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Cancer development is characterized by chromosomal instability, manifesting in frequent occurrences of different genomic alteration mechanisms ranging in extent and impact. Mathematical modeling can help evaluate the role of each mutational process during tumor progression, however existing frameworks can only capture certain aspects of chromosomal instability (CIN). We present CINner, a mathematical framework for modeling genomic diversity and selection during tumor evolution. The main advantage of CINner is its flexibility to incorporate many genomic events that directly impact cellular fitness, from driver gene mutations to copy number alterations (CNAs), including focal amplifications and deletions, missegregations and whole-genome duplication (WGD). We apply CINner to find chromosome-arm selection parameters that drive tumorigenesis in the absence of WGD in chromosomally stable cancer types from the Pan-Cancer Analysis of Whole Genomes (PCAWG, n = 718 ). We found that the selection parameters predict WGD prevalence among different chromosomally unstable tumors, hinting that the selective advantage of WGD cells hinges on their tolerance for aneuploidy and escape from nullisomy. Analysis of inference results using CINner across cancer types in The Cancer Genome Atlas ( n = 8207 ) further reveals that the inferred selection parameters reflect the bias between tumor suppressor genes and oncogenes on specific genomic regions. Direct application of CINner to model the WGD proportion and fraction of genome altered (FGA) in PCAWG uncovers the increase in CNA probabilities associated with WGD in each cancer type. CINner can also be utilized to study chromosomally stable cancer types, by applying a selection model based on driver gene mutations and focal amplifications or deletions (chronic lymphocytic leukemia in PCAWG, n = 95 ). Finally, we used CINner to analyze the impact of CNA probabilities, chromosome selection parameters, tumor growth dynamics and population size on cancer fitness and heterogeneity. We expect that CINner will provide a powerful modeling tool for the oncology community to quantify the impact of newly uncovered genomic alteration mechanisms on shaping tumor progression and adaptation.

Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised clustering followed by manual annotation or via ‘mapping’ to existing data. However, manual interpretation scales poorly to large datasets, mapping approaches require purified or pre-annotated data and both are prone to batch effects. To overcome these issues, we present CellAssign, a probabilistic model that leverages prior knowledge of cell-type marker genes to annotate single-cell RNA sequencing data into predefined or de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while controlling for batch and sample effects. We demonstrate the advantages of CellAssign through extensive simulations and analysis of tumor microenvironment composition in high-grade serous ovarian cancer and follicular lymphoma.

Antisense oligonucleotides (ASOs) conjugated to triantennary N-acetyl galactosamine (GalNAc) ligands represent an emerging approach to antisense therapy. Our current generation of GalNAc-ASO conjugates link the GalNAc to the 5′-terminus of the ASO. The conjugation reaction can be accomplished using solution-phase or solid-phase techniques. Here we show a direct comparison of a solution-phase and a solid-phase conjugation strategy. The solution-phase approach, using amine-pentafluorophenyl (PFP) ester coupling, is higher yielding and gives material of slightly higher purity, but requires several additional unit operations and longer production time. The solid-phase approach, using a protected GalNAc ligand phosphoramidite, is more expedient, but results in lower yield and purity. Both strategies efficiently deliver conjugated material in excellent purity.

In Scotland, a third of all deaths of people experiencing homelessness (PExH) are street-drug-related, and less than half of their multiple physical- and mental health conditions are treated. New, holistic interventions are required to address these health inequalities. PHOENIx (Pharmacist Homeless Outreach Engagement and Non-medical Independent prescribing Rx) is delivered on outreach by National Health Service (NHS) pharmacist independent prescribers in partnership with third sector homelessness charity workers. We describe participant's perspectives of PHOENIx. This study aims to understand experiences of the PHOENIx intervention by participants recruited into the active arm of a pilot randomised controlled trial (RCT). Semi-structured in-person interviews explored participants' evaluation of the intervention. In this study, the four components (coherence, cognitive participation, collective action, reflexive monitoring) of the Normalisation Process Theory (NPT) framework underpinned data collection and analyses. We identified four themes that were interpreted within the NPT framework that describe participant evaluation of the PHOENIx intervention: differentiating the intervention from usual care (coherence), embedding connection and consistency in practice (cognitive participation), implementation of practical and emotional operational work (collective action), and lack of power and a commitment to long-term support (reflexive monitoring). Participants successfully engaged with the intervention. Facilitators for participant motivation included the relationship-based work created by the PHOENIx team. This included operational work to fulfil both the practical and emotional needs of participants. Barriers included concern regarding power imbalances within the sector, a lack of long-term support and the impact of the intervention concluding. Findings identify and describe participants' evaluations of the PHOENIx intervention. NPT is a theoretical framework facilitating understanding of experiences, highlighting both facilitators and barriers to sustained engagement and investment. Our findings inform future developments regarding a subsequent definitive RCT of PHOENIx, despite challenges brought about by challenging micro and macro-economic and political landscapes.

