WaveCNV: allele-specific copy number alterations in primary tumors and xenograft models from next-generation sequencing.

MotivationCopy number variations (CNVs) are a major source of genomic variability and are especially significant in cancer. Until recently microarray technologies have been used to characterize CNVs in genomes. However, advances in next-generation sequencing technology offer significant opportunities to deduce copy number directly from genome sequencing data. Unfortunately cancer genomes differ from normal genomes in several aspects that make them far less amenable to copy number detection. For example, cancer genomes are often aneuploid and an admixture of diploid/non-tumor cell fractions. Also patient-derived xenograft models can be laden with mouse contamination that strongly affects accurate assignment of copy number. Hence, there is a need to develop analytical tools that can take into account cancer-specific parameters for detecting CNVs directly from genome sequencing data.ResultsWe have developed WaveCNV, a software package to identify copy number alterations by detecting breakpoints of CNVs using translation-invariant discrete wavelet transforms and assign digitized copy numbers to each event using next-generation sequencing data. We also assign alleles specifying the chromosomal ratio following duplication/loss. We verified copy number calls using both microarray (correlation coefficient 0.97) and quantitative polymerase chain reaction (correlation coefficient 0.94) and found them to be highly concordant. We demonstrate its utility in pancreatic primary and xenograft sequencing data.Availability and implementationSource code and executables are available at https://github.com/WaveCNV. The segmentation algorithm is implemented in MATLAB, and copy number assignment is implemented [email protected] informationSupplementary data are available at Bioinformatics online

Open Problems in Extracellular RNA Data Analysis: Insights From an ERCC Online Workshop.

We now know RNA can survive the harsh environment of biofluids when encapsulated in vesicles or by associating with lipoproteins or RNA binding proteins. These extracellular RNA (exRNA) play a role in intercellular signaling, serve as biomarkers of disease, and form the basis of new strategies for disease treatment. The Extracellular RNA Communication Consortium (ERCC) hosted a two-day online workshop (April 19-20, 2021) on the unique challenges of exRNA data analysis. The goal was to foster an open dialog about best practices and discuss open problems in the field, focusing initially on small exRNA sequencing data. Video recordings of workshop presentations and discussions are available (https://exRNA.org/exRNAdata2021-videos/). There were three target audiences: experimentalists who generate exRNA sequencing data, computational and data scientists who work with those groups to analyze their data, and experimental and data scientists new to the field. Here we summarize issues explored during the workshop, including progress on an effort to develop an exRNA data analysis challenge to engage the community in solving some of these open problems

Intratumoral heterogeneity and clonal evolution in liver cancer

Clonal evolution of a tumor ecosystem depends on different selection pressures that are principally immune and treatment mediated. We integrate RNA-seq, DNA sequencing, TCR-seq and SNP array data across multiple regions of liver cancer specimens to map spatio-temporal interactions between cancer and immune cells. We investigate how these interactions reflect intra-tumor heterogeneity (ITH) by correlating regional neo-epitope and viral antigen burden with the regional adaptive immune response. Regional expression of passenger mutations dominantly recruits adaptive responses as opposed to hepatitis B virus and cancer-testis antigens. We detect different clonal expansion of the adaptive immune system in distant regions of the same tumor. An ITH-based gene signature improves single-biopsy patient survival predictions and an expression survey of 38,553 single cells across 7 regions of 2 patients further reveals heterogeneity in liver cancer. These data quantify transcriptomic ITH and how the different components of the HCC ecosystem interact during cancer evolution

Complex paths and perturbations for slow-roll cosmologies

We extend the model orignally proposed by Unruh and Jheeta in [6] in two ways. First we derive the complex action to third order in the slope of the slow-roll potential and find that it depends on precisely which endpoint is chosen in complex T space, and that it also has phase contributions to the semi-classical wave function. Secondly, we derive the reduced Hamiltonian action to second order in classical, non-homogenous scalar metric and matter fluctuations about an arbitrary FRW background. We analyze the I = 1 mode for the closed FRW universe and prove it is a gauge mode. We find a contour in T space for which this reduced action has a simple character. We use this character to make an argument concerning the appropriateness of using Linde's or Hawking's sign in determining the amplitude contribution to the wave-function from these classical fluctuations.Science, Faculty ofPhysics and Astronomy, Department ofGraduat

