Reconstructing tumor clonal lineage trees incorporating single-nucleotide variants, copy number alterations and structural variations.

MOTIVATION: Cancer develops through a process of clonal evolution in which an initially healthy cell gives rise to progeny gradually differentiating through the accumulation of genetic and epigenetic mutations. These mutations can take various forms, including single-nucleotide variants (SNVs), copy number alterations (CNAs) or structural variations (SVs), with each variant type providing complementary insights into tumor evolution as well as offering distinct challenges to phylogenetic inference. RESULTS: In this work, we develop a tumor phylogeny method, TUSV-ext, which incorporates SNVs, CNAs and SVs into a single inference framework. We demonstrate on simulated data that the method produces accurate tree inferences in the presence of all three variant types. We further demonstrate the method through application to real prostate tumor data, showing how our approach to coordinated phylogeny inference and clonal construction with all three variant types can reveal a more complicated clonal structure than is suggested by prior work, consistent with extensive polyclonal seeding or migration. AVAILABILITY AND IMPLEMENTATION: https://github.com/CMUSchwartzLab/TUSV-ext. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Semi-deconvolution of bulk and single-cell RNA-seq data with application to metastatic progression in breast cancer.

MOTIVATION: Identifying cell types and their abundances and how these evolve during tumor progression is critical to understanding the mechanisms of metastasis and identifying predictors of metastatic potential that can guide the development of new diagnostics or therapeutics. Single-cell RNA sequencing (scRNA-seq) has been especially promising in resolving heterogeneity of expression programs at the single-cell level, but is not always feasible, e.g. for large cohort studies or longitudinal analysis of archived samples. In such cases, clonal subpopulations may still be inferred via genomic deconvolution, but deconvolution methods have limited ability to resolve fine clonal structure and may require reference cell type profiles that are missing or imprecise. Prior methods can eliminate the need for reference profiles but show unstable performance when few bulk samples are available. RESULTS: In this work, we develop a new method using reference scRNA-seq to interpret sample collections for which only bulk RNA-seq is available for some samples, e.g. clonally resolving archived primary tissues using scRNA-seq from metastases. By integrating such information in a Quadratic Programming framework, our method can recover more accurate cell types and corresponding cell type abundances in bulk samples. Application to a breast tumor bone metastases dataset confirms the power of scRNA-seq data to improve cell type inference and quantification in same-patient bulk samples. AVAILABILITY AND IMPLEMENTATION: Source code is available on Github at https://github.com/CMUSchwartzLab/RADs.

Tumor heterogeneity assessed by sequencing and fluorescence in situ hybridization (FISH) data.

MOTIVATION: Computational reconstruction of clonal evolution in cancers has become a crucial tool for understanding how tumors initiate and progress and how this process varies across patients. The field still struggles, however, with special challenges of applying phylogenetic methods to cancers, such as the prevalence and importance of copy number alteration (CNA) and structural variation events in tumor evolution, which are difficult to profile accurately by prevailing sequencing methods in such a way that subsequent reconstruction by phylogenetic inference algorithms is accurate. RESULTS: In this work, we develop computational methods to combine sequencing with multiplex interphase fluorescence in situ hybridization to exploit the complementary advantages of each technology in inferring accurate models of clonal CNA evolution accounting for both focal changes and aneuploidy at whole-genome scales. By integrating such information in an integer linear programming framework, we demonstrate on simulated data that incorporation of FISH data substantially improves accurate inference of focal CNA and ploidy changes in clonal evolution from deconvolving bulk sequence data. Analysis of real glioblastoma data for which FISH, bulk sequence and single cell sequence are all available confirms the power of FISH to enhance accurate reconstruction of clonal copy number evolution in conjunction with bulk and optionally single-cell sequence data. AVAILABILITY AND IMPLEMENTATION: Source code is available on Github at https://github.com/CMUSchwartzLab/FISH_deconvolution. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Robust and accurate deconvolution of tumor populations uncovers evolutionary mechanisms of breast cancer metastasis.

MOTIVATION: Cancer develops and progresses through a clonal evolutionary process. Understanding progression to metastasis is of particular clinical importance, but is not easily analyzed by recent methods because it generally requires studying samples gathered years apart, for which modern single-cell sequencing is rarely an option. Revealing the clonal evolution mechanisms in the metastatic transition thus still depends on unmixing tumor subpopulations from bulk genomic data. METHODS: We develop a novel toolkit called robust and accurate deconvolution (RAD) to deconvolve biologically meaningful tumor populations from multiple transcriptomic samples spanning the two progression states. RAD uses gene module compression to mitigate considerable noise in RNA, and a hybrid optimizer to achieve a robust and accurate solution. Finally, we apply a phylogenetic algorithm to infer how associated cell populations adapt across the metastatic transition via changes in expression programs and cell-type composition. RESULTS: We validated the superior robustness and accuracy of RAD over alternative algorithms on a real dataset, and validated the effectiveness of gene module compression on both simulated and real bulk RNA data. We further applied the methods to a breast cancer metastasis dataset, and discovered common early events that promote tumor progression and migration to different metastatic sites, such as dysregulation of ECM-receptor, focal adhesion and PI3k-Akt pathways. AVAILABILITY AND IMPLEMENTATION: The source code of the RAD package, models, experiments and technical details such as parameters, is available at https://github.com/CMUSchwartzLab/RAD. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Clonal Evolution Simulator for Planning Somatic Evolution Studies.