Background Single-cell RNA sequencing (scRNA-seq) is a powerful tool for studying complex biological systems, such as tumor heterogeneity and tissue microenvironments. However, the sources of technical and biological variation in primary solid tumor tissues and patient-derived mouse xenografts for scRNA-seq are not well understood. Results We use low temperature (6 °C) protease and collagenase (37 °C) to identify the transcriptional signatures associated with tissue dissociation across a diverse scRNA-seq dataset comprising 155,165 cells from patient cancer tissues, patient-derived breast cancer xenografts, and cancer cell lines. We observe substantial variation in standard quality control metrics of cell viability across conditions and tissues. From the contrast between tissue protease dissociation at 37 °C or 6 °C, we observe that collagenase digestion results in a stress response. We derive a core gene set of 512 heat shock and stress response genes, including FOS and JUN, induced by collagenase (37 °C), which are minimized by dissociation with a cold active protease (6 °C). While induction of these genes was highly conserved across all cell types, cell type-specific responses to collagenase digestion were observed in patient tissues. Conclusions The method and conditions of tumor dissociation influence cell yield and transcriptome state and are both tissue- and cell-type dependent. Interpretation of stress pathway expression differences in cancer single-cell studies, including components of surface immune recognition such as MHC class I, may be especially confounded. We define a core set of 512 genes that can assist with the identification of such effects in dissociated scRNA-seq experiments.

Sohrab Shah, Samuel Aparicio and colleagues analyze whole genomes and single cells from ovarian cancers in the peritoneal cavity to establish patterns of disease spread. They determine the clonal relationships between multiple tumor sites and characterize the migratory potential of genomically diverse clones. We performed phylogenetic analysis of high-grade serous ovarian cancers (68 samples from seven patients), identifying constituent clones and quantifying their relative abundances at multiple intraperitoneal sites. Through whole-genome and single-nucleus sequencing, we identified evolutionary features including mutation loss, convergence of the structural genome and temporal activation of mutational processes that patterned clonal progression. We then determined the precise clonal mixtures comprising each tumor sample. The majority of sites were clonally pure or composed of clones from a single phylogenetic clade. However, each patient contained at least one site composed of polyphyletic clones. Five patients exhibited monoclonal and unidirectional seeding from the ovary to intraperitoneal sites, and two patients demonstrated polyclonal spread and reseeding. Our findings indicate that at least two distinct modes of intraperitoneal spread operate in clonal dissemination and highlight the distribution of migratory potential over clonal populations comprising high-grade serous ovarian cancers.

Chromosomal instability (CIN) and epigenetic alterations are characteristics of advanced and metastatic cancers 1 – 4 , but whether they are mechanistically linked is unknown. Here we show that missegregation of mitotic chromosomes, their sequestration in micronuclei 5 , 6 and subsequent rupture of the micronuclear envelope 7 profoundly disrupt normal histone post-translational modifications (PTMs), a phenomenon conserved across humans and mice, as well as in cancer and non-transformed cells. Some of the changes in histone PTMs occur because of the rupture of the micronuclear envelope, whereas others are inherited from mitotic abnormalities before the micronucleus is formed. Using orthogonal approaches, we demonstrate that micronuclei exhibit extensive differences in chromatin accessibility, with a strong positional bias between promoters and distal or intergenic regions, in line with observed redistributions of histone PTMs. Inducing CIN causes widespread epigenetic dysregulation, and chromosomes that transit in micronuclei experience heritable abnormalities in their accessibility long after they have been reincorporated into the primary nucleus. Thus, as well as altering genomic copy number, CIN promotes epigenetic reprogramming and heterogeneity in cancer. Missegregated chromosomes that are sequestrated in micronuclei are subject to changes in histone modifications leading to abnormalities in chromatin accessibility that remain long after the chromosomes have been reincorporated into the primary nucleus.