Quantum backreactions in slow-roll and de Sitter spacetimes

This thesis is comprised of three projects. In the first, I consider fluctuations in a perfect irrotational fluid coupled to gravity in an Einstein static universe background. I show that a linearization instability occurs in Einstein static spacetimes despite the presence of matter, and that this instability can only avoided by inducing spatially homogeneous perturbations of the spacetime. Since the first order homogeneous perturbations in this case are well known to be exponentially (dynamically) unstable, the tactic of neglecting these modes to create a long-lived, perturbed Einstein static universe does not work, even if all higher order (L > 1) modes are dynamically stable. The main conclusion is that Einstein static is unconditionally unstable at first order in perturbation theory despite the presence of a large class of neutrally stable, inhomogeneous, modes. In the second, I examine the importance of second order corrections to linearized cosmological perturbation theory in an inflationary background, taken to be a spatially flat FRW spacetime. The full second order problem is solved in the sense that I evaluate the effect of the superhorizon second order corrections on the inhomogeneous and homogeneous modes of the linearized flucuations. In order to quantify their physical significance I study their effective equation of state by looking at the perturbed energy density and isotropic pressure to second order. I define the energy density (isotropic pressure) in terms of the (averaged) eigenvalues associated with timelike (spacelike) eigenvectors of a total stress energy for the metric and matter fluctuations, and find that the second order contributions to the dispersion of these eigenvalues becomes of the same order or exceeds that of the linear contributions. This occurs for a wide range of initial conditions for slow-roll inflation and results in a constraint on the small slow-roll parameter of that model. The main conclusion is that the linearized approximation of a slowly rolling spacetime may, under reasonable circumstances, be intrinsically sick since higher order contributions are comparable to, or substantially larger than, the linear contributions. In the third and final project, backreactions are considered in a pure de Sitter space whose cosmological constant is generated by the potential of scalar field. The leading order effect of matter backreactions on the gravitational field is considered. The initial value problem for the perturbed Einstein equations is proven to generically possess linearization instabilites. I furthermore show that these linearization instabilities can be avoided by assuming strict de Sitter invariance of the quantum states of the linearized fluctuations. This invariance constraint applies to the entire spectrum of states, from the vacuum to the excited states, and is in that sense much stronger than the usual Poincare invariance of the Minkowski vaccum. Some sketches are presented on how to construct de Sitter invariant states. The main conclusion is that to leading order in their effect on the gravitational field, the quantum states of the matter and metric fluctuations must be de Sitter invariant.Science, Faculty ofPhysics and Astronomy, Department ofGraduat

Tumoral and immune heterogeneity in an anti-PD-1 responsive glioblastoma: a case study

Clinical benefit of immune checkpoint blockade in glioblastoma (GBM) is rare, and we hypothesize that tumor clonal evolution and the immune microenvironment are key determinants of response. Here, we present a detailed molecular characterization of the intratumoral and immune heterogeneity in an IDH wild-type, MGMT-negative GBM patient who plausibly benefited from anti-PD-1 therapy with an unusually long 25-mo overall survival time. We leveraged multiplex immunohistochemistry, RNA-seq, and whole-exome data from the primary tumor and three resected regions of recurrent disease to survey regional tumor-immune interactions, genomic instability, mutation burden, and expression profiles. We found significant regional heterogeneity in the neoantigenic and immune landscape, with a differential T-cell signature among recurrent sectors, a uniform loss of focal amplifications in EGFR, and a novel subclonal EGFR mutation. Comparisons with recently reported correlates of checkpoint blockade in GBM and with TCGA-GBM revealed appreciable intratumoral heterogeneity that may have contributed to a differential PD-1 blockade response

WaveCNV: allele-specific copy number alterations in primary tumors and xenograft models from next-generation sequencing.

MotivationCopy number variations (CNVs) are a major source of genomic variability and are especially significant in cancer. Until recently microarray technologies have been used to characterize CNVs in genomes. However, advances in next-generation sequencing technology offer significant opportunities to deduce copy number directly from genome sequencing data. Unfortunately cancer genomes differ from normal genomes in several aspects that make them far less amenable to copy number detection. For example, cancer genomes are often aneuploid and an admixture of diploid/non-tumor cell fractions. Also patient-derived xenograft models can be laden with mouse contamination that strongly affects accurate assignment of copy number. Hence, there is a need to develop analytical tools that can take into account cancer-specific parameters for detecting CNVs directly from genome sequencing data.ResultsWe have developed WaveCNV, a software package to identify copy number alterations by detecting breakpoints of CNVs using translation-invariant discrete wavelet transforms and assign digitized copy numbers to each event using next-generation sequencing data. We also assign alleles specifying the chromosomal ratio following duplication/loss. We verified copy number calls using both microarray (correlation coefficient 0.97) and quantitative polymerase chain reaction (correlation coefficient 0.94) and found them to be highly concordant. We demonstrate its utility in pancreatic primary and xenograft sequencing data.Availability and implementationSource code and executables are available at https://github.com/WaveCNV. The segmentation algorithm is implemented in MATLAB, and copy number assignment is implemented [email protected] informationSupplementary data are available at Bioinformatics online

A Systems Approach Identifies Networks and Genes Linking Sleep and Stress: Implications for Neuropsychiatric Disorders

Sleep dysfunction and stress susceptibility are comorbid complex traits that often precede and predispose patients to a variety of neuropsychiatric diseases. Here, we demonstrate multilevel organizations of genetic landscape, candidate genes, and molecular networks associated with 328 stress and sleep traits in a chronically stressed population of 338 (C57BL/6J × A/J) F2 mice. We constructed striatal gene co-expression networks, revealing functionally and cell-type-specific gene co-regulations important for stress and sleep. Using a composite ranking system, we identified network modules most relevant for 15 independent phenotypic categories, highlighting a mitochondria/synaptic module that links sleep and stress. The key network regulators of this module are overrepresented with genes implicated in neuropsychiatric diseases. Our work suggests that the interplay among sleep, stress, and neuropathology emerges from genetic influences on gene expression and their collective organization through complex molecular networks, providing a framework for interrogating the mechanisms underlying sleep, stress susceptibility, and related neuropsychiatric disorders