineage-specific gene duplications and gene loss among the Laurasiatherian and Euarchontoglires species included in the study. Interestingly, cetaceans present an increased loss of hair-type keratin genes when compared to other terrestrial mammals. As suggested by the ‘less-is-more’ hypothesis, we do not rule out the possibility that the gene loss of hair-type keratin genes in these species might be associated to the hairless phenotype and could have been adaptive in response to new selective pressures imposed by the colonization of a new habitat. Our study provides support for the idea that pseudogenes are not simply ‘genomic fossils’ but instead have adaptive roles during the evolutionary process.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-869) contains supplementary material, which is available to authorized users.     

Sequencing methods and datasets to improve functional interpretation of sleeping beauty mutagenesis screens

Background

Animal models of cancer are useful to generate complementary datasets for comparison to human tumor data. Insertional mutagenesis screens, such as those utilizing the Sleeping Beauty (SB) transposon system, provide a model that recapitulates the spontaneous development and progression of human disease. This approach has been widely used to model a variety of cancers in mice. Comprehensive mutation profiles are generated for individual tumors through amplification of transposon insertion sites followed by high-throughput sequencing. Subsequent statistical analyses identify common insertion sites (CISs), which are predicted to be functionally involved in tumorigenesis. Current methods utilized for SB insertion site analysis have some significant limitations. For one, they do not account for transposon footprints – a class of mutation generated following transposon remobilization. Existing methods also discard quantitative sequence data due to uncertainty regarding the extent to which it accurately reflects mutation abundance within a heterogeneous tumor. Additionally, computational analyses generally assume that all potential insertion sites have an equal probability of being detected under non-selective conditions, an assumption without sufficient relevant data. The goal of our study was to address these potential confounding factors in order to enhance functional interpretation of insertion site data from tumors.

Results

We describe here a novel method to detect footprints generated by transposon remobilization, which revealed minimal evidence of positive selection in tumors. We also present extensive characterization data demonstrating an ability to reproducibly assign semi-quantitative information to individual insertion sites within a tumor sample. Finally, we identify apparent biases for detection of inserted transposons in several genomic regions that may lead to the identification of false positive CISs.

Conclusion

The information we provide can be used to refine analyses of data from insertional mutagenesis screens, improving functional interpretation of results and facilitating the identification of genes important in cancer development and progression.

Electronic supplementary material

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SHEAR: sample heterogeneity estimation and assembly by reference

Background

Personal genome assembly is a critical process when studying tumor genomes and other highly divergent sequences. The accuracy of downstream analyses, such as RNA-seq and ChIP-seq, can be greatly enhanced by using personal genomic sequences rather than standard references. Unfortunately, reads sequenced from these types of samples often have a heterogeneous mix of various subpopulations with different variants, making assembly extremely difficult using existing assembly tools. To address these challenges, we developed SHEAR (Sample Heterogeneity Estimation and Assembly by Reference; http://vk.cs.umn.edu/SHEAR), a tool that predicts SVs, accounts for heterogeneous variants by estimating their representative percentages, and generates personal genomic sequences to be used for downstream analysis.

Results

By making use of structural variant detection algorithms, SHEAR offers improved performance in the form of a stronger ability to handle difficult structural variant types and better computational efficiency. We compare against the lead competing approach using a variety of simulated scenarios as well as real tumor cell line data with known heterogeneous variants. SHEAR is shown to successfully estimate heterogeneity percentages in both cases, and demonstrates an improved efficiency and better ability to handle tandem duplications.

Conclusion

SHEAR allows for accurate and efficient SV detection and personal genomic sequence generation. It is also able to account for heterogeneous sequencing samples, such as from tumor tissue, by estimating the subpopulation percentage for each heterogeneous variant.

Electronic supplementary material

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Combination of proteasome and HDAC inhibitor enhances HPV16 E7-specific CD8+ T cell immune response and antitumor effects in a preclinical cervical cancer model

Background

Bortezomib, a proteasome inhibitor and suberoylanilide hydroxamic acid (SAHA, also known as Vorinostat), a histone deacetylase inhibitor, have been recognized as potent chemotherapeutic drugs. Bortezomib and SAHA are FDA-approved for the treatment of cutaneous T cell lymphoma and multiple myeloma/mantle cell lymphoma, respectively. Furthermore, the combination of the bortezomib and SAHA has been tested in a variety of preclinical models and in clinical trials and may be ideal for the treatment of cancer. However, it remains unclear how this treatment strategy affects the host immune response against tumors.

Results

Here, we used a well-defined E6/E7-expressing tumor model to examine how the immune system can be motivated to act against tumor cells expressing tumor antigens. We demonstrate that the combination of bortezomib and SAHA elicits potent antitumor effects in TC-1 tumor-bearing mice. Additionally, we are the first to show that treatment with bortezomib and SAHA leads to tumor-specific immunity by rendering tumor cells more susceptible to killing by antigen-specific CD8+ T cells than treatment with either drug alone.

Conclusions

The current study serves an important foundation for the future clinical application of both drugs for the treatment of cervical cancer.

Electronic supplementary material

The online version of this article (doi:10.1186/s12929-014-0111-1) contains supplementary material, which is available to authorized users.     

Methylome analysis identifies a Wilms tumor epigenetic biomarker detectable in blood

Background

Wilms tumor is the most common pediatric renal malignancy and there is a clinical need for a molecular biomarker to assess treatment response and predict relapse. The known mutated genes in this tumor type show low mutation frequencies, whereas aberrant methylation at 11p15 is by far the most common aberration. We therefore analyzed the epigenome, rather than the genome, to identify ubiquitous tumor-specific biomarkers.

Results

Methylome analysis of matched normal kidney and Wilms tumor identifies 309 preliminary methylation variable positions which we translate into three differentially methylated regions (DMRs) for use as tumor-specific biomarkers. Using two novel algorithms we show that these three DMRs are not confounded by cell type composition. We further show that these DMRs are not methylated in embryonic blastema but are intermediately methylated in Wilms tumor precursor lesions. We validate the biomarker DMRs using two independent sample sets of normal kidney and Wilms tumor and seven Wilms tumor histological subtypes, achieving 100% and 98% correct classification, respectively. As proof-of-principle for clinical utility, we successfully use biomarker DMR-2 in a pilot analysis of cell-free circulating DNA to monitor tumor response during treatment in ten patients.

Conclusions

These findings define the most common methylated regions in Wilms tumor known to date which are not associated with their embryonic origin or precursor stage. We show that this tumor-specific methylated DNA is released into the blood circulation where it can be detected non-invasively showing potential for clinical utility.

Electronic supplementary material

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