Follicular lymphoma (FL) is an indolent, yet incurable B cell malignancy. A subset of patients experience an increased mortality rate driven by two distinct clinical end points: histological transformation and early progression after immunochemotherapy. The nature of tumor clonal dynamics leading to these clinical end points is poorly understood, and previously determined genetic alterations do not explain the majority of transformed cases or accurately predict early progressive disease. We contend that detailed knowledge of the expansion patterns of specific cell populations plus their associated mutations would provide insight into therapeutic strategies and disease biology over the time course of FL clinical histories. Using a combination of whole genome sequencing, targeted deep sequencing, and digital droplet PCR on matched diagnostic and relapse specimens, we deciphered the constituent clonal populations in 15 transformation cases and 6 progression cases, and measured the change in clonal population abundance over time. We observed widely divergent patterns of clonal dynamics in transformed cases relative to progressed cases. Transformation specimens were generally composed of clones that were rare or absent in diagnostic specimens, consistent with dramatic clonal expansions that came to dominate the transformation specimens. This pattern was independent of time to transformation and treatment modality. By contrast, early progression specimens were composed of clones that were already present in the diagnostic specimens and exhibited only moderate clonal dynamics, even in the presence of immunochemotherapy. Analysis of somatic mutations impacting 94 genes was undertaken in an extension cohort consisting of 395 samples from 277 patients in order to decipher disrupted biology in the two clinical end points. We found 12 genes that were more commonly mutated in transformed samples than in the preceding FL tumors, including TP53, B2M, CCND3, GNA13, S1PR2, and P2RY8. Moreover, ten genes were more commonly mutated in diagnostic specimens of patients with early progression, including TP53, BTG1, MKI67, and XBP1. Our results illuminate contrasting modes of evolution shaping the clinical histories of transformation and progression. They have implications for interpretation of evolutionary dynamics in the context of treatment-induced selective pressures, and indicate that transformation and progression will require different clinical management strategies.

Subclonal copy number alterations are a prevalent feature in tumors with high chromosomal instability and result in heterogeneous cancer cell populations with distinct phenotypes. However, the extent to which subclonal copy number alterations contribute to clone-specific phenotypes remains poorly understood. We develop TreeAlign, which computationally integrates independently sampled single-cell DNA and RNA sequencing data from the same cell population. TreeAlign accurately encodes dosage effects from subclonal copy number alterations, the impact of allelic imbalance on allele-specific transcription, and obviates the need to define genotypic clones from a phylogeny a priori, leading to highly granular definitions of clones with distinct expression programs. These improvements enable clone-clone gene expression comparisons with higher resolution and identification of expression programs that are genomically independent. Our approach sets the stage for dissecting the relative contribution of fixed genomic alterations and dynamic epigenetic processes on gene expression programs in cancer. Quantifying the impact of copy-number alterations (CNAs) on gene expression at the subclone level in cancer remains a challenge. Here, the authors develop TreeAlign, a method that integrates sample-matched single-cell DNA and RNA sequencing data to infer the impact of CNAs on subclonal gene expression.

Measuring gene expression of tumor clones at single-cell resolution links functional consequences to somatic alterations. Without scalable methods to simultaneously assay DNA and RNA from the same single cell, parallel single-cell DNA and RNA measurements from independent cell populations must be mapped for genome-transcriptome association. We present clonealign, which assigns gene expression states to cancer clones using single-cell RNA and DNA sequencing independently sampled from a heterogeneous population. We apply clonealign to triple-negative breast cancer patient-derived xenografts and high-grade serous ovarian cancer cell lines and discover clone-specific dysregulated biological pathways not visible using either sequencing method alone.