Somatic evolution plays a key role in development, cell differentiation, and normal aging, but also in diseases such as cancer. Understanding mechanisms of somatic mutability and how they can vary between cell lineages will likely play a crucial role in biological discovery and medical applications. This need has led to a proliferation of new technologies for profiling single-cell variation, each with distinctive capabilities and limitations that can be leveraged alone or in combination with other technologies. The enormous space of options for assaying somatic variation, however, presents unsolved informatics problems with regard to selecting optimal combinations of technologies for designing appropriate studies for any particular scientific questions. Versatile simulation tools are needed to explore and optimize potential study designs if researchers are to deploy multiomic technologies most effectively. In this study, we present a simulator allowing for the generation of synthetic data from a wide range of clonal lineages, variant classes, and sequencing technology choices, intended to provide a platform for effective study design in somatic lineage analysis. Users can input various properties of the somatic evolutionary system, mutation classes, and biotechnology options, and then generate samples of synthetic sequence reads and their corresponding ground truth parameters for a given study design. We demonstrate the utility of the simulator for testing and optimizing study designs for various experimental queries.

Joint Clustering of Single-Cell Sequencing and Fluorescence In Situ Hybridization Data for Reconstructing Clonal Heterogeneity in Cancers.

Aneuploidy and whole genome duplication (WGD) events are common features of cancers associated with poor outcomes, but the ways they influence trajectories of clonal evolution are poorly understood. Phylogenetic methods for reconstructing clonal evolution from genomic data have proven a powerful tool for understanding how clonal evolution occurs in the process of cancer progression, but extant methods so far have limited the ability to resolve tumor evolution via ploidy changes. This limitation exists in part because single-cell DNA-sequencing (scSeq), which has been crucial to developing detailed profiles of clonal evolution, has difficulty in resolving ploidy changes and WGD. Multiplex interphase fluorescence in situ hybridization (miFISH) provides a more unambiguous signal of single-cell ploidy changes but it is limited to profiling small numbers of single markers. Here, we develop a joint clustering method to combine these two data sources with the goal of better resolving ploidy changes in tumor evolution. We develop a probabilistic framework to maximize the probability of latent variables given the pre-clustered datasets, which we optimize via Markov chain Monte Carlo sampling combined with linear regression. We validate the method by using simulated data derived from a glioblastoma (GBM) case profiled by both scSeq and miFISH. We further apply the method to two GBM cases with scSeq and miFISH data by reconstructing a phylogenetic tree from the joint clustering results, demonstrating their synergistic value in understanding how focal copy number changes and WGD events can collectively contribute to tumor progression.

Neural Network Deconvolution Method for Resolving Pathway-Level Progression of Tumor Clonal Expression Programs With Application to Breast Cancer Brain Metastases.

Metastasis is the primary mechanism by which cancer results in mortality and there are currently no reliable treatment options once it occurs, making the metastatic process a critical target for new diagnostics and therapeutics. Treating metastasis before it appears is challenging, however, in part because metastases may be quite distinct genomically from the primary tumors from which they presumably emerged. Phylogenetic studies of cancer development have suggested that changes in tumor genomics over stages of progression often result from shifts in the abundance of clonal cellular populations, as late stages of progression may derive from or select for clonal populations rare in the primary tumor. The present study develops computational methods to infer clonal heterogeneity and dynamics across progression stages via deconvolution and clonal phylogeny reconstruction of pathway-level expression signatures in order to reconstruct how these processes might influence average changes in genomic signatures over progression. We show, via application to a study of gene expression in a collection of matched breast primary tumor and metastatic samples, that the method can infer coarse-grained substructure and stromal infiltration across the metastatic transition. The results suggest that genomic changes observed in metastasis, such as gain of the ErbB signaling pathway, are likely caused by early events in clonal evolution followed by expansion of minor clonal populations in metastasis, a finding that may have translational implications for early detection or prevention of metastasis.

Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data.

Characterizing intratumor heterogeneity (ITH) is crucial to understanding cancer development, but it is hampered by limits of available data sources. Bulk DNA sequencing is the most common technology to assess ITH, but involves the analysis of a mixture of many genetically distinct cells in each sample, which must then be computationally deconvolved. Single-cell sequencing is a promising alternative, but its limitations-for example, high noise, difficulty scaling to large populations, technical artifacts, and large data sets-have so far made it impractical for studying cohorts of sufficient size to identify statistically robust features of tumor evolution. We have developed strategies for deconvolution and tumor phylogenetics combining limited amounts of bulk and single-cell data to gain some advantages of single-cell resolution with much lower cost, with specific focus on deconvolving genomic copy number data. We developed a mixed membership model for clonal deconvolution via non-negative matrix factorization balancing deconvolution quality with similarity to single-cell samples via an associated efficient coordinate descent algorithm. We then improve on that algorithm by integrating deconvolution with clonal phylogeny inference, using a mixed integer linear programming model to incorporate a minimum evolution phylogenetic tree cost in the problem objective. We demonstrate the effectiveness of these methods on semisimulated data of known ground truth, showing improved deconvolution accuracy relative to bulk data alone.

Promotion of behavior and neuronal function by reactive oxygen species in C. elegans.

Reactive oxygen species (ROS) are well known to elicit a plethora of detrimental effects on cellular functions by causing damages to proteins, lipids and nucleic acids. Neurons are particularly vulnerable to ROS, and nearly all forms of neurodegenerative diseases are associated with oxidative stress. Here, we report the surprising finding that exposing C. elegans to low doses of H2O2 promotes, rather than compromises, sensory behavior and the function of sensory neurons such as ASH. This beneficial effect of H2O2 is mediated by an evolutionarily conserved peroxiredoxin-p38/MAPK signaling cascade. We further show that p38/MAPK signals to AKT and the TRPV channel OSM-9, a sensory channel in ASH neurons. AKT phosphorylates OSM-9, and such phosphorylation is required for H2O2-induced potentiation of sensory behavior and ASH neuron function. Our results uncover a beneficial effect of ROS on neurons, revealing unexpected complexity of the action of oxidative stressors in the nervous system.

The Neuronal Kinesin UNC-104/KIF1A Is a Key Regulator of Synaptic Aging and Insulin Signaling-Regulated Memory.

Aging is the greatest risk factor for a number of neurodegenerative diseases, such as Alzheimer's and Parkinson's disease. Furthermore, normal aging is associated with a decline in sensory, motor, and cognitive functions. Emerging evidence suggests that synapse alterations, rather than neuronal cell death, are the causes of neuronal dysfunctions in normal aging and in early stages of neurodegenerative diseases. However, little is known about the mechanisms underlying age-related synaptic decline. Here, we uncover a surprising role of the anterograde molecular motor UNC-104/KIF1A as a key regulator of neural circuit deterioration in aging C. elegans. Through analyses of synapse protein localization, synaptic transmission, and animal behaviors, we find that reduced function of UNC-104 accelerates motor circuit dysfunction with age, whereas upregulation of UNC-104 significantly improves motor function at advanced ages and also mildly extends lifespan. In addition, UNC-104-overexpressing animals outperform wild-type controls in associative learning and memory tests. Further genetic analyses suggest that UNC-104 functions downstream of the DAF-2-signaling pathway and is regulated by the FOXO transcription factor DAF-16, which contributes to the effects of DAF-2 in neuronal aging. Together, our cellular, electrophysiological, and behavioral analyses highlight the importance of axonal transport in the maintenance of synaptic structural integrity and function during aging and raise the possibility of targeting kinesins to slow age-related neural circuit dysfunction.

Functional aging in the nervous system contributes to age-dependent motor activity decline in C. elegans.

Aging is characterized by a progressive decline in multiple physiological functions (i.e., functional aging). As animals age, they exhibit a gradual loss in motor activity, but the underlying mechanisms remain unclear. Here we approach this question in C. elegans by functionally characterizing its aging nervous system and muscles. We find that motor neurons exhibit a progressive functional decline, beginning in early life. Surprisingly, body-wall muscles, which were previously thought to undergo functional aging, do not manifest such a decline until mid-late life. Notably, motor neurons first develop a deficit in synaptic vesicle fusion followed by that in quantal size and vesicle docking/priming, revealing specific functional deteriorations in synaptic transmission. Pharmacological stimulation of synaptic transmission can improve motor activity in aged animals. These results uncover a critical role for the nervous system in age-dependent motor activity decline in C. elegans and provide insights into how functional aging occurs in this organism.

Highly efficient arsenic removal using a composite of ultrafine magnetite nanoparticles interlinked by silane coupling agents.

Arsenic (As) contamination in groundwater is a great environmental health concern and is often the result of contact between groundwater and arsenic-containing rocks or sediments and from variation of pH and redox potentials in the subsurface. In the past decade, magnetite nanoparticles (MNPs) have been shown to have high adsorption activity towards As. Alerted by the reported cytotoxicity of 5­12 nm MNP, we studied the adsorption behavior of 1.15 nm MNP and a MNP composite (MNPC), MNPs interlinked by silane coupling agents. With an initial concentration of As at 25 mg L(-1), MNPs exhibited high adsorption capacity for As(V) and As (III), 206.9 mg·g(-1) and 168.6 mg·g(-1) under anaerobic conditions, respectively, and 109.9 mg·g(-1) and 108.6 mg·g(-1) under aerobic conditions, respectively. Under aerobic conditions, MNPC achieved even higher adsorption capacity than MNP, 165.1 mg·g(-1) on As(V) and 157.9 mg·mg(-1) on As(III). For As(V) at 50 mg L(-1), MNPC achieved an adsorption capacity as high as 341.8 mg·g(-1), the highest in the literature. A kinetic study indicated that this adsorption reaction can reach equilibrium within 15 min and the rate constant of As(V) is about 1.9 times higher than that of As(III). These results suggested that MNPC can serve as a highly effective adsorbent for fast removal of As.